International Journal of Circuits, Systems and Signal Processing

E-ISSN: 1998-4464
Volume 14, 2020

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

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Volume 14, 2020

Title of the Paper: Experimental Measurements of a Low Power CMOS Analog MPPT Power Conditioning Circuit for Energy Harvesting Applications


Authors: Francarl Galea, Owen Casha, Ivan Grech, Edward Gatt, Joseph Micallef

Pages: 1192-1197


Abstract: This paper presents the complete measured performance and characterization of a fabricated power conditioning integrated circuit for energy harvesters with on-chip maximum power point tracking (MPPT) and external energy storage. This ultra-low power circuit employs an AC/DC-to-DC converter compatible with both AC and DC voltage energy harvesters. The MPPT design follows the perturbation and observation algorithm. This MPPT is capable of tracking the maximum power point of types of energy harvesters. The circuit is implemented using the AMS CMOS 0:35 μm high voltage technology and all the circuit blocks use analog electronic techniques, with the transistors operating in the sub-threshold region, in order to obtain a minimum power consumption. This power conditioning circuit consumes less than 2 μW while featuring an input voltage range of -0:5V to -50V and a power range from 10 μW to 200mW.

Title of the Paper: First Steps Toward a Simple but Efficient Model-free Control Synthesis for Variable-speed Wind Turbines


Authors: Frederic Lafont, Jean-Francois Balmat, Cedric Join, Michel Fliess

Pages: 1181-1191


Abstract: In Although variable-speed three-blade wind turbines are nowadays quite popular, their control remains a challenging task. We propose a new easily implementable model-free control approach with the corresponding intelligent controllers. Several convincing computer simulations, including some fault accommodations, shows that model-free controllers are more efficient and robust than classic proportional-integral controllers.

Title of the Paper: Signal and Video Processing: Developing the Noise Music Trend in Digital Edugaming


Authors: Ieva Gintere

Pages: 1176-1180


Abstract: In digital educational gaming, there are presently no games devoted to the trends of contemporary music. Also, there are no studies of noise-related sound in contemporary gaming discourse, yet noise is presently one of the most current trends in the arthouse world. The author of this study is carrying out a post-doctoral research into contemporary audio-visual art and digital game theory. Next to the other trends of modern gaming, the study incorporates analysis of noise-related artefacts that are of particular interest to the author taking into account her musicological education. The author intends to transfer knowledge gathered in the research process to the general public with an aim to facilitate the comprehension of noise music. Noise-related sound does not belong to the traditional system of musical expression thereof it requires an explanation and justification in order to be well apprehended. It has been explained in the literature of musicology, but serious gaming would help to disseminate these results and thus support intellectual education. The new experimental game Art Space explores the noise effect in order to deepen the understanding of this fuzzy area of contemporary culture. The game incorporates the historical background of noise music and its contemporary examples in the academic and alternative genres. The methods used in this study are literature analysis (theory of gaming and musicology), analyses of game sound scores and music examples. The mission of the research project and the innovative game Art Space is to pave the way to a new type of edugame that supports the documentation and analysis of aesthetical trends today.

Title of the Paper: Signal Processing: New Stochastic Feature of Unvoiced Pronunciation for Whisper Speech Modeling and Synthesis


Authors: X. D. Zhuang, H. Zhu, N. E. Mastorakis

Pages: 1162-1175


Abstract: Whisper is an indispensable way in speech communication, especially for private conversation or human-machine interaction in public places such as library and hospital. Whisper is unvoiced pronunciation, and voiceless sound is usually considered as noise-like signals. However, unvoiced sound has unique acoustic features and can carry enough information for effective communication. Although it is a significant form of communication, currently there is much less research work on whisper signal than common speech and voiced pronunciation. Our work extends the research of unvoiced pronunciation signal by introducing a novel signal feature, which is further applied in unvoiced signal modeling and whisper sound synthesis. The statistics of amplitude for each frequency component is studied individually, based on which a new feature of “consistent standard deviation coefficient” is revealed for the amplitude spectrum of unvoiced pronunciation. A synthesis method for unvoiced pronunciation is proposed based on the new feature, which is implemented by STFT with artificially generated short-time spectrum with random amplitude and phase. The synthesis results have identical quality of auditory perception as the original pronunciation, and have similar autocorrelation as that of the original signal, which proves the effectiveness of the proposed stochastic model of short-time spectrum for unvoiced pronunciation.

Title of the Paper: Heuristic Algorithms for Surveyor Standby Location Planning with Multiple Plans


Authors: Rawee Suwandechochai, Wasin Padungwech

Pages: 1154-1161


Abstract: The evaluation of resource carrying capacity of Xiong'an New Area can clearly recognize the endowment characteristics, carrying capacity and carrying level of resources in Xiong'an New Area, which could provide certain theoretical guidance for the sustainable development of economy, society and ecology in the New Area. Based on the analysis of the current situation of resources in the New Area, following the main principles of scientific, dynamic, reflecting the coordination of human land relationship and sustainable development, from three aspects of land resources, water resources and mineral resources, 13 indexes of the per capita cultivated land area, per capita construction land area, per unit cultivated land productivity, per capita water resources, per unit effective irrigation area, utilization rate of water resources, water use efficiency, ore production per unit land use and geothermal field area are selected to build the evaluation index system of resource carrying capacity of the New Area. Based on the fuzzy comprehensive evaluation method, the resource carrying capacity of Xiong'an New Area from 2014 to 2018 is evaluated with the administrative areas of Xiong county, An’xin county and Rongcheng county (including Baiyangdian water area) as the main evaluation unit, which could provide policy guidance for the land, water and mineral resources management of the New Area.

Title of the Paper: A Fuzzy Comprehensive Evaluation Method of Area Resource Carrying Capacity


Authors: Qin Wang

Pages: 1137-1153


Abstract: The evaluation of resource carrying capacity of Xiong'an New Area can clearly recognize the endowment characteristics, carrying capacity and carrying level of resources in Xiong'an New Area, which could provide certain theoretical guidance for the sustainable development of economy, society and ecology in the New Area. Based on the analysis of the current situation of resources in the New Area, following the main principles of scientific, dynamic, reflecting the coordination of human land relationship and sustainable development, from three aspects of land resources, water resources and mineral resources, 13 indexes of the per capita cultivated land area, per capita construction land area, per unit cultivated land productivity, per capita water resources, per unit effective irrigation area, utilization rate of water resources, water use efficiency, ore production per unit land use and geothermal field area are selected to build the evaluation index system of resource carrying capacity of the New Area. Based on the fuzzy comprehensive evaluation method, the resource carrying capacity of Xiong'an New Area from 2014 to 2018 is evaluated with the administrative areas of Xiong county, An’xin county and Rongcheng county (including Baiyangdian water area) as the main evaluation unit, which could provide policy guidance for the land, water and mineral resources management of the New Area.

Title of the Paper: Bearings-only multitarget tracking based onRao-Blackwellized particle CPHD filter


Authors: Jungen Zhang

Pages: 1129-1136


Abstract: Following Mahler’s framework forinformation fusion, this paper develops a implementationof cardinalized probability hypothesis density (CPHD)filter for bearings-only multitarget tracking.Rao-Blackwellized method is introduced in the CPHDfiltering framework for mixed linear/nonlinear state spacemodels. The sequential Monte Carlo (SMC) method is usedto predict and estimate the nonlinear state of targets.Kalman filter (KF) is adopted to estimate the linear stateswith the information embedded in the estimated nonlinearstates. The multitarget state estimates are extracted byutilizing the kernel density estimation (KDE) theory andmean-shift algorithm to enhance tracking performance.Moreover, the computational load of the filter is analyzedby introducing equivalent flop measure. Finally, theperformance of the proposed Rao-Blackwellized particleCPHD filter is evaluated through a challengingbearings-only multitarget tracking simulation experiment.

Title of the Paper: System Analysis and Reliability Analysis for Environmental Test Chambers


Authors: Chaoyang Gu, Lin Zhai, Wei Gu, Yuyu Sun, Chengjing Han, Xianwen Zhou, Wangqiang Niu

Pages: 1115-1128


Abstract: The purpose of this paper is to present the results of the application of various models to estimate the reliability of environmental test chambers, especially, the methodology proposed by the International Electrotechnical Commission (IEC), using the Homogeneous Poisson process (HPP) and Non-Homogeneous Poisson process (NHPP) models, is adopted first, and then, a non-monotonic trend test and bathtub curve intensity function not covered by IEC are used, and supplementary analysis is used to characterize the resulting failure intensity. For the first time, the stochastic process model was applied to evaluate the reliability of 20 environmental test chambers. The results show that the IEC standards process is suitable for the reliability evaluation of a single chamber, and 16 chambers conform to the HPP model and 4 chambers conform to the NHPP model. However, there is the power-law model (PLP) rejection cases among the overall description of multiple chambers by the IEC model. The rejected cases were analyzed again by using a non-monotonic trend test and constructing a double Weibull process tub curve strength function, and the 3-stage time interval of the bathtub curve failure is obtained, which is in line with the actual operation data. The Ward clustering method is used for the mean time between failures of 20 chambers, resulting in 4 types of chamber groups with different reliability values (71,52,100,130 days), which is of great significance for studying the reliability of the environmental test chamber and carrying out customized maintenance.

Title of the Paper: Research on Vision System Calibration Method of Forestry Mobile Robots


Authors: Ruting Yao, Yili Zheng, Fengjun Chen, Jian Wu, Hui Wang

Pages: 1107-1114


Abstract: Forestry mobile robots can effectively solve the problems of low efficiency and poor safety in the forestry operation process. To realize the autonomous navigation of forestry mobile robots, a vision system consisting of a monocular camera and two-dimensional LiDAR and its calibration method are investigated. First, the adaptive algorithm is used to synchronize the data captured by the two in time. Second, a calibration board with a convex checkerboard is designed for the spatial calibration of the devices. The nonlinear least squares algorithm is employed to solve and optimize the external parameters. The experimental results show that the time synchronization precision of this calibration method is 0.0082s, the communication rate is 23Hz, and the gradient tolerance of spatial calibration is 8.55e−07. The calibration results satisfy the requirements of real-time operation and accuracy of the forestry mobile robot vision system. Furthermore, the engineering applications of the vision system are discussed herein. This study lays the foundation for further forestry mobile robots research, which is relevant to intelligent forest machines.

Title of the Paper: Determination the Modes Characteristics in the Complex Cross-Section Waveguides


Authors: Mohammed Yousef AL-Gawagzeh

Pages: 1103-1106


Abstract: In this research the Expressions that determine the eigenfunctions and modes eigenvalues in waveguides with a composite sectorial cross-section are obtained. The possibility of characteristics changing for eigenvalues by changing the parameters that characterizing the cross-sectional shape was studied. The field modes of waveguide based on Ritz method was was determined the characteristics of the quasi – H_mn modes in a cruciform sector waveguide, and quasi – H_mn modes in a composite sector waveguide with an arbitrary number of was also shown the advantage of using the cross-section waveguide in single mode optical fiber wavelength range. The eigenvalues (χ) and the normalized coefficients (a )for quasi – H_mn modes in terms of Bessel functions (Q_m,P_m ) and their combinations was obtained.

Title of the Paper: Brain Tumor identification by Convolution Neural Network with Fuzzy C-mean Model Using MR Brain Images


Authors: Abd El Kader Isselmou, Guizhi Xu, Zhang Shuai, Sani Saminu, Imran Javaid, Isah Salim Ahmad

Pages: 1096-1102


Abstract: Medical image computing techniques are essential in helping the doctors to support their decision in the diagnosis of the patients. Due to the complexity of the brain structure, we choose to use MR brain images because of their quality and the highest resolution. The objective of this article is to detect brain tumor using convolution neural network with fuzzy c-means model, the advantage of the proposed model is the ability to achieve excellent performance using accuracy, sensitivity, specificity, overall dice and recall values better than the previous models that are already published. In addition, the novel model can identify the brain tumor, using different types of MR images. The proposed model obtained accuracy with 98%.

Title of the Paper: A Modeling Method of Cylindrical Turning Processing Behavior


Authors: Hongtao Li, Gang Zhao, Yi Chen Zhang, Yingxin Ji

Pages: 1089-1095


Abstract: This paper put forward the theory of processing behavior through research on conceptual system of advanced manufacturing technologies and production modes such as cloud manufacturing and systematic classification of processing technology, and gave the concept of processing behavior primitive element. The processing behavior was classified and the relationship between different levels of processing behavior was clarified. To realize the standardization of NC processing technology for parts of automatic turning programming. Embed turning behavior into the design elements, realize the combination of design and manufacturing, shorten the time and space of coordination and product cycle, reduce product design and manufacturing costs, and achieve green, low consumption, high quality, high efficiency and effective manufacturing of turning parts.

Title of the Paper: The Influence Law of Multi-point Cumulative Blasting in Slope


Authors: Liu Han, Jisen Shu, Yanjun He, Tao Chen, Changchao She

Pages: 1074-1088


Abstract: In order to study the cumulative blasting stress wave produced by the vibration of soft rock slope, the deformation regularity of the influence of blasting, caused by the horizontal and axial acceleration along with other additional stress waves were analyzed. The vibration displacement formula along with the vibration source stress wave superimposed model were established. The research also deduced the deformation rule for the gangue slope of Lijiahao Coal Mines as a vibration signal recorder was used to collect the blast vibration wave and the simulated analysis of displacement monitoring profile of the X and Y displacement rule under the influence of blast vibration were obtained. The results indicate that with the increase of the shot point distance, the displacement in the Y direction shows a logarithmic decline rule while the change in displacement of the X direction is small. The result also shows that the greater the initiation of multipoint shot firing, the greater the slope deformation and displacement of the rock mass and as a result multipoint non-simultaneous blasting and control detonation time difference, can effectively reduce the effects of vibration on the slope deformation.

Title of the Paper: Open Systems Science: Digital Transformation and Developing Business Model toward Smart Farms’ Platform


Authors: Rania E. Ibrahim, Amr Elramly, Hoda M Hassan

Pages: 1054-1073


Abstract: This paper describes efforts by National Authority for Remote Sensing and Space Sciences (NARSS) to help the Egyptian government to manage and monitor the national projects. We successfully developed a geospatial data sharing portal (NARSSGeoPortal) as part of the government need to build national Decision Support System (DSS). We were able to solve the software development issues as well as the satellite imagery sourcing issues, but the main challenge remains around how to collect complete and correct data from the public about their private businesses nationwide. The most challenging is how to engage the public and encourage the business owners who are the main sources of data to provide the government Geoportal with data about their businesses. It is also challenging to engage the scientists and experts from government research centers into the data sharing Geoportal. Furthermore, it is a challenge to integrate the government research centers with the public businesses’ daily operation. The data sharing Geoportal is built for all national projects and government authorities, however, in this paper we focus on the Agriculture authorities and farming businesses where the challenge is how to collect correct and complete data per acre about the seeds, fertilizers, water, pest control and all other farm related data that the satellite imagery does not provide. The goal is to integrate the farms into unified national monitoring, and control system while developing advanced smart farms with the use of Internet of Things (IoT). The proposed collaboration‎‎ agriculture platform fills the gap between two groups. The first group includes the government authorities, financial institutions, and research centers. The second group includes farmers, supply chain, and agriculture engineers. The platform show how employment can be generated by transforming the national ecosystem. The paper also fills a major gap in industry as well as in academia by providing the first Bluetooth Low Energy computer aided design tool that will facilitate testing, designing, deploying, managing and debugging of real Bluetooth Low Energy networks.

Title of the Paper: Prediction of Casing Damage: A Data-Driven, Machine Learning Approach


Authors: Yanhong Zhao, Hanqiao Jiang, Hongqi Li

Pages: 1047-1053


Abstract: Casing damage is the result of a number of factors in the long process of oilfield development, so it must be correctly judged and repaired in time to ensure the normal production of the oil fields. With the development of data science, it has always been an imperative problem remained to be solved. In this paper, we adopt a data-driven and the machine learning approach to casing damage forecasts. Firstly, from the fields of geology, engineering and development, a lot of history data is collected and processed. Then, based on these dynamic and static data samples, the random forest algorithm is used to create the casing damage prediction model. Finally, after the model is tested in two fault blocks, the results indicate that accuracy rates are 91% and 75%, which proves the validity and performance of the mode.

Title of the Paper: Safe Position Detection Based on Safety System-on-Chip (SSoC) for Wireless IoT Application


Authors: Josef Börcsök, Michael Schwarz, Muhammad Ikram Hafiz, Mohamed Abdelawwad, Ahmed Alsuleiman

Pages: 1040-1046

Abstract: Cyber Physical Systems (CPS) are predestined for use in Industry 4.0 applications. However, the interaction between the virtual and physical world also creates risks that is essential to be controlled. In highly automated industrial systems, for example, robots are used in confined spaces together with working humans. The risk posed by such systems endangers, among others, the people working there. This paper presents an approach to ensure the safety of the situation described above, which makes the workspace of industrial robots safer by implementing a safe workspace detection system. This system comprises several detection sensors implemented in a 2oo3 safety architecture and a Safety System on a Chip (SSoC) based on a safe 1oo2 system architecture. The safety-related redundancy provided by the detection and calculation elements enables a safe position detection of the robotic arm in the 3-dimensional space. The presented system monitors the position of the robotic arm and thus supports the safety of the surrounding objects and the people working there by leading to a safe standstill or to a reduced speed of movement of the robot as soon as the defined and permitted working space is left.

