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SSRN 2023年

作者： Qin, Zhaoming Dong, Nanqing Liu, Di Cao, Junwei Automatic Control Laboratory EPFL Lausanne1015 Switzerland Department of Computer Science University of Oxford OxfordOX1 3QD United Kingdom Department of Electrical Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

As a data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems. In a simplified scenario, centralized training, the most common paradigm for MARL, precludes large-scale deployment of MARL in communication-constrained cloud-edge environments. As a remedy, distributed training shows unparalleled advantages in real-life application scenarios compared with its centralized counterpart. However, distributed training still faces the challenge of system scalability, e.g., the high communication burden during coordinating individual agents, and needs to comply with data governance in terms of privacy. In this work, we propose a novel MARL solution to address these two practical issues. The proposed solution is based on actor-critic methods, where the global critic is a learned function of individual critics computed solely based on local observations of residences. In this way, the private data of residences is preserved completely and the communication cost is reduced significantly. The simulation experiments demonstrate that the proposed framework can achieve comparable performance to the state-of-the-art actor-critic framework without data governance and communication constraints. © 2023, The Authors. All rights reserved.

关键词： Edge computing

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Distributed optimal load frequency control with stochastic wind power generation

arXiv

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arXiv 2021年

作者： Silani, Amirreza Cucuzzella, Michele Scherpen, Jacquelien M.A. Yazdanpanah, Mohammad Javad Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

Motivated by the inadequacy of conventional control methods for power networks with a large share of renewable generation, in this paper we study the (stochastic) passivity property of wind turbines based on the Doubly Fed Induction Generator (DFIG). Differently from the majority of the results in the literature, where renewable generation is ignored or assumed to be constant, we model wind power generation as a stochastic process, where wind speed is described by a class of stochastic differential equations. Then, we design a distributed control scheme that achieves load frequency control and economic dispatch, ensuring the stochastic stability of the controlled network. © 2021, CC BY.

关键词： Stochastic systems

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Distributed Optimal Load Frequency control with Stochastic Wind Power Generation

Distributed Optimal Load Frequency Control with Stochastic W...

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European control Conference (ECC)

作者： Amirreza Silani Michele Cucuzzella Jacquelien M. A. Scherpen Mohammad Javad Yazdanpanah Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen The Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran University of Pavia Pavia Italy

关键词： Renewable energy sources Wind speed Decentralized control Stochastic processes Doubly fed induction generators Differential equations Wind power generation

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Differential Diagnostic Value of Machine Learning–Based Models for Embolic Stroke

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Clinical and Applied Thrombosis/Hemostasis 2023年 29卷 10760296231203663-10760296231203663页

作者： Kuo, HsunYu Liu, Tsai-Wei Huang, Yo-Ping Chin, Shy-Chyi Ro, Long-Sun Kuo, Hung-Chou Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Neurology Chang Gung Memorial Hospital at Linkou Medical Center Chang Gung University College of Medicine Taoyuan Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu Taiwan Institute of Electrical and Electronics Engineers Taipei Taiwan Institution of Engineering and Technology Taipei Taiwan Fellow of Chinese Automatic Control Society Taipei Taiwan Medical Imaging and Intervention Chang Gung Memorial Hospital at Linkou Medical Center Chang Gung University College of Medicine Taoyuan Taiwan

Cancer-associated thrombosis (CAT) and atrial fibrillation (AF)-related stroke are two subtypes of acute embolic stroke with distinct lesion patterns on diffusion weighted imaging (DWI). This pilot study aimed to evaluate the feasibility and performance of DWI-based machine learning models for differentiating between CAT and AF-related stroke. Patients with CAT and AF-related stroke were enrolled. In this pilot study with a small sample size, DWI images were augmented by flipping and/or contrast shifting to build convolutional neural network (CNN) predicative models. DWI images from 29 patients, including 9 patients with CAT and 20 with AF-related stroke, were analyzed. Training and testing accuracies of the DWI-based CNN model were 87.1% and 78.6%, respectively. Training and testing accuracies were 95.2% and 85.7%, respectively, for the second CNN model that combined DWI images with demographic/clinical characteristics. There were no significant differences in sensitivity, specificity, accuracy, and AUC between two CNN models (all P = n.s.). The DWI-based CNN model using data augmentation may be useful for differentiating CAT from AF-related stroke. © The Author(s) 2023.

关键词： atrial fibrillation cancer-associated thrombosis data augmentation differential diagnosis diffusion-weighted imaging machine learning

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Towards Narrowing the Search in Bounded-Suboptimal Safe Interval Path Planning 14

Towards Narrowing the Search in Bounded-Suboptimal Safe Inte...

