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2022 Chinese Automation Congress, CAC 2022

作者： Li, Huimiao Yang, Xiaozhou Zhai, Chao Hubei Key Laboratory of Advanced Control and Intelligent Automation Fo Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Future Resilient Systems Singapore-ETH Centre Create Campus Singapore

ISBN: (纸本)9781665465335

In the past decades, cascading blackouts have caused serious damages to power systems and affected the normal operation of society, so it is crucial to quickly restore the damaged power system to normal state. In this paper, a reinforcement learning (RL) approach is developed to achieve the robust restoration of generators in power systems. Firstly, the performance recovery of damaged power system is modeled as markov decision process, and constraints such as line voltage and active power output of generators are considered. Secondly, a RL algorithm is proposed to search the optimal control strategy for generator units recovery. Based on the proposed restoration approach, Q-learning algorithm is employed to obtain the optimal strategy for power supply of generaors in power grids. Finally, numerical simulations are carried out on IEEE 9-bus system under different scenarios of external disturbances and certainties. The simulation results demonstrate the effectiveness and feasibility of the proposed approach. © 2022 IEEE.

关键词： Reinforcement learning

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3D Reconstruction of Weakly Textured Objects Based On Denoising Polarized Images 41

3D Reconstruction of Weakly Textured Objects Based On Denois...

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第41届中国控制会议

作者： Youai Wang Yi Liu Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Eduction CRRC Zhuzhou Locomotive Co. Ltd.

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Traditional 3D visual reconstruction technology based on feature extraction is difficult to estimate the surface depth information of a smooth object with a single texture,while the polarization 3 D reconstruction technology can recover the surface normal information from the image pixel by pixel,which has the characteristics of high precision,low texture dependence and strong material ***,the normals recovered from the polarization information have the problem of multi-value ambiguity,which needs to be corrected before the accurate normal information can be *** the same time,the image collected by the polarization camera contains a lot of noise,which will greatly affect the accuracy of reconstruction *** this paper,the characteristics of polarization information are used to denoise the polarization image,and the reference normal information is used as the prior information for correcting the polarization ***,the degree of polarization and angle of polarization are calculated based on the polarized image,then the two are fused and denoised by the Block Matching threedimensional(BM3D) method,and then the multi-valued ambiguity of polarization normal is corrected according to the reference normal ***,the corrected polarization normal is integrated to obtain a high-precision 3D shape of the object.

关键词： Polarization Denoising 3D Reconstruction Weakly Textured Objects Normal Multi-value Correction

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Recent developments of the reconstruction in magnetic particle imaging

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 290-302页

作者： Lin Yin Wei Li Yang Du Kun Wang Zhenyu Liu Hui Hui Jie Tian CAS Key Laboratory of Molecular Imaging the State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Molecular Imaging Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Medical Imaging Center the First Affiliated HospitalJinan UniversityGuangdong 510632China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100083China

Magnetic particle imaging(MPI)is an emerging molecular imaging technique with high sensitivity and temporal-spatial *** reconstruction is an important research topic in MPI,which converts an induced voltage signal into the image of superparamagnetic iron oxide particles concentration *** reconstruction primarily involves system matrix-and x-space-based *** this review,we provide a detailed overview of the research status and future research trends of these two *** addition,we review the application of deep learning methods in MPI reconstruction and the current open sources of ***,research opinions on MPI reconstruction are *** hope this review promotes the use of MPI in clinical applications.

关键词： Magnetic particle imaging Image reconstruction System matrix X-space

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Frequency Regulation Reserve optimization Considering Flexible Resource Complementary Aggregation

Frequency Regulation Reserve Optimization Considering Flexib...

