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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Wu, Wenjie Qiu, Lin Li, Hui Liu, Xing Du, Chao Fang, Youtong Zhejiang University College of Electrical Engineering Hangzhou China Florida State University Department of Electrical and Computer Engineering TallahasseeFL United States Shaanxi University of Science & Technology College of Electrical and Control Engineering Xian China

ISBN: (纸本)9798350316445

This paper presents a novel predictive control architecture for power converters that addresses the challenges of model mismatch and parameter sensitivity in the finite control-set model predictive control (FCS-MPC) framework. The proposed method employs a dynamic-linearization-based approach, which replaces the detailed model used in the FCS-MPC controller with a virtual equivalent data model, creating a data-enabled FCS-MPC architecture. By relying solely on input-output data, the proposed method exhibits considerable robustness against parameter perturbations while retaining the attractive features of the conventional FCS-MPC method. The effectiveness of the proposed design is demonstrated through comparative simulations results on a two-level grid-tied inverter. © 2023 IEEE.

关键词： Grid-tied inverter Model predictive control (MPC) Robustness

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of Systems science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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Emergency Vehicle Direction Detection Using Mel-Frequency Cepstral Coefficients and Deep Learning

Emergency Vehicle Direction Detection Using Mel-Frequency Ce...

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IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： Shourya Bhushan Manish Chaturvedi Department of Electrical and Computer Science Engineering IITRAM Ahmedabad India

ISBN: (数字)9798350387278

ISBN: (纸本)9798350387285

Emergency vehicles face difficulty in navigating through traffic and locating the shortest route to the incident site. In the developing countries or under disaster conditions when limited communication and Intelligent Transportation System (ITS) infrastructure is available, the situation becomes more challenging. This paper addresses these challenges by leveraging the unique sound generated by emergency vehicles to detect the direction of the emergency vehicles. We use the Mel-frequency cepstral coefficients (MFCC) and a Convolutional Neural Network (CNN) stacked with Long Short-Term Memory (LSTM) layers to accurately detect the movement direction of outgoing emergency vehicles at a junction. MFCC facilitates effective feature extraction from audio signals, while the CNN-LSTM architecture enables robust temporal and spatial pattern recognition. The information can be used to adapt the traffic lights at the downstream junction to enable the uninterrupted movement of the emergency vehicle through the junction. The experiment results demonstrate 98.56% accuracy in detecting movement direction of an emergency vehicle. The solution can improve emergency response under resource constrained environment and in turn improve public safety and well-being.

关键词： Accuracy Predictive models Emergency services Feature extraction Public security Convolutional neural networks Mel frequency cepstral coefficient Junctions Long short term memory Testing

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Improved Proximal Policy Optimization Algorithm for Multi-objective Disassembly Line Balancing Considering Hazardous Tasks 21

Improved Proximal Policy Optimization Algorithm for Multi-ob...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Guo, Zeyu Guo, Xiwang Wang, Jiacun Qin, Shujin Qi, Liang Kang, Qi Liaoning Petrochemical University Information and Control Engineering Fushun China Monmouth University Computer Science and Software Eng. Wes Long BranchNJ United States Shangqiu Normal University Economics and Management Shangqiu China Shandong Technology University Computer Science Department Qingdao China New Jersey Institute of Technology Electrical and Computer Engineering Newark United States

ISBN: (纸本)9798350365221

As disassembly lines evolve, keeping workers safe from all potential dangers has become paramount. This work proposes an improved approach, Proximal Policy Optimization for Disassembly Line(PPO-DL), to address Multi-objective Dis-assembly Line Balancing Problem(MDLBP) that integrates task switching time and hazardous tasks. The MDLBP model aims to optimize disassembly net profit, balance workstation utilization, and mitigate penalties from hazardous tasks. The proposed PPO-DL modifies the action space of PPO to accommodate MD LBP characteristics, ensuring efficient learning by restricting actions to valid ranges through action masking. Comparative experiments with PPO, Q-learning, and A2C demonstrate PPO-DL's superiority in finding near-optimal solutions across various scenarios. © 2024 IEEE.

