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Adaptive Critics for Decentralized Stabilization of Constrained-Input Nonlinear Interconnected systems

IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2022年第7期52卷 4187-4199页

作者： Yang, Xiong Zhou, Yingjiang Dong, Na Wei, Qinglai Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

This article considers the decentralized stabilization problem of continuous-time nonlinear systems subject to unmatched interconnections and asymmetric input constraints. Initially, with the nonquadratic value functions being introduced to constrained auxiliary subsystems, the decentralized stabilization problem is converted into an array of nonlinear optimal control problems. It is proved that all solutions of these nonlinear optimal control problems together assure asymptotic stability of the entire system. Then, in the framework of adaptive critics, the critic-only architecture is built to solve the Hamilton-Jacobi-Bellman equations associated with these solutions. The critic-only architecture is implemented via critic neural networks (NNs) with their weight vectors being tuned through an improved gradient descent method. A remarkable feature of the present gradient descent approach is that it simultaneously utilizes previously stored and instantaneous state data, which makes the persistence of excitation conditions relaxed. After that, with Lyapunov's techniques being employed, asymptotic stability of the closed-loop auxiliary subsystems and uniform ultimate boundedness of the critic NNs' weight estimation errors are demonstrated. Finally, simulations of an unmatched interconnected nonlinear plant are provided to validate the present decentralized control method.

关键词： Optimal control Interconnected systems Nonlinear systems Decentralized control Asymptotic stability Stability analysis Artificial neural networks Adaptive critics adaptive dynamic programming (ADP) asymmetric input constraints decentralized control reinforcement learning (RL)

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Exploring the Economic Feasibility of Advanced Air Mobility in the Early Stages

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 4826-4830页

作者： Jingqiu Guo Long Chen Lingxi Li Xiaoxiang Na Ljubo Vlacic Fei-Yue Wang Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University–Purdue University Indianapolis (IUPUI) Indianapolis IN USA Department of Engineering University of Cambridge Cambridge U.K. Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith University Nathan QLD Australia State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Engineering Macau University of Science and Technology Macau China

Advanced Air Mobility (AAM) envisages a sustainable, safe, convenient, and affordable air transport system. In socio-technical transition of AAM, there are a number of trade-offs in ecosystem that need to be studied. Three perspectives on economic feasibility are explored: first, based on history of VTOL services and value of time estimates, we discuss whether AAM can provide customers with competitive mobility services; second, what are the stakeholders’ insights on the deployment of AAM; last, the experience in the development of autonomous driving technology, such as parallel intelligence, can inform future AAM research.

关键词： Active appearance model Costs Economics Transportation Helicopters Aircraft propulsion Public transportation

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Investigate the Neuro Mechanisms of Stereoscopic Visual Fatigue

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第7期26卷 2963-2973页

作者： Yue, Kang Guo, Mei Liu, Yue Hu, Haochen Lu, Kai Chen, Shanshan Wang, Danli Beijing Inst Technol Sch Opt & Photon Beijing Engn Res Ctr Mixed Real & Adv Display Beijing 100081 Peoples R China Beijing Film Acad Adv Innovat Ctr Future Visual Entertainment AICFV Beijing 100088 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Stereoscopic visual fatigue (SVF) due to prolonged immersion in the virtual environment can lead to negative user experience, thus hindering the development of virtual reality (VR) industry. Previous studies have focused on investigating the evaluation indicators associated with SVF, while few studies have been conducted to reveal the underlying neural mechanism, especially in VR applications. In this paper, a modified Go/NoGo paradigm was adopted to induce SVF in VR environment with Go trials for maintaining participants' attention and NoGo trials for investigating the neural effects under SVF. Random dot stereograms (RDSs) with 11 disparities were presented to evoke the depth-related visual evoked potentials (DVEPs) during 64-channel EEG recordings. EEG datasets collected from 15 participants in NoGo trials were selected to conduct individual processing and group analysis, in which the characteristics of the DVEPs components for various fatigue degrees were compared and independent components were clustered to explore the original cortex areas related to SVF. Point-by-point permutation statistics revealed that DVEPs sample points from 230 ms to 280 ms (component P2) in most brain areas changed significantly when SVF increased. Additionally, independent component analysis (ICA) identified that component P2 which originated from posterior cingulate cortex and precuneus, was associated statistically with SVF. We believe that SVF is rather a conscious status concerning the changes of self-awareness or self-location awareness than the performance reduction of retinal image processing. Moreover, we suggest that indicators representing higher conscious state may be a better indicator for SVF evaluation in VR environments.

