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6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Amesimenu, Governor David Kwabena Chang, Kuo-Chi Sung, Tien-Wen Wang, Hsiao-Chuan Shyirambere, Gilbert Chu, Kai-Chun Hsu, Tsui-Lien School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Institute of Environmental Engineering National Taiwan University Taipei Taiwan Department of Business Management Fujian University of Technology Fuzhou China Institute of Construction Engineering and Management National Central University Taoyuan Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

The existence of high armature current is the main limitation while starting a DC series motor. This initial current can have dangerous effects on the DC motor such as damage of windings and reduction in the lifetime of the machine. There are two basic methods of starting of DC motor namely the resistor starting method and the soft start using solid-state devices. In this project, to lessen the inrush current, the solid-state method was adopted to start the motor smoothly. The system uses Arduino Atmega328p-PU Microcontroller to send pulses to the motor driver which regulates the running of the motor. By focusing on DC series motor, power electronic converters and microcontrollers, the system was modeled and simulated in Proteus ISIS Software. The results recorded from the soft starter system were compared with a direct online starting system considering the starting current and voltage of the motor with an open loop control system. The soft starter system was able to decrease the direct online current value from 0.996A to 0.449A which represents approximately 28.1% reduction of the starting current to protect the motor. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DC motors

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Study of the Intelligent Algorithm of Hilbert-Huang Transform in Advanced Power System 6th

Study of the Intelligent Algorithm of Hilbert-Huang Transfor...

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6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Zhang, Cheng Liu, Jia-Jing Chang, Kuo-Chi Wang, Hsiao-Chuan Lin, Yuh-Chung Chu, Kai-Chun Hsu, Tsui-Lien School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Institute of Environmental Engineering National Taiwan University Taipei Taiwan Department of Business Management Fujian University of Technology Fuzhou China Institute of Construction Engineering and Management National Central University Taoyuan Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

With the rapid increase in population and electricity consumption, power grid has long formed a large-scale interconnection of systems. The power system is a complex multi-dimensional dynamic system, the traditional system parameter processing method has gradually shown its limitations, which affects the stability and reliability analysis of the system. This study introduces a popular time-frequency intelligent application analysis methodology for nonlinear nonstationary signalsHilbert-huang transform (HHT) algorithm, then summaries the applications of HHT method for low frequency oscillation of advanced power system, power quality detection and harmonic analysis with combining the research achievements of domestic and foreign scholars in recent years. However, this study discusses the interpolation function and endpoint effect of HHT method in practice and further research on its application in advanced power system. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hilbert-Huang transform

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Design of Leakage Current Monitoring System for Low Voltage Power Grid Based on NB-IoT 6th

Design of Leakage Current Monitoring System for Low Voltage ...

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Xu, GuanWei Zheng, RongJin Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Institute of Engineering Smart Grid Simulation Analysis and Integrated Control of Fujian University Engineering Research Center Fuzhou Fujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9783030697167

Leakage current of low-voltage power grid lines will cause major safety hazards such as personal electric shock and electrical fire. Traditional leakage current protectors can not play their due protection role due to their quality problems and aging, and other reasons. When leakage current accidents occur, traditional manual troubleshooting is inefficient. Aiming at the safety of leakage current in a low-voltage power grid, a new leakage current monitoring system is designed based on NB-IoT technology. The system includes the detection and transmission of leakage current and the construction of the cloud platform, which can realize the real-time monitoring of leakage current in low-voltage power grid and prevent and solve the leakage current safety problems. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Leakage currents

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Study of Advanced Power Load Management Based on the Low-Cost Internet of Things and Synchronous Photovoltaic Systems 6th

Study of Advanced Power Load Management Based on the Low-Cos...