关键词： Assembly machines

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Reinforcement Learning for Control System Optimization in electrical engineering

Reinforcement Learning for Control System Optimization in El...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Bhadula, Shuchi Alabdeli, Haider Badhoutiya, Arti Deepak, A. Bhardwaj, Nitin Anitha, G. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Electrical Engineering Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This research investigates the use of reinforcement learning (RL) algorithms for optimal control systems in electrical engineering. The article discusses four popular RL algorithms - Q-learning, Deep Q Network (DQN), as well as Policy Gradient Methods (PGM) and Proximal Policy Optimization (PPO) density. A simulation in an electric control system was performed to assess these strategies. The proposed research was designed and made with the intention of improving adaptivity, delays in responding to time actions on adopting circumstances such as UAV placement navigation paradigm, wave energy conversions, there is an aspect of HVAC systems around water circulation networks control. The results demonstrated the robustness of DQN and PPO, with system efficiency values of 0.92 and 0.91, response times of 0.08s and 0.09s, respectively, and in that case they were specific circumstances showcasing stable behavior system compared to training on an experience replay memory basis Comparative analysis with relative work highlighted the competitive performance of RL algorithms as compared to other methods, which further validated their practical application. Sensitivity analysis demonstrated the DQN and PPO adaptation to a change in influential factors, proving that they are capable of functioning under changing conditions. The computational efficiency analysis showed that DQN and PPO had faster convergence speeds compared to Q-learning and Policy Gradient Methods. According to this research, RL offers vital bits of knowledge into the transformative potential that these algorithms can bring to electrical engineering and in this way make them successful instruments for versatile networks. © 2024 IEEE.

关键词： Reinforcement learning

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Ammonia Sensing Performance of an Al-Doped SnO Thin Film Decorated With Platinum Nanoparticles

IEEE Electron Device Letters

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IEEE Electron Device Letters 2023年第12期44卷 2043-2046页

作者： Zhu, Bo-Xun Chiu, Chao-Chun Jian, Jia-Jin Lin, Kun-Wei Hsu, Wei-Chou Liu, Wen-Chau National Cheng Kung University Institute of Microelectronics Department of Electrical Engineering Tainan70101 Taiwan Chaoyang University of Technology Department of Computer Science and Information Engineering Taichung41349 Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Institute of Microelectronics Department of Electrical Tainan70101 Taiwan

A new ammonia (NH3) sensor based on the incorporation of an Al-doped SnO2 (ATO) thin layer and platinum (Pt) nanoparticles (NPs) is produced. The employed Pt NPs effectively enhance the ammonia sensing behaviors due to enlarged specific surface area and advanced catalytic ability of noble Pt metal. Compositional, elemental, and morphological analyses are made to check the designed structure. In the experiment, good ammonia detecting performance including an advanced sensing response of 817.2 with a small response (recovery) time of 35 s (7 s) under 1000 ppm NH3 gas and an extremely lower detectable level of 20 ppb NH3 are obtained at 275°C. Moreover, the presented sensor demonstrates prominent long-term (120 d) stability toward ammonia gas. © 1980-2012 IEEE.

关键词： Surface treatment

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Programmable Adaptive Security Scanning for Networked Microgrids

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engineering 2021年第8期7卷 1087-1100页

作者： Zimin Jiang Zefan Tang Peng Zhang Yanyuan Qin Department of Electrical and Computer Engineering Stony Brook UniversityStony BrookNY 11794USA Department of Computer Science and Engineering University of ConnecticutStorrsCT 06269USA

Communication-dependent and software-based distributed energy resources(DERs)are extensively integrated into modern microgrids,providing extensive benefits such as increased distributed controllability,scalability,and ***,malicious cyber-attackers can exploit various potential *** this study,a programmable adaptive security scanning(PASS)approach is presented to protect DER inverters against various power-bot ***,three different types of attacks,namely controller manipulation,replay,and injection attacks,are *** approach employs both software-defined networking technique and a novel coordinated detection method capable of enabling programmable and scalable networked microgrids(NMs)in an ultra-resilient,time-saving,and autonomous *** coordinated detection method efficiently identifies the location and type of power-bot attacks without disrupting normal NM *** simulation results validate the efficacy and practicality of the PASS for securing NMs.

