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Annual Conference of Industrial electronics Society

作者： Zhenxiao Yin Xiaobing Dai Zewen Yang Yang Shen Georges Hattab Hang Zhao Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Chair of Information-oriented Control Technical University of Munich Munich Germany Center for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Department of Electronic & Computer Engineering The Hong Kong University of Science and Technology Hong Kong China

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations.

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Dual Role of Pulmonary Surfactant Corona in Modulating Carbon Nanotube Toxicity and Benzo[A]Pyrene Bioaccessibility

SSRN

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SSRN 2023年

作者： Luo, Zhen Xu, Dongfang Xu, Yan Zhao, Jian Hu, Guoqing Yue, Tongtao Department of Engineering Mechanics State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China Institute of Coastal Environmental Pollution Control Key Laboratory of Marine Environment and Ecology Ministry of Education Frontiers Science Center for Deep Ocean Multispheres and Earth System Ocean University of China Qingdao266100 China College of Electronic Engineering and Automation Shandong University of Science and Technology Qingdao266590 China

Inhaled carbon nanotubes (CNTs) can deposit in the deep lung, where they interact with pulmonary surfactant (PS) to form coronas, potentially altering the fate and toxicity profile of CNTs. However, the presence of other contaminants in combination with CNTs may affect these interactions. In this study, we investigated the effects of coexisting contaminants, specifically benzo[a]pyrene (BaP), on the interaction between CNTs and PS. We used passive dosing and fluorescence-based techniques to demonstrate that PS increases the solubility of BaP pre-adsorbed on CNTs. Subsequently, molecular dynamics simulations were performed to gain insight into the intricate interactions between CNTs, BaP, and PS. Our results suggest that the PS corona plays a dual role in modulating the toxicity of CNTs. First, it mitigates toxicity by reducing the hydrophobicity and apparent aspect ratio of CNTs. Second, it exacerbates toxicity due to energetically dominant PS-BaP interactions, which increase the solubility and thus bioaccessibility of BaPs. Importantly, these interactions are strongly influenced by the size and aggregation state of the CNTs. Our findings highlight the need to consider the bioaccessibility of coexisting contaminants when assessing the inhalation toxicity of PS-modified CNTs. © 2023, The Authors. All rights reserved.

关键词： Toxicity

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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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Inverse learning of black-box aggregator for robust Nash equilibrium

arXiv

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Nearest-Better Network for Fitness Landscape Analysis of Continuous Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年

作者： Diao, Yiya Li, Changhe Zeng, Sanyou Yang, Shengxiang Coello, Carlos A. Coello China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Anhui University of Sciences and Technology School of Artificial Intelligence Hefei China China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Department of Computer Science CINVESTAV-IPN 07360 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L. Monterrey Mexico

Fitness landscape analysis (FLA) is quite important in evolutionary computation. In this paper, we propose a novel FLA method, the nearest-better network (NBN), which uses the nearest-better relationship to simplify the original fitness landscape of continuous optimization problems. We introduce an efficient algorithm to calculate NBN for continuous problems. We also propose four numerical measurements and a 3D visualization method based on NBN. Experiments show that compared to the other main FLA methods, the four numerical measurements proposed here can effectively measure the four intended features: neutrality, ruggedness, modality, and basin of attraction, respectively, and common features of the fitness landscape can be maintained in 3D NBN visualization, regardless of the scale of the problem. NBN also provides a view of how algorithms search in high-dimensional problems with the help of the 3D NBN visualization. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Is the skip connection provable to reform the neural network loss landscape? 29

Is the skip connection provable to reform the neural network...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wang, Lifu Shen, Bo Zhao, Ning Zhang, Zhiyuan Department of Electronic and Information Engineering Beijing Jiaotong University Beijing China Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China

ISBN: (纸本)9780999241165

The residual network is now one of the most effective structures in deep learning, which utilizes the skip connections to "guarantee" the performance will not get worse. However, the non-convexity of the neural network makes it unclear whether the skip connections do provably improve the learning ability since the nonlinearity may create many local minima. In some previous works [Freeman and Bruna, 2016], it is shown that despite the non-convexity, the loss landscape of the two-layer ReLU network has good properties when the number m of hidden nodes is very large. In this paper, we follow this line to study the topology (sub-level sets) of the loss landscape of deep ReLU neural networks with a skip connection and theoretically prove that the skip connection network inherits the good properties of the two-layer network and skip connections can help to control the connectedness of the sub-level sets, such that any local minima worse than the global minima of some two-layer ReLU network will be very "shallow". The "depth" of these local minima are at most O(m(?-1)/n), where n is the input dimension, ? © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Deep learning

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Application of Improved PSO-ELM in Auto Insurance Customer Risk Level Prediction 17

Application of Improved PSO-ELM in Auto Insurance Customer R...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Chen, Xi Xu, Liang Wang, Yalong Zhai, Xiang Guo, Xiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory Applications in Complicated Systems School of Electrical and Electronic Engineering Tianjin300384 China Picc Property and Casualty Limited Operation Sharing Department Beijing100022 China

ISBN: (纸本)9781728164151

In order to improve the prediction accuracy of automobile insurance customer's risk level, a prediction model of automobile insurance customer's risk level based on GSPSO - ELM is proposed. The premium, insurance amount, number of risks, gender, driving age and other indicators were used as the main criteria to predict the risk level. The simulation results show that the proposed GSPSO - ELM prediction model can not only overcome the local optimal, and has higher precision of prediction, forecasting results of the mean square error (mse), decision coefficient and mean absolute percentage error and dynamic inertia weight PSO algorithm to optimize extreme learning machine, linear decreasing inertia weight PSO algorithm to optimize extreme learning machine model are improved. © 2020 IEEE.

