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第42届中国控制会议

作者： Yaojun Liu Xingwen Liu Nyima Tashi Lab.of Complex Systems & Intelligent Control Key Lab.of Electronic Information of State Ethnic Affairs CommissionCollege of Electrical EngineeringSouthwest Minzu University Engineering Research Center for Tibetan Information Processing School of Information Science and TechnologyTibet University

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural *** memory can greatly influence the emergence of cooperative behavior,memory-based research on evolutionary games has proliferated in recent *** the present paper,inspired by the well-known "Stanford marshmallow experiment"(SME),we proposed a multi-memory mechanism in Snowdrift game(SDG) on spatial lattice networks whose core lies in what follows:Players are divided into two groups according to individual self-control;different memory lengths are set by group and by time in a single *** simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary *** specifically,as memory length increases to an intermediate length,the cooperation level increases as *** mechanism with short memory length,the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative *** findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation.

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Online Progressive Instance-Balanced Sampling for Weakly Supervised Vibration Damper Detection

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Chen, Minghao Tian, Yunong Li, Zhishuo Li, En Liang, Zize Inst Automat Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Academyof Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Engn Lab Ind Vis & Intelligent Equipment Technol Beijing 100190 Peoples R China

The detection of vibration dampers is important for power systems. Deep learning-based damper detection needs massive annotations, which are labor-intensive and time-consuming. Therefore, weakly supervised object detection (WSOD) is considered. Part domination is the main problem in weakly supervised vibration damper detection. Current WSOD methods neglect that overwhelming negative instances exist in each image during the training phase, which would mislead the training and make detection results stuck in the most discriminative parts of objects. To tackle this problem, an online progressive instance-balanced sampling (OPIS) algorithm based on hard sampling and soft sampling is proposed in this article. The algorithm includes two modules: a progressive instance balance (PIB) module and a progressive instance reweighting (PIR) module. The PIB module, combining random sampling and intersection over union (IoU)-balanced sampling, progressively mines hard negative instances while balancing positive instances and negative instances. The PIR module further utilizes classifier scores and IoUs of adjacent refinements to reweight the weights of positive instances to make the network focus on positive instances. Extensive experimental results on the vibration damper, pattern analysis, statistical modelling and computational learning visual object classes (PASCAL VOC) 2007, and PASCAL VOC 2012 datasets demonstrate that the proposed method can significantly improve the baseline, which is also comparable to many existing methods. In addition, compared with the baseline, the proposed method requires no extra network parameters, and the supplementary training overheads are small.

关键词： Shock absorbers Vibrations Object detection Proposals Training Sampling methods Convolutional neural networks Instance balance multiple instance learning (MIL) progressive sampling vibration damper detection weakly supervised object detection (WSOD)

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Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for intelligent Vehicles

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IEEE TRANSACTIONS ON intelligent VEHICLES 2022年第3期7卷 417-440页

作者： Hu, Zhongxu Lou, Shanhe Xing, Yang Wang, Xiao Cao, Dongpu Lv, Chen Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 637460 Singapore Cranfield Univ Ctr Autonomous & Cyber Phys Syst Cranfield MK43 0AL Beds England Chinese Acad Sci Inst Automat Qingdao Acad Intelligent Ind Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China

Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system

关键词： Vehicles Digital twins Data models Behavioral sciences Safety Autonomous vehicles Reliability Driver digital twin human-centric deisgn intelligent vehicles human-machine interactions cyber-physical systems

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A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

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Plant Diseases and Pests 2024年第2期15卷 9-16页

作者： Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning *** demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model ***,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial *** issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data *** address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

关键词： Rice disease and pest identification Convolutional neural networks Distributed training Federated learning(FL) Open-source data sharing platform

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Measurement Invariance Investigation for Performance of Deep Learning Architectures

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IEEE ACCESS 2022年 10卷 78070-78087页

作者： Chen, Dewang Lu, Yuqi Hsu, Chih-Yu Fujian Univ Technol Sch Transportat Fuzhou 350118 Fujian Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Fujian Univ Technol Intelligent Transportat Syst Res Ctr Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou 350118 Fujian Peoples R China

Models can be compared against each other. Such comparison helps to find strengths and weaknesses of different approaches and gives additional possibilities for model validation. Multi-class classification is a classification task where each image is assigned to one and only one label. Confusion matrix, Precision, Recall, and F1 Score are popular performance metrics. The common sense is that the performance of any architecture is dependent on the sizes of data set. The goodness of the architecture of deep learning models for different data sets is critical issue. This paper implements Pearson correlation coefficient and the multivariate linear regression method to assess the accuracy of deep learning architectures with five performance indicators. The five performance indicators are training loss rate, robustness, training time, number of model parameters and computation complexity. There are five image datasets used to test four deep learning models: Alexnet, GoogLeNet, ResNet, MobileNet to obtain the values of accuracy and other five indicators. The most important contribution of the article is to show that the accuracy indicator related to training loss rate and training time indicators are not dependent on the selection of the data group. According to the definition of Measurement Invariance (MI), the measurement invariance is demonstrated by the linear regression analysis and inner product of the unit normal vector of the linear regression planes.

