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19th IEEE International Symposium on Biomedical Imaging (IEEE ISBI)

作者： Gong, Lixin Zhou, Pinghong Dong, Di Hu, Hao Tian, Jie Northeastern Univ Coll Med Shenyang Peoples R China Northeastern Univ Biol Informat Engn Sch Shenyang Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst CAS Key Lab Mol ImagingBeijing Key Lab Mol Imagi Beijing Peoples R China Fudan Univ Zhongshan Hosp Endoscopy Ctr Shanghai Peoples R China Fudan Univ Zhongshan Hosp Endoscopy Res Inst Shanghai Peoples R China

ISBN: (纸本)9781665429238

The supervised-learning-based diagnosis model needs lots of labeled data. However, there are major obstacles in obtaining massive labeled data while unlabeled data is rich. To assist in improving the detection of early gastric cancer, we constructed an unsupervised-feature-driven diagnosis model (UNFD-EGCM) based on contrastive learning, including the unsupervised feature extraction (UFE) and supervised linear layer (SLL) modules. The UFE was trained by training data without labels to extract features, while the SLL was trained by labeled training data to give the final predictions. We also trained and evaluated our model under simulated low-shot scenarios by gradually decreasing the number of labeled training data. The results showed that our UNFD-EGCM out-performed the baseline in the test cohort, and the superiority is kept even when our model used only 40% of labeled training data used by the baseline. This shows the possibility of mining information from unlabeled medical data.

关键词： Unsupervised Learning Low-Shot Gastric Cancer Contrastive Learning

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Emerging Trends in Intelligent Vehicles: The IEEE TIV Perspective

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第8期8卷 3983-3995页

作者： Zhang, Hui Guo, Junbo Luo, Guiyang Li, Lingxi Na, Xiaoxiang Wang, Xiao Teng, Siyu Ma, Siji Li, Yidong Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Indiana Univ Purdue Univ Indianapolis Dept Elect & Comp Engn Purdue Sch Engn & Technol Indianapolis IN 46202 USA Univ Cambridge Dept Engn Cambridge CB2 1PZ England Minist Educ Engn Res Ctr Autonomous Unmanned Syst Technol Hefei 230601 Anhui Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Hong Kong Baptist Univ Hong Kong 999077 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China

This article is focused on bibliographic analysis and collaboration pattern analysis of the textpapers published in the IEEE Transactions on Intelligent Vehicles (TIV) from January 2019 to January 2023. We have identified the authors, institutions, and countries/regions that are the most productive and have the highest impact. It is found that research on intelligent vehicles is dominated by researchers and institutions from China. Researchers from the US are the second largest contributor to the transaction, whilst those from Germany rank the third. It is also found that, Fei-Yue Wang, Mohan Manubhai Trivedi, and J. Christian Gerdes are the three most productive and influential authors at IEEE TIV, and the Institute of Automation, Chinese Academy of Sciences, National University of Defense Technology, and the University of California, are three most productive and influential institutions. In addition, three networks are generated (i.e., the co-authorship, co-keyword, and author-keyword) to mine collaboration patterns among authors and keywords. Collaboration relationship between researchers and hot research topics are also investigated. Furthermore, an open-source tool is developed to automatically collect metadata, perform bibliographic analysis, and mine collaboration patterns.

关键词： Bibliographic analysis collaboration pattern intelligent vehicles (IV)

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Kinematics Analysis of a Manipulator for the Power Transmission Line Maintenance Robot

Kinematics Analysis of a Manipulator for the Power Transmiss...

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第42届中国控制会议

作者： Minghao Chen En Li Feng Zhang Rui Guo Zize Liang Yuwei Zhang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences The School of Artificial Intelligence University of Chinese Academy of Sciences Shandong Luneng Intelligence Technology Co. Ltd. The State Grid Shandong Electric Power Company

With the remarkable development of the power grid, traditional manual maintenance modes cannot meet the requirement of efficiency and reliability. Robotic technology is applied for power line maintenance to bring down risks and ensure inspectors' safety. Based on the power transmission line maintenance platform, a 5-DOF manipulator is designed to perform the maintenance tasks for the quad bundle conductors. Then the kinematics analysis of the manipulator is analyzed. The manipulator doesn't meet the Pieper criterion. So an improved adaptive particle swarm optimization(APSO) is proposed for figuring out the inverse kinematics. The weights of the pose and the constraint of the prismatic joint are introduced to the algorithm. What's more, half of the particles are initialized around the incipient value of joints and half of the particles are randomly initialized with the constraint of joints in this algorithm. Results show that the manipulator is able to work in the environment of quad bundle conductors and the proposed algorithm can improve the precision of inverse kinematics and prevent the leap of joints.