关键词： Forecasting

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Phase chimera states on non-local hyperrings

arXiv

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arXiv 2023年

作者： Muolo, Riccardo Njougouo, Thierry Gambuzza, Lucia Valentina Carletti, Timoteo Frasca, Mattia Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Department of Mathematics University of Namur NamurB5000 Belgium naXys Namur Institute for Complex Systems University of Namur NamurB5000 Belgium Faculty of Computer Science University of Namur NamurB5000 Belgium Department of Electrical and Electronic Engineering University of Buea P.O. Box 63 Buea Cameroon Department of Electrical Electronics and Computer Science Engineering University of Catania Catania95125 Italy Istituto di Analisi dei Sistemi ed Informatica "A. Ruberti" IASI-CNR Roma00185 Italy

Chimera states are dynamical states where regions of synchronous trajectories coexist with incoherent ones. A significant amount of research has been devoted to study chimera states in systems of identical oscillators, non-locally coupled through pairwise interactions. Nevertheless, there is an increasing evidence, also supported by available data, that complex systems are composed by multiple units experiencing many-body interactions, that can be modeled by using higher-order structures beyond the paradigm of classic pairwise networks. In this work we investigate whether phase chimera states appear in this framework, by focusing on a novel topology solely involving many-body, non-local and non-regular interactions, hereby named non-local d-hyperring, being (d + 1) the order of the interactions. We present the theory by using the paradigmatic Stuart-Landau oscillators as node dynamics, and show that phase chimera states emerge in a variety of structures and with different coupling functions. For comparison, we show that, when higher-order interactions are"flattened" to pairwise ones, the chimera behavior is weaker and more elusive. © 2023, CC BY.

关键词： Dynamics

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A MULTIPORT EMBEDDED DC POWER FLOW controlLER FOR ISOP-DCT

A MULTIPORT EMBEDDED DC POWER FLOW CONTROLLER FOR ISOP-DCT

引用

2021 Annual Meeting of CSEE Study Committee of HVDC and Power electronics, HVDC 2021

作者： Tian, Chaohui Zhang, Jianwen Zhou, Jianqiao Zang, Jiajie Li, Zheng Xi, Dongmin Wang, Fu Zhang, Aozhe Liu, Linzhao School of Information Sciences and Technology Donghua University No.2999 People North Rd. Shanghai China Department of Electrical Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University The Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education No.800 Dongchuan Rd. Shanghai China Simon Fraser University Mechatronic Systems Engineering 250-13450 102 Ave Surrey Canada School of Electric Power Inner Mongolia University of Technology Aimin St. 49 Hohhot China

ISBN: (纸本)9781839537035

The DC transformers (DCTs) can achieve flexible power flow control in the ring-type and meshed-type multi-terminal DC grids with less loss. However, due to the limited power flow control range of the DCTs, the power flow controller (PFC) is expected to boost the system's controllability. Unlike the conventional DCTs, this paper proposes an embedded DC power flow controller (e-DCPFC) formed by a PFC and an input-series-output-parallel (ISOP) DCT. This device has the features of multiple ports and extended bidirectional power flow control capability. The operational principle of the e-DCPFC is analysed in detail. And a modified input voltage sharing (m-IVS) control strategy is proposed in this paper. This control scheme not only maintains the input capacitor voltage balanced of the ISOP structure but also controls the power of the extra ports provided by the e-DCPFC. The feasibility and effectiveness of the proposed topology and control strategy are verified via MATLAB simulation mode. © 2021 IET Conference Proceedings. All rights reserved.

关键词： Electric load flow

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Urban Digital Twins for Intelligent Road Inspection

Urban Digital Twins for Intelligent Road Inspection

引用

IEEE International Conference on Big Data

作者： Rui Fan Yikang Zhang Sicen Guo Jiahang Li Yi Feng Shuai Su Yanting Zhang Wenshuo Wang Yu Jiang Mohammud Junaid Bocus Xingyi Zhu Qijun Chen the Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous Systems and State Key Laboratory of Intelligent Autonomous Systems Robotics & Artificial Intelligence Laboratory (RAIL) Tongji University Shanghai P. R. China Department of Computer Science & Technology Donghua University Shanghai P. R. China Department of Civil Engineering McGill University Montreal Canada CTO office at ClearMotion Billerica MA USA Department of Electrical & Electronic Engineering the University of Bristol BS8 1UB U.K Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji University Shanghai P. R. China

ISBN: (纸本)9781665480468

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022.

关键词： Roads Data acquisition Inspection Big Data Rough surfaces Digital twins

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