关键词： Convolutional neural networks Training Licenses Predictive models Diseases Deep learning Neural networks Deep learning model multivariate linear regression Pearson correlation coefficient measurement invariance performance indicators

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Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2023年 80卷

作者： Li, Wenhao Zhang, Haiou Wang, Guilan Xiong, Gang Zhao, Meihua Li, Guokuan Li, Runsheng Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Mat Sci & Engn Wuhan 430074 Hubei Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent M Cloud Comp Ctr Donggguan 523808 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 Wuhan Natl Lab Optoelect Wuhan 430074 Hubei Peoples R China

Wire and arc additive manufacturing (WAAM) is an emerging manufacturing technology that is widely used in different manufacturing industries. To achieve fully automated production, WAAM requires a dependable, efficient, and automatic defect detection system. Although machine learning is dominant in the object detection domain, classic algorithms have defect detection difficulty in WAAM due to complex defect types and noisy detection environments. This paper presents a deep learning-based novel automatic defect detection solution, you only look once (YOLO)-attention, based on YOLOv4, which achieves both fast and accurate defect detection for WAAM. YOLO-attention makes improvements on three existing object detection models: the channel-wise attention mechanism, multiple spatial pyramid pooling, and exponential moving average. The evaluation on the WAAM defect dataset shows that our model obtains a 94.5 mean average precision (mAP) with at least 42 frames per second. This method has been applied to additive manufacturing of single-pass, multi-pass deposition and parts. It demonstrates its feasibility in practical industrial applications and has potential as a vision-based methodology that can be implemented in real-time defect detection systems.

关键词： Wire and arc additive manufacturing Defect detection Online Deep learning

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A Parallel Flexible Manufacturing System: Smart Manufacturing in the Age of Smart Industry

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IEEE systems MAN AND CYBERNETICS MAGAZINE 2023年第4期9卷 69-76页

作者： Hu, Bin Zhang, XiaoDong Liu, XinJie Ding, TianJu Dong, XiSong Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Yantai Inst Technol Sch Informat Engn Dept Software Engn Yantai 264025 Peoples R China Yantai Univ Engn Yantai 264005 Peoples R China Yantai Inst Technol Yantai 264025 Peoples R China IEEE Piscataway NJ USA

This article proposes a parallel flexible manufacturing system (FMS), which is divided into three subparallel systems, including a parallel manufacturing system, a parallel inspection system, and a parallel loading-unloading system. By building a chess parallel manufacturing production line, the lightweight practice of a parallel FMS is completed. Based on the parallel system ACP idea, the parallel FMS can realize the agility advantage of flexible manufacturing to quickly respond to diverse needs;realize the ability to quickly correct defective products;and predict and optimize the production process through the analysis and modeling of production data, reducing the product quality defect rate. This article also proposes the concept of parallel flexible manufacturing triplets, adding a microphysical world to the artificial world and the physical world, highlighting the rapid iteration characteristics of flexible manufacturing.

关键词： Industries Frequency modulation Production Predictive models Inspection Product design Quality assessment

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Manifestations of 9754 gout patients in a Chinese clinical center: A 10-year observational study

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Revue du Rhumatisme (Edition Francaise) 2022年第1期89卷 65-71页

