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63rd IEEE Conference on Decision and control, CDC 2024

作者： Xing, Yu Bizyaeva, Anastasia Johansson, Karl H. Kth Royal Institute of Technology and with Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm Sweden Cornell University Sibley School of Mechanical and Aerospace Engineering IthacaNY United States

ISBN: (纸本)9798350316339

This paper studies community detection for a nonlinear opinion dynamics model from its equilibria. It is assumed that the underlying network is generated from a stochastic block model with two communities, where agents are assigned with community labels and edges are added independently based on these labels. Agents update their opinions following a nonlinear rule that incorporates saturation effects on interactions. It is shown that clustering based on a single equilibrium can detect most community labels (i.e., achieving almost exact recovery), if the two communities differ in size and link probabilities. When the two communities are identical in size and link probabilities, and the intercommunity connections are denser than intra-community ones, the algorithm can achieve almost exact recovery under negative influence weights but fails under positive influence weights. Utilizing fixed point equations and spectral methods, we also propose a detection algorithm based on multiple equilibria, which can detect communities with positive influence weights. Numerical experiments demonstrate the performance of the proposed algorithms. © 2024 IEEE.

关键词： Nash equilibrium

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Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

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computers, Materials & Continua 2022年第6期71卷 4627-4641页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Mohamed Saber Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Electronics and Communications Engineering Faculty of EngineeringDelta University for Science and TechnologyMansoura11152Egypt

Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state *** the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various *** Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of *** may be used to select the optimum EEG channels for use in Brain-computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be *** enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the ***(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.

关键词： Signals metaheuristics optimization feature selection multilayer perceptron support vector machines

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Fed-Autoformer: A Federated Learning-Based Battery Anomaly Detection Approach for Electric Vehicle Charging with Privacy Preservation 30th

Fed-Autoformer: A Federated Learning-Based Battery Anomaly D...

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30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Yan, Xuhan Yang, Shunbo Guo, Yiguo Fu, Yimu Wu, Sixing Li, Jianbin School of Control and Computer Engineering North China Electric Power University Beijing China Economic Technology Research Institute State Grid Shandong Electric Power Company Shandong Jinan City China

ISBN: (纸本)9783031829062

The battery management system ensures the safe operation of electric vehicles (EVs) by detecting abnormalities in the battery energy storage system. However, the extensive and widely dispersed data in EV battery data, along with the presence of users’ private details, raise a demand for effective anomaly detection and privacy protection. To tackle those two challenges, we propose Fed-Autoformer, a distributed anomaly detection architecture based on real vehicle charging data to detect battery anomalies and protect privacy. Thus, we first propose a novel anomaly detection model for EV charging data based on the Autoformer encoder architecture. In particular, it enables an autocorrelation attention mechanism to extract and consider the intrinsic correlation of battery data features (e.g., voltage, current, state of charge (SOC), etc.) for anomaly detection. In addition, we introduce a federated learning architecture to protect the privacy of battery charging data and improve the federated learning training process based on the differences between vehicle batteries in charging stations to ensure detection accuracy while protecting the privacy of EV charging data. Finally, we comprehensively analyze the feasibility and effectiveness of our proposed architecture using a real EV charging dataset. Our proposed federated learning approach for EV battery anomaly detection not only effectively protects privacy through federated learning but also improves the accuracy by 3.2% compared to most mainstream time series anomaly detection models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Charging stations

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Accelerating Data Transfer in Dataflow Architectures Through a Look-Ahead Acknowledgment Mechanism

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Journal of computer Science & Technology 2022年第4期37卷 942-959页

作者： Yu-Jing Feng De-Jian Li Xu Tan Xiao-Chun Ye Dong-Rui Fan Wen-Ming Li Da Wang Hao Zhang Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Beijing Smartchip Microelectronics Technology Company Limited Beijing 100000China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100190China

The dataflow architecture,which is characterized by a lack of a redundant unified control logic,has been shown to have an advantage over the control-flow architecture as it improves the computational performance and power efficiency,especially of applications used in high-performance computing(HPC).Importantly,the high computational efficiency of systems using the dataflow architecture is achieved by allowing program kernels to be activated in a simultaneous ***,a proper acknowledgment mechanism is required to distinguish the data that logically belongs to different *** solutions include the tagged-token matching mechanism in which the data is sent before acknowledgments are received but retried after rejection,or a handshake mechanism in which the data is only sent after acknowledgments are ***,these mechanisms are characterized by both inefficient data transfer and increased area *** performance of the dataflow architecture depends on the efficiency of data *** order to optimize the efficiency of data transfer in existing dataflow architectures with a minimal increase in area and power cost,we propose a Look-Ahead Acknowledgment(LAA)*** accelerates the execution flow by speculatively acknowledging ahead without *** simulation analysis based on a handshake mechanism shows that our LAA increases the average utilization of computational units by 23.9%,with a reduction in the average execution time by 17.4%and an increase in the average power efficiency of dataflow processors by 22.4%.Crucially,our novel approach results in a relatively small increase in the area and power consumption of the on-chip logic of less than 0.9%.In conclusion,the evaluation results suggest that Look-Ahead Acknowledgment is an effective improvement for data transfer in existing dataflow architectures.

关键词： dataflow model control-flow model high-performance computing application data transfer power efficiency

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Hunger games search optimization with deep learning model for sustainable supply chain management

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Discover Internet of Things 2023年第1期3卷 10页

作者： Xu, Zheng Jain, Deepak Kumar Neelakandan, S. Abawajy, Jemal School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University Shanghai201209 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China School of Artificial Intelligence Dalian China Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science and Engineering R.M.K Engineering College Taluk India Deakin University Geelong Australia

The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use. © The Author(s) 2023.

