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第36届中国控制会议

作者： Lushan Zhou Liling Ma Junzheng Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

ISBN: (纸本)9781538629185

In this paper,a fault tolerant control method based on active disturbance rejection control(ADRC) and radial basis function neural network(RBFNN) is proposed for a class of multi-input-multi-output nonlinear system with actuator faults,components faults and sensor *** proposed method does not rely on the plant *** regarding the faults and plant uncertainties as the disturbance,through the observation of extended state observer and the compensation of feedback control signal,this method achieves the fault tolerance control of the plant with component fault and actuator *** sensor faults,in this work,radial basis function neural network is applied to estimate the real output of the *** this output estimation is utilized by active disturbance rejection control to achieve the fault tolerance of ***,the effectiveness of the proposed method is validated by the simulation results of the three-tank system.

关键词： Fault tolerant control active disturbance rejection control radial basis function neural network

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Fault Detection of a 6-DOF Hydraulic Platform Based on Equivalent-Input-Disturbance Approach 29

Fault Detection of a 6-DOF Hydraulic Platform Based on Equiv...

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第29届中国控制与决策会议

作者： Qianqiu Li Jinhua She Min Wu Pingping Lin School of Information Science and Engineering Central South University School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

ISBN: (纸本)9781509046584

This paper presents a method of fault detection for a 6-degree-of-freedom hydraulic platform based on the equivalent-input-disturbance(EID) ***,a model of the platform is derived based on the Lagrange ***,the types of actuator faults are ***,an optimal feedback controller is designed and the EID approach is incorporated to detect and suppress actuator *** results demonstrate the effectiveness of the method.

关键词： Equivalent input disturbance 6 degrees of freedom Fault detection Optimal feedback controller

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Decoupling control for Parallel Robot Based on Equivalent-Input-Disturbance Approach 29

Decoupling Control for Parallel Robot Based on Equivalent-In...

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第29届中国控制与决策会议

作者： Pingping Lin Jinhua She Min Wu Qianqiu Li School of Information Science and Engineering Central South University School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

ISBN: (纸本)9781509046584

This paper discusses the problem of posture control for a parallel *** parallel robot features multiple variables and large *** control accuracy is strongly affected by the parameters which are coupled with each other,and by exogenous disturbances.A decoupling control strategy is presented based on the equivalent-inputdisturbance(EID) approach to improve control ***,decoupling control is used to eliminate the main coupling effect between multiple ***,the EID method is used to further improve decoupling effect and suppress the external *** results show that the control strategy is effective for the decoupling control of parallel robot.

关键词： Parallel Robot Decoupling control Equivalent Input Disturbance(EID) PID

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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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Generative Adversarial Networks:Introduction and Outlook

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 588-598页

作者： Kunfeng Wang Chao Gou Yanjie Duan Yilun Lin Xinhu Zheng Fei-Yue Wang IEEE State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Qingdao Academy of Intelligent Industries State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences Department of Computer Science and Engineering University of Minnesota Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology

Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning *** goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that *** their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background,theoretic and implementation models, and application ***, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence,with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.

关键词： ACP approach adversarial learning generative adversarial networks(GANs) generative models parallel intelligence zero-sum game

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Economic Issues in Bitcoin Mining and Blockchain Research

Economic Issues in Bitcoin Mining and Blockchain Research

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2018 IEEE intelligent Vehicles Symposium, IV 2018

作者： Qin, Rui Yuan, Yong Wang, Shuai Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Research Center of Military Computational Experiments and Parallel Systems National University of Defense Technology Changsha China

ISBN: (纸本)9781538644522

With the development of the blockchain technology, Bitcoin mining has become more and more popular. This paper aims to provide a three-level framework of the economic issues in Bitcoin mining research, from the levels of mining pools, individual miners and blockchain network. We also offer an overview of relevant research efforts in literature. Considering the uncertainty, diversity and complexity of the Bitcoin ecosystems, we propose a novel research framework based on the ACP theory, which can be used to explore the economic issues in the Bitcoin ecosystems. This paper aims to provide a preliminary investigation to the economic issues faced by participants in the Bitcoin ecosystems, and stimulate the attentions and interests of researchers in this field. © 2018 IEEE.