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6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Turatsinze, Elias Chang, Kuo-Chi Li, Pei-Qiang Chang, Cheng-Kuo Chu, Kai-Chun Zhou, Yu-Wen Omer, Abdalaziz Altayeb Ibrahim School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Institute of Environmental Engineering National Taiwan University Taipei Taiwan Department of Business Management Fujian University of Technology Fuzhou China Institute of Construction Engineering and Management National Central University Taoyuan Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

The main problem with todays power consumption is to determine which load is consuming much power so that it can be disconnected to reduce high electrical bills. The main concern of todays researches is to find an alternative way of installing a grid-connected PV system or other renewable resources free of harmful gases so that they add up to the electrical grids to provide enough power. This study presents how to synchronize a photovoltaic system with a grid to save money and electrical load management based on IoT technology. However, current sensing is used to sense the current when loads are connected and send the instantaneous value of current to the Arduino Uno which is programmed to find the total power consumed and then ESP 8266 WiFi module uploads the data to the Thingspeak for analysis and monitoring where utility company or users can access the information about the usage of electricity since many PV systems are in remote areas. A simulation of an inverter with PLL circuit for generating the desired signal waveform and frequency is done in MATLAB/Simulink and the objective was successfully achieved which showed that the output voltage is a sinusoidal waveform of the same frequency and same phase angle and a current sensor can be installed at the output of the inverter to sense the power generated for analysis and monitoring of the proposed PV system. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Research on Penetration Strategy of UAV 2020

Research on Penetration Strategy of UAV

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Daojun Han Hongzhen Xiang Haolin Chen Yi Xie Institute of Data and Knowledge Engineering Henan University Kaifeng PR China School of Computer and Information Engineering Henan University Kaifeng PR China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng PR China

ISBN: (纸本)9781450387828

Taking a military operation as an example, this paper makes an in-depth study of what tactics a single UAV should adopt when facing the interception of multiple enemy UAV groups. According to the analysis of the impact diagram, the aerial range of the UAV standoff defines a threat area, that is, when the UAV arrives at the threat area, it needs to adopt a strategy for penetration, or it will be intercepted. The strategy adopted by UAV in the threat area is analyzed, and the decision control method is made.

关键词： Decision control Threat area Influence chart analysis UAV penetration

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DGA-Net: Dynamic gaussian attention network for sentence semantic matching

arXiv

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arXiv 2021年

作者： Zhang, Kun Lv, Guangyi Wang, Meng Chen, Enhong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science and Technology University of Science and Technology of China China

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, where much recent progress has been made by advancement of representation learning techniques and inspiration of human behaviors. Among all these methods, attention mechanism plays an essential role by selecting important parts effectively. However, current attention methods either focus on all the important parts in a static way or only select one important part at one attention step dynamically, which leaves a large space for further improvement. To this end, in this paper, we design a novel Dynamic Gaussian Attention Network (DGA-Net) to combine the advantages of current static and dynamic attention methods. More specifically, we first leverage pre-trained language model to encode the input sentences and construct semantic representations from a global perspective. Then, we develop a Dynamic Gaussian Attention (DGA) to dynamically capture the important parts and corresponding local contexts from a detailed perspective. Finally, we combine the global information and detailed local information together to decide the semantic relation of sentences comprehensively and precisely. Extensive experiments on two popular sentence semantic matching tasks demonstrate that our proposed DGA-Net is effective in improving the ability of attention mechanism. © 2021, CC BY.

关键词： Gaussian distribution

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Cloud-assisted Road Condition Monitoring with Privacy Protection in VANETs

Cloud-assisted Road Condition Monitoring with Privacy Protec...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Lemei Da Yujue Wang Yong Ding Bo Qin Xiaochun Zhou Hai Liang Huiyong Wang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Pengcheng Laboratory Cyberspace Security Research Center Shenzhen China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education School of Information Renmin University of China Beijing China Shenzhen Olym Information Security Technology Co. Ltd. Shenzhen China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

Vehicular ad hoc network (VANET) is one of the fastest developing technologies in intelligent transportation systems (ITS), which has made great contributions to improving traffic congestion and reducing traffic accidents. As it is deployed in an open environment, security and privacy are threatened to a certain extent. Moreover, there are huge data exchanges in high traffic areas, which require VANET system to improve computing efficiency while ensuring communication security. To solve the above issues, this paper proposes a cloud-assisted road condition monitoring (RCM) system. The trusted authority (TA) monitors the road conditions with the help of the cloud server. The vehicle collects the road condition information of the road section managed by the roadside unit (RU), and only the vehicles authorized by the administrative roadside unit can successfully upload the road condition reports to the cloud server. The cloud server divides the road condition reports into different equivalence classes, in this way to report the emergency to the TA when the reported quantity exceeds the threshold. Security analysis showed that the proposed RCM system can effectively protect the security and privacy of road condition reports in VANETs.

