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International Conference on Computer Vision (ICCV)

作者： Zimeng Zhao Xi Zhao Yangang Wang Southeast University China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781665428132

This paper aims to reconstruct physically plausible hand motion from monocular color images. Existing frame-by-frame estimating approaches can not guarantee the physical plausibility (e.g. penetration, jittering) directly. In this paper, we embed physical constraints on the per-frame estimated motions in both spatial and temporal space. Our key idea is to adopt a self-supervised learning strategy to train a novel encoder-decoder, named TravelNet, whose training motion data is prepared by the physics engine using discrete pose states. TravelNet captures key pose states from hand motion sequences as compact motion descriptors, inspired by the concept of keyframes in animation. Finally, it manages to extract those key states out of perturbations without manual annotations, and reconstruct the motions preserving details and physical plausibility. In the experiments, we show that the outputs of the TravelNet contain both finger synergism and time consistency. Through the proposed framework, hand motions can be accurately reconstructed and flexibly re-edited, which is superior to the state-of-the-art methods.

关键词： Training Computer vision Perturbation methods Color Manuals Space exploration Reliability

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Data-Driven Distributed MPC for Load Frequency control of Networked Nonlinear Power systems

Data-Driven Distributed MPC for Load Frequency Control of Ne...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Yubin Jia Jun Zhou Panxiao Yong Jun Guo School of Automation Southeast University Nanjing Jiangsu China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing P. R. China

ISBN: (纸本)9781665476881

In this paper we proposed a data-driven distributed model predictive control (MPC) for the load frequency control of the multi-area interconnection nonlinear power system. The data-driven Koopman operator method is used to deal with the nonlinear wind power generation system in each area. The distributed Koopman operator algorithm is proposed to deal with the huge amount of data in the large-scale distributed system. Based on the distributed linear model obtained by the distributed Koopman operator algorithm, the distributed MPC is applied to realize the load frequency control (LFC) of the multi-area power system. The cooperation-based cost function is optimized by the MPC controller in each area, and the global optimal solution can be achieved by the proposed method. The effectiveness and practicability of the proposed algorithm are demonstrated by the simulation.

关键词： Distributed databases Wind power generation Prediction algorithms Cost function Power systems Nonlinear systems Frequency control

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An IMU Error Correction Method Based on Multithreshold ZV Detection and SRCKF

An IMU Error Correction Method Based on Multithreshold ZV De...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jialiang Luo Shichao Chen Yisheng Lv Wenqiao Sun Fan Yang Ruijie Xu The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Transportation and Economics Research Institute and the Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University Jinan China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hong Kong China

With the rapid development and widespread application of Inertial measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update Technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field.

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Reinforcement Learning based Data-driven Optimal control Strategy for systems with Disturbance

Reinforcement Learning based Data-driven Optimal Control Str...

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Data Driven control and Learning systems (DDCLS)

作者： Zhong-Xin Fan Shihua Li Rongjie Liu School of Automation Southeast University Nanjing P. R. China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing P. R. China Department of Statistics Florida State University Tallahassee FL USA

This paper proposes a partially model-free optimal control strategy for a class of continuous-time systems in a data-driven way. Although a series of optimal control have achieving superior performance, the following challenges still exist: (i) The controller designed based on the nominal system is difficult to cope with sudden disturbances. (ii) Feedback control is highly dependent on system dynamics and generally requires full state information. A novel composite control method combining output feedback reinforcement learning and input-output disturbance observer for these two challenges is concluded in this paper. Firstly, an output feedback policy iteration (PI) algorithm is given to acquire the feedback gain iteratively. Simultaneously, the observer continuously provides estimates of the disturbance. system dynamic information and states information are not needed to be known in advance in our approach, thus offering a higher degree of robustness and practical implementation prospects. Finally, an example is given to show the effectiveness of the proposed controller.

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Unknown-frequency harmonic disturbance estimation based speed regulation for single-frame control moment gyroscope

Unknown-frequency harmonic disturbance estimation based spee...

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2020 Jiangsu Annual Conference on Automation, JACA 2020

作者： Yu, Yuxuan Yang, Jun Li, Shihua School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Jiangsu Nanjing210096 China

ISBN: (纸本)9781839535635

In the single frame control moment gyroscope (CMG) servo system, the permanent magnet synchronous motor (PMSM) is used to drive the frame. However, CMG is always affected by the multi-source strong coupling disturbance, and the disturbance form is mostly harmonic, so it is very important to accurately estimate the harmonic disturbance. In this paper, an estimation method of harmonic disturbance with unknown frequency and amplitude is proposed, and then the disturbance estimation is compensated by feedforward. The simulation results show the feasibility and performance of the proposed control method © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Permanent magnets

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AGI in Metaverse for Smart Cities and Societies: A Cyber Physical Social Approach

AGI in Metaverse for Smart Cities and Societies: A Cyber Phy...