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14th International Symposium on Combinatorial Search, SoCS 2021

作者： Rybecky, Tomas Kulich, Miroslav Andreychuk, Anton Yakovlev, Konstantin Department of Cybernetics Faculty of Electrical Engineering Czech Technical University Czech Republic Czech Institute for Informatics Robotics and Cybernetics Czech Technical University Czech Republic Federal Research Center for Computer Science and Control of Russian Academy of Sciences Russia Russia National Research University Higher School of Economics Russia

ISBN: (纸本)9781713834557

Path planning in the presence of dynamic obstacles is challenging as the time dimension has to be considered. A prominent approach to tackle this problem known to be complete and optimal is the A*-based Safe-interval Path Planning (SIPP). Bounded-suboptimal variants of SIPP employing the ideas of Weighted A* (WSIPP) and Focal Search (FocalSIPP) have been introduced recently, trading-off optimality for decreased planning time. In this paper, we revisit FocalSIPP and design several secondary heuristics for Focal Search with the intention to narrow the search in the direction of a preplanned optimal single-agent path not considering dynamic obstacles. The experimental results on various maps show that the designed heuristics generally outperform the hops-to-the-goal heuristic used in the original FocalSIPP and successfully compete with WSIPP as well. Copyright © 2021, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Motion planning

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Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections

arXiv

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arXiv 2022年

作者： Bai, Zhengwei Hao, Peng Shangguan, Wei Cai, Baigen Barth, Matthew J. The Department of Electrical and Computer Engineering University of California at Riverside RiversideCA92507 United States The Center for Environmental Research and Technology University of California at Riverside RiversideCA92507 United States The State Key Laboratory of Rail Traffic Control and Safety The Beijing Engineering Research Center of EMC GNSS Technology for Rail Transportation School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and automated vehicles are quickly becoming one of the transformative solutions to many transportation problems. However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system. In this study, we proposed a hybrid reinforcement learning (HRL) framework which combines the rule-based strategy and the deep reinforcement learning (deep RL) to support connected eco-driving at signalized intersections in mixed traffic. Vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I) communications to achieve higher mobility and energy efficiency in mixed connected traffic. The HRL framework has three components: a rule-based driving manager that operates the collaboration between the rule-based policies and the RL policy;a multi-stream neural network that extracts the hidden features of vision and V2I information;and a deep RL-based policy network that generate both longitudinal and lateral eco-driving actions. In order to evaluate our approach, we developed a Unity-based simulator and designed a mixed-traffic intersection scenario. Moreover, several baselines were implemented to compare with our new design, and numerical experiments were conducted to test the performance of the HRL model. The experiments show that our HRL method can reduce energy consumption by 12.70% and save 11.75% travel time when compared with a state-of-the-art model-based Eco-Driving approach. © 2022, CC BY-NC-ND.

关键词： Reinforcement learning

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

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Target Defense against Sequentially Arriving Intruders

Target Defense against Sequentially Arriving Intruders

引用

IEEE Conference on Decision and control

作者： Arman Pourghorban Michael Dorothy Daigo Shishika Alexander Von Moll Dipankar Maity Department of Electrical and Computer Engineering The University of North Carolina at Charlotte NC USA Computational and Information Sciences Directorate Army Research Laboratory APG MD USA Mechanical Engineering Department George Mason University Fairfax VA USA Air Force Research Laboratory WPAFB Control Science Center OH USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We consider a variant of the target defense problem where a single defender is tasked to capture a sequence of incoming intruders. The intruders’ objective is to breach the target boundary without being captured by the defender. As soon as the current intruder breaches the target or gets captured by the defender, the next intruder appears at a random location on a fixed circle surrounding the target. Therefore, the defender’s final location at the end of the current game becomes its initial location for the next game. Thus, the players pick strategies that are advantageous for the current as well as for the future games. Depending on the information available to the players, each game is divided into two phases: partial information and full information phase. Under some assumptions on the sensing and speed capabilities, we analyze the agents’ strategies in both phases. We derive equilibrium strategies for both the players to optimize the capture percentage using the notions of engagement surface and capture circle. We quantify the percentage of capture for both finite and infinite sequences of incoming intruders.

关键词： Level set Games Sensors Task analysis IEEE Regions

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A Gas Recognition Method Based on PCA and PSO-LSSVM 19

A Gas Recognition Method Based on PCA and PSO-LSSVM

引用

19th International Conference on Optical Communications and Networks, ICOCN 2021

作者： Song, Tingting Xia, Wanyu Yan, Zhanwei Song, Kai Chen, Deyun Chen, Yinsheng National Experimental Teaching Demonstration Center for Measurement and Control Technology and Instrumentation Harbin University of Science and Technology Harbin150080 China Postdoctoral Research Station of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9781665424462

Gases in the real environment always exist in the form of mixtures, and effective identification of gas types to reduce the occurrence of safety incidents has become an important direction in the field of gas analysis research. This paper proposes a mixed gas identification method based on PCA and PSO-LSSVM. This method uses principal component analysis (PCA) to reduce the dimensionality of the sensor array output signal, and uses the Relief algorithm to select data. The particle swarm algorithm is used to iteratively optimize the relevant parameters in the least squares support vector machine model, and the PSO-LSSVM model is constructed to qualitatively analyze the composition of the mixed gas. This article uses a public data set of mixed gases of ethylene, methane and carbon monoxide to conduct experiments. The experimental results show that the method used in this paper can effectively identify the type of mixed gas, and the recognition accuracy rate reaches 91.67%. The method proposed in this paper can provide a research foundation for the follow-up analysis of the mixed gas concentration. © 2021 IEEE.

关键词： Particle swarm optimization (PSO)

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