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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Zirui Huang Wenbo Mao Yaping Li Kaifeng Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China China Electric Power Research Institute CO. Ltd Nanjing China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

This paper proposes a fast-solving strategy that uses the complementarity of flexible resources to aggregate and participate in frequency regulation. Firstly, this paper proposes a frequency regulation performance index which can consider the adjustment rate, response delay time, adjustable capacity and adjustment cost of flexible resources. Then, based on the index, this paper uses the K-means algorithm to cluster the resources with similar performance. Then, this paper proposes an aggregation strategy considering the complementary of multiple flexible resources. Finally, this paper improves the unit combination optimization model, so that flexible resources can participate in the optimization in the form of aggregation, and the ratio of resources can be determined in advance, reducing the number of optimization parameters and improve the efficiency of solution. In this paper, the simulation is carried out on the IEEE-118 node. The results show that the proposed scheme can effectively improve the rate of optimization solution and effectively avoid the curse of dimensionality in the face of a large number of flexible resources.

关键词： Costs optimization models Face recognition Smart grids Performance analysis Delays Time factors Indexes optimization Frequency control

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A Finite-Time Posture control Strategy for the Swarm Underactuated Robots 5th

A Finite-Time Posture Control Strategy for the Swarm Under...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Huang, Zixin Qin, Xiangyu Wei, Shaoqi Wang, Lejun Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan430200 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789811939976

Under the background of industrial machinery automation. The swarm robots are required to cooperate to complete complex and accurate works in finite-time and create more relative residual value. Based on the inherent advantages of underactuated robot, the research of finite time posture control strategy has higher theoretical and practical value. Firstly, the dynamic model of a single Pendubot of planar swarm underactuated robots was built. However, the robots reach to the same objective from different initial states and keep being at the same posture, so it was necessary to plan a feasible trajectory. Then, the planned trajectory parameters are optimized to make the underactuated robots relized the objectives in finite time. Finally, an unified tracking controller is designed for the underactuated robots. Simulation results of the swarm underactuated robots demonstrated the effectiveness of the proposed control strategy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Robots

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Tracking performance limitations of MIMO discrete-time networked control systems with multiple constraints

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Science China(Information Sciences) 2023年第8期66卷 267-268页

作者： Xiaowei JIANG Bin ZHANG Xiangyong CHEN Huaicheng YAN School of Automation China University of Geosciences Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation and Electrical Engineering Linyi University Key Laboratory of Smart Manufacturing in Energy Chemical Process of the Ministry of Education

Dear editor,Networked control systems(NCSs) have attracted widespread attention in some fields because they offer the advantages of reduced cabling, greater resource sharing,and ease of installation and maintenance [1]. However, in an unreliable network environment, network constraints can significantly limit the performance of NCSs.

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Impulsive control of Heterogeneous Multi-agent systems with Saturation Constraint and Time Delay

Impulsive Control of Heterogeneous Multi-agent Systems with ...

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Chinese Automation Congress (CAC)

作者： Ranran Jiao Bo Li Xiangyong Chen Xiaowei Jiang Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper addresses the problem of leader-following consensus in heterogeneous multi-agent systems (HMAS) with input saturation and communication delay. Given the practical differences between leader and follower dimensions, we design virtual leaders to address this problem by allowing actual leaders and followers to communicate with virtual leaders separately, thus enabling indirect communication between actual leaders and followers. Additionally, the control protocol proposed in this paper takes into account the effects of input saturation and time delay that exist in real communication. At last, a simulation example is presented to demonstrate the effectiveness of the designed control protocol.

关键词： Energy consumption Protocols Automation Event detection Delay effects control systems Delays Multi-agent systems

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Optimizing Bounding Box Regression in complex Backgrounds

Optimizing Bounding Box Regression in Complex Backgrounds

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2024 China Automation Congress, CAC 2024

作者： Chen, Jun Zhang, Haiyan Yu, Anjun Wang, Yiwei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Jiangxi Ganyue Expressway Co.Ltd. Nanchang China School of Automation China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798350368604

