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第34届中国控制与决策会议

作者： Lin Tian Wenchao Xue Long Cheng The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences The Key Laboratory of Systems and Control National Center for Mathematics and Interdisciplinary SciencesAcademy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

This paper proposes a hand position tracking algorithm based on optimized consistent extended Kalman filter(CEKF).By introducing the previous work of the authors and analyzing the parameter of the original CEKF algorithm,the key parameter that is negatively correlated with the degree of the estimation of uncertain dynamics is ***,two metaheuristic methods,the Genetic Algorithm(GA) and the Particle Swarm Optimization(PSO) are used to optimize the original CEKF *** quantify the performance of the algorithms,the root-mean-square error(RMSE) is employed as the performance ***,the numerical simulation and practical experiment of the hand position tracking are carried out,and the optimized algorithm achieves 9.52% and 10.94% improvements of the performance,respectively.

关键词： Consistency Extended Kalman Filter Genetic Algorithm Particle Swarm Optimization

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A Minimax Probability Machine for Nondecomposable Performance Measures

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2023年第5期34卷 2353-2365页

作者： Luo, Junru Qiao, Hong Zhang, Bo Changzhou Univ Sch Comp Sci & Artificial Intelligence Changzhou 213100 Jiangsu Peoples R China Changzhou Univ Aliyun Sch Big Data Changzhou 213100 Jiangsu Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci LSEC Beijing 100190 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

Imbalanced classification tasks are widespread in many real-world applications. For such classification tasks, in comparison with the accuracy rate (AR), it is usually much more appropriate to use nondecomposable performance measures such as the area under the receiver operating characteristic curve (AUC) and the $F_beta$ measure as the classification criterion since the label class is imbalanced. On the other hand, the minimax probability machine is a popular method for binary classification problems and aims at learning a linear classifier by maximizing the AR, which makes it unsuitable to deal with imbalanced classification tasks. The purpose of this article is to develop a new minimax probability machine for the $F_beta$ measure, called minimax probability machine for the $F_beta$ -measures (MPMF), which can be used to deal with imbalanced classification tasks. A brief discussion is also given on how to extend the MPMF model for several other nondecomposable performance measures listed in the article. To solve the MPMF model effectively, we derive its equivalent form which can then be solved by an alternating descent method to learn a linear classifier. Further, the kernel trick is employed to derive a nonlinear MPMF model to learn a nonlinear classifier. Several experiments on real-world benchmark datasets demonstrate the effectiveness of our new model.

关键词： Measurement Task analysis Covariance matrices Support vector machines Prediction algorithms Minimization Kernel Imbalanced classification minimax probability machine nondecomposable performance measures

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Three-dimensional target tracking control for a biomimetic underwater vehicle 30

Three-dimensional target tracking control for a biomimetic u...

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30th International Ocean and Polar Engineering Conference, ISOPE 2020

作者： Bai, Ge Wang, Rui Wang, Yu Wang, Shuo State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781880653845

This paper presents a target tracking control method for a biomimetic underwater vehicle named RobCutt-II. The RobCutt-II is propelled by double undulatory fins which are distributed on both sides. In order to implement target tracking and observation in a certain distance, we propose a method by decoupling the target tracking control problem into distance, heading and depth control problems. Based on this idea, we design a target tracking controller composed of a navigation system, a distance controller, a heading controller, a depth controller and a switching logic controller. The simulation results verify the effectiveness of the proposed method on the biomimetic underwater vehicle. © 2020 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： controllers

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A Sustainable Ecology of Mobility: DAO-Based Autonomous Vehicular Services

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第12期8卷 4682-4684页

作者： Li, Juanjuan Zhou, Min Xue, Xiao Zhu, Jing Han, Shuangshuang Li, Lefei Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300072 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Beijing Huairou Acad Parallel Sensing Beijing 101400 Peoples R China Tsinghua Univ Dept Ind Engn Beijing 100080 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China

As a brief summary of the Distributed/Decentralized Hybrid Workshop on sustainable vehicular service ecology, this letter discusses the challenges of autonomous vehicular services and the potential of decentralized autonomous organizations and operations (DAOs) to address them. It outlines the future research issues on the integration of blockchain-based TRUE DAOs (TAOs) within vehicular services, including the ecological framework and the corresponding organizational, cooperational, executional and application technologies, to support a sustainable ecology of mobility.