Title of the Paper: An Improved Neural Network Algorithm for Remote Sensing Image Classification


Authors: Liang Zhao

Pages: 1034-1039



Abstract: The appearance of hyperspectral remote sensing image further improves the accuracy of remote sensing image classification, but the data of hyperspectral remote sensing image is large, and the processing hyperspectral remote sensing image has high complexity and low efficiency. A remote sensing image classification algorithm based on improved bilinear recurrent neural network (BLRNN) model is proposed, this paper gives the definition of bilinear recurrent neural Network and the description of network structure, and optimizes and improves the bilinear recurrent neural network from two aspects of network structure and pruning process, and uses the genetic algorithm global search to trim. Compared with the original feature, PCA and BPNN algorithm, the results show that the BLRNN algorithm has been greatly improved in classification accuracy and classification time, and the image processing efficiency is improved.

Title of the Paper: Simulation and Mathematical Description of Mechanical Shocks Caused by Brake Actuation on Electric Motors


Authors: David Schepers, Josef Börcsök, Lauri Bodenröder, Florian Rieger

Pages: 1030-1033


Abstract: When a brake engages or releases on an electric motor, a mechanical shock is generated. These so-called brake shocks propagate across the motor housing and the motor shaft, affecting safety relevant mechanical and electronic components. The nature of the interference may be irreversible, i.e. mechanical damage, or reversible, e.g. interference of signal measurement or data transmission. Especially component failures or faulty signal values on rotary encoders are undesirable from a safety point of view. Current shock testing procedures are insufficient to simulate real brake shock characteristics and to identify valid shock limits regarding these shocks. In the first part of this paper, the characteristics of brake shocks are presented and compared to pyroshocks with similar characteristics. Furthermore, it shows that the Pseudo-Velocity Shock Response Spectrum (PVSRS) appears to be the best mathematical method to describe the severity of brake shocks with respect to their potential of damaging encoder components or influencing electrical signals. In the second part a testing machine will be introduced, which is able to generate mechanical shocks with comparable characteristics of real mechanical brake shocks for up to several million cycles. During further research, endurance tests shall be performed with the machine to determine the resilience of safety-related components against mechanical brake shocks. The long-term goal is to define scientifically confirmed test criteria for a standardized shock testing procedure to be applied on safety-related components on electric motors. It is intended to include this testing procedure in an international safety-related standard, like IEC 61800-5-3.

Title of the Paper: Brain Tumor Classification & Segmentation by Using Advanced DNN, CNN & ResNet-50 Neural Networks


Authors: Imran Javaid, Shuai Zhang, Abd El Kader Isselmou, Souha Kamhi, Isah Salim Ahmad, Ummay Kulsum

Pages: 1011-1029

Abstract: In the medical domain, brain image classification is an extremely challenging field. Medical images play a vital role in making the doctor's precise diagnosis and in the surgery process. Adopting intelligent algorithms makes it feasible to detect the lesions of medical images quickly, and it is especially necessary to extract features from medical images. Several studies have integrated multiple algorithms toward medical images domain. Concerning feature extraction from the medical image, a vast amount of data is analyzed to achieve processing results, helping physicians deliver more precise case diagnoses. Image processing mechanism becomes extensive usage in medical science to advance the early detection and treatment aspects. In this aspect, this paper takes tumor, and healthy images as the research object and primarily performs image processing and data augmentation process to feed the dataset to the neural networks. Deep neural networks (DNN), to date, have shown outstanding achievement in classification and segmentation tasks. Carrying this concept into consideration, in this study, we adopted a pre-trained model Resnet_50 for image analysis. The paper proposed three diverse neural networks, particularly DNN, CNN, and ResNet-50. Finally, the splitting dataset is individually assigned to each simplified neural network. Once the image is classified as a tumor accurately, the OTSU segmentation is employed to extract the tumor alone. It can be examined from the experimental outcomes that the ResNet-50 algorithm shows high accuracy 0.996, precision 1.00 with best F1 score 1.0, and minimum test losses of 0.0269 in terms of Brain tumor classification. Extensive experiments prove our offered tumor detection segmentation efficiency and accuracy. To this end, our approach is comprehensive sufficient and only requires minimum pre-and post-processing, which allows its adoption in various medical image classification & segmentation tasks.

Title of the Paper: Investigation of a Safety Parameter Observer for Wireless Communication


Authors: Michael H. Schwarz, Larissa Gaus, Josef Börcsök

Pages: 1005-1010


Abstract: This paper investigates the possibilities to monitor the degree of disturbances of a wireless communication and to use this information to calculate online the necessary safety parameters in order to estimate the probability of failure per hour (PFH) and the safety integrity level (SIL). Depending of the degree of disturbances, addition actions are performed with the intention of keeping the current safety level. A hardware set-up is introduced to get process data and to evaluate the results.

Title of the Paper: Neural Network Implementations on the coastal water quality of Manora channel for the years 1996 to 2014.


Authors: Sidra Ghayas, Junaid Sagheer Siddiquie, Suboohi Safdar, Asif Mansoor

Pages: 996-1004



Abstract: Neural Networks is an Important Part of Computational Intelligence, Systems Theory and Signal Processing and finds numerous important applications in Science and Engineering. Sea water quality contaminates due to the severe untreated domestic, sewage and industrial pollutants. Presence of ammonia in seawater causes the deterioration of coastal water in terms of diminution of oxygen levels which suffocates the marine lives, fishes and mangroves. Industrial, sewage and domestic effluents carried by Lyari River contaminate the Manora channel, Karachi. The aim of study is to make the clear and transparent step-wise use of Artificial Neural Networks for the data driven water quality parameters models of Manora channel (Lyari river outfall zone N 24-51-26, E 66-58-01), Karachi (Pakistan) as well as to compare the pollutant contaminant ratio with the national environmental quality standard limits and other sampling sites of Manora channel and southern east Karachi coast. In this study, Manora channel Physico-chemical water quality parameters are assessed by using Artificial Neural Network taking Biochemical Oxygen Demand (BOD), chemical oxygen Demand (COD), Bicarbonates, potential Hydrogen(pH) , Chloride(Cl) as input and Ammonia(NH3)as output. Mean Square Error and R square are used for the model assessments statistical metrics. The computational work has been done by using R-studio. This is also found that Manora channel has the contaminated level of ammonia along the other sampling stations of both southern Karachi coast (N 24-47-03 E 67-08-39) as well as the other sampling site of Manora channel Karachi coast (N 24-50-15, E 66-58-01). In spite of all contamination Ammonia is found to be within National Environmental Quality Standards limits of Pakistan.

Title of the Paper: Study of the Discrete Wavelet Transform Based Designed System Under Different Simulation Conditions


Authors: Simmi Garg, Anuj Kumar Sharma, Anand Kumar Tyagi

Pages: 990-995



Abstract: Wavelet Transforms is an Important Part of, Systems Theory and Signal Processing and finds numerous important applications in Science and Engineering. In this paper, we investigated the performance of proposed scheme coded Discrete wavelet transform based Orthogonal frequency division multiplexing scheme over Additive white Gaussian noise channel using Pulse Amplitude Modulation in terms of Energy bits per noise ratio values. The simulation has been done using MATLAB software and results are compared with ½ rate convolution coded Discrete wavelet transform based Orthogonal frequency division multiplexing system. It is found by MATLAB simulations that the performance of proposed scheme coded Discrete wavelet transform based Orthogonal frequency division multiplexing outperforms than that of ½ rate convolution encoded Discrete wavelet transform based Orthogonal frequency division multiplexing with 16-Pulse Amplitude Modulation. Along with this, different orders of reverse biorthogonal and biorthogonal wavelets are implemented to simulate the proposed system with 16-Pulse Amplitude Modulation scheme. The performance of proposed system is compared and it is found that proposed system performs better than conventional system under all different simulation conditions. This study finds important applications in Signal Processing.

Title of the Paper: Big Data Classification for the Analysis MEL Scale Features Using KNN Parameterization


Authors: Volodymyr Osadchyy, Ruslan V. Skuratovskii

Pages: 978-989



Abstract: The role of human speech is intensified by the emotion it conveys. The parameterization of the vector obtained from the sentence divided into the containing emotional-informational part and the informational part is effectively applied. There are several characteristics and features of speech that differentiate it among utterances, i.e. various prosodic features like pitch, timbre, loudness and vocal tone which categorize speech into several emotions. They were supplemented by us with a new classification feature of speech, which consists in dividing a sentence into an emotionally loaded part of the sentence and a part that carries only informational load. Therefore, the sample speech is changed when it is subjected to various emotional environments. As the identification of the speaker’s emotional states can be done based on the Mel scale, MFCC is one such variant to study the emotional aspects of a speaker’s utterances. In this work, we implement a model to identify several emotional states from MFCC for two datasets, classify emotions for them on the basis of MFCC features and give the comparison of both. Overall, this work implements the classification model based on dataset minimization that is done by taking the mean of features for the improvement of the classification accuracy rate in different machine learning algorithms.

Title of the Paper: The Artificial Intelligence and Design of Multibody Systems with Predicted Dynamic Behavior


Authors: Vladimir Poliakov

Pages: 972-977


Abstract: The problem of complicated dynamic system optimization is very difficult for human intellect. The design of these systems comprises typical tasks of artificial intelligence – big data analysis, decision making, etc. In this article, we applied artificial intelligence approach to optimize the properties of the multibody dynamic system. It is very important to study the whole carrying system of a high-speed railroad in its integrity because the elements of the system interact and influence each other simultaneously. The system should include the train of several cars, the track upper structure, and the bridges. It is possible to synthesize the optimal system with predicted behavior that meets various constraints on dynamic parameters and interaction between the elements of the system.

Title of the Paper: A Novel Method of Optimization for Stochastic Control System


Authors: Yupeng Wen

Pages: 966-971



Abstract: Stochastic phenomena widely exist in the nature and real dynamic systems. The existence of random phenomena will make the system performance degrade greatly, and even cause instability. For the sake of improving the stability of stochastic control system, this paper proposed a novel method of optimization for stochastic control system by control model and max-plus algebraic algorithm. The simulation results indicate that the optimization method can effectively optimize the stochastic system. The input of the stochastic control system is stable to a certain extent, which weakens the random interference of the input signal in the external environment, thus improving the stability of the stochastic control system.

Title of the Paper: Source Fabrication Detection Model based on Key-value Variables in Reactive Protocols of VANET


Authors: Fadlallah Chbib, Walid Fahs, Jamal Haydar, Lyes Khoukhi, Rida Khatoun

Pages: 959-965



Abstract: The vehicular communication hasbeen considered as the most promising wirelesscommunication technology in the computernetwork scenario, the beginning of which hasmarked a great change for the passengers in therange of safety application. The development ofvehicular communication increased securitythreats and weaknesses. Vehicularcommunication is exposed to severalvulnerabilities such as Denial of Service attacks(DoS), Black hole and fabrication attacks.Fabrication attack consists of a malicious nodethat modifies information in the packet causingcritical damage in the network like congestionand high delay. In this paper, we propose a novelfabrication model which consists of twoalgorithms one for source attack and another foranti-source attack. In such attack, the maliciousnode fabricates the source address of the routerequest message; this means that the maliciousnode selects randomly from the routing table asource address that is different from the inputsource address and the source address of thecurrent node and forwards the message to itsneighbors. In the anti-attack, our novel proposedalgorithm has the role to identify the sourceattack during the communication. We create twovariables at each node in order to check if theinput source address in RREQ is equal to theoutput source address in RREQ; otherwise, thenode is identified as a malicious node and anurgent message is broadcasted to all nodes toremove it from their routing table. The proposedalgorithm targets to minimize the delay ofpackets. Our simulation is done using SUMO0.22 simulator, NS-2.35 and awk scripts; thesimulation was applied on Hamra area (Beirut,Lebanon). The results show good improvementsin terms of packet delivery ratio and the end toend delay.

Title of the Paper: How Varying the Dipole Lengths of a Uniform Linear Array Affects the Performance of an ESPRIT based Direction Finding Algorithm


Authors: Gerald Pacaba Arada

Pages: 952-958



Abstract: Popularly used to collect data for the direction-of-arrival estimation of an incident source. Since the dipole elements of the uniform linear array are electromagnetically and mutually coupled, the presence of mutual coupling may degrade the performance of any direction finding algorithms. In order to mitigate mutual coupling, several references introduced modifications of the well-known ESPRIT algorithm. These modifications involve discarding the data collected by linear array at the two ends. However, the assumption of several literature that the mutual coupling matrix to be both Toeplitz and banded are invalid. This is pointed out by the “method of moments” based computer simulation tool used in this paper. The Toeplitz-and-banded coupling-matrix assumption leads to the mutual coupling being mis-modeled. Furthermore, previous studies failed to consider how varying the dipoles’ electrical length affect the performance of the direction-of-arrival estimation under mutual coupling.

Title of the Paper: Data Compression and Protection Through Representation With Combinations and Spiral Path


Authors: Elda Cina, Hiba Tabbara, Esmerald Aliaj

Pages: 942-951



Abstract: We are living in Industry 4.0 era where enormous data need to be stored and processed. Though hardware is also becoming more abundant, its limitation in medium transmission speed and memory storage still beats this need. Therefore, data compression seems always an emerging necessity. Another big threat to data is also the unauthorized access especially while dealing with sensitive data. In this paper, we introduce an enhancement of a 2D signals compression with security tools through cryptography. Spiral path compression technique uses data representation through combinations to achieve satisfactory lossless compression rate but it also offers intrinsic encryption of data. Instead of representing an image as a matrix of pixels, we represent it through a group of index numbers, each belonging to a part of the image called mini-images. Every index is performed through a spiral path inside the mini-image starting from the most repeated pixel value. The histogram not only helps on defining these starting points of spirals but also decreases the number of bits needed to represent the index. Since there are many starting points possible for each mini-image, we use a random distribution to decide which of them to be selected. We also use a matrix of private keys to make possible the protection of the image from unauthorized use. We conclude that using this technique, we can achieve satisfactory compression rates compared to actual compression rates used nowadays and many other cryptographic possibilities are available for future studies.

Title of the Paper: Data Model for Evaluation of Audience Feedback in Lectures


Authors: Hawraa Al Abedi, Ahmad Koubeissi

Pages: 932-941



Abstract: In this article, we discuss the importance of interactivity during lectures and means to evaluate the feedback of the audience. We conduct a literature review on the subject and propose accordingly, a data model for the evaluation of audience feedback in lectures at microscopic and macroscopic levels. We propose a detailed scenario and examine how we are able to simulate such a scenario using the designated data model.

Title of the Paper: Tetra-parameter Fish Feeding Machine


Authors: Ertie Abana, Maureen Baricaua, Rochelle Joyce Casibang, Aldene Paulino Babaran, Vincent Joseph Gaspar, Fritz Gerald Puzon

Pages: 923-931



Abstract: This study developed an automated machine that automatically controls the feeding routine of fish by checking four parameters that will serve as a prerequisite before dispensing the required amount of commercial feeds. The parameters to be checked are time, precipitation, the water temperature of the pond, and behavior of the fishes. The machine is also capable of notifying the owner or caretaker via text message if fishes have been fed successfully or not and if the level of the feeds is low. The machine utilizes sensors, namely a raindrop sensor, temperature sensor, and water flow sensor in which data are gathered through the aid of a microcontroller. After undergoing several trials, it was revealed that the fish feeding machine was able to implement the capabilities of the manual process of feeding done by a fish farmer. It also dispensed the required weight of feeds on time after satisfying the parameters. The machine was also reliable in terms of sending notifications to the owner through text message since results convey that they were received within 10 seconds if the signal is fine.

Title of the Paper: Predicting Loan Approval of Bank Direct Marketing Data Using Ensemble Machine Learning Algorithms


Authors: Hossam Meshref

Pages: 914-922



Abstract: The Bank Marketing data set at Kaggle is mostly used in predicting if bank clients will subscribe a long-term deposit. We believe that this data set could provide more useful information such as predicting whether a bank client could be approved for a loan. This is a critical choice that has to be made by decision makers at the bank. Building a prediction model for such high-stakes decision does not only require high model prediction accuracy, but also needs a reasonable prediction interpretation. In this research, different ensemble machine learning techniques have been deployed such as Bagging and Boosting. Our research results showed that the loan approval prediction model has an accuracy of 83.97%, which is approximately 25% better than most state-of-the-art other loan prediction models found in the literature. As well, the model interpretation efforts done in this research was able to explain a few critical cases that the bank decision makers may encounter; therefore, the high accuracy of the designed models was accompanied with a trust in prediction. We believe that the achieved model accuracy accompanied with the provided interpretation information are vitally needed for decision makers to understand how to maintain balance between security and reliability of their financial lending system, while providing fair credit opportunities to their clients.

