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2nd International Conference on Advanced Algorithms and Signal Image processing, AASIP 2022

作者： Li, Jianning Zhang, Junzhao Zhang, Xinyu Wang, Shuai College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Software Institute Nanjing University Jiangsu Nanjing210093 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Shanxi Province 1331 Project-Smart City Based on Big Data Lighting Data Sharing and Public Service Engineering Technology Research Center Shanxi Taiyuan030024 China Shanxi Intelligent Digital Lighting Union Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Cultural Tourism Industry Research Institute Shanxi Taiyuan030024 China

ISBN: (纸本)9781510660571

In the construction process, wearing safety helmet can effectively reduce the injury caused by falling objects and hard objects. In order to detect the wearing of safety helmet during the construction process, a lightweight safety helmet wearing detection algorithm YOLOv5-CS is proposed and applied to the embedded equipment at the construction site. The algorithm is based on the improvement of YOLOv5 target detection algorithm. The improved CAShuffle module is used to form the backbone feature extraction network, which reduces the size of the original algorithm and ensures the detection accuracy of the algorithm;DIoU as the loss function, which makes the algorithm more conducive to detecting small targets such as hard hats;modify dataset, add the dataset on the basis of the original dataset and mark the safety helmet and the anchor frame of the person separately, and judge whether the worker wears the safety helmet by analyzing positional relationship of the identified anchor frame, use the algorithm combining K-means and genetic algorithm to predict the anchor frame of the dataset, and use Mosaic algorithm to enhance the data. The results show that the parameter quantity of YOLOv5-CS algorithm is 2.4M, less than one third of YOLOv5;The average precision decreased by only 4.2%;640x640 pictures can be recognized at 31 frames per second on Jetson Nano, a embedded platform. © 2022 SPIE.

关键词： Signal detection

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Dynamic exploitation Gaussian bare-bones bat algorithm for optimal reactive power dispatch to improve the safety and stability of power system

arXiv

引用

arXiv 2021年

作者： Qu, Zhaoyang Dong, Yunchang Mugemanyi, Sylvère Yu, Tong Bo, Xiaoyong Li, Huashun Li, Yang Rugema, François Xavier Bananeza, Christophe School of Electrical Engineering Northeast Electric Power University Jilin China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin China Guangxi Power Grid Co. Ltd. Electric Power Research Institute Nanning China State Grid Jilin Electric Power Co. Ltd. Jilin Power Supply Company Jilin China School of Energy and Electrical Engineering Hohai University Nanjing China

In this paper, a novel Gaussian bare-bones bat algorithm (GBBBA) and its modified version named as dynamic exploitation Gaussian bare-bones bat algorithm (DeGBBBA) are proposed for solving optimal reactive power dispatch (ORPD) problem. The optimal reactive power dispatch (ORPD) plays a fundamental role in ensuring stable, secure, reliable as well as economical operation of the power system. The ORPD problem is formulated as a complex and nonlinear optimization problem of mixed integers including both discrete and continuous control variables. Bat algorithm (BA) is one of the most popular metaheuristic algorithms which mimics the echolocation of the microbats and which has also outperformed some other metaheuristic algorithms in solving various optimization problems. Nevertheless, the standard BA may fail to balance exploration and exploitation for some optimization problems and hence it may often fall into local optima. The proposed GBBBA employs the Gaussian distribution in updating the bat positions in an effort to mitigate the premature convergence problem associated with the standard BA. The GBBBA takes advantages of Gaussian sampling which begins from exploration and continues to exploitation. DeGBBBA is an advanced variant of GBBBA in which a modified Gaussian distribution is introduced so as to allow the dynamic adaptation of exploitation and exploitation in the proposed algorithm. Both GBBBA and DeGBBBA are used to determine the optimal settings of generator bus voltages, tap setting transformers and shunt reactive sources in order to minimize the active power loss, total voltage deviations and voltage stability index. The performance and effectiveness of the proposed algorithms are demonstrated on three power system test bus including the standard IEEE 14-bus, IEEE 57-bus and IEEE 118-bus. The results provided by the proposed algorithms have been compared with the results obtained by other algorithms reported in the literature. Simulation results show that

关键词： Gaussian distribution

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research on the Improved YOLOv5 Algorithm Fall Monitoring System for Home Care for the Elderly

Research on the Improved YOLOv5 Algorithm Fall Monitoring Sy...