In the context of electrical power operation sites, the automated and accurate detection of whether workers are properly equipped with safety gear, such as safety clothing, helmets, and safety ropes for high-altitude operations, is crucial for ensuring the safety of personnel. Traditional object detection models often struggle to accurately identify these critical pieces of safety equipment in complex environments, due to variable object sizes, overlapping objects, and substantial background noise. To specifically address these challenges, we propose a novel loss function-Enhanced Alignment Intersection over Union (EAIoU) loss. This loss function is designed to enhance the model’s ability to discriminate between closely spaced and overlapping safety gear in real-time, significantly improving robustness against scale variations and complex backgrounds. In experiments based on the YOLOv8s architecture, our model achieved a mean Average Precision (mAP) of 92.1% on a dataset specific to electrical power operations, markedly outperforming traditional IoU loss functions. Additionally, EAIoU demonstrated improved performance on the COCO dataset, indicating its generalization capability across various complex scenarios. This research not only enhances the accuracy and real-time performance of safety equipment detection at electric power operation sites but also opens new avenues for advanced object detection technology in complex, multi-object environments. © 2024 IEEE.

关键词： Safety devices

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A Transfer Learning Model for Gesture Recognition Based on the Deep Features Extracted by CNN

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第5期2卷 447-458页

作者： Zou, Yongxiang Cheng, Long The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

The surface electromyogram (sEMG) based hand gesture recognition is prevalent in human-computer interface systems. However, the generalization of the recognition model does not perform well on cross-subject and cross-day. Transfer learning, which applies the pretrained model to another task, has demonstrated its effectiveness in solving this kind of problem. In this regard, this article first proposes a multiscale kernel convolutional neural network (MKCNN) model to extract and fuse multiscale features of the multichannel sEMG signals. Based on the proposed MKCNN model, a transfer learning model named TL-MKCNN combines the MKCNN and its Siamese network by a custom distribution normalization module (DNM) and a distribution alignment module (DAM) to realize domain adaptation. The DNM can cluster the deep features extracted from different domains to their category center points embedded in the feature space, and the DAM further aligns the overall distribution of the deep features from different domains. The MKCNN model and the TL-MKCNN model are tested on various benchmark databases to verify the effectiveness of the transfer learning framework. The experimental results show that, on the benchmark database NinaPro DB6, the average accuracies of TL-MKCNN can achieve 97.22% on within-session, 74.48% on cross-subject, and 90.30% on cross-day, which are 4.31%, 11.58%, and 5.51% higher than those of the MKCNN model on within-session, cross-subject, and cross-day, respectively. Compared with the state-of-the-art works, the TL-MKCNN obtains 13.38% and 37.88% accuracy improvement on cross-subject and cross-day, respectively. Impact Statement-Intelligent robot technology has dramatically promoted economic development and social progress. The HCI system is widely implemented in the robotics field because it can fill the gap between human intentions and robotic control, and a more robust generalization HCI system can make the interaction between humans and robots more convenient

关键词： Economic and social effects

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Fault detection method of photovoltaic system based on power change of array 4

Fault detection method of photovoltaic system based on power...

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4th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2021

作者： Tan, Chao Li, Chenxi Zhang, Kanjian Wei, Haikun Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

ISBN: (纸本)9781450385053

The fault detection of photovoltaic power generation system is of great significance in photovoltaic plant management. The conventional fault detection method of photovoltaic power system requires additional sensors, and the fault detection scheme needs to be redesigned if the parameters of the photovoltaic plant change. Therefore, for large photovoltaic plants, it is necessary to develop a method that does not require the use of external sensors or the detailed parameters of the photovoltaic plant. This paper proposes a photovoltaic system fault detection method based on photovoltaic array current and voltage data. This method does not require additional sensor data such as irradiance and temperature. The failure of photovoltaic system can be detected through the change of power, and the abnormal situation of photovoltaic power station can still be detected under cloudy and shaded conditions. The advantages of this method are that there is no need for complex and diverse sensor equipment, the calculation is simple and the fault detection ability is accurate. The experimental data of Padang Poverty Power Station of State Grid has been verified. © 2021 ACM.

关键词： Fault detection

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