Title of the Paper: Performance Evaluation of 5G/WiFi-6 Coexistence


Authors: Aymen Zreikat

Pages: 903-913



Abstract: The fifth-generation mobile communication network (5G) is the promising technology nowadays to provide not only a higher speed compared to 4G but also a revolution of services to cover different industrial sectors such as health, production, energy, and many. WiFi-6 is a new wireless local area network (WLAN) technology that is suitable to work in an office, home, and dense areas. As a new technology, 5G has some limitations concerning coverage and capacity. To overcome these limitations, one possible solution is to use the free unlicensed spectrum available in Wi-Fi technology, therefore, a complement solution of 5G/WiFi-6 coexistence is proposed to make both technologies complement each other in providing a better quality of service for the end-user concerning a higher speed, low latency, and higher capacity. According to OFDM modulation, the proposed model divides the cell into two virtual zones, the inner zone represents WiFi-6 technology surrounded by 5G technology. All resources will be shared between the two technologies taken into consideration that more bandwidth should be given to the most inner zone with high intensity of traffic. The call admission control algorithm will be based on a minimum bit rate to be given to each zone that should be satisfied to admit the call. The model is solved using MOSEL-2 simulation language, to study different performance parameters such as BER, utilization, blocking probability, latency, throughput, and aggregate average bit rate in both zones. The simulation results show that coexistence causes some degradation in 5G performance, however, a positive effect on the overall cell performance is achieved by balancing the load all over the whole cell.

Title of the Paper: Motion Control of Small Autonomous Underwater Vehicle in Presence of Parameters Uncertainties


Authors: Jerzy Garus, Mariusz Giergiel

Pages: 888-902



Abstract: A dynamic model of the underwater vehicle is usually established with parameters uncertainties due to the non-linear and time-varying nature of hydrodynamic forces from the surrounding fluid and external environmental disturbances. The paper investigates the motion control problem of the vehicle in tridimensional space based on model reference adaptive control. A developed autopilot consists of three independent controllers with a parameter adaptation law implemented. A control performance is guaranteed by suitably choosing design parameters. The effectiveness and robustness of the proposed control scheme for trajectory tracking in surge, depth and yaw dynamics is tested through simulations studies.

Title of the Paper: A Novel Model for Train Operation Adjustment in High-Speed Railway based on Max-plus Algebra


Authors: Zhong-bo Liu

Pages: 881-887



Abstract: The running time of high-speed train is generally not late, and it can run normally in most cases. However, when severe weather conditions, train components and equipment, accidents or emergencies occur, it will lead to train operation delay and traffic congestion. Therefore, when an accident occurs, we need to adjust the train time or route timely and accurately. As an important algebraic system, max-plus algebra is widely used in the field of industrial production control. In industrial production, the most production mode is the discrete system , but the characteristics and the ability of discrete systems depends on the periodic of system and the number of workpiece produced by the system in unit time, and the characteristics of the system are closely related to the properties of the matrix, especially, the eigenvalues and eigenvectors of the matrix in the sense of max-plus algebra. Therefore, this paper studies the max-plus algebra theory and the solution of eigenvalues and eigenvectors of matrices in the sense of max-plus algebra, establish the operation time matrix to optimize the train operation adjustment model of high-speed railway, and analyze the failure propagation model.

Title of the Paper: Discontinuous Legendre Wavelet Galerkin Method for Optimal Control of Time Delayed Systems


Authors: Xiaoyang Zheng, Chengyou Luo, Zhaohui Zhang

Pages: 875-880



Abstract: Time-delay systems arise in many important applications in science and engineering and optimal control of delay differential equations are of theoretical and practical importance. This paper presents discontinuous Legendre wavelet Galerkin (DLWG) approach for solving optimal control problem of time-delayed systems. This new method demonstrates that operational matrices of derivative, delay and product are lower dimensions and sparse because of calculation only on each subinterval. The advantages are implemented to solve algebraic equations transformed from the time-delayed systems with less storage space and execution time. Finally, an experiment is included to illustrate the effectiveness and applicability of the proposed method.

Title of the Paper: Attitude Output of Strapdown Inertial Navigation System Based on Laser Gyro


Authors: Jianzhong Wang, Yan Zhang, Jijun Yan

Pages: 863-874



Abstract: Aiming at the real-time problems of signal acquisition, attitude calculation and data exchange of strapdown inertial navigation system, the data exchange between the core device of three-axis screw instrument and three-axis accelerometer sensor inertial unit (IMU) is analyzed. The RS-232 serial interface and can bus interface are adopted, which can not meet the requirements of high-speed sampling and real-time data transmission of each sensor. A new method based on FPGA dual port RAM and dual DSP is proposed Speed data access mode, through the main control CPU clock synchronization, can effectively solve the bottleneck problem of data communication between IMU attitude data and core equipment, and realize the rapid response ability of vehicle navigation system. Experiments and simulations show that the highest frequency attitude update rate of the method can reach 2000kHz, which can effectively solve the input and output data and navigation calculation ability, and improve the maneuverability of the carrier.

Title of the Paper: A Path-Server Traffic Scheduling Algorithm for Wireless Network Load based on SDN


Authors: Jie Zhou

Pages: 855-862


Abstract: At present, the research on flow scheduling optimization around SDN has become the focus of international attention. Based on the current traffic control algorithm, especially the shortcomings of traffic scheduling optimization, Path-Server Traffic Scheduling (PSTS) algorithm is proposed in this paper to solve problems. Based on the real-time monitoring of network on Ryu controller, network topology, network load and server load information were obtained to choose the optimal routing scheme, thus achieving the goal of improving the overall network performance. Through the interaction between the components, the work flow of the Ryu controller is designed, and the channel quality information among users is maintained by the link state management module. When the small base station receives the data packet sent by the SDN exchange price, it will temporarily store it in the data cache module. Then, according to the amount of cached data and the channel quality of each user, the optimal time slot of the wireless network resource allocation scheme is comprehensively determined, and the data packet is sent to the corresponding client. In the view of proposed design, the Mininet simulation platform is used to simulate the SDN network in this paper. Based on the simulation platform, the performance of the algorithm is analyzed and compared. Besides, bandwidth utilization, average transmission delay, system utility and terminal usage change, interrupt rate and terminal usage change of different algorithms in SDN wireless network are analyzed and compared through data. All experiments have proved that the research content proposed in this paper has an obvious effect on network load control, which shows network performance

Title of the Paper: Artificial Neural Network Performance Boost using Probabilistic Recovery with Fast Cascade Training


Authors: Andreas Maniatopoulos, Alexandros Gazis, Venetis P. Pallikaras, Nikolaos Mitianoudis

Pages: 847-854



Abstract: Pattern Recognition and Classification is considered one of the most promising applications in the scientific field of Artificial Neural Networks (ANN). However, regardless of the vast scientific advances in almost every aspect of the technology and mathematics, neural networks still need to be fairly large and complex (i.e., deep), in order to provide robust results. In this article, we propose a novel ANN architecture approach that aims to combine two fairly small Neural Networks based on an introduced probability term of correct classification. Additionally, we present a second ANN, used to reclassify the potentially incorrect results by using the most probable error-free results as additional training data with the predicted labels. The proposed method achieves a rapid decrease in the mean square error compared to other large and complex ANN architectures with a similar execution time. Our approach demonstrates increased effectiveness when applied to various databases, related to wine, iris, the Modified National Institute of Standards and Technology (MNIST) database, the Canadian Institute for Advanced Research (Cifar32), and Fashion MNIST classification problems.

Title of the Paper: Short-Term Load Forecasting of Power System Based on Improved BP Neural Network


Authors: Sufen Li

Pages: 840-846



Abstract: Power system load is a stochastic and non-stationary process. Due to the influence of various factors, some bad data may exist in the load observation value. These data are mixed into the normal load data to participate in the training of neural network, which seriously affects the accuracy of load forecasting. Short-term load forecasting is the basis of power system operation and analysis, improving the precision of load forecasting is an important means to ensure the scientific decision-making of power system optimization. In order to improve the precision of short term load forecasting in power system, a short-term load forecasting model based on genetic algorithm is proposed to optimize BP neural network. Firstly, using genetic algorithm to optimize the initial weights and thresholds of BP neural network to improve the prediction accuracy of BP neural network; Through the comparison and analysis before and after the model optimization, the experimental results with smaller prediction error were obtained. The simulation results show that the short-term load forecasting model established by this method has faster convergence rate and higher prediction precision.

Title of the Paper: Study on Human Resource Allocation Efficiency Based on DEA Analysis


Authors: Guannan Bao, Fanlei Zeng, Mingwei Wang

Pages: 826-832



Abstract: As a new resource, human resource has attracted wide attention in many social fields. The competition of human resource is the competition of productivity between different enterprises. With the reform and opening up, national economy of China continues to develop rapidly. The sound macro-economy and rapidly development of capital market lead the banking industry to have a good development prospect. Whether the human resource allocation of commercial Banks is effective or not has a great impact on the bank earning. This study aims to further clarify the human resource allocation efficiency of banks to improve the efficiency and realize the improvement of bank profits. In this study, computer software was used to calculate the scale efficiency, pure technical efficiency, the change of scale interval and data envelopment analysis (DEA) efficiency of some selected representative banks according to the DEA model. The analysis showed that the DEA scale of many banks could reach the valid value 1, and the human resource allocation efficiency was optimized. The pure technical efficiency of some banks was valid value 1, but the scale efficiency was low and ineffective, which needed to be enlarged appropriately. And the DEA efficiency of some banks was invalid, which was caused by the valid value 1 of the scale efficiency and relatively low pure technical efficiency, and such banks need to make reasonable allocation of human resources.

Title of the Paper: Alternative methods to derive the Black-Scholes-Merton equation


Authors: Nattakorn Phewchean, Renato Costa, Masnita Misiran, Yongwimon Lenbury

Pages: 821-825



Abstract: We investigate the derivation of option pricing involving several assets following the Geometric Brownian Motion (GBM). First, we propose some derivations based on the basic ideas of the assets. Next, we consider the trivial case where we have n assets. Finally, we consider different drifts, volatilities and Wiener processes but now from n stochastic assets taking into account a fixed-income.

Title of the Paper: A Prediction Method of Network Security Situation based on QPSO-SVM


Authors: Jian-an Zhang, Hui Luo

Pages: 815-820



Abstract: In network security situation awareness system, situation prediction is the key point. The traditional intrusion detection method lacks scalability in the face of the changing network structure and lacks adaptability in the face of unknown attack types. In order to ensure and improve the accuracy of situation prediction, a QPSO-SVM prediction model is proposed by combining the optimization performance of quantum particle swarm optimization and the prediction accuracy of support vector machines. By adding the original sequence to the original sequence, this model weakens the irregular disturbance in the original sequence and enhances the regularity of the sequence. Compared with the traditional SVM and PSOSVM, the superiority of the prediction precision is better, the prediction accuracy can be ensured, and the validity of the model is tested by the simulation experiment.

Title of the Paper: Weak form market efficiency: A case study of Asia-Pacific markets


Authors: Nattawut Phanrattinon, Yongwimon Lenbury, Masnita Misiran, Nattakorn Phewchean

Pages: 807-814



Abstract: This study aims to test the weak form market efficiency for five developed markets, nine emerging markets and three frontier markets in the Asia-Pacific region. The tools applied in the test of this form of market efficiency are serial correlation test, runs test and unit root test. The analysis is performed by using logarithm return for the period of 2008 to 2018. For all markets in our research, the results strongly reject the weak form efficiency when the unit root tests are carried out, while the results from the Durbin-Watson test are in complete contrast. However, in the runs test and variance ratio test, the results provide mixed evidences of weak form efficiency of the markets

Title of the Paper: The Neural Modules Network with Collective Relearning for the Recognition of Diseases: Fault- Tolerant Structures and Reliability Assessment


Authors: Iraj Elyasi Komari, Mykola Fedorenko, Vyacheslav Kharchenko, Yevhenia Yehorova, Nikolaos Bardis, Liudmyla Lutai

Pages: 792-800

Abstract: The article presents the architecture of multi-level information-analytical system (IAS) based on the neural modules network (NMN). This network consists of neural modules which are placed at the three levels (local, region and nation geographically distributed medical centers). Procedures of learning and collectiverelearning of neural modules consider region particularities and are based on analysis, generalization and exchange of experience related to diagnosis of diseases. These procedures provide modification and filtering parameters used as input for the further learning of local and regional neural modules.A few fault-tolerant structures of NMN-based IAS are researched taking into account different options of server and communication redundancy. Reliability block diagrams for redundant IAS structures are developed and formulas for calculation of probability of upstate are analyzed.

Title of the Paper: Possibility of Using the Spiral Single Mode Optical Fiber for Dispersion Compensation


Authors: Mohammed Yousef AL-Gawagzeh

Pages: 788-791


Abstract: This research show the possibility of using the Anisotropy of spiral single mode optical fiber for compensation the main three types (material,waveguide,and polarized) of is also study the effect of energies mutual transformation of optical pulsed signals that carried by orthogonal waves (HEe11) and (HEo11) on the generation the chromatic and polarized dispersions and inter symbol distortions of example of polarized modal dispersion compensation is gives.

Title of the Paper: Drone Fleet Survivability Evaluation Based on Lanchester’s Modified Deterministic Model


Authors: Herman Fesenko, Vyacheslav Kharchenko, Nikos Bardis, Ah-Lian Kor, Yevhen Brezhniev

Pages: 775-781

Abstract: An algorithmic approach for the assessment of the survivability is proposed that is based on Lanchester’s modified deterministic model. Methods are suggested for increasing the available time capability for nuclear power plant monitoring and coverage, using the required or a limited number of the operable drones,. Dependencies of the variance between the residual fleet damage and permissible drone fleet damage on monitoring time as well as dependencies of the monitoring time on the recovery group productivity are analysed.

Title of the Paper: Comparison between Optical Fiber Amplifiers of Information Signals


Authors: Mohammed Yousef AL-Gawagzeh

Pages: 769-774


Abstract: This research is devoted for reviewing a differe nt types of fiber optical amplifiers for construction the optical system of transmission such erbium-doped fiber amplifi er (EDFA), Thulium-doped fiber amplifier (TDFA), Compelle d Combinational Dispersion (CCD), their principles of wo,r k advantages, and disadvantages, with purpose of increasing eth pass band spectrum and to decrease some disadvantages su ch attenuation and dispersion. also considered a suggested metho d of simultaneous stokes and anti-stokes strengthening of compelled combinational dispersion in fibers

Title of the Paper: Risk Analysis and Support for the Integrated Rescue System on Emergencies


Authors: M. Dzermansky, R. Pekaj

Pages: 764-768

Abstract: The protection of the population is one of the basic aspects of the contemporary world. As well as an integrated rescue system that performs the tasks of protection of the population and has the task of minimizing the impact of emergencies and their rescue and liquidation work. Today, emergency plans include individual analyzes that help point out possible risks and also help integrated rescue system units prepare for them

Title of the Paper: A CPSO Algorithm for Optimization of Wireless Sensor Network Positioning


Authors: Xing Jin

Pages: 758-763


Abstract: Wireless sensor network technology is widely used, and most applications depend on node location. Aiming at the problem that the signal intensity indication (RSSI) is susceptible to the environment, an improved chaotic particle swarm optimization (CPSO) is proposed in this paper in order to improve the positioning performance of the sensor node based on the research of the existing location algorithm. The convergence of the algorithm is better than the PSO algorithm. The results of the RSSI measurement data in both indoor and outdoor communication environments show that, compared with the general weighting algorithm and the traditional PSO algorithm, the improved combined optimization algorithm can greatly improve the effect of the ranging error on the positioning error and improve the positioning performance greatly

Title of the Paper: Harmonics of Double-fed Wind Power System with Grid-Tied Dual-PWM Converter


Authors: Qiaoli He

Pages: 743-750


Abstract: To fill the gaps of the double-fed wind power system, this paper conducts a study for the scarcity and integration of social resources. The LF harmonics on the DC and grid sides are surveyed based on the double Fourier transform algorithm, in conjunction with the power balance theory. A study model has also been built herein. The findings show that the calculated values of the HF harmonic components in the DFIG rotor current almost coincide with the simulation results, regardless of whether the wind velocity is 7 m/s or 19 m/s. When the three-phase voltage of the grid is unbalanced, the stator current contains the grid side basebands with LF harmonics of odd times, among which, the fundamental frequency of triple grid side baseband is the most distinct. It is thus clear that the simulation can capture relevant voltage and current data for the wind power system running in the balance and unbalanced states of grid voltages. it is therefore proved that the theoretical analysis is accurate and reliable

Title of the Paper: Construction of a Power Quality Monitoring System based on the Wavelet Transform Change-Point Detection Algorithm on the Virtual Instrument Platform


Authors: Shi Lei, Wang Na

Pages: 736-742


Abstract: It is aimed to carry out the investigation on power quality detection, promote the realization of efficient transmission of network data, and expand the application of wavelet transform change-point detection algorithm in the monitoring system. The voltage deviation is used as a starting point to explore the detection of power quality. First, it describes the harmonics of the public power grid and the limits of harmonic voltage. Second, based on the virtual instrument platform, the power quality monitoring system based on wavelet transform change-point detection algorithm is completed. Finally, by adding a monitoring terminal and a service terminal, the design of the monitoring system server is completed. Through the analysis of the experimental results, it is found that in the monitoring system, the current waveform and the three-phase voltage can be accurately displayed. The combined design of the networked monitoring system and the system server enables the system to complete the rapid transmission of data related to power quality, while having a good monitoring effect. For the optimization of networked monitoring experienceof the server, the application of wavelet transform in power quality measurement is realized. The power quality monitoring system proposed has a strong practicality in power quality monitoring.