引用

Computer Information Science and Application technology (CISAT), International Conference on

作者： Wanchao Xu Junchao Zhang Xinyu Zhang Fengxia Li Jinyi Tao Rongli Zhao College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan Shanxi China Shanxi Provincial Engineering Research Center of Electric Drive and Internet of Things Taiyuan Shanxi College of Software Nanjing University Nanjing Jiangsu National Intelligent Lighting and Smart Wearable Technology Innovation Center Taiyuan Shanxi Province Engineering Technology Research Center of Smart City Lighting Data Sharing and Public Service Platform Based on Big DataShanxi Province Taiyuan Shanxi Province Research Institute of New Materials and Chemical Engineering Shanxi Zhejiang University Taiyuan Shanxi

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

In view of the risk of fall for the elderly in the home care environment, a surveillance video display fall detection method of the improved YOLOv5 algorithm is improved, and the Transformer module that increases the human attention mechanism is combined with the CSPDarknet structure of the backbone part of the YOLOv5 algorithm. The system collects video data through the camera, trains and verifies the video data, and then judges the action and posture of the target person. It connects the WIFI module of Orange School to send the action and posture information to the guardian’s mobile phone to remind the guardian to timely treat the person who fell down, so as to improve the rescue efficiency. On the basis of improving the algorithm structure, the loss function was improved and conducted repeated training. The experimental results showed that the improved algorithm mAP improved by 2.8%, the $F 1$ score by 2.2%, the number of processing frames per second by 10% during inference, and achieved the fall detection accuracy of 98.6%.

关键词： YOLO Training Accuracy Attention mechanisms Surveillance Transformers Inference algorithms Older adults Fall detection Wireless fidelity

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Modeling method for the coupling relations of microgrid cyber-physical systems driven by hybrid spatiotemporal events

arXiv

引用

arXiv 2021年

作者： Bo, Xiaoyong Chen, Xiaoyu Li, Huashun Dong, Yunchang Qu, Zhaoyang Wang, Lei Li, Yang School of Electrical Engineering Northeast Electric Power University Jilin132012 China Electrical and Information Engineering College Jilin Agricultural Science and Technology University Jilin132101 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin132012 China State Grid Jilin Electric Power Co. Ltd. Baishan Power Supply Company Baishan134300 China State Grid Jilin Electric Power Co. Ltd. Jilin Power Supply Company Jilin132011 China

The essence of the microgrid cyber-physical system (CPS) lies in the cyclical conversion of information flow and energy flow. Most of the existing coupling models are modeled with static networks and interface structures, in which the closed-loop data flow characteristic is not fully considered. It is difficult for these models to accurately describe spatiotemporal deduction processes, such as microgrid CPS attack identification, risk propagation, safety assessment, defense control, and cascading failure. To address this problem, a modeling method for the coupling relations of microgrid CPS driven by hybrid spatiotemporal events is proposed in the present work. First, according to the topological correlation and coupling logic of the microgrid CPS, the cyclical conversion mechanism of information flow and energy flow is analyzed, and a microgrid CPS architecture with multi-agents as the core is constructed. Next, the spatiotemporal evolution characteristic of the CPS is described by hybrid automata, and the task coordination mechanism of the multi-agent CPS terminal is designed. On this basis, a discrete-continuous correlation and terminal structure characteristic representation method of the CPS based on heterogeneous multi-groups are then proposed. Finally, four spatiotemporal events, namely state perception, network communication, intelligent decision-making, and action control, are defined. Considering the constraints of the temporal conversion of information flow and energy flow, a microgrid CPS coupling model is established, the effectiveness of which is verified by simulating false data injection attack (FDIA) scenarios. © 2021, CC BY-NC-ND.