作者： Gao, Qianhui Cheng, Xiaoyu Merriman, Tony R. Wang, Can Cui, Lingling Zhang, Hui Sun, Wenyan Wang, Jing Wang, Feiyue Li, Changgui Lu, Jie Shandong Provincial Key Laboratory of Metabolic Diseases and Qingdao Key Laboratory of Gout Department of Endocrinology and Metabolism the Affiliated Hospital of Qingdao University Qingdao 266003 China Institute of Metabolic Diseases Qingdao University Qingdao 266003 China Department of Biochemistry University of Otago Dunedin New Zealand Division of Clinical Immunology and Rheumatology University of Alabama Birmingham Alabama United States Institute of Smart Healthcare Systems Qingdao Academy of Intelligent Industries Qingdao 266109 China The State Key Laboratory for Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Objectif: La prévalence de la goutte augmente, mais les études analysant ses caractéristiques cliniques dans de grands échantillons sont insuffisantes. Cette étude s'attache à clarifier les changements dans les manifestations cliniques de la goutte en Chine au cours des dix dernières années et à analyser les caractéristiques cliniques et les facteurs de risque de la goutte précoce. Méthodes: Les manifestations cliniques de la goutte ont été comparées chez 9754 patients reçus pour la première fois dans notre établissement entre 2008 et 2012 (premier groupe) ou entre 2013 et 2018 (dernier groupe). La goutte apparue ≤ 30 ans a été définie comme goutte précoce et > 30 ans, comme goutte tardive. Les caractéristiques cliniques et les facteurs de risque ont été comparés entre les groupes. Résultats: La goutte est apparue 4,14 ans plus tôt dans le dernier groupe (2013-2018, 5979 patients) et le pourcentage de goutte précoce (3827 patients) y était plus élevé. La durée de la maladie était significativement plus courte (5,72 ± 0,09 contre 6,01 ± 0,11, p < 0,05) et la proportion de patients présentant des tophi était plus faible dans le dernier groupe (20,0% contre 22,0%, p<0,05). Le dernier groupe comptait davantage de patients obèses (37,7% contre 32,6%, p < 0,001) et l'uricémie y était significativement plus élevée (485,5 ± 1,5 Μmol/l contre 465,0 ± 1,9 Μmol/l). Comme la goutte survenait à un âge plus jeune, nous avons appliqué une régression logistique multivariée et déterminé que des antécédents familiaux positifs, le sexe masculin, l'obésité, une uricémie élevée et la consommation de sodas sucrés étaient des facteurs prédictifs indépendants de goutte précoce. Conclusion: Les manifestations de la goutte en Chine ont évolué au cours des dix dernières années, avec notamment une tendance à une apparition plus précoce. © 2021

关键词： Apparition précoce Drop Early onset Facteurs de risque Goutte Risk factors

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Relation-Aware Facial Expression Recognition

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2022年第3期14卷 1143-1154页

作者： Xia, Yifan Yu, Hui Wang, Xiao Jian, Muwei Wang, Fei-Yue Univ Portsmouth Sch Creat Technol Portsmouth PO1 2DJ Hants England Qingdao Acad Intelligent Ind Parallel Intelligence Innovat Ctr Qingdao 266109 Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Research on facial expression recognition has been moving from the constrained lab scenarios to the in-the-wild situations and has made progress in recent years. However, it is still very challenging to deal with facial expression in the wild due to large poses and occlusion as well as illumination and intensity variations. Generally, existing methods mainly take the whole face as a uniform source of features for facial expression analysis. Actually, physiology and psychology research shows that some crucial regions, such as the eye and mouth, reflect the differences of different facial expressions, which have close relationships with emotion expression. Inspired by this observation, a novel relation-aware facial expression recognition method called relation convolutional neural network (ReCNN) is proposed in this article, which can adaptively capture the relationship between crucial regions and facial expressions leading to the focus on the most discriminative regions for recognition. We have evaluated the proposed ReCNN on two large in-the-wild databases: 1) AffectNet and 2) RAF-DB. Extensive experiments on these databases show that our method has superior recognition accuracy compared with state-of-the-art methods and the relationship between crucial regions and facial expressions is beneficial to improve the performance of facial expression recognition.

关键词： Face recognition Feature extraction Mouth Image recognition Deep learning Databases Convolutional neural networks Deep convolutional neural networks facial expression in the wild facial expression recognition relation convolutional neural network (ReCNN) relation-aware

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Data-Driven control of Distributed Event-Triggered Network systems

arXiv

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arXiv 2022年

作者： Wang, Xin Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart70550 Germany Department of Control Science and Engineering Tongji University Shanghai201804 China State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a.k.a. network systems). To this end, we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling, under which a model-based stability criterion for the closed-loop network system is derived, by leveraging a discrete-time looped-functional approach. Marrying the model-based criterion with a data-driven system representation recently developed in the literature, a purely data-driven stability criterion expressed in the form of linear matrix inequalities (LMIs) is established. Meanwhile, the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data. Finally, numerical results corroborate the efficacy of the proposed distributed data-driven ETS in cutting off data transmissions and the co-design procedure. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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