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Chinese Automation Congress (CAC)

作者： Mingxuan Cai Qi Liu Yuxiang Liu Bo Yang Department of Automation Shanghai Jiao Tong University shanghai China Key Laboratory of System Control and Information Processing Shanghai Engineering Research Center of Intelligent Control and Management Ministry of Education of China Shanghai China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate fault diagnosis plays a key role in the safe and efficient operation of electrical motors. Aiming at multiclass motor fault diagnosis under complex working conditions, a signal fusion-based fault diagnosis framework is proposed. Measurement parameters from different sensors are first transformed by wavelet denoising and fast Fourier transform (FFT). Then, multiple parameters spectrum and working condition information are integrated and converted into high-dimension matrices. Finally, a channel-attention-based convolution neural network (CA-CNN) is proposed to extract fault patterns from a reconstructed matrix and output diagnosis results. Experiment results on both simulated and experimental datasets verify the effectiveness of the proposed method.

关键词： Fault diagnosis Employee welfare Wavelet transforms Accuracy Fast Fourier transforms Motors Sensors Numerical models Velocity measurement Matrix converters

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Pyramid Pooling-based Local Profiles for Graph Classification

Pyramid Pooling-based Local Profiles for Graph Classificatio...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Wu, Chengpei Lou, Yang Li, Junli College of Computer Science Sichuan Normal University Chengdu610066 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan Graduate School of Information Science and Technology Osaka University Osaka565-0871 Japan

ISBN: (纸本)9798350337020

Many natural and engineering systems can be modeled and represented in the forms of graph data, and then studied using graph theory and network analysis tools. Graph representation learning aims at generating lower-dimensional representations from higher-dimensional graph data, which is a crucial step that facilitates the follow-up tasks, such as node and graph classifications. In this paper, we present a simple but effective graph representation learning method, namely the pyramid pooling-based local profile (PPLP), which enables local nodal profiles to be transformed into a graph representation, with multi-scale features extracted. PPLP can be either embedded into a graph neural network as the readout layer, or perform independently as a graph embedding algorithm. The resultant representations of PPLP are for graph-level tasks. PPLP is experimentally tested by performing graph classification tasks on ten representative datasets, either as the readout layer of different graph neural networks, or as an independent graph embedding algorithm. Experimental results demonstrate that: 1) when embedded into graph neural networks, PPLP outperforms the widely-used global pooling-based readout methods;2) as an independent graph embedding algorithm, PPLP performs fairly good, especially on the social network datasets. The investigation confirms PPLP as a simple but promising method for graph-level tasks. © 2023 IEEE.

关键词： graph classification graph neural network Graph representation learning pyramid pooling

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Modulating surface cation vacancies of nickel-cobalt oxides as efficient catalysts for lithium-oxygen batteries

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Journal of Materials Science & Technology 2023年第8期139卷 147-155页

作者： Yuan Yang Linna Dai Jianwei Qiu Zhibiao Hu Peng Wang Yongtao Zhao Huanhuan Guo Pengchao Si Rui Song Center for Robotics School of Control Science and EngineeringShandong UniversityJinan 250061China Research Center for Carbon Nanomaterials Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials(Ministry of Education)School of Materials Science and EngineeringShandong UniversityJinan 250061China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China College of Chemistry and Chemical Engineering Xingtai UniversityXingtai 054001China

Rechargeable lithium-oxygen batteries(LOBs)have received incremental attention owing to their high energy density and applicability to mobile devices and electric ***,the lack of robust,low-cost,and environmentally benign bifunctional catalysts is a major impediment to the commercial *** introduction of vacancies is one of the effective strategies to enhance the performance of cathode catalysts for lithium-oxygen batteries,but the preparation is *** this work,needle-like microsphere cathode catalysts of nickel-cobalt oxide containing cationic vacancies are constructed by controlling the annealing *** is demonstrated that the presence of cationic vacancies can modulate the electronic structure of the catalyst,reduce the energy barrier for the oxygen electrode re-action,meanwhile enhance the bifunctional catalytic ***,the nickel-cobalt oxide-based LOB with cationic vacancies exhibits large specific capacity(12,205 mAh g^(-1)at 200 mA g^(-1))and good *** work provides worthwhile insight into the formation and catalytic enhancement mech-anism of transition metal oxide catalysts with cationic vacancies,and to some extent,the creation of efficient and low-cost oxygen electrocatalysts for LOBs.

