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Chinese Automation Congress (CAC)

作者： Yulin Kang Menzji Shi Kexiang Wang Long You Jianzfeng Yue Boxian Lin Kaiyu Qin School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu China National Laboratory on Adaptive Optics Chengdu China Chinese Aeronautical Establishment Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Uncertainties, such as modeling inaccuracies, external disturbances, and actuator attacks are widely spread in the control channels of the networked networked agent system, which affect the accuracy and reliability of the cooperative control to a certain extent. With this in mind, this paper focuses on the robust bipartite consensus control problem of uncertain nonlinear networked networked agent systems subject to actuator attacks. In particular, the communication topology among agents is described by a signed graph, which contains not only cooperative interactions but also competitive interactions among agents. Theoretically, the neural network-based robust consensus controller is designed. On the one hand, compound uncertainties, like modeling inaccuracies and external disturbances are approximated by interpolated fitting of Gaussian radial basis functions. The adaptive laws for approximate parameters are generated online based on the stability conditions of the closed-loop error system. On the other hand, the estimation of the unknown actuator attack factor is achieved based on the adaptive control technique, which results in the effective compensation of the attack-induced control attenuation. Finally, the effectiveness of the proposed robust consensus control scheme is validated using some simulation examples.

关键词： Hands Actuators Adaptation models Uncertainty Consensus control Attenuation Stability analysis Robustness Topology Compounds

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High-Efficient Removal of 2,4 Dichlorophenol by Co-Degradation and Adsorption with Embedded Sr/Fe/Fexoy/Srfe12o19 Heterojunctions Loading on Biochar

SSRN

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SSRN 2022年

作者： Dai, Yumei Liu, Chen Fan, Yuhan Peng, Kang Li, Rou Chen, Ming College of Environmental Science and Engineering Key Laboratory of Environmental Biology and Pollution Control Hunan University Ministry of Education Changsha410082 China Key Laboratory of Reservoir Aquatic Environment Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China

Functionalized biochar-supported Fe/Sr (NaBH4-Fe/Sr-BC, BSFBC) nanocomposites synthesized by one-pot method with NaBH4 reduction were used to remove 2,4-dichlorophenol (2,4-DCP) in aqueous solution. The modified biochar adsorbent with W(Sr/Fe): W(BC) = 5% (BSFBC5%) showed 98.7% adsorption and removal rate of 2,4-DCP within 60 min. As confirmed by scanning electron microscopy and EDS, biochar acted as a support material that dispersed the Fe/Sr nanoparticles, greatly reducing their aggregation and correspondingly improving the reactivity of adsorption and degradation of 2,4-DCP. The presence of Sr/Fe/FexOy/SrFe12O19 heterojunctions nanoparticles on BSFBC5% was confirmed by X-ray diffraction and X-ray photoelectron spectroscopy. The key parameters controlling the degradation of 2,4-DCP, such as the initial pH of the solution, adsorbent dosage and reaction time, were systematically explored. In addition, BSFBC5% showed good ability in adsorption of 2,4-DCP and degradation of total organic carbon. Kinetic analysis showed that the depletion of 2,4-DCP was consistent with the Freundlich models as well as the pseudo-second-order kinetic model. Possible adsorption and catalytic degradation mechanisms are proposed, while the products of degradation were identified by chromatographic techniques with UV and mass spectrometric detection. This study shows that BSFBC5% is promising as a new candidate for environmental pollution remediation of water sources contaminated with chlorophenols. © 2022, The Authors. All rights reserved.

关键词： Degradation

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Intention-Aware Planner for Robust and Safe Aerial Tracking

Intention-Aware Planner for Robust and Safe Aerial Tracking

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Qiuyu Ren Huan Yu Jiajun Dai Zhi Zheng Jun Meng Li Xu Chao Xu Fei Gao Yanjun Cao The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China The Robotics Institute of Zhejiang University Yuyao China Huzhou Institute Zhejiang University Huzhou China Huzhou Laboratory of Autonomous Intelligent Systems Huzhou China College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to speci cally evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method.

关键词： Target tracking Semantics Prediction methods Turning Prediction algorithms Robustness Safety Planning Trajectory optimization Quadrotors

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Dual-channel Dynamical Event-Triggered Load Frequency control under Attack for Wind Power Systems with Multiple Time-varying Delay

Dual-channel Dynamical Event-Triggered Load Frequency Contro...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Hanmei Zhou Qishui Zhong Shaoyu Hu Jin Yang Kaibo Shi School of Aeronautics and Astronautics University of Electronic Science and Technology Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu China School of Aeronautics and Astronautics University of Electronic Science and Technology Chengdu China School of Electronic Information and Electrical Engineering Chengdu University Chengdu China

In order to stabilize a multi-time-delay interconnected wind power system, a dual-channel dynamic event-triggered load frequency control scheme is proposed, considering network attacks in this paper. Firstly, this paper designs load frequency control of wind power system incorporating the effect of time-varying delay both in the states and control input. Secondly, in order to reduce the occupancy of communication bandwidth, a novel transmission scheme with two asynchronous event triggers network channels has been designed. Further, a malicious bounded time-delay deception attack model is proposed to enhance the security. By constructing a discrete-type Lyapunov-Krasovskii functional, stability criteria with H ∞ performance index are established. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed control method.