关键词： Condition monitoring Privacy data privacy Roads Vehicular ad hoc networks Forgery Sensors

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Research on Visualization Algorithm of Association Rules Based on Concept Lattice 2020

Research on Visualization Algorithm of Association Rules Bas...

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Xiajiong Shen Lijie Bao Lei Zhang Institute of Data and Knowledge Engineering Henan University Kaifeng PR China School of Computer and Information Engineering Henan University Kaifeng PR China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng PR China

ISBN: (纸本)9781450387828

The traditional association rule representation method has no way to show the essential relationship between concepts and lacks understanding of the concept level. Concept lattice is a kind of data structure which can show the relationship between concepts, concept lattice also has unique advantages in the visualization and discovery of potential knowledge of association rules. In this paper, a visualization algorithm of association rules based on concept lattice is proposed. The algorithm visually displays the association rules extracted from concept lattice. Finally, this article combines the chronic disease record data of a certain hospital to analyze and visualize the association rules. The implementation results show that the visualization method has good effects in knowledge analysis and data display.

关键词： Visualization Concept lattice Association rules knowledge discovery

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Personalized Recommendation Based On Entity Attributes and Graph Features

Personalized Recommendation Based On Entity Attributes and G...

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IEEE International Conference on Big knowledge (ICBK)

作者： Yi Zhu Bingbing Dong Zhiqing Sha School of Information Engineering Yangzhou University Yangzhou China Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Instituse of Big Knowledge Science Hefei University of Technology Hefei China

ISBN: (纸本)9781665438599

With the rapid increase in the amount of website data, it has been a more difficult task for users to get the infor-mation they are interested in. Personalized recommendation is an important bridge to find the information which users really need on the website. Many recent studies have introduced additional attribute information about users and/or items to the rating matrix for alleviating the problem of data sparsity. In order to make full use of the attribute information and scoring matrix, deep learning based recommendation methods are proposed, especially the autoencoder model has attracted much attention because of its strong ability to learn hidden features. However, most of the existing autoencoder- based models require that the dimension of the input layer is equal to the dimension of the output layer, which may increase model complexity and certain information loss when using attribute information. In addition, as users' awareness of privacy protection increases, user attribute information is difficult to obtain. To address the above problems, in this paper, we propose a hybrid personalized recommendation model, which uses a semi-autoencoder to jointly embed the item's score vector and internal graph features (short for Co-Agpre). Specifically, we regard the user-item historical interaction matrix as a bipartite graph, and the Laplacian of the user-item co-occurrence graph is utilized to obtain the graph features of the item for solving the problem of sparse attributes. Then a semi-autoencoder is introduced to learn the hidden features of the item and perform rating prediction. The proposed model can flexibly use information from different sources to reduce the complexity of the model. Experiments on two real-world datasets demonstrate the effectiveness of the proposed Co-Agpre compared with state-of-the-art methods.

关键词： Deep learning Privacy Laplace equations Conferences Predictive models Feature extraction Complexity theory

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Analyzing the Spillover Effects of the Binance Incident on Financial Markets: A Machine Learning Approach

SSRN

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SSRN 2024年

作者： Feng, Lingbing Qi, Jiajun Liu, Ye Wang, Wei School of Statistics and Data Science Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China Key Laboratory of Data Science in Finance and Economics Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China Research Centre for Financial Development and Risk Prevention Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China School of Applied Economics Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China School of Management and Engineering Nanjing University Jiangsu Nanjing210093 China School of Economics and Business Administration Chongqing University Chongqing400044 China

This paper integrates event study with machine learning to analyze the spillover effects of the "Binance Incident" in the cryptocurrency market on financial markets. Utilizing the Lasso model, this paper predicts normal returns during the event window, outperforming traditional market models. Findings reveal positive effects on precious metal markets due to their safe-haven nature, while energy markets exhibit negative effects due to reduced investor confidence and weaker safe-haven attributes. Fiat currencies show negligible reactions. Summarizing across assets, this paper finds a negative overall impact of this adverse event on the financial markets. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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