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From Formula One to Autonomous One: History, Achievements, and Future Perspectives

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第5期8卷 3217-3223页

作者： Li, Bai Gao, Ting Ma, Siji Zhang, Youmin Acarman, Tankut Cao, Kai Xu, Tian'ao Zhang, Tantan Wang, Fei-Yue Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Taipa 999078 Macau Peoples R China Concordia Univ Concordia Inst Aerosp Design & Innovat Dept Mech Ind & Aerosp Engn Montreal PQ H3G 1M8 Canada Galatasaray Univ Dept Comp Engn TR-34349 Istanbul Turkiye Dongfeng USharing Technol Co Ltd Wuhan 430056 Peoples R China Swiss Fed Inst Technol Dept Mech & Proc Engn CH-8092 Zurich Switzerland Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This letter is the first report from a series of IEEE TIV's Decentralized and Hybrid Workshops (DHWs) on Intelligent Vehicles for Education (IV4E). The role of intelligent vehicles in promoting education for all ages through autonomous racing was discussed during a recent DHW. Over the past decade, autonomous racing has emerged due to advancements in self-driving technologies. While still focused on extreme speed, autonomous racing differs from conventional automobile racing in its development philosophy, as human drivers are no longer involved. The absence of human drivers should be regarded as a new chance to increase competitiveness and entertainment value. This letter discusses opportunities to promote education-oriented autonomous racing. Recall that the flagship car race is Formula 1, where "formula" denotes technical restrictions that should be satisfied strictly. We name the new race series Autonomous 1 or A1, leveraging the power of autonomous intelligence in education. The achievements made in Formula 1 and typical autonomous races are reviewed, followed by discussions about A1's future perspectives. Specifically, A1 needs to maintain race consistency, update rules, and provide personalized commentary to support all-age education.

关键词： Automobiles History Safety Sports control systems Color Accidents Autonomous racing formula 1 autonomous 1 car race intelligent vehicles for education (IV4E)

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Pavement Defect Detection With Deep Learning: A Comprehensive Survey

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4292-4311页

作者： Fan, Lili Wang, Dandan Wang, Junhao Li, Yunjie Cao, Yifeng Liu, Yi Chen, Xiaoming Wang, Yutong Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Jiangsu Ind Innovat Ctr Intelligent Equipment Co L Beijing 100081 Peoples R China Lab Electromagnet Space Cognit & Intelligent Contr Beijing 100081 Peoples R China Dalian Minzu Univ Sch Sci Dalian 116600 Peoples R China Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada Jiangsu Ind Innovat Ctr Intelligent Equipment Co L Nanjing 213300 Jiangsu Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Pavement defect detection is of profound significance regarding road safety, so it has been a trending research topic. In the past years, deep learning based methods have turned into a key technology, with advantages of high accuracy, strong robustness, and adaptability to complex pavement environments. This paper first reviews the methods based on image processing and 3D imaging. As for image-based processing techniques, they can be classified into three types based on how to label the collected data: fully supervised learning, unsupervised learning, and other methods. Different methods are further classified and compared with each other. Second, the pavement detection methods based on 3D data are sorted out, thereby summarizing their benefits, drawbacks, and application scenarios. Third, the study proposed the major challenges in the field of pavement defect detection, introduced validated datasets and evaluation metrics. Finally, on the basis of reviewing the literature in pavement defect detection, the promising direction is put forward.

关键词： Deep learning pavement defect detection computer vision image processing 3D image

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Gait Planning and Motion control Based on Vrep Simulation for Quadruped Robot

Gait Planning and Motion Control Based on Vrep Simulation fo...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Linqi Zhou Zhihua Chen Jun Liu Zhi Liu Yumeng Chen Liting Zhang key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot’s foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper.

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A process transfer model-based optimal compensation control strategy for batch process using just-in-time learning and trust region method

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2021年第1期358卷 606-632页

作者： Chu, Fei Cheng, Xiang Peng, Chuang Jia, Runda Chen, Tao Wei, Qinglai China Univ Min & Technol Sch Informat & Control Engn Minist Educ Underground Space Intelligent Control Engn Res Ct Xuzhou 221116 Jiangsu Peoples R China Beijing Gen Res Inst Min & Met Beijing Key Lab Proc Automat Min & Met State 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 Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Univ Surrey Dept Chem & Proc Engn Guildford Surrey England