关键词： Ecology Intelligent vehicles Decision making Blockchains Autonomous vehicles Ecosystems Stakeholders Autonomous systems Hybrid intelligent systems Distributed management Decentralized applications Sustainable development Mobility vehicular services decentralized autonomous organizations and operations human-oriented operating systems

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Self-Learning Robust control Synthesis and Trajectory Tracking of Uncertain Dynamics

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第1期52卷 278-286页

作者： Wang, Ding Cheng, Long Yan, Jun Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Beijing Artificial Intelligence Inst Beijing 100124 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ H3G 1M8 Canada

In this article, we investigate the self-learning robust control synthesis and tracking design of general uncertain dynamical systems. Based on the adaptive critic learning, the robust stabilization method is developed with the help of conducting problem transformation. In addition, by considering the optimal control solution with a discounted cost function, the established method is extended to address the robust trajectory tracking design problem. The Lyapunov stability analysis is also conducted for proving the robustness of the related control plants. Finally, the simulation verification with the three case studies is provided in terms of robust stabilization and trajectory tracking, respectively.

关键词： Robust control Optimal control Cost function Trajectory tracking Nonlinear systems Feedback control Dynamical systems Adaptive critic learning control synthesis neural networks optimization robust stabilization tracking design

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Data Calibration Algorithm for Artificial Lateral Line Sensor of Robotic Fish on Improved LSTM 40

Data Calibration Algorithm for Artificial Lateral Line Senso...

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40th Chinese control Conference (CCC)

作者： Ou, Yaming Zhang, Zhuoliang Zhou, Chao Zhou, Bo Southeast Univ Sch Automat Nanjing 210096 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Naval Res Acad Beijing 100072 Peoples R China

ISBN: (纸本)9789881563804

All the time, the measurement of swimming speed has long been a major research challenge in the field of robotic fish. The artificial lateral line system can accurately measure the speed without modifying the structure of the robot fish too much. In this paper, we developed a cantilever artificial lateral line(ALL) sensor based on piezoresistive effect by imitating the "SNs" cell structure of real fish, and established an accurate physical model for it considering the complex underwater boundary layer effect, which is conducive to the selection of hardware parameters such as sensor size and stiffness. In addition, a flowmeter-based speed calibration experimental platform was developed, based on which we proposed an "end to end" speed calibration algorithm based LSTM without filtering. Then a potential mapping relationship between the electrical signal of the sensor and the speed value of the flowmeter was obtained, which laid a strong foundation for the subsequent measurement of free-swimming speed of robotic fish by artificial lateral line system composed of this sensor.

关键词： velocity measurement robotic fish artificial lateral line sensor data processing

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What will robots be like in the future?

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National Science Review 2019年第5期6卷 1059-1061页

作者： Yanfeng Lu Weijie Zhao the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences NSR

Robots are changing our lives:sweeping robots patrol our living rooms; interactive robots accompany our children; industrial robots assemble vehicles; rescue robots search and save lives in catastrophes; medical robots perform surgeries in *** better understand robots’challenges and impact, National Science Review (NSR) interviewed Professor Toshio Fukuda, who is one of the world’s most representative robotics experts and has developed a number of bionic robots and micro/nano-robots.