Title of the Paper: Application of Deep Learning in Power Load Analysis


Authors: Xinhua Duan

Pages: 726-735


Abstract: Aiming at the problems of slow model training speed and poor prediction effect of traditional power load prediction algorithm, a parallel load prediction method based on deep learning is proposed. The method is based on the MapReduce parallel calculating framework, and the deep belief network model, which is used to parallel training the sample data with the historical load and the weather information, and the model of the training model to predict the load value. The experimental results show that the average root-mean-square error between the predicted power load value and the actual value of the prediction method in this paper is 2.86%. The prediction accuracy is higher than the traditional method, and the training and prediction time are effectively reduced, which can adapt to the prediction requirements of large-scale power data.

Title of the Paper: Power System Frequency Prediction after Disturbance based on Deep Learning


Authors: Wei Huang

Pages: 716-725


Abstract: In order to ensure the safe and stable operation of power system, enrich the means of power grid analysis and control, and expand the application of deep intelligent learning methods in power grid systems, the application of deep learning intelligent machine learning method in frequency prediction of large power grid is explored. First, on the basis of deep learning, the frequency response mode of large power grid is analyzed and the key characteristic quantities that affect the frequency response mode are extracted. Second, the deep belief neural network (DBN. DNN) frequency prediction model is constructed. Also, the training and testing of the model are introduced. Finally, the input and output based on the DBN.CNN prediction model and the network structure design of the model are analyzed. The prediction performance of the model is evaluated. The results show that when the number of neurons in the hidden layer is 50, the model achieves the optimal prediction effect. Increasing the number of training samples helps to improve the modeling ability and prediction accuracy of the model. For frequency prediction problems, the number of training samples should be set to ≥400, and the number of hidden layers corresponding to the model should be 5. When the number of hidden layer neurons is 10, the prediction accuracy of the DBN/DNN network is poor. When the number of hidden layer neurons is 50, the model can achieve the best prediction effect. Overall, the DBN.DNN prediction model has good prediction performance. The RMSE of the forecast data is O. 0073Hz can basically meet the actual application requirements. Therefore, the frequency prediction method based on deep belief neural network has certain advantages in accuracy and efficiency.

Title of the Paper: On-line Detection Method of Power Quality Detection Device based on High-precision Standard Time Synchronization Technology


Authors: Rui Chen

Pages: 708-715


Abstract: To improve power quality and improve the operational efficiency and accuracy of industrial automation machinery, the on-line detection method of power quality detection device based on high precision standard time synchronization technology is studied. Firstly, the research situation of power quality and time synchronization technology is briefly analyzed, and the Network Time Protocol (NTP) time synchronization technology and the calculation method of basic power quality are introduced. Then, based on the high-precision standard time synchronization technology, the system scheme of the power quality detection device is designed. Finally, the system is tested, and the test results are in line with the test expectation, indicating that the system can make a particularly accurate measurement of power quality. The power quality detection system designed in this research improved the detection performance of power quality and is of great significance to the research on online detection of power quality

Title of the Paper: Space Vector Transients of Three-phase Transformers


Authors: Diego Bellan

Pages: 700-707


Abstract: This paper is devoted to the transient analysis of three-phase transformers by means of the space vector tool. Space vector definition is based on the Clarke transformation, operating in the time domain. Thus, the space vector is better suited to transient analysis when compared with approximate approaches based on the phasor symmetrical-component transformation. Space-vector equivalent circuits are derived for three-phase transformers with Wye and Delta connections. A case study, consisting in the transient due to a capacitor bank insertion, shows that the proposed space-vector approach can clearly evidence the asymmetrical transient behavior of the phase variables in terms of different peak levels and oscillations amplitude.

Title of the Paper: An Efficient Intelligent Power Detection Method for Photovoltaic System


Authors: Ayman M. Mansour, Jalal Abdallah, Mohammad A. Obeidat

Pages: 686-699


Abstract: Jordan has experienced a significant increase in both peak load and annual electricity demand within the last decade due to the growth of the economy and population. Photovoltaic (PV) system is one of the most popular renewable energy source in Jordan. PV system is highly nonlinear with unpredictable behavior since it is always subject to many external factors such as severe weather conditions, irradiance level, sheds, temperature, etc. This makes it difficult to maintain maximum power production around its operation ranges. In this paper, an intelligent technique is used to predict and identify the working ability of the PV system under different weather factors in Tafila Technical University (TTU) in Jordan. It helps in optimizing power productions for different operation points. The PV system in Tafila with size 1 MWp PV generated 5.4 GWh since 2017. It saves about € 1.5 million in three years. A real power data from the PV system and a weather data from world weather online site of TTU location are used in this study. Decision tree technique is employed to identify the relation between the output power and weather factors. The results show that the system accuracy is 82.01% during the training phase and 93.425 % on the validation set.

Title of the Paper: An Algorithm of Tracking and Controlling Network Attack Node based on Adaptive Neural Networks


Authors: Wei Wang

Pages: 669-678


Abstract: In order to obtain certain and comprehensive information for formulating network attack strategy, a complex network attack method is proposed in this paper. The attackers’ income, loss, cost and encountered risk in network attack are analyzed and index system is established to evaluate attack effect of network node with dynamic Bayesian network. This method can overcome defects of static evaluation which is relied on single index of network topology. Simulation experiment shows that this method combines more nodes and observation during the attack. It can avoid the gap between actual attack effect and theoretical expectation when attack is implemented by relying on static evaluation. In the meanwhile, it is more accurate in attack precision and of high attack efficiency

Title of the Paper: Circuit Health State Estimation via an Integrated Deep Neural Network


Authors: Ming Xiang, Yigang He, Hui Zhang, Chaolong Zhang, Zhaorong Zeng, Baoran An

Pages: 661-668


Abstract: The approaching of ubiquitous power internet of things is accelerating the industry to become more integrated and intricated. It is almost inevitable for a system to encounter failures during its whole life span. Thus, it is imperative to monitor the operating system from a system-level perspective to avoid potential catastrophes. Intuitively, inclusive prior knowledge is required for prognostics and health management (PHM). However, due to time-varying parameters and external conditions, the system is usually too complex to neatly fit into a prior-built model. This paper presents a novel pragmatic method, encompassing the convolutional autoencoder (CAE) and long short-term memory recurrent neural network (LSTM-RNN), to track the health state of a circuit. Briefly, the proposed method can be divided into two steps. First, degradation characteristics are extracted by using the time-domain features and CAE to prepare for the later health state estimation step. Then, the LSTM-RNN is used to finish the predictive process, i.e., to map the extracted abstract features to the health state. In addition, the degradation of a practical circuit considering the angular distance is discussed to quantify the health state of the circuit system. Furthermore, a case study based on that prognostics scheme is conducted to verify the proposed method. The comparison with other existing popular methods indicates the superiority of the proposed methodology

Title of the Paper: A Regional Industry Intelligence Business Platform based on Adaptive Clustering


Authors: Junjie Liu, Danlin Cai, Daxin Zhu, Siyu Huang

Pages: 656-660


Abstract: How to grasp comprehensive, timely, effective and accurate business competitive intelligence has become an urgent and critical issue for regional industrial clusters. Therefore, a Industrial Internet Platform for Regional Economic based on Adaptive Clustering called IIPRE is developed. The multi thread oriented extraction technology is used to collect the business intelligence data of specific industries. The data is clustered by rule-based machine learning, and the business information data model is used for analysis. Finally, the visualization report is generated by big data visualization software. The system uses Intelligent Retrieval technology to automatically complete the functions from acquisition to processing automatically, to generate data information to meet the application requirements, uses Automatic Classification Technology to provide automatic classification function combining machine-based automatic learning and rule-based information, and uses Personalized Display Technology to provide customizable personalized display pages for individuals, to organize and adjust the hot information, thematic information, clustering, related words, early warning, statistics and other information released by the system. The construction of the platform provides enterprises with comprehensive, timely, effective and accurate business competitive intelligence services, improves the strategic planning, competitive intelligence acquisition and industrial information sharing capabilities of regional industrial clusters, and will achieve tremendous economic and social benefits

Title of the Paper: A Lean Green Implementation Evaluation Method based on Fuzzy Analytic Net Process and Fuzzy Complex Proportional Assessment


Authors: Xiaoyong Zhu, Hua Zhang

Pages: 646-655


Abstract: For the issue that the optimal selection of lean green implementation tools for performance effect, a method combining fuzzy analytic net process (FANP) with fuzzy complex proportional assessment (COPRAS) is proposed. Firstly, the main properties of the lean green performance and candidate tools are determined based on the production requirements and expert knowledge. Then, the fuzzy comparison matrix of each attribute is constructed according to the expert judgment represented by triangular fuzzy number, and the FANP method is used to obtain the importance weight of each attribute. After that, the fuzzy decision matrix between attributes with tools alternatives is constructed, and multiplying it with the attribute weight so as to obtain the weighted normalized decision matrix. Finally, the fuzzy COPRAS method is used to analyze the decision matrix to get the utility of each tools alternative. Based on the model and analysis, these results make us understand and be familiar with the most influential practice tools on performance benefits. These findings are expected to policy makers and industrial practitioners to focus on key elements of the success of the manufacturing business to facilitate both lean and green manufacturing implementation and assessment, and contribute to sustainable growth.

Title of the Paper: Research on the Coupling Coordinative Degree of Tourism Development and Poverty Alleviation Effects in China based on the Model of DPSIR--An Example of Guizhou


Authors: Yanling Ma

Pages: 637-645


Abstract: Based on the framework of driving force-pressure-state-response, a DPSIR framework for the coupling and coordination mechanism between tourism development and poverty alleviation effects was constructed by comprehensively considering the poverty alleviation process and poverty vulnerability characteristics of Guizhou Province. On this basis, by using the data from 2006-2017, the paper made an empirical analysis on the development relation of tourism development and ecological environment coupling in Guizhou Province by using the coupled model. The results showed that the degree of coupling between tourism development level and poverty alleviation effects showed multiple different stages as human production activities progress. The comprehensive evaluation index of tourism development and poverty alleviation effects in Guizhou Province showed an upward trend from 2012 to 2019, and the rate of increase was fast. The degree of coupling was continuously rising rapidly, experienced two stages: Firstly, the period from the severe imbalance period to the imminent imbalance period from 2012 to 2013. Secondly, the imbalance period to the primary coordination period from 2014 to 2016. Lastly, the primary coordination period to the good coordination period from 2017 to 2019. A transition from a serious imbalance to a well-coordinated phase was achieved. According to the different developmental stages of the coupling coordination mechanism, we put forward some measures for the coordinated development between tourism development and poverty alleviation effects in Guizhou Province.

Title of the Paper: Large Medical Equipment in Hospital Management and its Economic Benefits


Authors: Maojun Wang

Pages: 629-636


Abstract: Large medical equipment management is one of the important parts in the management of hospital, which has great meaning to the hospital development. However, the current use of medical equipment generally has the problem of high cost and low efficiency, which brings great difficulty to hospital management. Based on the statistics of investment cost, operation cost and medical equipment income of some large medical equipment in a hospital in Suzhou in recent three years, the economic benefits of large medical equipment were analyzed in aspects of payback period and method of rate of return on investment. The results demonstrated that the investment cost and operation cost of the large medical equipment in the hospital management were both high, and the economic benefits were significantly different between different equipment. Then based on this, some Suggestions were put forward for equipment management, and the role of economic benefit analysis in hospital management was illustrated to provide some theoretical reference for further research.

Title of the Paper: A New Approach to Solve Perturbed Symmetric Eigenvalue Problems


Authors: O. Dadah, H. Ait rimouch, A. Mousrij, O. Koubaiti, N. Mastorakis

Pages: 622-628


Abstract: The objective of this study is to ef-ciently resolve a perturbed symmetric eigen-value problem, without resolving a completelynew eigenvalue problem. When the size of aninitial eigenvalue problem is large, its multipletimes solving for each set of perturbations can becomputationally expensive and undesired. Thistype of problems is frequently encountered inthe dynamic analysis of mechanical structures.This study deals with a perturbed symmetriceigenvalue problem. It propose to develop atechnique that transforms the perturbed sym-metric eigenvalue problem, of a large size, toa symmetric polynomial eigenvalue problem ofa much reduced size. To accomplish this, weonly need the introduced perturbations, the sym-metric positive-de nite matrices representing theunperturbed system and its rst eigensolutions.The originality lies in the structure of the ob-tained formulation, where the contribution of theunknown eignsolutions of the unperturbed sys-tem is included. The e ectiveness of the pro-posed method is illustrated with numerical tests.High quality results, compared to other existingmethods that use exact reanalysis, can be ob-tained in a reduced calculation time, even if theintroduced perturbations are very signi cant.

Title of the Paper: An Image Reconstruction Model under Poisson Noise using Multiscale Compressed Sensing


Authors: Pai Zhang

Pages: 616-621


Abstract: For the influence of poisson noise images, in order to get rid of poisson noise, this paper put forward image reconstruction method by using multiscale compressed sensing. the algorithm can approximate the optimal sparse representation of the image edge details such as the characteristics of theShearlet domain based multi-scale compressed sensing method. The image is decomposed into the high-frequency subbands byShearlet, and the compressed sensing is applied into each subband to reconstruct the image. In this paper, A total variation of RL iterative algorithm constructed by nonlinear projection algorithm based on closed convex set is explored as the reconstruction method, which use derivation of the nonlinear projection instead of total variation. In mathematics, Shearlet has been proved to be a better tool for edge characterization than traditional wavelet. By using the nonlinear projection scheme to constrain the residual coefficients in the Shearlet domain, a better estimation can be obtained from the Shearlet representation. Numerical examples show that the denoising effect of these methods is very good, which is better than the correlation method based on Curvelet transform. In addition, the number of iterations required by our scheme is far less than that of our competitors.

Title of the Paper: On Complexity of Adaptive Splines


Authors: Yuri K. Demjanovich

Pages: 607-615


Abstract: The paper discusses various methods of adaptive spline approximations for the flow of function values. It is considered an adaptive compression algorithm, which, for a priori given , has the properties 1) the complexity of the algorithm is proportional to the length of the original flow, 2) by the piecewise linear interpolation of the compression result, it is possible to restore the original flow with an accuracy of 3) the compression result is close to optimal and has 0(M) of arithmetic operations. The effectiveness of this approach is demonstrated on rapidly changing initial flows of numerical information in the digital experiment . In addition, the paper presents an exact two-sided estimate for the number 0(M2 ) of arithmetic operations for the optimal solution of the problem of compressing an informational numerical flow of length M with the possibility of recovering this flow with a predetermined accuracy. Provided that the original flow is convex, a compression algorithm is developed with an accurate twosided estimate of the number 0(Mlog2M) and with the possibility of recovery with a prescribed accuracy.

Title of the Paper: A Multi-Scale Compressed Sensing Algorithm based on Variational Mode


Authors: Shuyao Tian, Pai Zhang, Hongju Lin

Pages: 600-606


Abstract: The compressed sensing algorithm based on the hybrid sparse base (TFWBST+wave atom) usually uses two kinds of image sparse transformations to realize the sparse representation of structure and texture respectively. However, due to the lack of constraints on image texture and structure and the lack of orthogonality of the two sparse bases, the sparse coefficient of structure and the sparse coefficient of texture after transformation are often not good enough to reflect their respective components, that is, the texture coefficient often loses the detail information of texture. To overcome this phenomenon, this paper combines the compressed sensing algorithm based on hybrid base with the layered variational image decomposition method to form the variational multi-scale compressed sensing, which is to establish the CS image reconstruction model with minimal energy functional. The layered variational image decomposition decomposes image into different feature components by minimizing energy functional. The reconstruction of each layer by compressed sensing algorithm is very suitable for texture and detail reconstruction. In this model, TFWBST transform and wave atom are combined as a joint sparse dictionary, and the image decomposition is carried out under the (BV, G, E) variational framework, which is introduced into multi-scale compressed sensing technology to reconstruct the original image. In this new functional, TFWBST transform and wave atom are used to represent structure and texture respectively, and multiscale (BV, G, E) decomposition which can decompose an image into a sequence of image structure, texture and noise is added for restricting image parts. Experiments show that the new model is very robust for noise, and that can keep edges and textures stably than other multi-scale restoration and reconstruction of images.

Title of the Paper: An Improved White Patch Method for Image Illumination Estimation


Authors: Li Zhao, Wei Ma, Mengxia Tang, Songnan Chen

Pages: 594-599


Abstract: As one of the underlying pixel-based illumination estimation algorithms, the White Patch algorithm is an algorithm for calculating the global illumination RGB value of an image based on the specific assumption that the maximum reflected light on the scene is chromatic. The algorithm is harsh on the assumptions of scene illumination, and many images are difficult to satisfy this assumption constraint. In this paper, we propose an improved White Patch image illumination estimation method. Firstly, the image patch is extracted by using sliding window method, we then use the white patch algorithm to estimate the illumination color value of each patch, and finally the kernel density estimation is adopted to obtain the overall illumination color value of the image. The experimental results show that the improved White Patch images illumination estimation method proposed to this paper performs better on the illumination estimation of natural illumination scene images.

