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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Water Supply Prediction Based on Initialized Attention Residual Network

Water Supply Prediction Based on Initialized Attention Resid...

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第三十九届中国控制会议

作者： Yuhao Long Yeming Zhang Jingcheng Wang Miaoshun Bai Dept of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of China Shanghai Municipal Engineering Design Institute(Group)Co. Ltd Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University

Real-time and accurate water supply forecast is crucial for water supply control. However, most existing methods are likely affected by factors such as weather and holidays, which lead to a decline in the reliability of water supply *** this paper, we address a generic artificial neural network, Initialized Attention Residual Network(IARN), to tackle the time series prediction problem in water supply domain. Specifically, instead of continuing to use the recurrent neural network(RNN)in time-series tasks, we try to build a convolution neural network(CNN) to recede the disturb from other factors and get a more credible results. Experiments show that our method achieves state-of-the-art performance on several datasets in terms of accuracy,robustness and generalization ability. The source code of our method will available at https://***/SJTU-L-dd/IARN.

关键词： Neural networks Prediction methods Water supply Time-series analysis Attention module Residual network

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A hybrid model based on stacking and multi-correction mechanisms for urban water demand prediction ⁎

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IFAC-PapersOnLine 2020年第2期53卷 16685-16690页

作者： Yang Lan Jingcheng Wang Miaoshun Bai Ibrahim Brahmia Haotian Xu Piao Hu Yuhao Long Yeming Zhang Dept. of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of education of China Shanghai 200240 China. Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an Shaanxi710021 China. Shanghai Municipal engineering Design Institute (Group) Co. Ltd.

Water demand prediction is the key link for the effective operation of urban intelligent water supply system. Since the non-linearity and complex variability of water consumption, it is difficult for traditional water demand prediction models to guarantee high accuracy for a long period. Different holiday types and even tiny changes in temperatures can affect urban water demand seriously. This paper proposes a Stacking-based hybrid model which integrates multi-correction mechanisms to address these problems. A better stacking model is proposed to minimize the generalization error. The stability and reliability of predictions are improved through the design of multi-correction mechanisms such as high temperature weather compensation feature, holiday-type correction model and water quantity fluctuation correction model. Comparing different models before and after stacking also before and after correcting, the prediction accuracy of the proposed hybrid model is much higher and the predictions are more stable and reliable.

关键词： Water supply system Prediction methods Hybrid models Stacking methods Multi-correction mechanisms Neural-network models Time-series analysis

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An adaptive fault detection strategy for Cz crystal growth process

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Mu, Lingxia Feng, Nan Zhang, Zhenhua Zhang, Youmin Zhang, Jian Xian University of Technology Crystal Growth Equipment and Systems Integration National Engineering Research Center Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian710048 China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing100083 China Concordia University Department of Mechanical Industrial and Aerospace Engineering MontrealQCH3G 1M8 Canada

The monitoring and fault detection for the Czochralski (Cz) crystal growth process is important to ensure the quality of the produced mono-crystalline silicon. The Cz process is a typical multi-modes industrial process, which makes the detection difficult in distinguish the change caused by the fault or mode switching. In this paper, an adaptive fault detection strategy for Cz crystal growth process is proposed. Firstly, quality-related variables are selected from numerous process variables, based on which the training dataset is constructed. Then, a fault detection model is built based on slowly-varying feature and statics indicator. A model switching strategy and updating algorithm are designed to handle the abnormal situation caused by the mode switching or fault occurrence. Three representative experiments are conducted to verify the performance of the proposed strategy, including numerical cases, a publicly available dataset, and Cz crystal growth process from our laboratory. © 1963-2012 IEEE.

关键词： Crystal growth from melt

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A New Method for Deriving Weights in Group Fuzzy Analytic Hierarchy Process and Evaluation Measures

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IFAC-PapersOnLine 2020年第2期53卷 7941-7946页

作者： Lin Che Yeming Zhang Jingcheng Wang Miaoshun Bai Dept. of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Municipal Engineering Design Institute (Group) Co. Ltd. China Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an Shaanxi 710021 China

The paper discusses fuzzy comparison matrices, consistency check, weight prioritization methods and weight evaluation methods in fuzzy group analytic hierarchy process. There are various methods of weight prioritization, however, they are not critically evaluated. In the paper, two measures are introduced for the evaluation of the group weights. Then, a new method is proposed to improve the process of deriving weights and use it in an application compared to another common three methods. Our results show that the new method is a good method for deriving weights of indexes.

关键词： Group Fuzzy AHP Ari-Geo Method Assessment Measure Fuzzy Consistency Check Triangular Fuzzy Number

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Authors' Reply

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IEEE Sensors Journal 2018年第3期18卷 1336-1337页

作者： Ghanbari, Milad Yazdanpanah, Mohammad Javad School of Electrical and Computer Engineering University of Tehran Tehran*** Iran Control and Intelligent Processing Center of Excellence Advanced Control Systems Laboratory College of Engineering School of Electrical and Computer Engineering University of Tehran Tehran*** Iran

The experimental results of the paper titled 'Delay compensation of tilt sensors based on mems accelerometer using data fusion technique' was commented by Bodnicki and Luczak. In this note, we address the raised comments by the mentioned authors. Moreover, appropriate explanations are provided in order to make the procedure of the experimental section more clear. © 2001-2012 IEEE.

