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Line-of-sight based three-dimensional path following control for an underactuated robotic dolphin

Science China(Information Sciences) 2021年第1期64卷 215-226页

作者： Jincun LIU Zhenna LIU Junzhi YU State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking University Department of Electrical and Automation Shandong Labor Vocational and Technical College State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

This paper investigates the three-dimensional(3-D) path following control problem for an underactuated robotic dolphin. With a comprehensive consideration of the mechanical constraint and swimming principle of the robotic dolphin, a decoupling motion strategy is proposed to produce yaw and pitch maneuvers simultaneously. Then, kinematics and dynamics models for 3-D dolphin-like swimming are established,followed by simulations of the path following control. Furthermore, a novel lookahead based 3-D line-ofsight(LOS) guidance law is developed and implemented to obtain desired attitude angles with its simplicity,intuitiveness, and small computational footprint. Finally, simulation results illustrate the feasibility and effectiveness of the proposed path following control methods.

关键词： robotic dolphin path following 3-D LOS guidance law decoupling motion

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A Recurrent Attention and Interaction Model for Pedestrian Trajectory Prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1361-1370页

作者： Xuesong Li Yating Liu Kunfeng Wang Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the University of Chinese Academy of SciencesBeijing 100049China College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

The movement of pedestrians involves temporal continuity,spatial interactivity,and random *** a result,pedestrian trajectory prediction is rather *** existing trajectory prediction methods tend to focus on just one aspect of these challenges,ignoring the temporal information of the trajectory and making too many *** this paper,we propose a recurrent attention and interaction(RAI)model to predict pedestrian *** RAI model consists of a temporal attention module,spatial pooling module,and randomness modeling *** temporal attention module is proposed to assign different weights to the input sequence of a target,and reduce the speed deviation of different *** spatial pooling module is proposed to model not only the social information of neighbors in historical frames,but also the intention of neighbors in the current *** randomness modeling module is proposed to model the uncertainty and diversity of trajectories by introducing random *** conduct extensive experiments on several public *** results demonstrate that our method outperforms many that are state-ofthe-art.

关键词： Deep learning long short-term memory(LSTM) recurrent attention and interaction(RAI)model trajectory prediction

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Pedestrian Trajectory Prediction by Modeling the Interactions using Social LSTM Extensions

Pedestrian Trajectory Prediction by Modeling the Interaction...

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2022 Chinese Automation Congress, CAC 2022

作者： Si, Zeze Zhao, Hongxia Tang, Haina Zhu, Fenghua Lv, Yisheng Lu, Hao Institute of Autamation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Qingdao Academy of Intelligent Industries Qingdao266000 China

ISBN: (纸本)9781665465335

In many driving situations, human mobility is an important topic in trajectory prediction. Considering the pedestrian trajectory as a sequence generative task, a prediction algorithm based on Social Long Short-Term Memory (Social LSTM) is implemented. In order to simulate the social interaction between pedestrians, Social Pooling (S-Pooling) is used to aggregate the hidden state of pedestrians, while the attention mechanism is utilized to aggregate information differently according to the importance of surrounding pedestrians. Furthermore, Convolutional Neural Networks (CNN) is introduced into Social LSTM model to consider both the interaction between people and the characteristic of scene scale in the prediction process. Experiments are carried out against baseline methods, and the results demonstrated that combining Social LSTM with attention mechanism or CNN can improve the performance of pedestrian trajectory prediction. © 2022 IEEE.

关键词： Long short-term memory

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Enhanced Data Augmentation for Bearing Fault Diagnosis by Using a Spectral Flow Model

Enhanced Data Augmentation for Bearing Fault Diagnosis by Us...

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Prognostics and system Health management Conference (PHM)

作者： Xiaoyun Gong Mengxuan Hao Chuan Li Wenliao Du Yixiang Huang Henan International Joint Laboratory of Complex Mechanical Equipment Intelligent Monitoring and Control Zhengzhou University of Light Industry Zhengzhou China Research Center for System Health Maintenance Chongqing Technology and Business University Chongqing China The State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

While existing flow-based models have the potential for data enhancement and intelligent fault diagnosis of bearings, these methods are mainly based on the time domain to learn features. In general, time-domain and frequency-domain features are important for recognizing faults. Especially, the spectral features in the frequency domain can reveal highly distinct patterns associated with fault types. Therefore, learning spectral feature helps to obtain comprehensive fault information and achieve high fault diagnosis accuracy. In this work, a spectral flow model framework is developed to learn and generate realistic data from mechanical sensor signals. The performance of fault diagnosis under unbalanced data is improved by learning the information contained in the spectral features, which are utilized to generate insufficient fault data. Experimental results show that the present method can generate a large number of samples with higher similarity and better diversity on the bearing dataset. The generated samples may be used to expand the limited dataset and significantly improve the accuracy of the bearing fault diagnosis task.

