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Smart management of Sand-Breaking systems for Taklimakan SandSea Highway: A Parallel Intelligence and Fuzzy control Approach

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第6期11卷 7348-7358页

作者： Chang, Fangle Wang, Xiujuan Kang, Mengzhen Tao, Yu Yang, Jiahong Lei, Jiaqiang Wang, Fei-Yue Zhejiang Univ Ningbo Innovat Ctr Ningbo 315100 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog Urumqi 650100 Peoples R China

The planning, construction, and maintenance of sand-breaking systems around highway in Taklimakan SandSea mainly depend on expert's empirical knowledge, and face the problems of time-consuming management, unpredictable system performance, and few available data. This work proposes a management and control method of sand-breaking systems that follows artificial system, computational experiments, and parallel execution (ACP) theory. Expert knowledge was extracted to develop artificial sand-breaking system. conditional tabular generative adversarial network (CTGAN) was developed to achieve data augmentation. Computational experiments were implemented to evaluate artificial system performance by predicting whether the system can reach the set age limit. Using fuzzy control, the real sand-breaking system and the artificial one can learn from each other in parallel execution to provide decision support for sand-breaking system management. This approach provides a human-machine hybrid parallel intelligence system for complex sand-breaking systems management.

关键词： Road transportation Disasters Indexes Wind speed Vectors System performance Fuzzy control Conditional tabular generative adversarial network (CTGAN) fuzzy control parallel intelligence sand-breaking systems Taklimakan SandSea

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Self-attention mechanism in person re-identification models

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第4期81卷 4649-4667页

作者： Chen, Wenbai Lu, Yue Ma, Hang Chen, Qili Wu, Xibao Wu, Peiliang Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China Yanshan Univ Sch Informat & Engn Qinhuangdao 066004 Hebei Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In recent years, person re-identification based on video has become a hot topic in the field of person re-identification. The self-attention mechanism can improve the ability of deep neural networks in computer vision tasks such as image classification, image segmentation and natural language processing tasks. In order to verify whether the self-attention can improve the performance or not in person re-identification tasks, this paper applies two self-attention mechanisms, non-local attention and recurrent criss-cross attention to person re-identification model, and experiments are conducted on Market-1501, DukeMTMC-reID and MSMT17 person re-identification datasets. The results show that the self-attention mechanism can improve the accuracy of the person re-identification model. The accuracy is higher when the self-attention module is inserted into the convolutional layers of the re-identification network.

关键词： Person re-identification Deep neural network Self-attention Computer vision

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Online Competition of Trajectory Planning for Automated Parking: Benchmarks, Achievements, Learned Lessons, and Future Perspectives

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第1期8卷 16-21页

作者： Li, Bai Fan, Lili Ouyang, Yakun Tang, Shiqi Wang, Xiao Cao, Dongpu Wang, Fei-Yue State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230039 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Automated parking is a typical function in a self-driving car. The trajectory planning module directly reflects the intelligence level of an automated parking system. Although many competitions have been launched for autonomous driving, most of them focused on on-road driving scenarios. However, driving on a structured road greatly differs from parking in an unstructured environment. In addition, previous competitions typically competed on the overall driving performance instead of the trajectory planning performance. A trajectory planning competition of automated parking (TPCAP) has been recently organized. This event competed on parking-oriented planners without involving other modules, such as localization, perception, or tracking control. This study reports the TPCAP benchmarks, achievements, experiences, and future perspectives.

关键词： Trajectory Trajectory planning Benchmark testing Planning Source coding Location awareness Automobiles Automated parking trajectory planning motion planning autonomous driving autonomous racing

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Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion

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IEEE TRANSACTIONS ON NEURAL systems AND REHABILITATION ENGINEERING 2022年 30卷 2032-2041页

作者： Li, Chenguang Yang, Hongjun Cheng, Long Huang, Fubiao Zhao, Shuang Li, Dongyue Yan, Ruxiu Chinese Acad Sci Inst Automat State Key Lab Control & Management Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Beijing Boai Hosp China Rehabil Res Ctr Beijing 100068 Peoples R China