关键词： Kalman filters

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A two-level adaptive target recognition and tracking method based on vision for multi-robot system

A two-level adaptive target recognition and tracking method ...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Ren, Liang Cao, Zhiqiang Tan, Min Zhao, Peng Chen, Xuechao University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems CASIA Beijing China 21st Research Institute of China Electronics Technology Group Corporation Shanghai China BIT Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China

ISBN: (纸本)9781728163215

The vision-based target recognition and tracking have received much attention in the field of robotics. Existing methods mainly focus on the vision perception of individual robot with a single view, however, the performance is susceptible to illumination and occlusion. Multi-robot collaborative perception provides a potential solution to deal with the limitation of single-view observation, however, the challenging of environmental adaptability for multi-robot collaborative decision still remains unsolved. To solve this problem, this paper proposes a two-level adaptive target recognition and tracking method based on vision for multi-robot system. The problem of multi-robot target recognition and tracking is solved under a two-level framework, which contains the features fusion level of individual robot and the cooperation level of multi-robot system. In the first level, the features measuring results that influence the visual perception of individual robot are fused, while the second level combines the voting of each robot together to determine the target for multi-robot system. Both the features measuring weights and robots voting weights are adaptively updated according to their evaluation, which lead to a beneficial result where the features and robots with higher accuracy play major roles in the first and second levels, respectively. Therefore, a good adaptability to the environments can be guaranteed. The experimental results show that the proposed approach can realize the coordination of multi-robot system in target recognition and tracking with an effective performance. © 2019 IEEE.

关键词： Multipurpose robots

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EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

Research Square

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Research Square 2021年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine UCSD La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia ZHAW DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to the wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks, e.g., the attacker can add tiny adversarial perturbations to a test sample to fool the model, or poison the training data to insert a secret backdoor. Previous research has shown that adversarial attacks are also possible for EEG-based BCIs. However, only adversarial perturbations have been considered, and the approaches are theoretically sound but very difficult to implement in practice. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is more feasible in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs. © 2021, CC BY.

关键词： Electroencephalography

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Human-like Walking of a Biped Robot Actuated by Pneumatic Artificial Muscles and Springs*

Human-like Walking of a Biped Robot Actuated by Pneumatic Ar...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Yixiang Liu Qing Bi Xizhe Zang Yibin Li School of Control Science and Engineering Shandong University Jinan China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan China Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Human walking gait is much more natural looking and energy efficient compared with biped robots. This paper presents a bio-inspired approach to realizing more human-like biped robotic walking. For this purpose, a biped robot actuated coordinately by pneumatic artificial muscles and springs is developed, capable of exploiting passive compliance of the mechanical system in locomotion. And a control scheme for human-like walking is designed based on finite state machine. Experiments show that the biped robot can walk stably on the treadmill even with some small obstacles. The realized gait has the important features of human walking, including heel strike and toe off, stretched knees, and variation in the height of the body's center of mass, which demonstrates the effectiveness of the proposed approach.

关键词： Conferences Automation Computer aided software engineering

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CCA-Net: Zero-shot SSVEP classification via an integration of canonical correlation analysis and deep neural network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Deng, Yang Ji, Zhiwei Wang, Yijun Zhou, S. Kevin Harbin Institute of Technology School of Life Science and Technology Heilongjiang Harbin 150001 China University of Science and Technology of China Division of Life Sciences and Medicine School of Biomedical Engineering Anhui Hefei 230026 China University of Science and Technology of China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research Jiangsu Suzhou 215123 China Nanjing Agricultural University College of Artificial Intelligence Jiangsu Nanjing 210095 China Chinese Academy of Sciences State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Beijing 100083 China University of Science and Technology of China Key Laboratory of Precision and Intelligent Chemistry Anhui Hefei 230026 China Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Beijing 100190 China

Accurate decoding of electroencephalographic (EEG) signals is a crucial foundation for brain-computer interface (BCI) applications. Among various decoding approaches, those that do not require calibration are particularly significant for advancing BCI technologies into everyday life, as they alleviate user fatigue by eliminating the need for user-specific training. In this study, we integrate a deep neural network with canonical correlation analysis (CCA) to form the CCA-Net approach for decoding steady-state visually evoked potential (SSVEP) based BCI without user-specific calibration, i.e., at the zero-shot scenario. The CCA-Net aims to reduce the cross-subject domain gap for better transfer learning and make full use of the testing signal itself. Specifically, it adapts the cross-subject dual-domain fusion network (CSDuDoFN) by transferring the global multi-reference least-squares transformation coefficient from source subjects to both source and target data to reduce the cross-subject domain gap. In addition, CCA-Net enhances the transfer template-based canonical correlation analysis (tt-CCA) by introducing a subject-specific template to replace the sine-cosine reference template and adding an extra CCA coefficient to make full use of the testing signal itself, resulting in the Modified tt-CCA. Finally, the features extracted by CSDuDoFN and Modified tt-CCA are integrated to produce the final decoding result. Our approach leverages the strengths of both data-driven and model-driven methods and achieves state-of-the-art performance on three publicly available datasets, thus holding the potential to facilitate everyday BCI applications based on SSVEP. The reproducibility code is available at: https://***/Sungden/Zero-shot-SSVEP-classification. © 1963-2012 IEEE.

关键词： Brain-computer interface (BCI) canonical correlation analysis deep neural network steady-state visual evoked potential (SSVEP) zero-shot learning

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