关键词： Li-O_(2)batteries Metal oxides Cation vacancies Catalytic kinetic

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Security-Aware Reinforcement Learning under Linear Temporal Logic Specifications

Security-Aware Reinforcement Learning under Linear Temporal ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Bohan Cui keyi Zhu Shaoyuan Li Xiang Yin Department of Automation Shanghai Jiao Tong University Shanghai China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China

In this paper, we investigate the problem of reinforcement learning under linear temporal logic (LTL) specifications for Markov decision processes (MDPs) with security constraints. We consider an outside passive intruder (observer) that can observe the external output behavior of the system through an output projection. We assume that the secret of the system is a subset of the initial states. The security constraint requires that the observer can never infer for sure that the agent was initiated from a secret state. Our objective is to learn a control policy that achieves the LTL task while ensuring security. To solve the problem of shaping the reward for reinforcement learning, we propose an approach based on the initial-state estimator and the limit deterministic Büchi automata. We illustrate the proposed approach by a case study of mobile robot example.

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Deciphering decadal urban ozone trends from historical records since 1980

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National Science Review 2024年第11期11卷 117-129页

作者： Haolin Wang Xiao Lu Paul I.Palmer Lin Zhang Keding Lu Ke Li Tatsuya Nagashima Ja-Ho Koo Hiroshi Tanimoto Haichao Wang Meng Gao Cheng He Kai Wu Shaojia Fan Yuanhang Zhang School of Atmospheric Sciences Sun Yat-sen University and Key Laboratory of Tropical Atmosphere-Ocean System Ministry of Education Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) School of Geo Sciences University of Edinburgh National Centre for Earth Observation University of Edinburgh Laboratory for Climate and Ocean-Atmosphere Studies Department of Atmospheric and Oceanic SciencesSchool of PhysicsPeking University State Key Joint Laboratory of Environmental Simulation and Pollution Control The State Environmental Protection Key Laboratory of Atmospheric Ozone Pollution ControlCollege of Environmental Sciences and Engineering Peking University Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science &Technology National Institute for Environmental Studies Department of Atmospheric Sciences Yonsei University Department of Geography Hong Kong Baptist University Department of Civil and Environmental Engineering University of California

Ozone pollution is a major environmental threat to human health. Timely assessment of ozone trends is crucial for informing environmental policy. Here we show that for the most recent decade（2013–2022） in the northern hemisphere, warm-season（April–September） mean daily 8-h average maximum ozone increases much faster in urban regions with top ozone levels(mainly in the North China Plain, 1.2 ± 1.3 ppbv year-1) than in other, low-ozone regions(0.2 ± 0.9 ppbv year-1). These trends widen the ozone differences across urban regions, and increase extreme pollution levels and health threats from a global perspective. Comparison of historical trends in different urban regions reveals that ozone increases in China during 2013–2022 differ in magnitude and mechanisms to historical periods in other regions since *** reflects a unique chemical environment characterized by exceptionally high nitrogen oxides and aerosol concentrations, where reducing ozone precursor emissions leads to substantial ozone *** increase in China has slowed down in 2018–2022 compared to 2013–2017, driven by ongoing emission reductions, but with ozone-favorable weather conditions. Historical ozone evolution in Japan and South Korea indicates that ozone increases should be suppressed with continuous emission *** temperature and associated wildfires have also reversed ozone decreases in the USA and Europe,with anthropogenic ozone control slowing down in recent decades.

关键词： air quality surface ozone trends

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A New Hybrid Adaptive Self-Loading Fxlms and Cnn-Gru Net for Active Time-Varying Noise control

SSRN

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SSRN 2024年

作者： Zhu, Wenzhao Luo, Lei Key Laboratory of Intelligent Control and Neural Information Processing Education Ministry of China Yanshan University Qinhuangdao066004 China Key Laboratory of Optoelectronic Technology and Systems Education Ministry of China Chongqing University Chongqing400044 China

Active noise control (ANC) provides a lightweight and quick-to-deploy noise attenuation solution compared to traditional passive noise control. However, time-varying noise caused by the changing acoustic environment usually occurs in moving spaces, such as a car cabin, which further deteriorates the robustness of the adaptive filter and even deep networks in the ANC system. To use a deep ANC neural network with great denoising performance in a complex time-varying noise environment and reduce the interference of uncertain noise on the stability of the network, a new hybrid adaptive self-loading filtered-x least mean square (ASL-FxLMS) and convolutional neural network-gate recurrent unit (CNN-GRU) network is proposed. First, based on the CNN-LSTM structure, an improved CNN-GRU network with a multi-head attention mechanism is proposed to enhance attenuation performance for complicated periodical components in the time-varying noise and decrease the computing load as much as possible. In addition, an improved time-delay estimator is applied to improve the real-time ANC performance of CNN-GRU. Meanwhile, an adaptive self-loading FxLMS algorithm has been developed to deal with the uncertain components of time-varying noise. Moreover, to achieve balance attenuation, robustness, and tracking performance, the ASL-FxLMS and CNN-GRU are connected by a convex combination structure. Furthermore, theoretical analysis and simulations are also conducted to show the effectiveness of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Active noise control

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Fixed-time extended state observer-based trajectory tracking control for autonomous underwater vehicles

Fixed-time extended state observer-based trajectory tracking...