Title of the Paper: Innovative and Motivational Competence of Leaders and Its Transformation in the Context of Digitalization


Authors: E. Alekhina, V. Parakhina, O. Boris

Pages: 589-593


Abstract: The main factor that affected changing competencies in modern conditions is digitalization; the necessity of the interconnected development of innovative and motivational competence of managers is substantiated. The goal of this article is to highlight the most important characteristics of the innovative and motivational competence of organizational leaders. To analyze the competence factors, hypotheses about their change were made, which were tested using two methods: a sociological survey to assess the competencies priority and pairwise comparisons of competency characteristics in terms of their impact on the innovative activity of organizations. The competency model of a modern, innovatively active leader was formed, which included: psychological readiness for change and constant self-development, a desire to lead the organization to success, the need to be a leader based on innovative style of behavior, knowledge of advanced achievements in the organization's field of activity, experience in introducing inventions, skills to attract investment, understanding the risks of improvement. Our assumptions about the transformation of the leaders’ competencies are confirmed. However, estimates of the competencies shift towards motivational components were less significant than expected. Nevertheless, in the context of digitalization, it is very important for innovative leaders to constantly improve their motivational competencies in order to stimulate there highly professional creative workers who create and implement innovations.

Title of the Paper: Platform for Big Biomedical Data Streams Management and Analytics


Authors: Veska Gancheva

Pages: 580-588


Abstract: Major challenge in the analysis of clinical data and knowledge discovery is to suggest an integrated, advanced and efficient tools, methods and technologies for access and processing of progressively increasing amounts of data in multiple formats. The paper presents a platform for multidimensional large-scale biomedical data management and analytics, which covers all phases of data discovery, data integration, data preprocessing, data storage, data analytics and visualization. The goal is to suggest an intelligent solution as integrated, scalable workflow development environment consisting of a suite of software tools to automate the computational process in conducting scientific experiments.

Title of the Paper: On Wavelet Decomposition of the Singular Splines


Authors: Yu. K. Demjanovich, T. O. Evdokimova, O. N. Ivancova, D. M. Lebedinskii, A. Y. Ponomareva

Pages: 571-579


Abstract: One of the approaches to the problem of approximating functions with a singularity is the creation of an approximating apparatus based on splines with the same feature. For the wavelet decomposition of spline spaces it is important that the property of the embedding of these spaces is associated with embedding grids. The purpose of this paper is to consider ways of constructing spaces of splines with a predefined singularity and obtain their wavelet decomposition. Here the concept of generalized smoothness is used, within which the mentioned singularity is generalized smooth. This approach leads to the construction of a system of embedded spaces on embedded grids. A spline-wavelet decomposition of mentioned spaces is presented. Reconstruction formulas are done

Title of the Paper: Latency and Reliability Improvements of High Critical Tasks in Mixed Criticality Systems


Authors: Hakduran Koc, Vamsi Krishna Karanam

Pages: 561-570


Abstract: Reliability and execution latency of high critical tasks are crucial for a successful execution in a mixed criticality system with tight design constraints. In this paper, we focus on two main design problems, namely latency-constrained maximum reliability problem and reliability-constrained minimum latency problem, for the applications running tasks of different criticality. The target architecture can run in two operating modes: low criticality mode (normal operating mode) and high criticality mode. For the former problem, we first find the minimum execution latency assuming the system runs in low criticality mode. Then, using this latency as lower bound, we present a heuristic algorithm to improve the reliability of high critical tasks in the application. The proposed algorithm assigns high critical tasks to the highest reliable processing elements in the technology library, and then, schedules low critical tasks without exceeding the given latency constraint. Similarly, for the latter problem, we first determine the highest reliability assuming the system runs in low criticality mode. Then, considering the overall system reliability, the proposed approach reduces the latest completion time of high critical tasks by giving them priority over low critical ones when selecting processing elements. The experimental evaluation conducted using task graphs shows up to 14.69% reliability improvement and 20.05%, on the average, latency improvement for the high critical tasks in the system

Title of the Paper: The Generalized Haar Spaces and Their Adaptive Decomposition


Authors: Yuri K. Demjanovich, Tatjana A. Safonova, Mikhail A. Terekhov, V. Belyakova

Pages: 548-560



Title of the Paper: DC-DC Converter Topologies for LED Driver Circuit: A Review


Authors: Deepak Agrawal, Rajneesh Kumar Karn, Deepak Verma, Rakeshwri Agrawal

Pages: 542-547


Abstract: It has been noticed that in commercial lighting, in terms of efficiency light-lamps based on light-emitting diodes(LEDs) are far better as compared to those where traditional high-pressure sodium (HPS) lamps which are still in use in major underdeveloped and developing areas worldwide in specifically street lighting. The LED driver is an electrical device which controls power flow to the single LED or a string of the LEDs or controls to the current flowing through the LEDs. Available conventional topologies for LED drivers have several demerits such as flickering issues, high losses, luminance problems, low power factor, more number of switches etc. So, the need of the hour is to develop efficient, compact, long lifetime, high power factor and flicker-free LED drivers. The LED have numerous advantages such as high luminous efficiency, life span and it has no mercury in its composition. Therefore, recently researchers of this area has been setting a goal to utilize LED as a good alternative to save electricity from major parts of this planet.In this paper, various topologies of LED drivers are presented. This paper also portrays simulation of a LED driver which is based on the combination of the buck-boost converter as power factor correction stage (PFC) and buck converter as dc-dc power conversion (PC) stage. Both the stages are integrated using single switch only so it is basically integrated LED driver circuit.

Title of the Paper: Study of Temporal Correlations in the Urban Noise Monitoring Networkof Milan, Italy


Authors: Roberto Benocci, H. Eduardo Roman, Chiara Confalonieri, Giovanni Zambon

Pages: 533-541


Abstract: The European Life project, called DYNAMAP, has been devoted to provide a realimage of the noise generated by vehicular trafficin urban and suburban areas, developing a dynamic acoustic map based on a limited numberof low-cost permanent noise monitoring stations.In the urban area of Milan, the system has beenimplemented over the pilot area named Area 9.Traffic noise data, collected by the monitoringstations, each one representative of a numberof roads with similar characteristics (e.g. dailytraffic flow), are used to build-up a “real time”noise map. DYNAMAP has a statistical structure and this implies that information capturedby each sensor must be representative of an extended area, thus uncorrelated from other stations. The study of the correlations among thesensors represents a key-point in designing themonitoring network. Another important aspectregards the “contemporaneity” of noise fluctuations predicted by DYNAMAP with those effectively measured at an arbitrary location. Integration times heavily affect the result, with correlation coefficients up to 0.8-0.9 for updating timesof 1h. Higher correlations are observed when averaging over groups of roads with similar traffic flow characteristics

Title of the Paper: Research on Supply Chain Architecture of Logistics Network Platform based on Blockchain Technology


Authors: Lijuan Liu, Chao Li

Pages: 526-532


Abstract: With the development of productivity, supply chaincame into being. This study briefly introduced the blockchain andsupply chain technology, constructed the blockchain-based supplychain logistics platform for managing the information of the supplychain, carried out the interface test on the logistics platform system inthe laboratory, and made an analysis by taking XX flour factory inSichuan province as an example. The laboratory interface test resultsshowed that the system platform could be normally applied to the flourfactory’s supply chain management. The case analysis results showedthat the supply chain that applied blockchain technology couldeffectively resist abnormal data modification to ensure the reliability ofsupply chain information and quickly trace the supply chain problemsto improve the efficiency of supply chain management.

Title of the Paper: Research on E-commerce Payment Security and Privacy Protection based on Improved B2C Model


Authors: Zhihe Wang

Pages: 520-525


Abstract: The popularity of Internet and mobile terminals promotes the development of e-commerce. However, e-commerce which is not necessary to face to face poses a major challenge to the payment security and privacy protection of consumers. This paper briefly introduces the traditional business to customer (B2C) e-commerce model. A third-party privacy server was introduced to provide a double encryption algorithm for the model, and then the traditional and improved B2C models were simulated to verify the performance of the two models in the confidentiality of the transaction information and the security of the transaction information transmission process. The results showed that the intruder could not directly obtain the sensitive privacy information such as payment information and order information of consumers even if he invaded the database of the online store when the improved B2C model was used; after the transaction information was intercepted and decrypted under the two B2C models, the decryption integrity decreased with the increase of the transaction information quantity, while under the same transaction information quantity, the decryption integrity of the improved B2C model was lower

Title of the Paper: A Novel Way to Select the Optimal Electrical Power Demand Management Provider for Robust Smart Grid


Authors: Kyoung Jong Park

Pages: 511-519


Abstract: The smart grid is an integrated management of power demand and supply that cannot be achieved without efficient power demand management because it integrates information technology and shares power information in real time to maximize power efficiency. Efficient power demand management must prevent or minimize risks in advance between the customer and the demand management provider. This study proposes a method that can evaluate the risks that impede efficient power demand management and select the most robust demand management provider with respect to these risks. This paper applies the Grey system theory to obtain objectivity by calculating the quantitative value and risk ambiguity of uncertainty. Six power demand management service providers are evaluated through the opinion of four risk management experts considering eight risk factors with a view to selecting the optimum power demand management service provider for the consumer. In conclusion, this study applies the Grey system theory to the risk factors of six power demand management service providers, determines the ranking from the best power demand management service provider to the inferior power demand management service provider, and provides the most desirable provider to customers.

Title of the Paper: A Multi Fusion Data Mining Algorithm for Solar Energy Efficiency


Authors: Yue Lin, Zhan Shuo, Bai Jing, Kanae Shunshoku

Pages: 499-510


Abstract: The output power of renewable energy has the characteristics of random fluctuation, which have the harmful effect on stability of renewable power grid and causes the problem of low utilization ratio on renewable energy output power. Thus, this paper proposed a method to predict the output power of renewable energy based on data mining technology. Data mining is performed using linear regression algorithm, decision tree, and random forest. The simulation experiment results show the variation of solar radiation size and inclination angle, which improves solar panel position control accuracy and solar energy utilization in solar photovoltaic power generation systems. And this provides the scientific basis for theory and application of the efficiency of utilizing solar energy.

Title of the Paper: Radiated Susceptibility Analysis of Single-wire Transmission Lines by Means of Modified Stochastic Reduced-order Modeling


Authors: Diego Bellan

Pages: 492-498


Abstract: This work investigates the statistical radiated susceptibility of an electrically-short transmission line (TL) consisting of a single wire over a ground plane. The angular parameters of the impinging plane wave and the height of the wire over the ground plane are modelled as random variables with Gaussian/Uniform distributions. The statistical properties of the current in the TL terminations (i.e., mean value, standard deviation, and cumulative distribution function) are derived through a properly defined numerical methodology consisting in a modified version of the conventional Stochastic Reduced-Order Model (SROM) approach. The proposed methodology consists in a straightforward approximation of the input continuous random variables with small-size discrete random variables. In contrast with conventional SROM, no numerical pre-processing is needed. The modified SROM proposed in this paper demonstrates high efficiency when compared with classical Monte Carlo approach. The proposed technique can be applied to the statistical analysis of much more complex systems whose input/output relationship requires a huge computational burden and for which the conventional Monte Carlo approach is not suitable.

Title of the Paper: A Vision Calibration Method of Robot based on Halcon


Authors: Cheng Gao

Pages: 482-491


Abstract: Visual calibration is an important research direction in the field of robot vision control, and is also one of the current research hotspots. In this paper, the principle of software calibration is described in detail, and a software calibration method based on Halcon optimization is studied and designed. By using the operator in the function library, the internal and external parameters of the camera are calibrated. The influence of the terminal center of the robot and the radial distortion of the camera lens is fully considered. The method is used to establish the camera. The relationship between the image coordinated system and the robot world coordinated system. Experiments show that the method has high calibration accuracy and practicability, and is suitable for industrial robot vision system calibration.

Title of the Paper: Semi-supervised Optimization Algorithm based on Laplacian Eigenmaps


Authors: Qinjuan Luo, Jian Wen, Yutong Wu, Mingkai Wang

Pages: 474-481


Abstract: As a member of many dimensionality reduction algorithms, manifold learning is the hotspot of recent dimensionality reduction algorithm. Despite it is good at retaining the original space structure, there is no denying that its effect of classifying still has room for improvement. Based on Laplacian Eigenmap, which is one of the manifold learning algorithm, this paper committed to optimize the algorithm combined with a semi-supervised learning ideas, which can improve the recognition rate. Finally, the better method of two forms is tested in the surface electromyography system and plant leaf identification system. The experimental results show that this semi-supervised method does well in classifying

Title of the Paper: Approximations with Polynomial, Trigonometric, Exponential Splines of the Third Order and Boundary Value Problem


Authors: I. G. Burova, E. F. Muzafarova

Pages: 460-473


Abstract: This paper is devoted to the construction of local approximations of functions of one and two variables using the polynomial, the trigonometric, and the exponential splines. These splines are useful for visualizing flows of graphic information. Here, we also discuss the parallelization of computations. Some attention is paid to obtaining two-sided estimates of the approximations using interval analysis methods. Particular attention is paid to solving the boundary value problem by using the polynomial splines and the trigonometric splines of the third and fourth order approximation. Using the considered splines, formulas for a numerical differentiation are constructed. These formulas are used to construct computational schemes for solving a parabolic problem. Questions of approximation and stability of the obtained schemes are considered. Numerical examples are presented.

Title of the Paper: Three-channel Laser Diode Driver for Multimedia Laser Projectors


Authors: Svetozar Ilchev, Rumen Andreev, Zlatoliliya Ilcheva, Ekaterina Otsetova-Dudin

Pages: 451-459


Abstract: The paper describes the design, implementation andtest results of a three-channel laser diode driver intended for use inmultimedia laser projectors. Our development goals were to create acompact and power-efficient driver, which achieves good colormixing and supports high modulation frequencies. It is compatiblewith most laser diode configurations used in multimedia laserprojectors with a total optical output power of several watts. Thedriver is equipped with three protected signal modulation inputs. Itneeds a single regulated power supply between 9V and 18V andcontrols up to three cooling fans intended for the thermalmanagement of the laser diodes. Our initial tests show that the driverworks very well and is suitable for the long-term operation inprojector systems. In the future, we plan to perform more tests on thesignal feedback for various input signal combinations, which shouldresult in optimizations of the feedback configuration andimprovements of the modulation response of the driver.

Title of the Paper: Continuous Local Splines of the Fourth Order of Approximation and Boundary Value Problem


Authors: I. G. Burova

Pages: 440-450


Abstract: This paper discusses the construction of polynomial and non-polynomial splines of the fourth order of approximation. The behavior of the Lebesgue constants for the left, the right, and the middle continuous cubic polynomial splines are considered. The non-polynomial splines are used for the construction of the special central difference approximation. The approximation of functions, and the solving of the boundary problem with the polynomial and non-polynomial splines are discussed. Numerical examples are done.

Title of the Paper: The Effect of Tourism Development on Economic Growth in Taiwan: Export Growth as Mediator


Authors: Tzu-Kuang Hsu, I-Hsun Tsai

Pages: 435-439


Abstract: In this paper, we employed an innovative method, called a quantile mediation analysis, which combines a quantile regression and mediation analysis to examine the impact of tourism development on economic growth whether through export growth or not from 1990 to 2018 in Taiwan. The result of the traditional ordinary least square approach shows that Taiwan’s tourism development affects economic growth through the full mediation effect of export growth for the period of 1990-2018 and there is no direct relation from tourism development to economic growth. Moreover, the results of this innovative analysis indicate that Taiwan’s tourism development also affects economic growth through the full mediation effect of export growth at below 0.6 distributions of economic growth, but at above 0.6 distributions of economic growth, there exist direct and partially indirect effect from tourism development to economic growth. From the results, we suggest that Taiwan’s government should focus on the export growth if she wants to promote Taiwan’s economic growth when the economy is in a recession, not focus on tourism development.

Title of the Paper: Sharp Bounds on the Spectral Radius of Nonnegative Matrices and Comparison to the Frobenius’ Bounds


Authors: Maria Adam, Nicholas Assimakis, Fotis Babouklis

Pages: 423-434


Abstract: In this paper, a new upper bound and a new lower bound for the spectral radius of a nοnnegative matrix are proved by using similarity transformations. These bounds depend only on the elements of the nonnegative matrix and its row sums and are compared to the well-established upper and lower Frobenius’ bounds. The proposed bounds are always sharper or equal to the Frobenius’ bounds. The conditions under which the new bounds are sharper than the Frobenius' ones are determined. Illustrative examples are also provided in order to highlight the sharpness of the proposed bounds in comparison with the Frobenius’ bounds. An application to linear invariant discrete-time nonnegative systems is given and the stability of the systems is investigated. The proposed bounds are computed with complexity O(n2).