关键词： Visualization Electroencephalography Fatigue Stereo image processing Shape Task analysis Electrodes Depth perception depth-related visual evoked potential self-awareness stereoscopic visual fatigue virtual reality

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DeFACT in ManuVerse for Parallel Manufacturing: Foundation Models and Parallel Workers in Smart Factories

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第4期53卷 2188-2199页

作者： Yang, Jing Li, Shimeng Wang, Xiaoxing Lu, Jingwei Wu, Huaiyu Wang, Xiao 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 Beijing SANBODY Technol Co Ltd Beijing 214000 Peoples R China Qingdao Acad Intelligent Ind Qingdao 66109 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 266114 Peoples R China Qingdao Acad Intelligent Ind Qingdao 230031 Peoples R China

In cyber-physical-social systems, smart manufacturing has to overcome challenges, such as uncertainty, diversity, complexity in modeling, long-delayed responses to market changes, and human engineer dependency. DeFACT is a framework of parallel manufacturing in ManuVerse where the Decentralized Autonomous Organization-based interactions between parallel workers consisting of robotic, digital, and human workers are elaborated to transform from professional division to real-virtual division. In DeFACT, human workers are only responsible for 5% physical and mental work that is complex and creative, and the robotic and digital workers can take care of the rest. The perceptual and cognitive intelligence of digital workers are intensified by a manufacturing foundation model (MF-PC), where calibration and certification (C & C), and verification and validation (V & V) guarantee not only the accuracy of task models, but also the interpretability and controllability of feature learning. As a case study, the workflow of customized shoes of SANBODY Technology Company is illustrated to show how DeFACT breaks the time and space constraints, avoids production waste caused by aesthetic discrepancies with consumers, and truly realizes flexible manufacturing.

关键词： Artificial systems computational experiments and parallel execution (ACP) cyber-physical-social systems (CPSSs) decentralized autonomous organization (DAO) ManuVerse parallel manufacturing smart manufacturing

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Computational knowledge vision: paradigmatic knowledge based prescriptive learning and reasoning for perception and vision

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第8期55卷 5917-5952页

作者： Zheng, Wenbo Yan, Lan Gou, Chao Wang, Fei-Yue Wuhan Univ Technol Sch Comp & Artificial Intelligence Wuhan Peoples R China 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 Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China

This paper outlines a novel advanced framework that combines structurized knowledge and visual models-Computational Knowledge Vision. In advanced studies of image and visual perception, a visual model's understanding and reasoning ability often determines whether it works well in complex scenarios. This paper presents the state-of-the-art mainstream of vision models for visual perception. This paper then proposes a concept and basic framework of Computational Knowledge Vision that extends the knowledge engineering methodology to the computer vision field. In this paper, we first retrospect prior work related to Computational Knowledge Vision in the light of the connectionist and symbolist streams. We discuss neural network models, meta-learning models, graph models, and Transformer models in detail. We then illustrate a basic framework for Computational Knowledge Vision, whose essential techniques include structurized knowledge, knowledge projection, and conditional feedback. The goal of the framework is to enable visual models to gain the ability of representation, understanding, and reasoning. We also describe in-depth works in Computational Knowledge Vision and its extensions in other fields.