关键词： NSR China What will robots be like in the future

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Recommending Learning Objects Through Attentive Heterogeneous Graph Convolution and Operation-Aware Neural Network

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第4期35卷 4178-4189页

作者： Zhu, Yifan Lin, Qika Lu, Hao Shi, Kaize Liu, Donglei Chambua, James Wan, Shanshan Niu, Zhendong Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Jiaotong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Technol Sydney Australian Artificial Intelligence Inst AAII Ultimo NSW 2007 Australia Univ Dar es Salaam Comp Sci & Engn Dept Dar Es Salaam 35091 Tanzania Beijing Univ Civil Engn & Architecture Dept Comp Sci Beijing 100044 Peoples R China Univ Pittsburgh Sch Comp & Informat Pittsburgh PA 15260 USA

Massive Open Online Courses (MOOCs) have received unprecedented attention, in which learners can obtain a large number of learning objects anytime and anywhere. However, the increasing information overload on MOOCs inhibits the appropriate choice of learning objects by learners, leading to a low efficiency and high dropout rates in the learning process of this human-computer interaction scenario. E-learning recommendation systems have been studied to present learning objects directly to learners, thereby relieving such problem. However, in MOOC platforms, recommendation network structures which can selectively extract implicit feature such as heterogeneous learning preference and knowledge organization of learning objects are still not comprehensively studied. To this end, we propose a learning object recommendation model based on heterogeneous learning behavior and knowledge graph. To generate a unified representation of each entity and relation, we first propose an Attentive Composition based Graph Convolutional Network (ACGCN). By introducing an attention mechanism, information is amplified when updating the representation of the heterogeneous graph, which eliminates the impact of noise and improves the robustness of the model. Then, a Dense Feature based Operation-Aware Network (DFOAN) is utilized to capture implicit and complex learners' interactive behaviors, and to further provide a recommendation. Experimental results using two real-world datasets revealed that our proposed model has the best precision, recall, F1, and accuracy scores compared to those of several existing models.

关键词： Tensors Training Predictive models Neural networks Electronic learning Deep learning Videos Learning objects recommendation heterogeneous graph graph convolutional network attentive neural network

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Transfer learning for nonlinear batch process operation optimization

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JOURNAL OF PROCESS control 2021年 101卷 11-23页

作者： Chu, Fei Wang, Jiachen Zhao, Xu Zhang, Shuning Chen, Tao Jia, Runda Xiong, Gang China Univ Min & Technol Underground Space Intelligent Control Engn Res Ct Sch Informat & Control Engn Minist Educ Xuzhou 221116 Jiangsu Peoples R China Beijing Gen Res Inst Min & Met State Key Lab Proc Automat Min & Met Beijing Key Lab Proc Automat Min & Met Beijing 100160 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Ludong Univ Sch Informat & Elect Engn Yantai 264025 Peoples R China Univ Surrey Dept Chem & Proc Engn Guildford GU2 7XH Surrey England Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

This paper concerns with the JY-KPLS model based transfer learning for the operation optimization of nonlinear batch processes. Due to problems of data insufficiency and uncertainties in a new nonlinear batch process that has just been put into production, the model-(new) process mismatch is usually inevitable, which is also the main reason for the poor performance of the batch process. To solve this problem, this paper first adopts the JY-KPLS model to capture the behavior of the nonlinear batch process, and takes full advantage of the information in similar batch processes to assist the modeling and operation optimization of a new process. Then, a data selection based batch-to-batch optimization control strategy is proposed in this paper to reduce the adverse effects of this mismatch on the operation of the new batch process. Finally, the feasibility of the proposed method is demonstrated by simulations. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Transfer learning Nonlinear batch process Insufficient data Operation optimization Data selection

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Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation

arXiv

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arXiv 2022年

作者： Fan, Siqi Zhu, Fenghua Feng, Zunlei Lv, Yisheng Song, Mingli Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Tsinghua University Beijing China The College of Computer Science and Technology Zhejiang University Hangzhou China

Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collaborative Learning (CPCL), among which two predictive networks are trained in parallel, and the pseudo supervision is implemented based on both the agreement and disagreement of the two predictions. One network seeks common ground via intersection supervision and is supervised by the high-quality labels to ensure a more reliable supervision, while the other network reserves differences via union supervision and is supervised by all the pseudo labels to keep exploring with curiosity. Thus, the collaboration of conservative evolution and progressive exploration can be achieved. To reduce the influences of the suspicious pseudo labels, the loss is dynamic re-weighted according to the prediction confidence. Extensive experiments demonstrate that CPCL achieves state-of-the-art performance for semi-supervised semantic segmentation. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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