Title of the Paper: Retinal Image Enhancement using Ordering Gap Adjustment and Brightness Specification


Authors: P. Vonghirandecha, S. Kansomkeat, S. Intajag

Pages: 414-422


Abstract: Color retinal image enhancement plays an important role in improving an image quality suited for reliable diagnosis. For this problem domain, a simple and effective algorithm for image contrast and color balance enhancement namely Ordering Gap Adjustment and Brightness Specification (OGABS) was proposed. The OGABS algorithm first constructs a specified histogram by adjusting the gap of the input image histogram ordering by its probability density function under gap limiter and Hubbard’s dynamic range specifications. Then, the specified histograms are targets to redistribute the intensity values of the input image based on histogram matching. Finally, color balance is improved by specifying the image brightness based on Hubbard’s brightness specification. The OGABS algorithm is implemented by the MATLAB program and the performance of our algorithm has been evaluated against data from STARE and DiaretDB0 datasets. The results obtained show that our algorithm enhances the image contrast and creates a good color balance in a pleasing natural appearance with a standard color of lesions.

Title of the Paper: Image Classification Search System based on Deep Learning Method


Authors: Zhang Lin, Yang Fengshang

Pages: 407-413


Abstract: Image classification is to distinguish different types of images based on image information. It is an important basic issue in computer vision, and is also the fundamental for image detection, image segmentation, object tracking, and behavior analysis. Deep learning is a new field in machine learning research. Its motivation is to simulate the neural network of the human brain for analytical learning. Like the human brain, deep learning can interpret the data of images, sounds, and texts. The system is based on the Caffe deep learning framework. Firstly, the data set is trained and analyzed, and a model based on deep learning network is built to obtain the image feature information and corresponding data classification. Then the target image is expanded based on the bvlc-imagenet training set model, and finally achieve "search an image with an image" web application.

Title of the Paper: An Illumination Estimation Algorithm based on Outdoor Scene Classification


Authors: Ning Li, Chunxiao Li, Songnan Chen, Jiangming Kan

Pages: 400-406


Abstract: The illumination estimation algorithm belongs to the field of color constancy, aiming to restoring the color of image through estimating the RGB of scene illumination. In different scenarios, the performance of a general algorithm varies greatly. If the scene can be predicted, it can be inferred that the scenarios related optimal algorithms is better than a general algorithm for estimating illumination. In this paper, a novel algorithm based on outdoor scene classification was proposed: firstly, the support vector machine (svm) classifiers was used to identify scene types , and then the scenarios related optimal algorithms was selected, finally used the RGB values of scene illumination were calculated.

Title of the Paper: Robust Signals Detection Algorithm based on Cyclostationarity in Impulsive Noise


Authors: Shun Na, Penghui Li, Jing Zhang, Yang Liu, Yong Tie, Yongjun Jia

Pages: 392-399


Abstract: A robust method for detecting the communication signals impinging on an antenna with interference and non-Gaussian impulsive noise is introduced in this paper. Degradation of the conventional cyclic detector which based on max-output-SNR criterion in impulsive noise is shown both theoretically and experimentally. By fusing second-order cyclostationarity and fractional lower-order statistics, a type of cyclic fractional lower-order statistics is developed which is defined for exploiting cyclostationarity property. Then, a new robust type of detection algorithm is developed using the theory of optimal filtering based on max-output-SNR criterion and alpha-stable distribution, including the fractional lower-order cyclic matched filter, which is formulated for detecting the communication signals in the presence of interference and non-Gaussian alpha-stable distribution impulsive noise. It is shown that the new method is robust to Gaussian and non-Gaussian impulsive noises, and is immune to the interfering signals which occupy the same spectral band as that of the received signal. Simulation results show the robustness and effectiveness of the proposed algorithm.

Title of the Paper: A Control System of SEMG Signal based on Deep Learning


Authors: Weibo Song, Wei Wang, Fengjiao Jiang

Pages: 386-391


Abstract: The research of control system based on sEMG signal is a popular field at present. It collects bioelectricity of human body through surface electrode. It has the new characteristic of subject fusion, and it is the combination of engineering technology and medical theory, specifically the application of cross combination of control science and electrophysiology. In this paper, the human surface EMG signal is taken as the research object, and a manipulator control system based on one-dimensional convolutional neural network (CNN) is proposed, and the functions and implementation methods of each part of the system are analyzed. The experimental results show that the recognition accuracy of the training model is 0.973, and the design scheme of EMG signal recognition and classification system with deep learning method is feasible. The successful design of the system provides technical support and theoretical basis for the further study of electrophysiological signals

Title of the Paper: APTITUDE Framework for Learning Data Classification based on Machine Learning


Authors: Adelina Aleksieva-Petrova, Veska Gancheva, Milen Petrov

Pages: 379-385


Abstract: Learning analytics refers to the machine learning to provide predictions of learner success and prescriptions to learners and teachers. The main goal of paper is to proposed APTITUDE framework for learning data classification in order to achieve an adaptation and recommendations a course content or flow of course activities. This framework has applied model for student learning prediction based on machine learning. The five machine learning algorithms are used to provide learning data classification: random forest, Naïve Bayes, k-nearest neighbors, logistic regression and support vector machines

Title of the Paper: A Statistical Measurement of Randomness based on Pattern Vectors


Authors: Ray-Ming Chen

Pages: 372-378


Abstract: Randomness of data or signals has been applied and studied in various theoretical and industrial elds. There are many ways to de- ne and measure randomness. The most popular one probably is the statistical testing for random- ness. Among the approaches adopted, Runs Test is a highly used technique in testing the random- ness. In this article, we demonstrate the inef- cient aspects of Runs Test and put forward a new approach, or pattern-vector-based statistic, based on pattern vectors that could e ectively enhance the precision of testing randomness. A random binary sequence is supposedly to have less or no patterns. Based on this, we put for- ward our randomness-testing statistic. We also run an experiment to demonstrate how to apply this statistic and compare the eciency or failure rate with Runs Test in dealing with a set of ran- domly generated input sequences. Moreover, we devise a statistically-justi able measure of ran- domness for any given binary sequence. In the end, we demonstrate a way to combine this new device with Kalman lters to enhance the data assimilation.

Title of the Paper: Smooth Path Planning of Ackerman Chassis Robot based on Improved ant Colony Algorithm


Authors: Guannan Lei, Yili Zheng

Pages: 361-371


Abstract: In the domain of robotics and autonomous driving, the automatic path planning of vehicle collision-free motion is an essential task on the navigation level. It is found that the traditional path planning algorithm and the ployline path cannot fully meet the driving requirements of Ackerman chassis robot. In order to solve the autonomous navigation problem of Ackerman chassis mobile robot in structured environment, this paper presents a new improved algorithm. The method of configuration space can introduce the robot's own structural size parameters into the algorithm. Through convex polygon detection method, the local U-shaped area in the map is transformed into a closed area. The essence of these two strategies is to preprocess the map. The initial pheromone distribution is no longer globally uniform, but is distributed according to the terrain. The volatilization factor of pheromone is changed from static constant to dynamic one, which is combined with Poisson distribution law. This strategy makes the improved pheromone distribution law not only avoid the randomness and blindness in the initial stage of the algorithm, but also ensure the ant colony's exploration behavior and guiding role in the middle stage of the algorithm. Path smoothing is also a challenging task. This algorithm optimizes the path step by step by improving the evaluation function, removing redundant nodes and 2-turning algorithm. Thus, a collision free smooth path suitable for Ackerman robot is obtained. This paper combines a variety of algorithm improvement strategies, not only improving the performance of ant colony algorithm path exploration, but also planning a smooth curve path suitable rather than polyline for Ackerman mobile robot tracking. The algorithm is coded and simulated by MATLAB, and the feasibility and effectiveness of the algorithm are verified. This will provide an important basis for the subsequent algorithm migration and lay the foundation for the path tracking control of the Ackermann chassis robot.

Title of the Paper: An Automatic Monitoring Method of Slope Deformation in Open-pit mine based on BP Neural Network and GIS Technology


Authors: Yan-Jun He, Tao Chen, Liu Han

Pages: 353-360


Abstract: At present, the research on BP neural network has achieved good results in many industries and fields, but there are few projects in the application research of mineral resources mining. Under the social background of the rapid development of electronic information technology, BP neural network and GIS technology are combined to carry out research and application, which will provide a new research path for slope deformation monitoring and disaster prevention in mining area. Therefore, in the paper, the key technology of open-pit mine slope deformation automatic monitoring based on BP neural network and GIS technology was put forward. Firstly, the advantages of BP neural network were analyzed and BP neural network was selected as the prediction model of slope deformation. The artificial fish swarm algorithm was used to improve the BP neural network to improve the performance of the model. Based on the analysis and construction of GIS technology, the combination application of BP neural network and GIS technology was discussed. Through practice, the application effect of the technology was verified, and it has good theoretical and practical value

Title of the Paper: A Portable Environmental Parameter Monitor based on STM32


Authors: Pingchuan Zhang, Jie Liu and Sa Zhang

Pages: 346-352


Abstract: In this paper, a portable monitoring system with environmental parameters as breakthrough point is proposed, and focuses on the research and development of a portable instrument for monitoring the quality of indoor environmental parameters. This paper studies and designs a portable environment parameter detector based on STM32 development board, which collects monitoring data through external sensors. Write your own based on STM32 platform of data acquisition and processing procedures, the successful implementation of the, temperature, humidity, formaldehyde, CO, real-time monitoring of environmental parameters such as methane, and through the LCD screen display in real time. Testing environment parameter meter, real-time display of the corresponding data and collecting sample data processing analysis of a day, to achieve the desired goal.

Title of the Paper: An Image Fusion Algorithm based on Modified Contourlet Transform


Authors: Pai Zhang

Pages: 340-345


Abstract: Multi-focus image fusion has established itself as a useful tool for reducing the amount of raw data and it aims at overcoming imaging cameras’ finite depth of f ield by combining information from multiple images with t he same scene. Most of existing fusion algorithms use the method of multi-scale decompositions (MSD) to fuse the s ource images. MSD-based fusion algorithms provide much better performance than the conventional fusion methods .In the image fusion algorithm based on multi-scale decomposition, how to make full use of the characteristics of coefficients to fuse images is a key problem.This paper proposed a modified contourlet t ransform(MCT) based on wavelets and nonsubsampled directional filter banks(NSDFB). The image is decomposed in wavelet domain,and each highpass subband of wavelets is further decomposed into multiple directional subbands by using NSDFB. The MCT has the important features of directionality and translation invariance. Furthermore, the MCT and a novel region energy strategy are exploited to perform image fusion algorithm. simulation results shows t hat the proposed method can the fusion results visually and also improve in objective evaluating parameters.

Title of the Paper: Improving Patient Voice Intelligibility by using a Euclidian Distance-based Approach to Improve Voice Assistant Accuracy


Authors: A. M. Mutawa

Pages: 329-339


Abstract: Voice assistance (VA) is gaining domestic consumer attention in a variety of products, such as Amazon Alexa, Google Home, Apple’s Siri, and Microsoft’s Cortana. Furthermore, VA has recently shown its usefulness and ability to improve inpatient experience in hospitals and clinics. Nevertheless, none of the VA products has an accuracy rate greater than 90%. The accuracy decreases even more in noisy or public environments. Hence, improving VA accuracy in noisy environments requires a speech signal algorithm with good quality and intelligibility. There is great interest in developing an objective intelligibility measure that shows maximum correlation with subjective speech intelligibility and that can measure the effect of speech enhancement algorithms on the processing of noisy speech signals. In this paper, Euclidian distance-based speech intelligibility prediction is proposed to measure the correlation with subjective intelligibility in different noisy environments. This paper also presents a comparative analysis and general background research in speech intelligibility improvement. The results show that no single algorithm is effective in improving the intelligibility of speech signals.

Title of the Paper: A Magnetic Field Gradiometer based on the Laser Helium Optically Pumped Magnetic Field Sensors


Authors: Chao Wang, Zhijian Zhou, Defu Cheng and Jie Zhang

Pages: 318-328


Abstract: The theory of ows is one of the most important parts of Combinatorial Optimiza- tion and it has various applications. In this pa- per we study optimum (maximum or minimum) ows in directed bipartite dynamic network and is an extension of article [9]. In practical situa- tions, it is easy to see many time-varying opti- mum problems. In these instances, to account properly for the evolution of the underlying sys- tem overtime, we need to use dynamic network ow models. When the time is considered as a variable discrete values, these problems can be solved by constructing an equivalent, static time expanded network. This is a static approach

Title of the Paper: Optimum Flows in Directed Bipartite Dynamic Network. The Static Approach


Authors: Camelia Schiopu, Eleonor Ciurea

Pages: 309-317


Abstract: The theory of ows is one of the most important parts of Combinatorial Optimiza- tion and it has various applications. In this pa- per we study optimum (maximum or minimum) ows in directed bipartite dynamic network and is an extension of article [9]. In practical situa- tions, it is easy to see many time-varying opti- mum problems. In these instances, to account properly for the evolution of the underlying sys- tem overtime, we need to use dynamic network ow models. When the time is considered as a variable discrete values, these problems can be solved by constructing an equivalent, static time expanded network. This is a static approach

Title of the Paper: The Reasonable and Conscious Understanding System of reality Under Uncertainty


Authors: B. Khayut, L. Fabri, M. Avikhana

Pages: 296-308


Abstract: The modern autonomous Expert and Statistical Systems of Artificial Intelligence (AI) cannot continuously, independently and consciously think, learn and develop. This is happening because the models, methods and technologies of their processing in these systems cannot synchronously actualized (trained), function, independently, systemically, situationally, continuously, accurately and on their own in the conditions unpredictability, uncertainty of changing situations and lack of data, information and knowledge about the objects during the process of their continuous perception from the fuzzy environmental reality. Consequently, the need arises to create self-learning, self-developing and self-organized computational intelligent systems that continuously perceive and process changing data, information and knowledge in their changing, uncertainty and previously unknown situation in the surrounding reality. To solve the above problems and to create a system of General AI, we offer the new concept of creating a Computational Intelligent System of a Reasonable and Conscious Understanding of reality under uncertainty through of developed by us following models, methods and technologies of: a) perception the reality of environment, b) self-developing memory, c) situational control of data, information, knowledge, objects, models and processes, d) presentation, generalization and explanation of knowledge, e) fuzzy inference, f) decision making, g) reasoning and thinking, h) cognition, and h) Dialog Control in communication with human, robots and systems through of the intelligent interface, which integrating this functionality into a coherent Reasonable and Conscious Understanding System of reality Under Uncertainty.

Title of the Paper: Arabic Word Dependent Speaker Identification System Using Artificial Neural Network


Authors: Aws Al-Qaisi

Pages: 290-295


Abstract: The security of systems is a vital issue for any society. Hence, the need for authentication mechanisms that protect the confidentiality of users is important. This paper proposes a speech based security system that is able to identify Arabic speakers by using an Arabic word شكرا)) which means “Thank you”. The pre-processing steps are performed on the speech signals to enhance the signal to noise ratio. Features of speakers are obtained as Mel-Frequency Cepstral Coefficients (MFCC). Moreover, feature selection (FS) and radial basis function neural network (RBFNN) are implemented to classify and identify speakers. The proposed security system gives a 97.5% accuracy rate in its user identification process.

Title of the Paper: Cloud Based Mission Critical Calls at the Edge


Authors: I. Atanasov, E. Pencheva, A. Nametkov

Pages: 282-289


Abstract: Multi-access Edge Computing (MEC) technology outsources the cloud services at the edge of the mobile network for delay-sensitive, bandwidth hungry applications. The technology addresses the requirements of mission critical communications for ultralow latency and high reliability in a sustainable and affordable way. The paper studies MEC capabilities to handle mission critical calls exposing the network functions for traffic gating and rerouting. Following the RESTful approach to define MEC services, information flows, interfaces with data model and data format are presented. The injected latency by the service is theoretically evaluated.1

Title of the Paper: Bounds on Complexity when Sorting Reals


Authors: Marcel Jirina

Pages: 276-281


Abstract: A family of multivalue collocation methods for the numerical solution of differen- tial problems is proposed. These methods are developed in order to be suitable for the solu- tion of stiff problems, since they are highly stable and do not suffer from order reduction, as they have uniform order of convergence in the whole integration interval. In addition, they permits to have an effcient implementation, due to the fact that the coeffcient matrix of the nonlinear sys- tem for the computation of the internal stages has a lower triangular structure with one-point spectrum. The uniform order of convergence is numerically computed in order to experimentally verify theoretical results.

Title of the Paper: One-Point Spectrum Nordsieck Almost Collocation Methods


Authors: Dajana Conte, Raffaele D'Ambrosio, Maria Pia D'Arienzo, Beatrice Paternoster

Pages: 266-275


Abstract: A family of multivalue collocation methods for the numerical solution of differen- tial problems is proposed. These methods are developed in order to be suitable for the solu- tion of stiff problems, since they are highly stable and do not suffer from order reduction, as they have uniform order of convergence in the whole integration interval. In addition, they permits to have an effcient implementation, due to the fact that the coeffcient matrix of the nonlinear sys- tem for the computation of the internal stages has a lower triangular structure with one-point spectrum. The uniform order of convergence is numerically computed in order to experimentally verify theoretical results.