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Three hierarchy map building based on artificial label

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Open Cybernetics and Systemics Journal 2015年第1期9卷 2834-2841页

作者： Wu, Hao Tian, Guohui Pan, Chunwei Wang, Xin Yu, Jinshan Department of Control Science and Engineering University of Shandong JinanShandong250061 China School of Information and Electric Engineering Shandong Jianzhu University Jinan250101 China Shandong Provincial Key Laboratory of Intelligent Buildings Technology Shandong Jianzhu University Jinan250101 China

When service robots carry on mission planning and execution, they not only need structural information about the environment to navigation and localization, but also need to possess deeper knowledge to endow a robot with higher degrees of autonomy and intelligence. However it is hard for a robot to cognize the semantic description such as the function attributes of objects and rooms only relying on robot’s sensors itself. When the exploration scope of the robot is expanded to large regions such as a building, the problem becomes more prominent. In this paper, artificial labels are used to provide semantic concepts and relations of objects and rooms in a building, which can solve the complexity and limitations of semantic recognition and scene understanding only with robot’s vision. Imitating spatial cognizing mechanism of human, three hierarchy space model is built for a building service robot. With this space model, not only the plane structure of the environment for navigation is known, but also the abundant semantic information in different kinds of environment are obtained. Simulation results show that the three hierarchy indoor space model is applicable to service tasks of indoor robot, understanding human semantic statement, produce reasonable service path, and ensuring the security of robot running in building environment. © Hao et al.

关键词： Semantics

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Tongue image segmentation via color decomposition and thresholding

Tongue image segmentation via color decomposition and thresh...

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作者： Li, Zuoyong Yu, Zhaochai Liu, Weixia Xu, Yong Zhang, Daoqiang Cheng, Yong Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China The Fujian College's Research Base of Humanities and Social Science for Internet Innovation Research Center Minjiang University Fuzhou350121 China Straits Institute Minjiang University Fuzhou350121 China Bio-Computing Research Center Shenzhen Graduate School Harbin Institute of Technology Shenzhen518055 China Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Communication Engineering Nanjing Institute of Technology Nanjing211167 China

Tongue image segmentation is a crucial step in developing an automatic tongue diagnosis system. After exploring characteristics of image thresholding in different color spaces, we propose a simple and effective tongue image segmentation method. Specifically, the proposed method first extracts an initial tongue body region by performing image thresholding on the transformed hue component in HSI (Hue, Saturation, and Intensity) color space. Then, one of two image thresholding results on the red component of an original tongue image is adaptively selected to find the gap region between the tongue body root and the upper lip. Finally, the initial tongue body region is refined by removing fake object regions such as the upper lip to obtain final tongue image segmentation result. Experimental results on a tongue image dataset containing 200 subjects demonstrate the effectiveness of the proposed method. © 2018 John Wiley & Sons, Ltd.

关键词： Image segmentation

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Study on the Method of Image Restoration in the Environment of Dust

Study on the Method of Image Restoration in the Environment ...

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International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Yuanyu Wang Yuanzong Li Tianxu Zhang Department of Computer Fundamental Education Taiyuan University Of Technology Taiyuan China College of Mechanical Engineering Taiyuan University Of Technology Taiyuan China Institute of Pattern Recognition and A.I State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology Wuhan China

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, GA optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with automatic threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments.

关键词： Image edge detection Image restoration Scattering Degradation Gallium Atmospheric modeling Genetic algorithms

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A Survey on Big Data for Human Body Shape

A Survey on Big Data for Human Body Shape

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Hongli Peng Xiuqin Shang Chao Guo Gang Xiong Timo R.Nyberg Dong Fan Yiming Wang The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Information Science and Technology Beijing University of Chemical Technology Qingdao Academy of Intelligent Industries Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences Cloud Computing Center Chinese Academy of Sciences Department of Industrial Engineering and Management School of ScienceAalto University

In recent years, the rapid development of network and E-commerce technologies, promotes the development of human body big data. New technologies, such as 3D scanning and 3D printing will improve our life quality. The new socialized manufacturing network composed of Io T, advanced logistics network and 3D printers. Everyone is allowed to have chance to participate in any process of the whole production manufacturing lifecycle by some ways, such as crowdsourcing. Boosts the creation, production and consumption patterns of individuation, real-time transformation and E-commerce, possibly will lead to a new industrial revolution. In this revolutionary change wave, public concerns focus on the human body big data used for social manufacturing of wearable products or personalized medical treatment. Although many scholars have done a lot of research work, the collection of the human body shape big data still faces many challenges. There is no complete, separated theoretical research works in progress, it should be summarized and improved systematically. In this paper, we review that big data for human body shape is gaining momentum. The 3D scanning and 3D modeling of human body shape are summarized and concluded. The main applications of these big data are analyzed.