关键词： Multi agent systems

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Method for extracting patterns of coordinated network attacks on electric power CPS based on temporal-topological correlation

arXiv

引用

arXiv 2020年

作者： Wang, Lei Qu, Zhaoyang Li, Yang Hu, Kewei Sun, Jian Xue, Kai Cui, Mingshi School of Electric Engineering Northeast Electric Power University Jilin132012 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin132012 China State Grid Jilin Province Electric Power Supply Company Changchun130000 China EastInner Mongolia Electric Power Company Hohhot010000 China

In the analysis of coordinated network attacks on electric power cyber-physical system (CPS), it is difficult to restore the complete attack path, and the intent of the attack cannot be identified automatically. A method is therefore proposed for the extracting patterns of coordinated network attacks on electric power CPS based on temporal-topological correlation. First, the attack events are aggregated according to the alarm log of the cyber space, and a temporal-causal Bayesian network-based cyber attack recognition algorithm is proposed to parse out the cyber attack sequences of the same attacker. Then, according to the characteristic curves of different attack measurement data in physical space, a combination of physical attack event criteria algorithm is designed to distinguish the types of physical attack events. Finally, physical attack events and cyber attack sequences are matched via temporal-topological correlation, frequent patterns of attack sequences are extracted, and hidden multi-step attack patterns are found from scattered grid measurement data and information from alarm logs. The effectiveness and efficiency of the proposed method are verified by the testbed at Mississippi State University. Copyright © 2020, The Authors. All rights reserved.

关键词： Embedded systems

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power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization

arXiv

引用

arXiv 2020年

作者： Qu, Zhaoyang Xie, Qianhui Liu, Yuqing Li, Yang Wang, Lei Xu, Pengcheng Zhou, Yuguang Sun, Jian Xue, Kai Cui, Mingshi College of Information Engineering Northeast Electric Power University Jilin132012 China Jilin Engineering Technology Tesearch Center of Intelligent Electric Power Big Data Processing Jilin132012 China Department of Electrical and Electronic Engineering University of Bath Bath United Kingdom School of Electrical Engineering Northeast Electric Power University Jilin132012 China State Grid Jilin Electric Power Supply Company Changchun13000 China State Grid Neimenggu Eastern Electric Power Supply Company Huhehaote010000 China

Existing power cyber-physical system (CPS) risk prediction results are inaccurate as they fail to reflect the actual physical characteristics of the components and the specific operational status. A new method based on dependent Markov chain for power CPS risk area prediction is proposed in this paper. The load and constraints of the non-uniform power CPS coupling network are first characterized, and can be utilized as a node state judgment standard. Considering the component node isomerism and interdependence between the coupled networks, a power CPS risk regional prediction model based on dependent Markov chain is then constructed. A cross-adaptive gray wolf optimization algorithm improved by adaptive position adjustment strategy and cross-optimal solution strategy is subsequently developed to optimize the prediction model. Simulation results using the IEEE 39-BA 110 test system verify the effectiveness and superiority of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Cyber Physical System

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A New Ontology Meta-Matching Technique with a Hybrid Semantic Similarity Measure 15th

A New Ontology Meta-Matching Technique with a Hybrid Semanti...

引用

15th International Conference on intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2019

作者： Lu, Jiawei Xue, Xingsi Lin, Guoxiang Huang, Yikun College of Information Science and Engineering Fujian University of Technology Fuzhou China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou China Concord University College Fujian Normal University Fuzhou China

ISBN: (纸本)9789811397097

Ontology is the kernel technique of semantic web, which can be used to describe the concepts and their relationships in a particular domain. However, different domain experts would construct the ontologies according to different requirements, and there exists a heterogeneity problem among the ontologies, which hinders the interaction between ontology-based intelligent systems. Ontology matching technique can determine the links between heterogeneous concepts, which is an effective method for solving this problem. Semantic similarity measure is a function to calculate to what extent two concepts are similar to each other, which is the key component of ontology matching technique. Generally, multiple semantic similarity measures are used together to improve the accuracy of the concept recognition. How to combine these semantic similarity measures, i.e., the ontology meta-matching problem, is a challenge in the ontology matching domain. To address this challenge, this paper proposes a new ontology meta-matching technique, which applies a novel combination framework to aggregate two broad categories of similarity measures. The experiment uses the famous benchmark provided by the Ontology Alignment Evaluation Initiative (OAEI). Comparing results with the participants of OAEI shows the effectiveness of the proposal. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Ontology

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The recommendation method of power selling packages based on the optimal feature subset of power trading users

引用

Journal of Computers (Taiwan) 2019年第2期30卷 14-26页

作者： Qu, Zhao-Yang Feng, Rong-Qiang Qu, Nan Liu, Yao-Wei Yan, Jia Qu, Chong School of Information Engineering Northeast Electric Power University Jilin132012 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin132012 China Maintenaue Company of Jiangsu Power Company NanJing210000 China State Grid Jilin Electric Power Supply Company Jilin130000 China Fushun Power Supply Company of State Grid Liaoning Electric Power Supply Company LiaoNing113001 China