Title of the Paper: A Short Review of Some Mathematical Methods to Detect Fake News


Authors: Giuseppe Giordano, Serena Mottola, Beatrice Paternoster

Pages: 255-265


Abstract: In this work we aim to illustrate some mathematical methods recently appeared in the scientic literature to detect fake news. The problem of fake news is an increasingly present topic in our society, from public debate to scien- tic research. The number of fake news produced is constantly increasing especially for the advan- tages of those who spread them. In fact, emotion- ally compelling news, in line with our thoughts, capture our attention, and lead to clicks and views, in the hope of attracting advertising. Un- derstanding whether a news is false or not is not an easy problem to solve, given the large amount of data present on the internet. The detection mechanism should predict the information very quickly in order to stop the spread of fake news. This work is a review of four methods to de- tect fake news recently appeared in the litera- ture [22, 33, 39, 47]. Different methodologies are observed among the various methods: statistical approach, articial neural network, articial in- telligence and text approach. Furthermore, some results are shown.

Title of the Paper: Nonlinear Mechanics Study of Concrete T-beam Bridge With Cracking Damage Based on Numerical Simulation


Authors: Pulu Han

Pages: 249-254


Abstract: The cracking damage of concrete bridge will seriously affect the overall safety of a structure. In this study, based on the numerical simulation, finite element analysis was carried out on the concrete T beam through the ANSYS software, and the selection of elements and the constitutive relationship of materials in the numerical simulation were introduced. It was found from the results of numerical simulation that the cracks of T beam continued to develop under the action of load, the concrete entered the plastic state from the elastic state and the mid-span deflection increased with the increase of load. In the case of the change of cracks, the larger the crack height, the larger the crack range of the beam. With the increase of load, the structural rigidity continued to degenerate, and the compressive stress of the concrete also increased. The research in this paper proves the validity of numerical simulation in the study of nonlinear mechanics of beam bridge and also makes some contributions to the study of crack damage of beam bridge.

Title of the Paper: Circuit Theory Analysis of Parity-Time-symmetric Wireless Power Transfer System


Authors: Yu Jin, Wangqiang Niu, Wei Gu

Pages: 241-248


Abstract: Wireless power transfer (WPT) technology reduces the risks brought by connection between electrical equipment and power source, it has been widely used in various fields in recent years. To overcome issues of low transfer efficiency and poor robustness when coupling coefficient varies, a WPT system based on parity-time (PT) symmetric circuit is proposed, which consists of two RLC oscillators. The system state equation is obtained by circuit theory, then be analyzed to derive the system resonance frequency, transmission efficiency, and phase difference between Tx and Rx. A simulation based on PSIM is established to verify the theoretical derivation of transmission characteristics. The simulation results illustrate that the resonance frequency of the WPT system is adjusted automatically in the strong coupling region when the coupling coefficient changes, the output voltage across the load resistance always equals source voltage on the transmitter. Compared with the non-parity-time symmetric system, PT-symmetric WPT system could achieve higher transfer efficiency over a longer distance, this scheme can transfer power with constant efficiency of over 80% in a certain region. A set of simulations with variation load resistance are considered to verify the system robustness. All results are consistent with theoretical derivation and analysis

Title of the Paper: Factor Analysis Models in Enterprise Costs Management


Authors: G. Bakulina, V. Fedoskin, M. Pikushina, V. Kukhar, E. Kot

Pages: 232-240


Abstract: The process of substantiation, adoption and implementation of a managerial decision requires a lot of analytical work, which is based on the use of various economic calculations. Objective and accurate results of such an analysis are always in demand when developing and justifying managerial decisions. To estimate the impact of factor indicators on the effective feature, various factor analysis techniques have been developed based on such widely used research methods as the chain substitution method and the method of absolute differences. The main advantages of these methods are simplicity, efficiency and easy interpretation of the results. However, most of them do not give an accurate assessment of the influence of factors, since they do not take into account the sequence of replacement of indicators when performing calculations, depending on the degree of their significance. To analyze diversified production the problem arises how to estimate the impact of the composition of produced heterogeneous products on effective economic indicators, such as profit and total costs. Such a situation leads to the implementation of an incorrect production diversification strategy and errors in the formation of an optimal market composition. The article discusses and substantiates ways to eliminate identified problems in the construction of factor models on the example of agricultural production.

Title of the Paper: Isotropy Analysis of Parallel Six-Axis Accelerometer on Circular Hyperboloids


Authors: Chengxin Du, Jiaguo Tang, Chunzhan Yu, Qin Yin, Yili Zheng

Pages: 222-231


Abstract: This paper introduced the isotropic accelerometer using the circular hyperboloids method, which based on modified Gough-Stewart platform (GSP). By the static model of the accelerometer, the isotropy is defined on the acceleration matrix. On the basis of the isotropy condition, the relationship between isotropy index and geometric parameters of circular hyperboloids was investigated. Calculating the isotropy index by the optimization tool, this paper verified that it is feasible to achieve isotropy for the accelerometer. Then taking mass into account, a case is presented to optimize the parameters to construct isotropic accelerometer on circular hyperboloids. According to the 3D model of isotropic accelerometer, the static characteristic simulation was carried out by the finite element method. Based on the simulation experimental results, the calibration matrix was deduced, and the experimental isotropy index was obtained. Comparing the theoretical and experimental isotropy index, the method of circular hyperboloids was proved to be reliable and valid to construct isotropic accelerometer

Title of the Paper: Energy and Mode Filtering in a Graphene Channel With Unevenly Spaced Barriers with a Smooth Profile


Authors: Paolo Marconcini

Pages: 213-221


Abstract: We simulate the transport and shot noise behavior of graphene armchair ribbons with a series of parallel, unevenly spaced potential barriers with a smooth profile (which could result from the electrostatic effect of negatively biased gates). We analyze the effect of Klein tunneling and resonant tunneling on the individual modes propagating through the graphene channel, showing that this structure can behave as a mode and an energy filter for the charges injected from the contacts. Moreover, we study the different transport regimes (ballistic, strong localized, and diffusive) that can take place inside the graphene ribbon and the effect on the shot noise behavior of the device.

Title of the Paper: Markov-Modulated Linear Regression Parameter Estimation Using a Convolution of Exponential Densities


Authors: Nadezda Spiridovska

Pages: 205-212


Abstract: When one applies statistical models, he usually makes the assumption that the data or error come from a normal distribution. In order to verify or to estimate the parameters of the normal distribution, the typical approaches would be normality tests and model selections. In this article, we come up with two methods that could fit the data via normal distribution and measure the fitness. This would serve an alternative for model selection. Unlike statistical approaches - parametric or non-parametric statistics - we use approximation approaches to measure the optimal similarity between a given data set and its induced normal distributions and then search for the optimal normal distribution that has the best similarities. The degree of similarity is defined by two approaches: the overlapped area and the arccos function. The idea is to look at the patterns between data set induced step function and sampled normal distributions in the form of approximated step probability density functions. Our analytical approach could measure the degree of normality, or the similarity with normal distributions, which could then used in pioneering findings for related statistical inferences

Title of the Paper: Two Optimal Measurements of Normality for Finite Set of Discrete Data


Authors: Ray-Ming Chen

Pages: 197-204

Abstract: When one applies statistical models, he usually makes the assumption that the data or error come from a normal distribution. In order to verify or to estimate the parameters of the normal distribution, the typical approaches would be normality tests and model selections. In this article, we come up with two methods that could fit the data via normal distribution and measure the fitness. This would serve an alternative for model selection. Unlike statistical approaches - parametric or non-parametric statistics - we use approximation approaches to measure the optimal similarity between a given data set and its induced normal distributions and then search for the optimal normal distribution that has the best similarities. The degree of similarity is defined by two approaches: the overlapped area and the arccos function. The idea is to look at the patterns between data set induced step function and sampled normal distributions in the form of approximated step probability density functions. Our analytical approach could measure the degree of normality, or the similarity with normal distributions, which could then used in pioneering findings for related statistical inferences

Title of the Paper: Design of On chip Spiral Inductors for Millimeter Wave Frequency Synthesizers


Authors: Nithin M., Harish M. Kittur

Pages: 191-196

Abstract: The energy storing element, inductor plays a vital role in CMOS based high frequency integrated circuits, especially in signal generation and impedance matching blocks.An on chip inductor is considered as a critical component because its performance directly impacts the associated circuitry when it is used as a load device or as a matching element. Out of the various requirements of an inductor which resides inside a chip, the inductance value,quality factor and self resonance frequency with smaller area is often preferred. This paper focuses on the lumped model of inductors for high frequency circuits working in the Millimeter wave region from 30 GHz to 300 GHz. For millimeter wave oscillators,inductance value in the range of pico Henry are essential and hence a complete model of an inductor is presented. Using electromagnetic simulator SONNET, all the parameters are extracted. The extracted model is used in the design of an LC Oscillator for millimeter wave band. A Q factor of 26 is achieved for an inductor value close to 153 pH at 60 GHz.The circuits employing this inductor shows promising results when simulated using 45 nm CMOS pdks

Title of the Paper: A Study of Authenticated Communication Based on Magic Square and Goldbach’s Conjecture


Authors: Hui-Shan Li, Chenglian Liu

Pages: 184-190

Abstract: Although the magic square is a historical and universal study, its progress has been limited, to numeric games, which is closer to digital games or word games, and lacks the connection with mainstream mathematics. Recently, its study has extended from exciting mathematical games to various novel applications, such as image encryption, decryption processing, watermarking solutions, and student group learning problems, or different engineering applications. In terms of employment in information security, it is the blue ocean that requires more innovative research to enrich its content. In this study, we engage the magic square and Goldbach’s Conjecture to develop an innovative method to search prime numbers

Title of the Paper: A Back-stepping Control based on Bounded Function for Four-wheel Drive Omni-directional Mobile Robots


Authors: Jianping Chen, Jianbin Wang

Pages: 175-183

Abstract: As the simply structure and flexible design, back-stepping technique has been widely applied in robot trajectory tracking control. However, there is velocity jumping problem in conventional back-stepping tracking control for four-wheel drive omni-directional mobile robots. In this paper, an improved back-stepping controller based on a bounded function is proposed. To improve control performance, a smooth and bounded tracking velocity, arising from the function, is used to instead of the jumping velocity. Simulation results of tracking different paths and comparison with the conventional back-stepping technique show that the approach is effective, and the system has a good performance with smooth outputs.

Title of the Paper: Diameter Measurement of Cylindrical Products With Displacement Compensation Along the Optical Axis


Authors: V. V. Rakhmanov, S. V. Dvoynishnikov, D. O. Semenov

Pages: 169-174

Abstract: The problem of diameter measurement of cylindrical products using the adapted triangulation method is considered. A method to improve the accuracy of measurement is proposed by determining the displacement of an object in the work area along the optical axis. The description of the layout of the laboratory installation and the results of applying the method for exemplary objects are given.

Title of the Paper: Towards a Dynamic Multi-Agent Based Scaffolding Framework


Authors: Panayotis Papazoglou, Sarantos Psycharis, Konstantinos Kalovrektis

Pages: 160-168

Abstract: Students have different abilities, skills and background and thus the corresponding learning process is different. Moreover, the teacher strategy, the available equipment, etc, play a crucial role in the learning curve. Scaffolding is a learning approach for dynamically supporting student during the learning process. The final goal is to restrict this support and to increase the student autonomy. This paper presents a basic idea for developing a dynamic multi-agent computer based scaffolding framework. Multi-agent technology constitutes an adaptive approach regarding the needed scaffolding. This paper also shows the modelling approach regarding the multi agent concepts. Finally, some theoretical indicative learning paths for different students are presented.

Title of the Paper: Algorithms for Detection Gender Using Neural Networks


Authors: Maksat Kalimoldayev, Orken Mamyrbayev, Nurbapa Mekebayev, Aizat Kydyrbekova

Pages: 154-159

Abstract: In this paper, we investigate two neural architecture for gender detection tasks by utilizing Mel-frequency cepstral coefficients (MFCC) features which do not cover the voice related characteristics. One of our goals is to compare different neural architectures, multi-layers perceptron (MLP) and, convolutional neural networks (CNNs) for both tasks with various settings and learn the gender -specific features automatically.

Title of the Paper: A Note on the Cumulative and the Accumulated Hazard Function


Authors: Dejan Škanata

Pages: 149-153

Abstract: In probability and statistics, reliability theory and survival analysis, there exists a 20-year-old dilemma, initially raised by L.M. Leemis, on whether the cumulative or the accumulated hazard function in the discrete domain is more appropriate to be used in various types of applications. Here, we propose that priority should be given to the accumulated hazard function.

Title of the Paper: Model for 1/f Noise in Graphene and in More Common Semiconductors


Authors: Paolo Marconcini

Pages: 144-148

Abstract: Measurements performed on several graphene samples have shown the presence of a minimum of the flicker noise power spectral density near the charge neutrality point. This behavior is anomalous with respect to what is observed in more usual semiconductors. Here, we report our explanation for this difference. We simulate the 1/f noise behavior of devices made of graphene and of more common semiconductors, through a model based on the validity of the mass-action law and on the conservation of the charge neutrality. We conclude that the minimum of the flicker noise at the charge neutrality point can be observed only in very clean samples of materials with similar mobilities for electrons and holes.

Title of the Paper: Discovery of Incomplete Diagnostic Model based on Learning


Authors: Wang Xiaoyu, Li Chuang, Ye Liang

Pages: 137-143

Abstract: The model-based diagnosis uses the common reasoning of offline model and online observation to obtain whether and why faults occur. However, the diagnosis is based on the premise of complete model. Once there are unknown behaviors in the diagnosis process, the diagnosis results will not be obtained. In this paper, a method of incomplete model discovery based on online diagnosis process is proposed: In the online diagnosis process, the data of the complete model are learned and the model is trained and adjusted. When the incomplete behavior is found, the nature of the incomplete behavior is determined according to the historical diagnostic data and online observation data, and the corresponding transition/state/event is generated and added to the model to further obtain the definite diagnosis results.

Title of the Paper: Comparative Analysis of Two Generalized Methodologies for Circuit Optimization


Authors: Alexander Zemliak, Fernando Reyes, Sergio Vergara, Olga Felix

Pages: 131-136

Abstract: The design process for analog network design is formulated on the basis of the optimum control theory. The artificially introduced special control vector is defined for the redistribution of computational costs between network analysis and parametric optimization. This redistribution minimizes computer time. The problem of the minimal-time network design can be formulated in this case as a classical problem of the optimal control for some functional minimization. There is a principal difference between the new approach and before elaborated methodology. This difference is based on a higher level of the problem generalization. In this case the structural basis of design strategies is more complete and this circumstance gives possibility to obtain a great value of computer time gain. Numerical results demonstrate the effectiveness and prospects of a more generalized approach to circuit optimization.

Title of the Paper: Multivariable Constrained Adaptive Predictive Control based on Closed-loop Subspace Identification


Authors: Xiaosuo Luo, Xueshan Lin

Pages: 124-130

Abstract:In order to deal with nonlinear, time-varying, and multivariable constrained characteristics in closed-loop industrial processes, a multivariable constrained adaptive predictive control (CAPC) method based on closed-loop subspace identification is proposed. The state-space model is obtained through the closed-loop subspace identification algorithm, which is regarded as the system model. The algorithm is implemented online to update the R matrix with a receding window. By comparing the prediction errors before and after updating, it considers whether or not to update the system model. The model is then used to design the model predictive controller, which involves the solution of a quadratic program solving multivariable constraints. This paper presents a comparison between the performance of the proposed control method when applied to a 2-CSTR system, and that of an open-loop subspace CAPC method. The superiority of the proposed method is illustrated by the simulation results.

Title of the Paper: A Fast and Efficient Lossless Compression Technique for Greyscale Images


Authors: T. Kavitha, K. Jaya Sankar

Pages: 114-123

Abstract:The growth of cloud based remote healthcare and diagnosis services has resulted, Medical Service Providers (MSP) to share diagnositic data across diverse environement. This medical data are accessed across diverse platforms, such as, mobile and web services which needs huge memory for storage. Compression technique helps to address and solve storage requirements and provides for sharing medical data over transmission medium. Loss of data is not acceptable for medical image processing. As a result, this work considers lossless compression for medical in particular and in general any greyscale images. Modified Huffman encoding (MH) is one of the widely used technique for achieving lossless compression. However, due to longer bit length of codewords the existing Modified Huffman (MH) encoding technique is not efficient for medical imaging processing. Firstly, this work presents Modified Refined Huffman (MRH) for performing compression of greyscale and binary images by using diagonal scanning method. Secondly, to minimize the computing time parallel encoding method is used. Experiments are conducted for wide variety of images and performance is evaluated in terms of Compression Ratio, Computation Time and Memory Utilization. The proposed MRH achieves significant performance improvement in terms of Compression Ratio, Computation Time and Memory Usage over its state-of-the-art techniques, such as, LZW, CCITT G4, JBIG2 and Levenberg–Marquardt (LM) Neural Network algorithm. The overall results achieved show the applicability of MRH for different application services.