The advantages of maximally transferring similar process data for modeling make the process transfer model attract increasing attention in quality prediction and optimal control. Unfortunately, due to the difference between similar processes and the uncertainty of data-driven model, there are usually a more serious mismatch between the process transfer model and the actual process, which may result in the deterioration of process transfer model-based control strategies. In this research, a process transfer model based optimal compensation control strategy using just-in-time learning and trust region method is proposed to cope with this problem for batch processes. First, a novel JITL-JYKPLS (Just-in-time learning Joint-Y kernel partial least squares) model combining the JYKPLS (Joint-Y kernel partial least squares) process transfer model and just-in-time learning is proposed and employed to obtain the satisfactory approximation in a local region with the assistance of sufficient similar process data. Then, this paper integrates JITL-JYKPLS model with the trust region method to further compensate for the NCO (necessary condition of optimality) mismatch in the batch-to-batch optimization problem, and the problem of estimating experimental gradients is also avoided. Meanwhile, a more elaborate model update scheme is designed to supplement the lack of new data and gradually eliminate the adverse effects of partial differences between similar process production processes. Finally, the feasibility of the proposed optimal compensation control strategy is demonstrated through a simulated cobalt oxalate synthesis process. (C) 2020 Published by Elsevier Ltd on behalf of The Franklin Institute.

关键词： Deterioration

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Data augmentation using image generation for change detection 1

Data augmentation using image generation for change detectio...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Xuan Duan, Haibin Zhang, Hui Wang, Fei-Yue Peng Cheng Laboratory Shenzhen China Beihang University Beijing China Beijing Jiaotong University Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

Learning-based change detection (CD) in water scenarios is a key functionality for unmanned aerial vehicle (UAV). However, computer vision algorithms require large number of labeled datasets. Inspired by parallel intelligence, we propose a systematic framework for data generation. In this work, the framework consists of simulated scene and image generation network. In simulated scene, simulated images with pixel-level annotations are automatically generated. Then, image generation network uses paired images (real and simulated) to generate synthetic images. We use simulated and synthetic images in combination with publicly available real-world images to conduct experiments. The experimental results indicate that: 1) simulated images can be used in change detection research;2) synthetic images effectively improve the performance of supervised change detection model. © 2021 IEEE.

关键词： Change detection

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Multi-object tracking with hard-soft attention network and group-based cost minimization

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NEUROCOMPUTING 2021年 447卷 80-91页

作者： Liu, Yating Li, Xuesong Bai, Tianxiang Wang, Kunfeng Wang, Fei-Yue 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 Key Lab Informat Syst Engn Nanjing 210007 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

Multi-object tracking (MOT) has received constant attention from researchers with the development of deep learning and person re-identification (ReID). However, the occlusion caused tracking failure is still far from solved. In this paper, we propose a Hard-Soft Attention Network (HSAN) to improve the ReID performance and get robust appearance features of different targets. The pose information and attention mechanism are combined to distinguish between challenging targets. Besides, the unary and binary costs are constructed to ensure consistency and long-term tracking, which consider not only the appearance motion affinity of single tracks, but also the interactions between neighboring tracks. For that we cluster the tracks into different groups and choose reliable tracks as anchors to establish the two types of costs. Our HSAN appearance model is evaluated on the Market-1501, DUKE and CUHK03 ReID datasets and the MOT tracking method is conducted on MOTChallenge 15, 16 and 17. The experimental results demonstrate that our method can improve tracking accuracy and reduce fragments. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Multi-object tracking Attention mechanism Unary and binary costs Appearance-motion affinity

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A NSGA-II-Based Calibration Algorithm for Underwater Binocular Vision Measurement System

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2020年第3期69卷 794-803页

作者： Kong, Shihan Fang, Xi Chen, Xingyu Wu, Zhengxing Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Peking Univ Coll Engn BIC ESAT State Key Lab Turbulence & Complex SystDept Mech Beijing 100871 Peoples R China

In this paper, a nondominated sorting genetic algorithm (NSGA)-II-based calibration algorithm for the underwater binocular vision measurement system is proposed. The underwater measurement results will be incorrect with the binocular camera model in air. To deal with this problem, a refractive camera model and an akin triangulation are proposed to establish the nonlinear relationship with housing parameters between the object and its corresponding image plain points. A novel usage of checkerboard based on the relative position relationship of corners is employed to set three optimal goals, i.e., the distance difference, the vertical direction difference, and the parallel direction difference. The process of calibration is regarded as a multiobjective optimization and solved by NSGA-II. Finally, experimental results demonstrate the validity and effectiveness of the proposed calibration algorithm.

关键词： Calibration Cameras Mathematical model Optical refraction Absorption Nonlinear optics Optical distortion Akin triangular multiobjective optimization refractive camera model underwater calibration underwater measurement

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