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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Hub Ali Gang Xiong Huaiyu Wu Bin Hu Zhen Shen Hongxing Bai School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences China Hubei Jointown Technology Co. Ltd Artificial Intelligence Research Institute Wuhan China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

In this paper we consider a problem in task execution for multi-robot trajectory planning with collision avoidance in a shared working environment. Consider two or more robots generating trajectories towards their respective goal positions. The collision may occur if their trajectory coordinates are intersecting at a point or follow the same path segment simultaneously. The central planner is introduced to control robot motion in the collision state and to reduce the complexity of the multi-robot path planning system. The global path for every robot is generated by the $\mathrm{A}^{*}$ algorithm in a grid-based environment. The path has presented a sequence of optimal grid numbers and later transformed into Cartesian coordinates for smooth trajectory generation. The central planner takes an optimal grid sequence for every robot to analyze the collision state according to its cost value. It regenerates the trajectories to minimize the complexity cost value and replaces the previous trajectory based on minimum cost value. In the collision state, the central planner allows one robot at a time to pass along the conflict path segment and hold others in queue at a safety offset distance until the previous robot passes safely. The algorithm has been applied to robots working in a shared environment in complex maps and the simulations is performed with MATLAB to calculate the efficiency of this approach for handling collision states in a multi-robot path planning system.

关键词： Robot kinematics Trajectory Collision avoidance Task analysis Resource management

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A lightweight soft exoskeleton in lower limb assistance

A lightweight soft exoskeleton in lower limb assistance

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Chinese Automation Congress (CAC)

作者： Yu Zhang Zhuo Wang Chunjie Chen Tao Fang Ruimei Sun Yanjie Li School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China ShenZhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China BeiJing Aerospace Wanyuan Science Technology Co. Ltd. Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The soft exoskeleton is a wearable robot which is functioned to improve the capacity of human walking or loading. The metabolic of human is increase with the additional load, one integrated design method is used to reduce this effect in this paper. Meanwhile, in order to enhance the convenience and decrease the disturbance to wearer, the wireless module is used to replace the signal line. The nonlinear characteristics and uncertainty of flexible material make the control of soft exoskeleton system be a challenge, a switching controller is proposed to deal with this problem in this paper. It consists of Iterative learning control (ILC) based stable controller and admittance control based unstable controller, functioned with wearers walking stable and not. An experiment of force tracking is conducted to verify the controller performance in this paper, and the result demonstrate that the tracking is well after 20 iteration. In order to evaluate the performance of assistance of soft exoskeleton, five health adult male take part in metabolic experiment By compare the metabolic rate between the wear soft exoskeleton and not wear, the metabolic rate are decrease 7.33%, 14.56% and 10.45% with the walking speed of 3km/h, 5km/h and 7km/h, which are corresponding absolute values decrease of 0.46W/kg, 1.60W/kg and 1.21W/kg respectively.

关键词： AC motors Mechanical cables Wireless communication Pulleys Microprocessors Micromechanical devices Germanium

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Branch-and-cut-and-price for agile earth observation satellite scheduling

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European Journal of Operational Research 2025年

作者： Peng, Guansheng Wang, Jianjiang Song, Guopeng Gunawan, Aldy Xing, Lining Vansteenwegen, Pieter School of Advanced Engineering and Great Bay Institute for Advanced Study Great Bay University Dongguan523000 China Defense Innovation Institute Chinese Academy of Military Science Beijing100071 China College of Systems Engineering National University of Defense Technology Changsha410073 China National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha410073 China School of Computing and Information Systems Singapore Management University Singapore178902 Singapore Research Institute of Intelligent Control and Manufacturing System Jiangsu University of Technology Changzhou213001 China KU Leuven Institute for Mobility - CIB Leuven3001 Belgium

The Agile Earth Observation Satellite scheduling selects and sequences satellite observations of possible targets on the Earth's surface, each with a specific profit and multiple time windows. The objective is to maximize the collected profit of all observations completed under some operational constraints. The problem can be modeled as a variant of the Team Orienteering Problem with Time Windows (TOPTW). The key differences with the regular TOPTW are twofold: first, a time-dependent transition time is required for each pair of consecutive observations to adjust the camera's look angles. Second, the time windows of each target vary during different observation cycles, called "orbits". Some targets are invisible during certain orbits. We call this variant the Time-dependent Team Orienteering Problem with Variable Time Windows. In this paper, we present an efficient branch-and-cut-and-price (BCP) algorithm that exploits the problem's characteristics to solve it to optimality. Some algorithmic enhancements have been implemented, such as a Lagrangian bound, an ng-path relaxation, a primal heuristic, and subset-row inequalities. Extensive experiments on different configurations of benchmark instances demonstrate the superior performance of the proposed BCP algorithm and its algorithmic enhancements. Moreover, the primal heuristic yields a high-quality lower bound and outperforms state-of-the-art heuristics. Finally, we adopt our framework to solve the well-known TOPTW, and our algorithm is much faster than state-of-the-art exact algorithms. © 2025 Elsevier B.V.