关键词： Fault diagnosis Mechanical sensors Vibrations Training Accuracy Frequency-domain analysis Training data

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Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach

arXiv

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arXiv 2024年

作者： Zhu, Zhengqiu Zhao, Yong Chen, Bin Qiu, Sihang Xu, Kai Yin, Quanjun Huang, Jincai Liu, Zhong Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Hunan Province Changsha410073 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

The transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in Chatbots and Large Language Models (LLMs). Therefore, the advancement of parallel intelligence-powered Crowdsensing Intelligence (CSI) is witnessed, which is currently advancing towards linguistic intelligence. In this paper, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0. It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., inter-human, human-AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multi-agent systems, scenarios engineering and conversational human-AI cooperation. Finally, we present potential industrial applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI. © 2024, CC BY.

关键词： Multi agent systems

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Unneglectable Terrestrial Export of Dissolved Organic Carbon in China

SSRN

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SSRN 2024年

作者： Yu, Qian Li, Han Ge, Xiaodong Ma, Mingrui Zhao, Wenxin Yang, Jinya Mulder, Jan Zhang, Lei Zhao, Yu Duan, Lei State Key Laboratory of Pollution Control & Resource Reuse School of Environment Nanjing University Jiangsu Nanjing210023 China State Key Laboratory of Environmental Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life Sciences Box 5003 Ås1432 Norway State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Tsinghua University Beijing100084 China China

Transport of dissolved organic carbon (DOC) from land to inland waters is a crucial yet usually overlooked component of carbon budget. Its magnitude and response to global change are poorly quantified due to limited direct observation, especially in China. Here we compiled a database from 462 headwaters across China, primary based on our own observation, and applied a generalized additive model (GAM) to quantify terrestrial DOC export from 2000 to 2020. Our results reveal substantial underestimations in current assessments relying on riverine data. Over this period, terrestrial DOC export averaged 13.4 ± 1.5 Tg C yr−1, accounting for approximately 10% of national net biome productivity (NBP). Projections under SSP2-4.5 and carbon-neutral scenarios suggest an increase to 17.1 Tg C yr−1 in 2060, driven by decreasing atmospheric deposition and rising temperatures. This highlights the importance of considering DOC export in terrestrial carbon sink assessments, particularly as global change continues to evolve. © 2024, The Authors. All rights reserved.

关键词： Carbon sequestration

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Testing Intelligence: Accelerating the Verification and Validation of Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-14页

作者： Wang, Fei-Yue Hu, Jia Lai, Jintao State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China ZhongTe Distinguished Chair in Cooperative Automation Professor Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China

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Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1990-2004页

作者： Linyao Yang Chen Lv Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the China Electric Power Research Institute Beijing 100192China IEEE the Qingdao Academy of Intelligent Industries Qingdao 256200China the School of Information Science and Technology Nantong UniversityNantong 226019China the School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based *** recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power *** multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision *** achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical *** entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best *** address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart *** model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational ***,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one *** not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual *** comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

关键词： Entity alignment integer programming(IP) knowledge fusion knowledge graph embedding power dispatch

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Traffic Signal control Using Offline Reinforcement Learning

Traffic Signal Control Using Offline Reinforcement Learning

引用

2021 China Automation Congress, CAC 2021

作者： Dai, Xingyuan Zhao, Chen Li, Xiaoshuang Wang, Xiao Wang, Fei-Yue School of Artificial Intelligence University of Chinese Academy of Sciences China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781665426473

The problem of traffic signal control is essential but remains unsolved. Some researchers use online reinforcement learning, including the off-policy one, to derive an optimal control policy through interaction between agents and environments in simulation. However, it is difficult to deploy the policy in real transportation systems due to the gap between simulated and real traffic data. In this paper, we consider an offline reinforcement learning method to tackle the problem. First, we construct a realistic traffic environment and obtain offline data based on a classic actuated traffic signal controller. Then, we use an offline reinforcement learning algorithm, namely conservative Q-learning, to learn an efficient control policy via offline datasets. We conduct experiments on a typical road intersection and compare the conservative Q-learning policy with the actuated policy and two data-driven policies based on off-policy reinforcement learning and imitation learning. Empirical results indicate that in the offline-learning setting the conservative Q-learning policy performs significantly better than other baselines, including the actuated policy, but the other two data-driven policies perform poorly in test scenarios. © 2021 IEEE

关键词： Reinforcement learning

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Fusing Knowledge and Experience with Graph Convolutional Network for Cross-task Learning in Visual Cognitive Development 5th

Fusing Knowledge and Experience with Graph Convolutional Ne...

引用

5th International Conference on Cognitive systems and Signal Processing, ICCSIP 2020

作者： Zhang, Xinyue Yang, Xu Liu, Zhiyong Zhang, Lu Ren, Dongchun Fan, Mingyu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Meituan-Dianping Group Beijing100190 China

ISBN: (纸本)9789811623356

Visual cognitive ability is important for intelligent robots in unstructured and dynamic environments. The high reliance on large amounts of data prevents prior methods to handle this task. Therefore, we propose a model called knowledge-experience fusion graph (KEFG) network for novel inference. It exploits information from both knowledge and experience. With the employment of graph convolutional network (GCN), KEFG generates the predictive classifiers of the novel classes with few labeled samples. Experiments show that KEFG can decrease the training time by the fusion of the source information and also increase the classification accuracy in cross-task learning. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image recognition

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