Quantitative assessment of hand function can assist therapists in providing appropriate rehabilitation strategies, which plays an essential role in post-stroke rehabilitation. Conventionally, the assessment process relies heavily on clinical experience and lacks quantitative analysis. To quantitatively assess the hand motor function of patients with post-stroke hemiplegia, this study proposes a novel multi-modality fusion assessment framework. This framework includes three components: the kinematic feature extraction based on a graph convolutional network (HAGCN), the surface electromyography (sEMG) signal processing based on a multi-layer long short term memory (LSTM) network, and the quantitative assessment based on the multi-modality fusion. To the best of the authors' knowledge, this is the first study of applying a graph convolution network to assess the hand motor function. We also collect the kinematic data and sEMG data from 70 subjects who completed 28 types of hand movements. Therapists first graded patients using traditional motor assessment scales (Brunnstrom Scale and Fugl-Meyer Assessment Scale) and further refined the patient's motor assessment result by their experience. Then, we trained the HAGCN and LSTM networks and quantitatively assessed each patient based on the proposed assessment framework. Finally, the Spearman correlation coefficient (SC) between the assessment result of this study and the traditional scale are 0.908 and 0.967, demonstrating a significant correlation between the proposed assessment and the traditional scale scores. In addition, the SC value between the score of this study and the refined hand motor function is 0.997, indicating the "ceiling effect" of some traditional scales can be avoided.

关键词： Hemorrhaging Thumb Stroke (medical condition) Kinematics Feature extraction Wrist Correlation Hand motor function quantitative assessment multi-modality fusion graph convolutional network

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Local Linear Generalized Autoencoder-Based Incipient Fault Detection for Electrical Drive systems of High-Speed Trains

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第11期24卷 12422-12430页

作者： Cheng, Chao Ju, Yunfei Xu, Shuiqing Lv, Yisheng Chen, Hongtian Changchun Univ Technol Sch Comp Sci & Engn Changchun 130012 Peoples R China Hefei Univ Technol Coll Elect Engn & Automat Hefei 230009 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Features of incipient faults are tiny in high-speed trains' electrical drive systems. Noises and disturbances in the external environment and sensors can mask incipient faults. Therefore, fault detection (FD) of incipient faults is a challenge. This paper proposes a new FD scheme using a novel manifold learning method named local linear generalized autoencoder (LLGAE). The prominent characteristics of the LLGAE-based FD method are three-fold: 1) it can realize FD for electric drive systems even without the physical model or expertise;2) it still has good results for non-Gaussian electrical drives;3) it entirely takes into account the locally linear structure of samples. Mathematical derivations have proved the proposed method. Through an experimental platform of high-speed trains, the proposed method is validated.

关键词： Incipient fault fault detection (FD) manifold learning generalized autocoder (GAE) electrical drive systems high-speed trains

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Six-Dimensional Target Pose Estimation for Robot Autonomous Manipulation: Methodology and Verification

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2023年第1期15卷 186-197页

作者： Wang, Rui Su, Congjia Yu, Hao Wang, Shuo 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 Yizhun Med AI Co Ltd Beijing 100191 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China

The autonomous and precise grasping operation of robots is considered challenging in situations where there are different objects with different shapes and postures. In this study, we proposed a method of 6-D target pose estimation for robot autonomous manipulation. The proposed method is based on: 1) a fully convolutional neural network for scene semantic segmentation and 2) fast global registration to achieve target pose estimation. To verify the validity of the proposed algorithm, we built a robot grasping operation system and used the point cloud model of the target object and its pose estimation results to generate the robot grasping posture control strategy. Experimental results showed that the proposed method can achieve a six-degree-of-freedom pose estimation for arbitrarily placed target objects and complete the autonomous grasping of the target. Comparative experiments demonstrated that the proposed target pose estimation method achieved a significant improvement in average accuracy and real-time performance compared with traditional methods.

关键词： Autonomous manipulation robot semantic seg-mentation target pose estimation

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Electromagnetic Angular Position Sensing Using High-Magnetic-Permeability Materials

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IEEE SENSORS JOURNAL 2022年第12期22卷 11626-11636页

作者： Wang, Heng Wang, Shuangyi Rajamani, Rajesh South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou 511442 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Minnesota Dept Mech Engn Minneapolis MN 55455 USA

In this paper, a new electromagnetic angular position sensing method using high-magnetic-permeability metal (e.g., mu-metal) is proposed for measurement of joint angles of rotational mechanisms. An electromagnet and magnetic sensors are both located on one mechanical part and the mu-metal element is located on the other mechanical part with relative rotation to the first part around the joint. The mu-metal with high magnetic permeability is easily magnetized by the electromagnet and it exerts an additional magnetic field on the magnetic sensors dependent on the rotation angle nonlinearly. This influence is utilized for angle estimation using an extended Kalman filter. Two configurations of the angle sensing system are designed and modeled using numerical simulation. Experimental results show that the angle measurement error for both configurations is under 1% over their respective sensing range. The mu-metal based angle sensing system is also validated to be immune to ferromagnetic disturbances due to the use of an alternating magnetic field. The proposed angle sensing method is non-contacting and non-intrusive, and its off-joint configuration can measure joint angle without needing to be installed in the joint.