关键词： Networked microgrids Programmable adaptive security scanning Coordinated detection Software defined networking

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Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-Enabled Vehicular Networks

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Journal of Communications and Information Networks 2022年第3期7卷 269-277页

作者： Chang Liu Xuemeng Liu Shuangyang Li Weijie Yuan Derrick Wing Kwan Ng School of Electrical Engineering and Telecommunications the University of New South WalesSydneyNSW 2052Australia School of Electrical and Information Engineering the University of SydneySydneyNSW 2006Australia Department of Electrical Electronic and Computer Engineeringthe University of Western AustraliaPerth WA 6009Australia Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhen 518055China

Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication(ISAC),which highly depends on the accuracy of the channel prediction(CP),i.e.,predicting the angular parameters of ***,the performance of CP highly depends on the estimated historical channel stated information(CSI)with estimation errors,resulting in the performance degradation for most traditional CP *** further improve the prediction accuracy,in this paper,we focus on the ISAC in vehicle networks and propose a convolutional long-short term memory(CLSTM)recurrent neural network(CLRNet)to predict the angle of vehicles for the design of predictive *** the developed CLRNet,both the convolutional neural network(CNN)module and the LSTM module are adopted to exploit the spatial features and the temporal dependency from the estimated historical angles of vehicles to facilitate the angle ***,numerical results demonstrate that the developed CLRNet-based method is robust to the estimation error and can significantly outperform the state-of-the-art benchmarks,achieving an excellent sum-rate performance for ISAC systems.

关键词： integrated sensing and communication predictive beamforming deep learning convolutional longshort term neural network vehicular networks

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An end-to-end RNS CNN Accelerator

An end-to-end RNS CNN Accelerator

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Vasilis Sakellariou Vassilis Paliouras Ioannis Kouretas Hani Saleh Thanos Stouraitis Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi UAE Electrical and Computer Engineering Department University of Patras Patras Greece

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

This work presents a Residue Numbering System (RNS)-based Convolutional Neural Network (CNN) accelerator. The proposed system is fully RNS-based, requiring no intermediate conversions to a binary representation. RNS operation overhead is minimized by designing the architecture in such a way that the usage of the non-trivial RNS operations is amortized over a large number of MAC operations. This allows to exploit their periodic usage and further reduce power consumption through clock-gating. Implementation results on a 22-nm process, show that RNS can not only increase the maximum achievable frequency of the arithmetic circuits, but also results in 58% more energy-efficient processing, compared to the traditional binary counterparts. Compared to the state-of-the-art, RNS-based CNN accelerator, the proposed architecture is shown to be 8.5× more energy efficient, with an average energy efficiency of 1.91 TOPS/W.

关键词： Power demand Circuits Benchmark testing Energy efficiency Convolutional neural networks Artificial intelligence Arithmetic

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Wound Tissue Classification: A Comparative Analysis of Deep Neural Network Models

Wound Tissue Classification: A Comparative Analysis of Deep ...

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International Conference on Smart Systems and Technologies (SST)

作者： Antonio Antunović Emmanuel Karlo Nyarko Damir Filko Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Computer Engineering and Automation Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Wound tissue classification is an important task in medical imaging, with applications ranging from wound assessment to treatment planning. In this study, we investigated different neural network architectures and loss functions to improve the accuracy and efficiency of wound tissue classification. The study included eight different neural network architectures, including classic U-Net, MobileNet U-Net, Attention U-Net, Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net), Attention Recurrent Residual U-Net (R2AU-Net), Residual Network (ResNet-50), EfficientNet and SegFormer. Each architecture was trained separately with four different loss functions: categorical cross-entropy, weighted categorical cross-entropy, focal loss and soft dice loss. The comparative analysis revealed that the SegFormer architecture in conjunction with soft dice loss function achieved the most promising results on all classification metrics. The results of the study highlight the potential for further research in this area and emphasise the need for more comprehensive datasets to improve model performance.

关键词： Training Semantic segmentation Transformers Smart systems Robustness Distance measurement Data models Planning Decoding Residual neural networks

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