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control Conference (ANZCC), Australian and New Zealand

作者： Xiao Wang Jun Huang Yong-Lin Tian Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Macao Institute of Systems Engineering Macau University of Science and Technology Macao China

The development of artificial general intelligence (AGI) and metaverse technologies hold great potential to enhance the effectiveness of smart cities and societies. we present a cyberphysical-social approach to integrating AGI into metaverse-based smart cities and societies, with a specific focus on parallel intelligence. The proposed approach considers the interplay between the physical environment, digital systems, social dynamics, and emerging forms of parallel intelligence, as well as swarm intelligence and collective intelligence. By leveraging these forms of parallel intelligence, the integration of AGI can create more responsive and proactive infrastructure, personalized and efficient services, and foster greater community engagement. The metaverse acts as a platform for simulating real-world scenarios and testing and refining AGI models before deployment in the physical world, while also facilitating the development and coordination of parallel intelligence. However, ethical considerations must be taken into account to ensure responsible deployment and avoid exacerbating existing societal inequalities or infringing upon individual privacy rights. This report provides a comprehensive framework for the integration of AGI and parallel intelligence into metaverse-based digital and smart organizations, while emphasizing the importance of human ethics, social responsibility, and ecological sustainability.

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A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

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Plant Diseases and Pests 2024年第2期15卷 9-16页

作者： Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning *** demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model ***,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial *** issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data *** address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

关键词： Rice disease and pest identification Convolutional neural networks Distributed training Federated learning(FL) Open-source data sharing platform

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A Multi-population Integrated Approach for Capacitated Location Routing

arXiv

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arXiv 2024年

作者： He, Pengfei Hao, Jin-Kao Wu, Qinghua School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Computer Science LERIA University of Angers 2 Boulevard Lavoisier Angers49045 France School of Management Huazhong University of Science and Technology No. 1037 Luoyu Road Wuhan China

The capacitated location-routing problem involves determining the depots from a set of candidate capacitated depot locations and finding the required routes from the selected depots to serve a set of customers whereas minimizing a cost function that includes the cost of opening the chosen depots, the fixed utilization cost per vehicle used, and the total cost (distance) of the routes. This paper presents a multi-population integrated framework in which a multi-depot edge assembly crossover generates promising offspring solutions from the perspective of both depot location and route edge assembly. The method includes an effective neighborhood-based local search, a feasibility-restoring procedure and a diversification-oriented mutation. Of particular interest is the multi-population scheme which organizes the population into multiple subpopulations based on depot configurations. Extensive experiments on 281 benchmark instances from the literature show that the algorithm performs remarkably well, by improving 101 best-known results (new upper bounds) and matching 84 best-known results. Additional experiments are presented to gain insight into the role of the key elements of the algorithm. © 2024, CC BY-NC-ND.

关键词： Cost functions

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3D Single-pixel imaging with active sampling patterns and learning based reconstruction

arXiv

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arXiv 2022年

作者： Ma, Xinyue Wang, Chenxing School of Automation Southeast University 2# Sipailou Xuanwu Nanjing210096 China Key laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

Single-pixel imaging (SPI) is significant for applications constrained by transmission bandwidth or lighting band, where 3D SPI can be further realized through capturing signals carrying depth. Sampling strategy and reconstruction algorithm are the key issues of SPI. Traditionally, random patterns are often adopted for sampling, but this blindly passive strategy requires a high sampling rate, and even so, it is difficult to develop a reconstruction algorithm that can maintain higher accuracy and robustness. In this paper, an active strategy is proposed to perform sampling with targeted scanning by designed patterns, from which the spatial information can be easily reordered well. Then, deep learning methods are introduced further to achieve 3D reconstruction, and the ability of deep learning to reconstruct desired information under low sampling rates are analyzed. Abundant experiments verify that our method improves the precision of SPI even if the sampling rate is very low, which has the potential to be extended flexibly in similar systems according to practical needs. Copyright © 2022, The Authors. All rights reserved.

关键词： Pixels

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Seismocardiographic Signals Detection Based on Circulant Singular Spectrum Analysis through Millimeter Wave Radar

Seismocardiographic Signals Detection Based on Circulant Sin...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Haiyang Hu Yuhao Chen Shihua Li Jun Yang Zhenxing Sun School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781665446006

Aiming at the problem that Seismocardiography (SCG) signal detection cannot be promoted due to the bulkiness of the equipment and the SCG containing 7 fiducial points is difficult to extract, this paper proposes a non-contact SCG signal detection algorithm based on the circulant singular spectrum analysis (CiSSA) that runs on a millimeter-wave radar platform. In order to make the detection equipment portable and achieve non-contact detection, this paper uses a millimeter-wave radar working in the 60-64GHz frequency band to collect the signal returned from the surface of the human chest cavity. Then the signal is processed by static clutter elimination and maximum energy distance gate positioning algorithm to improve the signal-to-noise ratio, especially to reduce the interference problem caused by people’s tiny movement. For the key problem that the SCG curve extracted by traditional filtering methods are fuzzy and the fiducial points are difficult to identify, the CiSSA method is used to improve the accuracy and completeness of SCG curve extraction. By CiSSA, the signal is embedded into trajectory matrix, and then decomposed into component matrices containing various frequency characteristics. Next, the component matrices are grouped according to the frequency distribution characteristics of the SCG signal, and finally the result of the grouping is reconstructed into a high-precision SCG signal with clear and distinguishable fiducial points. Experiments show that the proposed SCG detection method can accurately extract the SCG curve, clearly distinguish 7 fiducial points of the SCG, and the success rate of detection can reach 97%. This provides the possibility for the popularization of SCG signal detection.

关键词： Vibrations Radar detection Millimeter wave radar Logic gates Trajectory Matrix decomposition Clutter

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