关键词： Sensors Magnetic sensors Robot sensing systems Magnetic field measurement Electromagnets Estimation Magnetic separation Angular position sensing position estimation high-magnetic-permeabilitymaterial disturbance rejection

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Discrete soft actor-critic with auto-encoder on vascular robotic system

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ROBOTICA 2023年第4期41卷 1115-1126页

作者： Li, Hao Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Gui, Mei-Jiang Xiang, Tian-Yu Wang, Jin-Li Hou, Zeng-Guang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

Instrument delivery is critical part in vascular intervention surgery. Due to the soft-body structure of instruments, the relationship between manipulation commands and instrument motion is non-linear, making instrument delivery challenging and time-consuming. Reinforcement learning has the potential to learn manipulation skills and automate instrument delivery with enhanced success rates and reduced workload of physicians. However, due to the sample inefficiency when using high-dimensional images, existing reinforcement learning algorithms are limited on realistic vascular robotic systems. To alleviate this problem, this paper proposes discrete soft actor-critic with auto-encoder (DSAC-AE) that augments SAC-discrete with an auxiliary reconstruction task. The algorithm is applied with distributed sample collection and parameter update in a robot-assisted preclinical environment. Experimental results indicate that guidewire delivery can be automatically implemented after 50k sampling steps in less than 15 h, demonstrating the proposed algorithm has the great potential to learn manipulation skill for vascular robotic systems.

关键词： surgical robots vascular robotic system automation reinforcement learning deep neural network

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Incremental few-shot object detection via knowledge transfer

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PATTERN RECOGNITION LETTERS 2022年 156卷 67-73页

作者： Feng, Hangtao Zhang, Lu Yang, Xu Liu, Zhiyong Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci China Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

As a challenging problem in machine learning, incremental few-shot object detection (iFSD) [1] aims to incrementally detect novel classes with few examples, while keeping the previous knowledge without revisiting base classes. Here, we propose two models based on the observation that when new memories come, new connections will be created between memory cells in the brain [2] . The first one, which is called the multi-class head (MCH) model, simulates how humans add new memory-connections that every time novel classes come, a classification branch is added to predict novel classification. And the second model, called the bi-path multi-class head (BPMCH) model, adds a new backbone, which is initialized with the weight of the base class backbone, to transfer more knowledge of the base class to the novel class. Considering accuracy and speed, we choose the Fully Convolutional One-Stage Object Detection (FCOS) [3] + Adaptive Training Sample Selection (ATSS) [4] detector as our baseline. Our models are first trained on the base classes with abundant examples and then finetuned on novel classes with few examples, which not only maintain the knowledge learned from the base class but also transfer the knowledge to the novel class. Extensive experiments show that our models outperform the state-of-theart model ONCE [1] on the COCO [5] and PASCAL VOC [6] by a large margin. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Machine learning Convolutional neural networks Transfer learning Incremental few-shot object detection

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Nuclear Pollution or Safe Discharge: Topic Evolution and Cognitive Analysis on Fukushimas Treated Radioactive Water

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第6期11卷 7338-7347页

作者： Liu, Xin Chen, Ziliang Wang, Fei-Yue Yang, Dawei Qin, Rui Pang, Mingjiang Ni, Qinghua Gao, Huiquan China Univ Petr East China Qingdao Inst Software Coll Comp Sci & Technol Qingdao 266580 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China

In the context of the Societies 5.0, a series of discussions on the emerging Fukushima treated radioactive water (FTRW) event was carried out, which has an impact on sustainable development in multiple fields including the economy, culture, and society. In order to comprehensively understand emerging topics and their evolution, explore the impact of people's cognition on their participation, and focus on people's attitudes and public participation in the FTRW event, we propose an evolution analysis framework (EAF) to analyze the massive multilingual comments and news collected from social media platforms in several countries. We design a multilingual topic extraction model (XLM-topic) to detect the patterns of topics and analyze their evolution. Potential relations between the FTRW event's elements are explored by relational reasoning based on a knowledge graph, which is established by entities and relations extracted from comments and news. Moreover, we predict the public attitudes and participation toward the FTRW event by utilizing our custom-designed public opinion cellular automata (POCA). The proposed POCA simulates the information dissemination, cognitive changes, and topic evolution among social groups in virtual spaces. It collaborates with XLM-topic to analyze trends in both physical and virtual spaces. Analysis results indicate that participants in different regions and countries have different attitudes and reactions toward the FTRW event, and the public's cognition on this event will interact with itself. Our study is conducive to promoting the integration and interaction of virtual space and physical space in the context of Societies 5.0, providing decision-making support for building a more harmonious and stable social environment.

关键词： Fukushima treated radioactive water internet public opinion social media analysis Societies 50 topic evolution trend prediction

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