关键词： Food supply

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Intelligent Execution of computer Vision Tasks in Delay-Constrained UAV-Aided Networks

Intelligent Execution of Computer Vision Tasks in Delay-Cons...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Varshney, Nancy Puligheddu, Corrado Chiasserini, Carla Fabiana Casetti, Claudio De, Swades Indian Institute of Technology Department of Electrical Engineering Delhi India Department of Electronics and Telecommunications Politecnico di Torino Italy Department of Control and Computer Engineering Politecnico di Torino Italy

ISBN: (纸本)9798350370218

Autonomous unmanned aerial vehicles (UAVs) are crucial in critical target tracking and disaster management services. However, challenges arise due to limited channel capacity causing large transmission delays and constraints imposed by UAV batteries when running computationally intensive object detection and tracking algorithms. To address this, we propose in-telligent offloading computer vision tasks to a high-computational edge server at millimeter wave (mmWave) frequency. Transmission at mmWave needs large transmission power and is susceptible to blockages that necessitate considering the link quality in the offloading policy. Additionally, the timely processing of frames containing objects of interest is essential for context-based UAV operations to achieve a low frame drop rate. In this work, we present a delayed-reward reinforcement learning framework to determine the offloading policy of computer vision tasks in a delay-constrained environment. This approach considers the importance of the content within frames, which is unknown to the UAV. The objective is to jointly reduce UAV energy consumption and frame drop rates, leveraging statistical information of both the channel and the frame semantics. Through extensive simulations, we demonstrate by considering statistical information of the communication channel and frame semantics, we achieve approximately 45% energy savings compared to the UAV's energy consumption when processing all frames locally and maintaining the drop rate of delay-constrained frames below 5%. © 2023 IEEE.

关键词： Communication channel delay frame semantics offloading unmanned aerial vehicle (UAV)

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Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

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computers, Materials & Continua 2022年第11期73卷 2241-2255页

作者： El-Sayed M.El-kenawy Zeeshan Shafi Khan Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Ali E.Takieldeen Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Science Department College of Engineering and Computer ScienceMustaqbal UniversityBuraidahSaudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Mechanical Engineering Qassim UniversityBuraidah51452Saudi Arabia

Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous *** is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital *** route planning methods for the mobile robot have been developed and *** to our best knowledge,no method offers an optimum solution among the existing *** Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental *** the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile *** paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)*** is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with *** order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are *** experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.

关键词： Mobile robot swarm optimization robot route planning neural networks

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Complex Permeability Determination for EE Core Based on Flexible PCB Fixture and Magnetic Flux Path Length Analysis 2

Complex Permeability Determination for EE Core Based on Flex...

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2nd China Power Supply Society Electromagnetic Compatibility Conference, CPEMC 2024

作者： Jie, Huamin Zhao, Zhenyu Chang, Yongqi Wang, Changdong Jiang, Shiqi Gao, Richard Xian-Ke See, Kye Yak School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering National Univeristy of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Institute of High Performance Computing A*STAR Singapore Singapore

ISBN: (纸本)9798331509552

The complex permeability of magnetic cores acts as a vital factor in electromagnetic interference (EMI) filtering choke design and optimization. Many efforts have been reported for toroid cores, but fewer target those with other shapes, such as EE, EI, UU, and UI cores. Owing to their commonly used in planar EMI filters, determining the complex permeability of them is of significance. Targeting EE cores, this paper presents a flexible printed circuit board (PCB)-based fixture adapter tailored designed for performing the measurement, while the corresponding core permeability can be calculated through magnetic flux path length analysis. The experiments validate the effectiveness and accuracy of the proposed methods, spanning a wide frequency range up to 120 MHz. ©2024 IEEE.

关键词： Magnetic flux

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Finite Sample Analysis for a Class of Subspace Identification Methods 63

Finite Sample Analysis for a Class of Subspace Identificatio...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： He, Jiabao Ziemann, Ingvar Rojas, Cristian R. Hjalmarsson, Hakan Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Pennsylvania PhiladelphiaPA19104 United States

ISBN: (纸本)9798350316339

While subspace identification methods (SIMs) are appealing due to their simple parameterization for MIMO systems and robust numerical realizations, a comprehensive statistical analysis of SIMs remains an open problem, especially in the non-asymptotic regime. In this work, we provide a finite sample analysis for a class of SIMs, which reveals that the convergence rates for estimating Markov parameters and system matrices are O(1 / SN), in line with classical asymptotic results. Based on the observation that the model format in classical SIMs is non-causal because of a projection step, we choose a parsimonious SIM that bypasses the projection step and strictly enforces a causal model to facilitate the analysis, where a bank of ARX models are estimated in parallel. Leveraging recent results from a finite sample analysis of an individual ARX model, we obtain a union error bound for an array of ARX models and proceed to derive error bounds for system matrices using robustness results for the singular value decomposition. © 2024 IEEE.

关键词： Singular value decomposition

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Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm

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CSEE Journal of Power and Energy Systems 2022年第4期8卷 1145-1156页

作者： Levent Yavuz Ahmet Soran AhmetÖnen Xiangjun Li S.M.Muyeen Electrical and Computer Engineering Department in Abdullah Gül University Kayseri 38080Turkey Department of Electrical and Computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscat123Oman Department of Electrical Engineering Qatar UniversityDoha 2713Qatar State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems China Electric Power Rescarch InstituteBeijing 100192China

This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to *** than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML *** the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)*** this purpose,power system failures are simulated by using the PSCA D-Python *** of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored ***,the proposed technique will be able to work on different systems,topologies,or data *** proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.

关键词： Decision tree(DT) ensemble machine learning algorithm fault detection islanding operation k-Nearest Neighbor(kNN) linear discriminant analysis(LDA) logistic regression(LR) Naive Bayes(NB) self-healing algorithm

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