关键词： Branch and bound method

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RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Netwo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Feng Xiao Cong Shen Yu Chen Tian Yang Shengyong Chen Zhijun Liao Jijun Tang School of Computer Science and Engineering Tianjin University of Technology Tianjin P.R.China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin P.R.China School of Basic Medical Sciences Fujian Medical University Fuzhou Fujian P.R.China College of Intelligence and Computing Tianjin University Tianjin P.R.China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin P.R.China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong P.R.China University of South Carolina Columbia SC U.S.A.

ISBN: (纸本)9781665429825

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score.

关键词： Deep learning Image segmentation Statistical analysis Lung Predictive models Feature extraction Generative adversarial networks

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SPNet: Superpixel Pyramid Network for Scene Parsing

SPNet: Superpixel Pyramid Network for Scene Parsing

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Chinese Automation Congress

作者： Bingbing Xu Fei Yang Jinfu Yang Suishuo Wu Yi Shan Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China

Scene parsing is the important part of computer vision research. And the deep coding-decoding network is widely applied to scene parsing. However, there are still some problems, such as ambiguity of object edge segmentation and uncertainty when segmenting small-size-objects in scene analysis. In this paper, we propose Superpixel Pyramid Network for Scene Parsing. First, a deep coding-decoding network is used to learn image features. Then, multi-scale spatial pyramid pooling structure is employed to enhance the performance of small-size-objects. Next, the Superpixel Segmentation is also applied to cope with the problem of ambiguity of object edge. Finally, a two-layer neural network classifier is applied to identify the fused features pixel-by-pixel. Extensive experimental results over ADE20K, PASCAL VOC 2012, and Camvid, demonstrated that the proposed method can obtain better performance counterparts than other.

关键词： Scene parsing Deep coding-decoding network Pyramid pooling structure Superpixel segmentation

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Temporal Knowledge Propagation for Image-to-Video Person Re-Identification

Temporal Knowledge Propagation for Image-to-Video Person Re-...

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International Conference on Computer Vision (ICCV)

作者： Xinqian Gu Bingpeng Ma Hong Chang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

In many scenarios of Person Re-identification (Re-ID), the gallery set consists of lots of surveillance videos and the query is just an image, thus Re-ID has to be conducted between image and videos. Compared with videos, still person images lack temporal information. Besides, the information asymmetry between image and video features increases the difficulty in matching images and videos. To solve this problem, we propose a novel Temporal Knowledge Propagation (TKP) method which propagates the temporal knowledge learned by the video representation network to the image representation network. Specifically, given the input videos, we enforce the image representation network to fit the outputs of video representation network in a shared feature space. With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated. With additional classification and integrated triplet losses, our model can learn expressive and discriminative image and video features for image-to-video re-identification. Extensive experiments demonstrate the effectiveness of our method and the overall results on two widely used datasets surpass the state-of-the-art methods by a large margin.

关键词： Videos Image representation Feature extraction Knowledge engineering Visualization Robustness Training

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Path Planning system for Smart Cars Used in Education

Path Planning System for Smart Cars Used in Education

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Guangyu Xiong Lulu Niu Yanfu Tian Jiehan Zhou Babak Mohajeri Timo R. Nyberg Silk Road Business School Sanya University(USY) China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Articial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Oulu Oulu Finland Aalto University Espoo Finland

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, we developed a path planning system for smart cars for teaching electronic engineering or computer science, which consists of the interactive platform for smart cars development and path planning. Designed by Visual C++, the interactive platform can call Matlab engine, allows users to choose path optimization algorithms such as genetic or A-star(A*) algorithm for different tasks and control smart cars through serial ports. The simulation and practice demonstrate that our interactive platform can help learners to plan paths and control intelligent vehicles without specially designing a user interface.