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作者： Zheng, Jiaqi Song, Lei Liu, Lingya Yu, Wenbin Zhu, Shanying Wang, Yiyin Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

In this paper, we investigate the trajectory tracking control problem for autonomous underwater vehicles (AUVs) with time-varying external disturbances of currents and waves in the sea. To solve the problem, a fixed-time extended state observer (FTESO)-based tracking controller is proposed. Firstly, by using the extended state observer, the FTESO is proposed for estimating the external disturbances and the velocities of the AUV within the fixed convergence time. Based on the FTESO, the fixed-time controller is proposed for reducing the tracking errors and improving robustness performance. Theoretical analysis is given, which shows that the unknown external disturbances can be compensated and tracking error can converge to zero within the fixed time. Simulation results show that the proposed FTESO controller not only guarantees the fixed-time convergence but also reduces the trajectory tracking errors of the AUV. © 2021 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd.

关键词： Autonomous underwater vehicles

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Terahertz metamaterial narrowband absorber based on metal split ring

Terahertz metamaterial narrowband absorber based on metal sp...

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2023 Applied Optics and Photonics China: Optical Sensing, Imaging, and Display Technology and Applications;and Biomedical Optics, AOPC 2023

作者： Ji, Xiaodi Ge, Hongyi Jiang, Yuying Wu, Xuyang Jia, Zhiyuan Sun, Zhenyu Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Henan China Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control Henan China College of Information Science and Engineering Henan University of Technology Henan Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Henan Zhengzhou450001 China

ISBN: (纸本)9781510672321

Multi-band terahertz metamaterial absorbers offer new perspectives to achieve perfect absorption and multipoint information matching, which enable an ever-growing number of applications. In this study, a dual-band terahertz metamaterial absorber based on the metal split ring is designed. The absorber has perfect absorption peaks at 1.15 THz and 2.47 THz, and the absorption rate is more than 99%. The absorber produces a harp peak with a bandwidth of 0.008 at 2.47 THz, which has an extremely high quality factor of 308. The distribution of the electric field and surface current at two resonance points is analyzed using the finite element integration method. Through full wave simulation calculation, the maximum sensitivity of the analyte refractive index of the absorber is 400 GHz/RIU, and the maximum sensitivity of the thickness is 35 GHz/μm. The results show that the absorber can achieve highly sensitive detection of trace substances. © 2023 SPIE. All rights reserved.

关键词： Metamaterials

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Communication and Computation Resource Allocation for Digital Twin in IIoT systems

Communication and Computation Resource Allocation for Digita...

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2021 China Automation Congress, CAC 2021

作者： Ren, Cheng Chen, Cailian Li, Peizhe Guan, Xinping Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9781665426473

In order to construct the digital twin (DT) of the manufacturing process in industrial IoT (IIoT) systems, multiple sensors and IIoT equipments (IIEs) are deployed for process monitoring (PM) and other tasks. Heterogeneous data generated from different tasks are transmitted through the shared wireless network to edge computing devices (ECDs) for further decisions. However, either communication or computation resources are limited in industrial environment, which should be allocated efficiently. In this paper, in order to improve the overall efficiency for DT construction, we adjust transmission power, allocate bandwidth and determine computing resource allocation for various tasks, which are jointly optimized by formulating a mixed integer nonlinear programming (MINLP) problem. We decompose the original problem into two subproblems and propose a value-of-information (Vol) based algorithm for PM tasks. Then, we characterize the relationship between state estimation convergence for PM and the maximum allowed offloading aomunt for other computation tasks at each time slot. Based on that relationship, we determine the transmission power and the computing resource allocation policy for IIEs. Finally, simulation results prove that our method can improve the overall efficiency while guaranteeing state estimation convergence requirements for PM systems and stability of computation task queues for other tasks. © 2021 IEEE

关键词： Resource allocation

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86-Gb/s Optical Chaos Communication over 100-km Fiber Transmission Using Semiconductor Lasers 49

86-Gb/s Optical Chaos Communication over 100-km Fiber Transm...

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49th European Conference on Optical Communications, ECOC 2023

作者： Mao, Xiaoxin Fan, Xiaoqi Wang, Longsheng Fu, Songnian Wang, Yuncai Wang, Anbang Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province Taiyuan030024 China College of Optoelectronics Taiyuan University of Technology Taiyuan030024 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China Guangdong Provincial Key Laboratory of Information Photonics Technology Guangzhou510006 China

An optical-chaos secure communication of 86-Gb/s 16-Ary QAM signal over 100-km fiber transmission is experimentally demonstrated under the 20%-overhead SD-FEC BER threshold of 2.0×10-2 by using wideband chaos syn... 详细信息

ISBN: (纸本)9781839539954

关键词： Semiconductor lasers

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