关键词： big data human body shape 3D scanning 3D modeling clothing footwear mass customization

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Exact Computation of Delay Margin by PID control: It Suffices to Solve a Unimodal Problem!

Exact Computation of Delay Margin by PID Control: It Suffice...

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第三十八届中国控制会议

作者： Jianqi Chen Dan Ma Yong Xu Jie Chen Department of Electronic Engineering City University of Hong Kong College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University The School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology

In this paper we study delay robustness of PID controllers in stabilizing systems containing uncertain, variable *** consider second-order unstable systems and seek analytical characterization and exact computation of the PID delay margin,where by PID delay margin we mean the maximal range of delay values within which the system can be robustly stabilized by a PID controller. Our contribution is threefold. First, we show that the delay margin achieved by PID control coincides with that by PD controllers. Second, we show that other than helping stabilize the delay-free part of a plant, the proportional control contributes no action to increase the delay margin. Finally, we show that the PID delay margin can be computed efficiently by solving a unimodal problem, that is, a univariate optimization problem that admits a unique maximum and hence is a convex optimization problem in one variable. This unimodal problem is one of pseudo-concave optimization and hence can be solved using standard convex optimization or gradient-based methods. As such, from a computational perspective, the PID delay margin problem is completely resolved in this paper! The results not only insure that the PID delay margin problem be readily solvable, but also provide fundamental conceptual insights into the PID control of delay systems, and analytical justifications to long-held engineering intuitions and heuristics, thus lending useful guidelines in the tuning and analytical design of PID controllers.

关键词： Delay margin robust stabilization PID controller uncertain time delay nonlinear programming pseudo-concavity

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Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms

arXiv

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arXiv 2025年

作者： Zhou, Junchi Wang, Haozhou Kato, Yoichiro Nampally, Tejasri Rajalakshmi, P. Balram, M. Katsura, Keisuke Lu, Hao Mu, Yue Yang, Wanneng Gao, Yangmingrui Xiao, Feng Chen, Hongtao Chen, Yuhao Li, Wenjuan Wang, Jingwen Yu, Fenghua Zhou, Jian Wang, Wensheng Hu, Xiaochun Yang, Yuanzhu Ding, Yanfeng Guo, Wei Liu, Shouyang Engineering Research Center of Plant Phenotyping Ministry of Education Jiangsu Collaborative Innovation Center for Modern Crop Production Academy for Advanced Interdisciplinary Studies Sanya Institute of Nanjing Agricultural University Nanjing Agricultural University Nanjing China Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan Department of Artificial Intelligence Indian Institute of Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad India Institute of Biotechnology Professor Jayashankar Telangana Agricultural State University Hyderabad India Graduate School of Agriculture Kyoto University Kyoto Japan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Crop Genetic Improvement National Center of Plant Gene Research Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural University Wuhan China State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China the Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing China Center for Geospatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China School of Information and Electrical Engineering Shenyang Agricultural University Shenyang China Rice Research Institute Jilin Academy of Agricultural Sciences Changchun China Institute of Crop Sciences National Key Facility for Crop Gene Resources and Genetic Improvement Chinese Academy of Agricultural Sciences Beijing China Yuan Long Ping High-Tech Agriculture Co. Ltd. Changsha China

Developing computer vision–based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights into eco-physiological processes. However, due to the fine structure of rice organs and complex illumination within the canopy, this task remains highly challenging, underscoring the need for a high-quality training dataset. Such datasets are scarce, both due to a lack of large, representative collections of rice field images and the time-intensive nature of annotation. To address this gap, we established the first comprehensive multi-class rice semantic segmentation dataset, RiceSEG. We gathered nearly 50,000 high-resolution, ground-based images from five major rice-growing countries (China, Japan, India, the Philippines, and Tanzania), encompassing over 6,000 genotypes across all growth stages. From these original images, 3,078 representative samples were selected and annotated with six classes (background, green vegetation, senescent vegetation, panicle, weeds, and duckweed) to form the RiceSEG dataset. Notably, the sub-dataset from China spans all major genotypes and rice-growing environments from the northeast to the south. Both state-of-the-art convolutional neural networks and transformer-based semantic segmentation models were used as baselines. While these models perform reasonably well in segmenting background and green vegetation, they face difficulties during the reproductive stage, when canopy structures are more complex and multiple classes are involved. These findings highlight the importance of our dataset for developing specialized segmentation models for rice and other crops. The RiceSEG dataset is publicly available at ***. © 2025, CC BY.

关键词： Semantic Segmentation

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