关键词： Blockchain

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Rethinking temporal object detection from robotic perspectives

arXiv

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arXiv 2019年

作者： Chen, Xingyu Wu, Zhengxing Yu, Junzhi Wen, Li State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Beijing Innovation Center Engineering Science and Advanced Technology Peking University Beijing100871 China School of Mechanical Engineering and Automation Beihang University Beijing100191 China

Video object detection (VID) has been vigorously studied for years but almost all literature adopts a static accuracy-based evaluation, i.e., average precision (AP). From a robotic perspective, the importance of recall continuity and localization stability is equal to that of accuracy, but the AP is insufficient to reflect detectors' performance across time. In this paper, non-reference assessments are proposed for continuity and stability based on object tracklets. These temporal evaluations can serve as supplements to static AP. Further, we develop an online tracklet refinement for improving detectors' temporal performance through short tracklet suppression, fragment filling, and temporal location fusion. In addition, we propose a small-overlap suppression to extend VID methods to single object tracking (SOT) task so that a flexible SOT-by-detection framework is then formed. Extensive experiments are conducted on ImageNet VID dataset and real-world robotic tasks, where the superiority of our proposed approaches are validated and verified. Codes will be publicly available. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

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Convolutional Neural Network Features Comparison Between Back-Propagation and Extreme Learning Machine

Convolutional Neural Network Features Comparison Between Bac...

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第37届中国控制会议

作者： Atmane Khellal Hongbin Ma Qing Fei School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Recently deep learning based architectures have been widely deployed in many problems of artificial *** deep learning models, Convolutional Neural Networks（CNN） have been reported in numerous successful applications such as object recognition, and natural language processing. The convolutional neural networks are trained by back-propagating the classification error using the Back-Propagation（BP） algorithm, which requires a large amount of data and slows the training process. To overcome these difficulties, a new fast and accurate approach based on Extreme Learning Machine（ELM） to train any convolutional neural network has been proposed. The developed framework（ELM-CNN） is based on the concept of autoencoding to learn the convolutional filters with biases, by reconstructing the normalized input and the intercept term. In this paper, systematic comparison with traditional back-propagation based training method（BP-CNN） has been made with respect to two aspects qualitative and quantitative. The experimental results on the popular MNIST dataset show that the ELM-CNN algorithm achieves competitive results in terms of generalization performance and up to 16 times faster than the back-propagation based training of CNN.

关键词： Convolutional Neural Network Extreme Learning Machine Features Comparison Visualization

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An Adaptive Sliding Mode controller with The Exponential and Power Reaching Law for Discrete systems

An Adaptive Sliding Mode Controller with The Exponential and...

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第37届中国控制会议

作者： Xiaofei Zhang Hongbin Ma Zhuang Li Wenchao Zao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

With the development of aeronautics and astronautics, the response speed of servo system need be faster. First, In order to improve the dynamic quality of servo system, the exponential and power reaching law, which combines the advantages of the exponential reaching law and the power reaching law, is introduced. Second, the chattering of the sliding mode controller（SMC） with the exponential and power reaching law for discrete systems is investigated. Finally, the adaptive sliding mode controller（ASMC） with the exponential and power reaching law is introduced. The stability of the ASMC with the exponential and power reaching law for discrete systems is analyzed, and the simulation of this approach on one joint of a six degrees of freedom robot is carried out. The experimental results indicate that the ASMC with the exponential and power reaching law is effective in reducing the time of arriving the sliding mode surface. The experimental results also indicate that the ASMC with the exponential and power reaching law may make output error reach zero in a shorter time.

关键词： adaptive sliding mode controller reaching law

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Deep Forest-Based Classification of Hyperspectral Images

Deep Forest-Based Classification of Hyperspectral Images

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Chinese control Conference

作者： Xu Yin Ruilin Wang Xiaobo Liu Yaoming Cai Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China School of Computer Science China University of Geosciences Wuhan P. R. China

The classification of hyperspectral images (HSIs) is a hot topic in the field of remote sensing technology. In recent years, convolutional neural network (CNN) has achieved great success for HSI classification. However, CNN has to do a great effort in parameters tuning which is time-consuming. Furthermore, a large number of samples are required to train CNN, nevertheless, it is expensive to obtain enough training samples from HSIs. In this paper, we propose a novel classification approach based on deep forest. To reduce the dimension of hyperspectral data, principal component analysis (PCA) is performed during the pre-processing. In contrast to the CNN, our method has fewer hyper-parameters and faster training speed. To the best of our knowledge, this is among the first deep forest-based hyperspectral spectral information classification. Extensive experiments are conducted on two real-world HSI datasets to show the proposed method is significantly superior to the state-of-the-art methods.

关键词： Forestry Hyperspectral imaging Training Principal component analysis Vegetation Radio frequency

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