By aiming at huge user groups in the power market and the difficult selection of power selling packages in the trading process, a recommendation method of power selling packages based on the optimal feature subset of power trading users was proposed. First of all, the Optimal Feature Subset of power Trading Users (OFSPTU) was defined, and the optimal subset discovery algorithm based on coverage rate with weighing increase was designed, the optimal user feature subset could be screened out reasonably from mass trading users. Next, the similar computing method of power selling packages based on attribute correlation was proposed, in the form of clustering and confirming package attribute weighing, the package similarity was calculated, obtaining the nearest neighbor of package projects. At last, the accurate recommendation of power selling packages based on the OFSPTU and package attribute was realized. The experiment shows that the effectiveness of OFSPTU and accuracy of the recommended algorithm. © 2019 Computer Society of the Republic of China. All rights reserved.

关键词： Sales

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electricity Sales Prediction Model of electricity Market of the Non-linear Regression Combination with the Optimal Weight

引用

Journal of Computers (Taiwan) 2019年第5期30卷 75-87S页

作者： Qu, Zhao-Yang Xie, Shu-Ya Feng, Rong-qiang Qu, Nan Lv, Hong-bo Chi, Zuo-Wei Qu, Chong Department of computer science and technology of the Institute of Information Engineering Northeast Electric Power University Jilin Jilin132012 China Intelligent Electric Power Big Data Processing Jilin Engineering Technology Research Center Jilin Jilin132012 China Maintenance Company of Jiangsu Power Company NanJing210000 China State Grid Jilin Electric Power Supply Company Jilin130000 China Fushun Power Supply Company of State Grid Liaoning Electric Power Supply Company LiaoNing113001 China Guizhou Power Grid Co. Ltd. Guiyang Baiyun Power Supply Bureau GuiYang550014 China

The model to estimate electric sales in electric market of the optimal weighting nonlinear regression combination is brought forth to improve the accuracy of electricity sales forecast. Firstly, the least square method is used to estimate the nonlinear regression equation in order to obtain the result sets of the prediction sequence. Secondly, the design method of minimizing the calculation of fitting variance and weighting com-bination to calculate the residual sequence and weighting of the Radial Basis Function (RBF) neural network and the least squares vector machine, finally the result sets of residual sequence concluded. At the end, a nonlinear decreasing strategy of inertia weight is introduced on the basis of cuckoo algorithm to optimize the extended search space of model parameters and the model solution. The simulation results show that the model can accurately and comprehensively reflect the law of electric sales and improve the accuracy of sales prediction. © 2019 Computer Society of the Republic of China. All rights reserved.

关键词： Sales

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Method for quantitative estimation of the risk propagation threshold in electric power CPS based on seepage probability

arXiv

引用

arXiv 2018年

作者： Zhaoyang, Q.U. Zhang, Yu Nan, Q.U. Wang, Lei Yang, L.I. Dong, Yunchang College of Information Engineering Northeast Electric Power University Jilin132012 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin132012 China Maintenaue Company of Jiangsu Power Company Nanjing210000 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China Energy Systems Division Argonne National Laboratory IL60439 United States

Because of the non-uniformity of the electric power CPS network and the dynamic nature of the risk propagation process, it is difficult to quantify the critical point of a cyber risk explosion. From the perspective of the dependency network, this paper proposes a method for quantitative evaluation of the risk propagation threshold of power CPS networks based on the percolation theory. First, the power CPS network is abstracted as a dual-layered network-directed unweighted graph according to topology correlation and coupling logic, and the asymmetrical balls-into-bins allocation method is used to establish a "one-to-many" and "partially coupled" non-uniform power CPS characterization model. Then, considering the directionality between the cyber layer and the physical layer link, the probability of percolation flow is introduced to establish the propagation dynamic equations for the internal coupling relationship of each layer. Finally, the risk propagation threshold is numerically quantified by defining the survival function of power CPS network nodes, and the validity of the proposed method is verified by the IEEE 30-bus system and 150-node Barabsi-Albert model. Copyright © 2018, The Authors. All rights reserved.

关键词： Solvents

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