Title of the Paper: Near-infrared Spectroscopy Detection Method for Compressive Strength of Fraxinus mandschurica


Authors: Hao Liang, Linyin Xing, Jian Wen, Chao Gao, Jianhui Lin

Pages: 108-113

Abstract: This study used near-infrared (NIR) spectroscopy as a non-destructive test to predict the compressive strength (i.e., modulus of rupture (MOR) and the modulus of elasticity (MOE)) of Fraxinus mandshurica parallel to the wood grain. Tests were conducted with 120 small and clear wood samples to obtain the diffuse NIR reflectance spectra of the radial and tangent surfaces of the wood samples. Standard normal variable transformation (SNV) combined with Savitzky-Golay (SG) convolution smoothing algorithm was used to filter the raw NIR spectra. Uninformative variables elimination (UVE) and a genetic algorithm (GA) were utilized to identify specific wavelengths in the spectra that directly correlated to compression strength. Finally, a partial least squares (PLS) regression model was developed with the identified wavelengths to determine the MOR and MOE of the samples. The results showed the correlation coefficients of the prediction models for MOR and MOE were 0.88 and 0.89, respectively. The root mean square errors of prediction for MOR and MOE models were 7.37 and 0.49, respectively. Based on these results, it is feasible to accurately estimate the compressive strength of Fraxinus mandshurica (parallel to the grain) using NIR spectroscopy.

Title of the Paper: A Low-cost Webcam-based Eye Tracker and Saccade Measurement System


Authors: Ahmad Aljaafreh, Murad Alaqtash, Naeem Al-Oudat, Jafar Abukhait, Ma’en Saleh

Pages: 102-107

Abstract:Eye movements are integrated with cognitive processes, which indeed make it a helpful research basis for the investigation of human practices. Eye movements can be deployed in discovering several cognitive processes of the brain. This research utilizes low-resolution webcam to develop an eye tracker and saccades measurement tool to extensively lower the gadgets expenses. A consistent algorithm is developed to suit the quality of the webcam using open-source software (Python) to record the time series of the eye location. Likewise, several algorithms are proposed to extract high-level eye movement saccadic measurements from the raw gaze outputs. A pilot study is performed on ten normal participants and Multiple Sclerosis (MS) patients. Experimental results demonstrate that the proposed system is quick, simple and efficient for eye tracking and saccade measurement. The developed tool can be used by clinicians and medical physicians for the diagnosis and identification of neurological disorders

Title of the Paper: An Excitation Circuit of the Cell in Optically Pumped Magnetometer


Authors: Chao Wang, Zhijian Zhou, Defu Cheng

Pages: 94-101

Abstract:Cell is the key component in an optically pumped magnetometer. It is necessary to light the cell before measurement and to maintain the illuminated state. The accuracy and stability of magnetic values from the instrument are closely related to the brightness and stability of the cell. The cell is also the largest power dissipation component in the sensor probe, so the overall energy consumption of the magnetometer is highly correlated with it. This paper studies the excitation circuit of cell in the magnetometer. Firstly, we demonstrate the resistivity characteristic of a cell using simulations. After that, based on the combination of signal source impedance and transmission line impedance, the matching network of excitation circuit is analyzed. We demonstrate that both T-network and Π-network can achieve the impedance matching of the transmitter circuit by a simulation experiment, under the condition of 50MHz signal, 10Ω source impedance, and 50Ω transmission line impedance. T-network shows the best performance in frequency selectivity and energy transfer. Finally, the simulation experiment also proves that a circuit composed of a self-coupled coil and an LC parallel resonant network can realize the impedance matching and the passband selection of the receiver circuit by optimizing values of the inductance and capacitance, and turns of the self-coupled coil simultaneously. The power consumption of the whole high-frequency excitation circuit of cell in the optically pumped magnetometer is only about 6W.

Title of the Paper: An Novel Atomic Scalar Magnetometer Using Laser


Authors: Chao Wang, Zhijian Zhou, Defu Cheng

Pages: 88-93

Abstract:The measurement precision of commercial atom scalar magnetometer is relatively backward compared with that of quantum magnetometer. However, the application of quantum magnetometers such as SERF requires more stringent environmental background requirements, which is not suitable for magnetic field measurement in the geomagnetic environment. The purpose of this paper is to design a 4He atom scalar magnetometer using ECDL laser. Compared with the conventional atomic scalar magnetometer, this magnetometer has higher measuring precision and can work normally in the geomagnetic environment. In order to achieve the above goals, the sensitivity formula of the atomic scalar magnetometer is first deduced and calculated, and the key physical factors that directly affect the sensitivity are the optical pumping rate, transverse relaxation rate, and longitudinal relaxation rate. Then, the light source and 4He cell are determined as key components which affect sensitivity. On this basis, the optical path of the 4He atomic scalar magnetometer using laser is designed in this paper. The light path ensures the stability of the laser wavelength of 1083.207nm by the saturation absorption spectrum method, and it ensures the circularly polarized light enters the 4He cell through the combination of various optical components. This paper also studies the electric excitation technology of the 4He cell. And, combined with simulation experiments, the High-Frequency discharge excitation circuit with high energy transfer efficiency and corresponding matching network are determined. Through the optical wavelength meter, it can be determined that the optical path designed in this paper can guarantee the wavelength stability of 1083.207nm for a long time. By analyzing the detection signals of PD, the circularly polarized light enters the 4He cell in the light circuit designed in this paper has a higher degree of polarization. The High-Frequency discharge excitation circuit designed in this paper can light up the cell smoothly, and the input power when the circuit works stably is about 6W. Finally, the static sensitivity of the magnetometer is 5pT/Hz1/2. The 4He atom scalar magnetometer using ECDL laser designed in this paper has high static sensitivity, which basically meets the design requirements, and the instrument can be used normally in the geomagnetic environment. However, the instrument still has a lot of room for improvement, including optical path and cell performance optimization, and we will continue to study in this direction.

Title of the Paper: Blockchain-based Biometric Election System


Authors: Ketevan Tsomaia, Archil Prangishvili, Levan Imnaishvili, Maguli Bedineishvili

Pages: 83-87

Abstract: The use of biometric technology in the electoral process has undoubtedly produced positive results in terms of protecting the electoral process, speeding up the results and enhancing the feeling of objectivity among the voters. But there is still room for falsification of election results, as the number of votes received by the candidates and the used ballot papers are kept centrally. It is also important to ensure the reliability of the templates for the biometric characteristics of the voters. In order to solve these problems, this work proposes the distributed database of key data, in particular, the blockchain storage technology. The electoral process scheme and the blockchain-based biometric election system architecture and protocols are elaborated according to the proposed method.

Title of the Paper: Low-Cost and Ultra-Low-Power Consuming RTUs for Use in IoT Systems


Authors: Ivan Ganchev, Zhanlin Ji, Máirtín O’Droma

Pages: 76-82

Abstract: This paper presents the design and realization of low-cost and ultra-low-power consuming remote transfer units (RTUs), working as communication gateways for collecting, aggregating, and forwarding IoT data to information centers (servers) in the cloud for further processing and data mining. Two types of RTUs, targeting different application scenarios and utilizing different communication standards, were designed – one, based on the General Packet Radio Service (GPRS) standard, and another – on the NarrowBand Internet of Things (NB-IoT) standard. The developed RTUs were experimentally tested and their use was successfully demonstrated in different IoT systems.

Title of the Paper: Research on Map Construction and Location of Laboratory Service Robot based on Iterative Closest Point


Authors: Jian Wu, Ruting Yao, Yili Zheng, Jinhao Liu

Pages: 69-75

Abstract: The development of mobile robots has led to their wide application in a variety of fields. This study focuses on the intelligent application of mobile robots in laboratory management, especially the environmental awareness and self-positioning of a robot in the laboratory. In this study, a wheeled mobile robot is selected and equipped with a 2D laser scanner. Based on this, a Robot Operating System (ROS) environment is built. The nearest neighbor iterative closest point (ICP) matching algorithm is utilized to perceive the laboratory service environment, construct the indoor map in real time, and locate the robot precisely. Subsequently, data collected in the corridors and indoor environment of the experimental building are used to test the accuracy of the ICP matching algorithm. The results showed that the minimum translation error is as low as 0.0003 m and that the minimum rotation angle error is less than 0.5°. In addition, the positioning and mapping of the robot were analyzed. The experimental results show that the ICP matching algorithm is well suited to map construction and positioning of the laboratory service robot. This is of great significance for further research on laboratory service robots.

Title of the Paper: Determination the Specific Parameters for Uniform Power Separation of Optical Signals


Authors: Al-Gawagzeh Mohammed Yousef

Pages: 63-68

Abstract: This paper will study the importance of using the features of anisotropic medium to decrease the effect of Attenuation and dispersion on the transmitted signals in the fiber optic system due to the power exchange between waves, a calculation for specific cases were made. Also to determine the half-length of the beat region ξ1 for a certain composition of the spiral single mode optical fibers depending on the parameters of the spiral to construct spiral single mode optical fibers based on photo elasticity. Also in this paper we will try to define specific parameters for uniform power separation of the optical signal

Title of the Paper: Transfer Learning-Based Convolutional Neural Network Image Recognition Method for Plant Leaves


Authors: Yue Zhao,Yili Zheng, Honglei Shi, Lu Zhang

Pages: 56-62

Abstract: To improve the accuracy of plant leaf image recognition with a small dataset of plant leaves, a convolution neural network (CNN) plant leaf image recognition method based on transfer learning is proposed. First, a plant leaf image database was expanded by pre-processing the original plant leaf images through random horizontal and vertical rotation and random zooming. The expanded dataset was then processed by mean removal and divided into training and testing sets at a ratio of 4:1. Second, transfer learning training was performed on the plant leaf dataset using existing models (AlexNet and InceptionV3) that were pre-trained on a large dataset. To ensure these models can be adapted to image recognition for plant leaves, the original parameters of the last fully connected layer were replaced, whereas those of all other convolution layers were retained. Finally, the method proposed in this paper was compared to support vector machine, deep belief network, and CNN through testing on the ICL database. A Tensorflow training network model was used in the comparison test, and the results were visualized by Tensorboard. The testing results showed a considerable improvement in recognition accuracy when using the pre-trained AlexNet and InceptionV3 models, where the training dataset accuracies were 95.31% and 95.4%, respectively.

Title of the Paper: Study and Research the Tensor of Dielectric Permittivity and Attenuation Transient in the Bended on Spiral Optical Fiber


Authors: Al-Gawagzeh Mohammed Yousef, Al-Hadidi Mohammed Rasoul

Pages: 49-55

Abstract: This paper investigates the assumption of spiralshaped index leads to an optimal result for the optical fiber modes. It also shows how to decrease the effect of optical fibers parameters such as attenuation and dispersion on the quality of transmitted signals, and to improve it into an acceptable form. The dielectric permeability tensor εij related to the curvature and torsion parameters ( χ , υ ) in the coordinates system ( r, φ ) was analyzed. The dependence of εij tensor on the bent on spiral optical fiber parameters was mathematically calculated. Also to study the exchange of power between the waves HEo 11 and HEe 11 .The Transient attenuation dependence on spiral parameters of optical fiber with a length of (one kilometer) as a case study will be studied also.

Title of the Paper: On the Prime Geodesic Theorem for SL4


Authors: Dzenan Gusic

Pages: 42-48

Abstract: In 1949, A. Selberg discovered a real variable (an elementary) proof of the prime number theorem. A number of authors have adapted Selberg’s method to achieve quite a good corresponding error term. The Riemann hypothesis has never been proved or disproved however. Any generalization of the prime number theorem to the more general situations is known in literature as a prime geodesic theorem. In this paper we derive yet another proof of the prime geodesic theorem for compact symmetric spaces formed as quotients of the Lie group SL4 (R). While the first known proof in this setting applies contour integration over square boundaries, our proof relies on an application of modified circular boundaries. Recently, A. Deitmar and M. Pavey applied such prime geodesic theorem to derive an asymptotic formula for class numbers of orders in totally complex quartic fields with no real quadratic subfields.

Title of the Paper: A Novel Sliding Mode Controller for Underactuated Vertical Takeoff and Landing Aircraft


Authors: Yu Wang

Pages: 34-41

Abstract: Compared with other control methods, the biggest advantage of using sliding mode variable structure control method lies in its strong robustness which could be used to directly handle the strong nonlinear flight control system. However, this control method requires switching between different switching surfaces, which will inevitably cause buffeting problems, so that the energy consumption increases. Therefore, how to overcome this disadvantage to achieve the superior performance of sliding mode variable structure control method is the current research focus. This paper studies the trajectory tracking of under-actuated VTOL aircraft with three degrees of freedom and two control inputs under various coupling effects. By the input and coordinate transformation, the dynamic equation of the system is transformed into decoupled standard under-actuated form and the sliding mode controller is designed. Then Lyapunov stability theorem is used to derive sliding mode control law which could ensure that the system asymptotically converges to the given trajectory. The simulation has demonstrated the effectiveness of this method

Title of the Paper: Bayesian Change Point Estimation Based on Masked Data in Exponential Distribution Parallel System


Authors: Yuejun Liu, Huaikou Miao

Pages: 28-33

Abstract: Change point reflects a qualitative change in things. It has gained some applications in the field of reliability. In order to estimate the position parameters of the change point, a Bayesian change point model based on masked data and Gibbs sampling was proposed. By filling in missing lifetime data and introducing latent variables, the simple likelihood function is obtained for exponential distribution parallel system under censored data. This paper describes the probability distributions and random generation methods of the missing lifetime variables and latent variables, and obtains the full conditional distributions of the change point position parameters and other unknown parameters. By Gibbs sampling and estimation of unknown parameters, the estimates of the mean, median, and quantile of the parameter posterior distribution are obtained. The specific steps of Gibbs sampling are introduced in detail. The convergence of Gibbs sampling is also diagnosed. Random simulation results show that the estimations are fairly accurate.

Title of the Paper: Approximate Formulas for Zeta Functions of Selberg’s Type in Quotients of SL4


Authors: Dzenan Gusic

Pages: 21-27

Abstract: The goal of the paper is to derive some approximate formulas for the logarithmic derivative of several zata functions of Selberg’s type for compact symmetric spaces formed as quotients of the Lie group SL4 (R). Such formulas, known in literature as Tutchmarsh-Landau style approximate formulas, are usually applied in order to obtain prime geodesic theorems in various settings of underlying locally symmetric spaces.

Title of the Paper: Stability Analysis of Networked Control Systems with Multi-Packet Dropout based on Switched System Approach


Authors: Zijian Dong, Liang Tian, Huanhuan Luo, Guiping Zhou, Li Wang

Pages: 13-20

Abstract: Networked control system models with packet dropout in multi-packet transmission were established under hypothetical conditions in this paper, and the system was seen as a switched system. The causes of packet dropout in networked control system are analyzed in view of single-packet transmission and multi-packet transmission respectively. Based on Lyapunov stability theory, the property of the networked control system with multi-packet dropout was analyzed from the point of view of an asynchronous dynamic system. The method which determined the multi-packet dropout boundary to keep the system steady was given. The simulation results show the influences of multi-packet dropout on the system performance and prove the validity of the analytical method proposed in this paper.

Title of the Paper: Identification of Small Unmanned Flying Objects


Authors: Lucjan Setlak, Rafał Kowalik

Pages: 7-12

Abstract: Detecting obstacles during the flight and avoiding them is desirable in the case of unmanned aerial vehicles intended for observation of a residential area, refers especially to lightweight micro-aviation vehicles of multi-rotor type, and is also a serious problem because their load capacity is limited, therefore only electronic sensors can be connected to the object. Usually the sensors built into the system are either based on a type vision (monocular or stereo camera) or on a laser camera. However, each of the sensors has its advantages and disadvantages, which is why the article presents the concept of a system for collecting data characterizing the flight of a UAV object and including them in the object identification process. The main purpose of this work is to perform selected studies (analysis, mathematical model, simulations) in the field of identification of small unmanned flying objects. A dynamic model describing UAV motion was developed, which took into account flight parameters using various identification methods. The structure of this work is contained in four chapters, in which, among others, the second chapter deals with the review of existing identification systems for small UAV objects, based on an analysis of the literature on the subject of research. The third chapter covers the issues related to aerodynamics and mechanics of small UAV objects and concerns linear longitudinal equations of UAVs based on Newton's second law. This chapter also describes the algorithm used for dynamic description along with incorrect filtering of "on-line" learning patterns and characterizes the least squares recursive method used for the simulation. Based on the analysis, mathematical models created, simulations performed and the results obtained based on them, practical conclusions presented in the final part of the article were formulated.

Title of the Paper: A Novel Network Flow Prediction Method based on Cuckoo Search Algorithm Optimizing BP Neural Network


Authors: Liqiang Fan

Pages: 1-6

Abstract: Network traffic modeling and forecasting is the basis of network management and security warning. According to the characteristics of the nonlinear network flows, chaos, polygon, etc., in order to improve the prediction accuracy of network traffic, and puts forward the a cuckoo search cable calculation method and BP neural network by network traffic prediction model, BP neural network is used by the network of the learning sample book training, die quasi cloth Valley bird found nest eggs to find the optimal model parameters and the mining network flow number in simulation experiment according to measure the trial model of can. Simulation results show that compared with the reference model, CS-BPNN improves the prediction accuracy of network traffic, network traffic trends are described more accurately, provides a new research tool with network traffic prediction.