关键词： Education Path planning Automobiles Task analysis Optimization Programming profession Matlab

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A weight initialization method for echo state network

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Kongzhi yu Juece/Control and Decision 2018年第2期33卷 356-360页

作者： Wang, Lei Qiao, Jun-Fei Li, Xiao-Li Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

To avoid singular problem and improve the performance of neural network, a weight initialization method for echo state network(WIESN) is proposed. With Cauchy inequality and linear algebra, the range of optimal initial weights, which is related to input dimension, reservoir dimension, input variables and reservoir state, is determined. The proposed method ensures that the outputs of neurons are in the active region. Simulation results show that the accuracy and training time of the proposed method is better than learning with random initialization. In addition, the time for weight initialization process is negligible comparing with the training process. © 2018, Editorial Office of Control and Decision. All right reserved.

关键词： Linear algebra

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EEG Based Depression Recognition by Combining Functional Brain Network and Traditional Biomarkers

EEG Based Depression Recognition by Combining Functional Bra...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuting Sun Huayu Chen Xuexiao Shao Liangliang Liu Xiaowei Li Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in BrainScience and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Beijing China Ministry of Education Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Lanzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Resting-state EEG data were collected from 24 major depressive patients (MDD) and 29 normal controls using 128-electrode geodesic sensor net. To better identify depression, we extracted multi-type of EEG features including linear features (L), nonlinear features (NL), functional connectivity features phase lagging index (PLI) and network measures (NM) to comprehensively characterize the EEG signals in patients with MDD. And machine learning algorithms and statistical analysis were used to evaluate the EEG features. Combined multi-types features (All: L+ NL + PLI + NM) outperformed single-type features for classifying depression. Analyzing the optimal features set we found that compared to other type features, PLI occupied the largest proportion of which functional connections in intra-hemisphere were much more than that of in inter-hemisphere. In addition, when using PLI features and All features, high frequency bands (alpha, beta) could achieve obviously higher classification accuracy than low frequency bands (delta, theta). Parietal-occipital lobe in the high frequency bands had great effect in depression identification. In conclusion, combined multi-types EEG features along with a robust classifier can better distinguish depressive patients from normal controls. And intra-hemispheric functional connections might be an effective biomarker to detect depression. Hence, this paper may provide objective and potential electrophysiological characteristics in depression recognition.

关键词： Electroencephalography Depression Feature extraction Electrodes Biomarkers Support vector machines Entropy

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SmartPV-AIoT: an AIoT-integrated framework for fault diagnosis and remote monitoring in photovoltaic systems

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Energy Conversion and Management: X 2025年

作者： Ehtisham Lodhi Xiaoli Liu Gang Xiong M. Adil Khan Zeeshan Lodhi Tahir Nawaz Adil Dilawar Sasu Tarkoma Feiyue Wang Zhejiang University-University of Illinois Urbana-Champaign Institute International Campus Haining 314400 China The SKL for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Computer Science University of Helsinki Helsinki 00560 Finland School of Transportation Fujian University of Technology Fuzhou China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Computer Science COMSATS University Islamabad Abbottabad Campus 22060 Pakistan Department of Electronics Technology University of Technology Nowshera 24100 Pakistan Department of Mechatronics Engineering National University of Sciences and Technology H-12 Islamabad 44000 Pakistan The SKL of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China

This paper presents SmartPV-AIoT, a cost-effective and edge-computing-based framework for real-time fault detection and environmental monitoring in photovoltaic (PV) systems. Addressing the challenges faced in remote or intermittently connected environments, the proposed system integrates electrical and environmental sensor data using a Raspberry Pi 4 and NodeMCU microcontroller. A lightweight Res-BiLSTM model, combining residual learning and bidirectional long short-term memory (BiLSTM) layers, is deployed at the edge to perform local fault classification by analyzing seven key parameters derived from I to V curve measurements and ambient conditions. Additionally, the framework employs multi-sensor data fusion by integrating voltage, current, irradiance, and temperature measurements to support comprehensive fault detection and predictive maintenance. The framework was validated on a 4.56  kW PV installation using a dataset of 2400 labeled samples representing normal operation, open-circuit, short-circuit, and partial shading conditions. The classifier achieved a fault detection accuracy of 96.67 %, with a maximum inference latency of 290  ms and low power overhead, making it suitable for real-time, on-site diagnostics. The system supports offline operation with automatic data synchronization upon connectivity restoration and includes mobile-based monitoring for remote accessibility. The proposed framework is designed for modular deployment and scales linearly with array size. Overall, SmartPV-AIoT offers a scalable, resilient, and practical solution for intelligent PV monitoring, particularly in resource-constrained or off-grid environments.

关键词： Photovoltaic (PV) Artificial intelligence of Things (AIoT) Green energy resources Deep learning Fault detection Fault classification Monitoring system Edge intelligence

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