关键词： Computer vision Knowledge engineering Deep learning Graph learning Meta-learning Transformer Artificial intelligence (AI)

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Taxi Demand Prediction Using Parallel Multi-Task Learning Model

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第2期23卷 794-803页

作者： Zhang, Chizhan Zhu, Fenghua Wang, Xiao Sun, Leilei Tang, Haina Lv, Yisheng Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Future Technol Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China China Nucl Power Engn Co Ltd State Key Lab Nucl Power Safety Monitoring Techno Shenzhen 518172 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Beihang Univ State Key Lab Software Dev Environm SKLSDE Beijing 100083 Peoples R China Beihang Univ Big Data Brain Comp Lab BDBC Lab Beijing 100083 Peoples R China

Accurate and real-time taxi demand prediction can help managers pre-allocate taxi resources in cities, which assists drivers quickly finding passengers and reduce passengers' waiting time. Most of the existing studies focus on mining spatial-temporal characteristics of taxi demand distributions, while lacking in modeling the correlations between taxi pick-up demand and the drop-off demand from the perspective of multi-task learning. In this article, we propose a multi-task learning model containing three parallel LSTM layers to co-predict taxi pick-up and drop-off demands, and compare the performance of single demand prediction methodology and that of two demands' co-prediction methodology. Experimental results on real-world datasets demonstrate that the pick-up demand and the drop-off demand do depend on each other, and the effectiveness of the proposed co-prediction methods.

关键词： Public transportation Predictive models Urban areas Task analysis Deep learning Data mining Correlation Taxi demand prediction pick-up drop-off demand multi-task learning LSTM deep learning

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A pixel-level deep segmentation network for automatic defect detection

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EXPERT systems WITH APPLICATIONS 2023年 215卷

作者： Yang, Lei Xu, Shuai Fan, Junfeng Li, En Liu, Yanhong Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Henan Peoples R China Robot Percept & Control Engn Lab Zhengzhou 450001 Henan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst c Beijing 100190 Peoples R China

Defect detection is a very important link for much manufacturing and processing applications which could be used for quality control and precise maintenance decision. However, faced with the weak-texture and low-contrast industrial environment, high-precision defect detection still faces a certain challenge due to diverse and complex of defects. Meanwhile, due to a minimal portion image pixels of defects, the pixel-level defect detection task is always against class-unbalance issue which also will affect the detection performance. Recently, with the strong automatic feature representation ability, deep learning has shown an excellent detection performance on defect identification and location. Nevertheless, it still has some demerits, such as insufficient processing of feature maps, lack of temporal modeling information, etc. To address these issues, on the basis of the encoder-decoder architecture, a pixel-level deep segmentation network is proposed for automatic defect detection to construct an end-to-end defect segmentation model. To realize effective feature representation, a residual attention network is proposed to construct the backbone network, which could also make the segmentation network better emphasize target regions. Meanwhile, to improve the network propagation ability of subtle context features, a bidirectional convolutional long short-term memory (ConvLSTM) block is introduced to optimize the skip connections to learn long-range spatial contexts. Besides, a weighted loss function is proposed for model training to address the class-unbalance issue. Combined with multiple public data sets, through qualitative and quantitative analysis, experimental results demonstrate that the proposed defect segmentation network achieves a better performance compared to other state-of-the-art segmentation methods.

关键词： Defect detection Deep convolutional neural network U-shape network ConvLSTM network

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TrajGen: Generating Realistic and Diverse Trajectories With Reactive and Feasible Agent Behaviors for Autonomous Driving

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第12期23卷 24474-24487页

作者： Zhang, Qichao Gao, Yinfeng Zhang, Yikang Guo, Youtian Ding, Dawei Wang, Yunpeng Sun, Peng Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Coll Artificial Intelligence Beijing 100049 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Baidu Inc Beijing 100085 Peoples R China

Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can be used for validation and verification of self-driving system performance without relying on expensive and time-consuming real-world testing. Existing simulators rely on heuristic-based behavior models for background vehicles, which cannot capture the complex interactive behaviors in real-world scenarios. Meanwhile, existing learning-based methods are typical insufficient, yielding behaviors of traffic participants that frequently collide or drive off the road especially for a long horizon. To address this issue, we propose TrajGen, a two-stage trajectory generation framework, which can capture more realistic and diverse behaviors directly from human demonstration. In particular, TrajGen consists of the multi-modal trajectory prediction stage and the reinforcement learning based trajectory modification stage. In the first stage, we propose a novel auxiliary RouteLoss for the trajectory prediction model to generate multi-modal diverse trajectories in the drivable area. In the second stage, reinforcement learning is used to track the predicted trajectories while avoiding collisions, which can improve the reactivity and feasibility of generated trajectories. In addition, we develop a simulator I-Sim that can provide support for data-driven agent behavior simulation and train reinforcement learning models in parallel based on naturalistic driving data. The vehicle model in I-Sim can guarantee that the generated trajectories by TrajGen satisfy vehicle kinematic constraints. Finally, we give comprehensive metrics to evaluate generated trajectories for simulation scenarios, which shows that TrajGen outperforms either trajectory prediction or inverse reinforcement learning in terms of fidelity, reactivity, feasibility, and diversity.

关键词： Trajectory Behavioral sciences Predictive models Measurement Kinematics Diversity reception Testing Simulation scenarios trajectory prediction reinforcement learning

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A Unified Framework for Multi-Guidewire Endpoint Localization in Fluoroscopy Images

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2022年第4期69卷 1406-1416页

作者： Li, Rui-Qi Xie, Xiao-Liang Zhou, Xiao-Hu Liu, Shi-Qi Ni, Zhen-Liang Zhou, Yan-Jie Bian, Gui-Bin Hou, Zeng-Guang 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 CAS Ctr Excellence Brain Sci & Technol Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn CAS Must Joint Lab Intelligence Sci & Technol Macau 999078 Peoples R China

Objective: In this paper, keypoint Localization Region-based CNN (KL R-CNN) is proposed, which can simultaneously accomplish the guidewire detection and endpoint localization in a unified model. Methods: KL R-CNN modifies Mask R-CNN by replacing the mask branch with a novel keypoint localization branch. Besides, some settings of Mask R-CNN are also modified to generate the keypoint localization results at a higher detail level. At the same time, based on the existing metrics of Average Precision (AP) and Percentage of Correct keypoints (PCK), a new metric named AP(PCK) is proposed to evaluate the overall performance on the multi-guidewire endpoint localization task. Compared with existing metrics, AP(PCK) is easy to use and its results are more intuitive. Results: Compared with existing methods, KL R-CNN has better performance when the threshold is loose, reaching a mean AP(PCK) of 90.65% when the threshold is 9 pixels. Conclusion: KL R-CNN achieves the state-of-the-art performance on the multi-guidewire endpoint localization task and has application potentials. Significance: KL R-CNN can achieve the localization of guidewire endpoints in fluoroscopy images, which is a prerequisite for computer-assisted percutaneous coronary intervention. KL R-CNN can also be extended to other multi-instrument localization tasks.

关键词： Location awareness Surgery Instruments Feature extraction Task analysis Proposals Measurement Guidewire endpoint keypoint localization surgical instrument

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Qingda Granule Attenuates Hypertension-Induced Cardiac Damage via Regulating Renin-Angiotensin System Pathway

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Chinese Journal of Integrative Medicine 2025年第5期31卷 402-411页

作者： LONG Lin-zi TAN Ling XU Feng-qin YANG Wen-wen LI Hong-zheng LIU Jian-gang WANG Ke ZHAO Zhi-ru WANG Yue-qi WANG Chao-ju WEN Yi-chao HUANGMing-yan QU Hua FU Chang-geng CHEN Ke-ji Department of Geriatrics Xiyuan Hospital of China Academy of Chinese Medical SciencesBeijing100091China Department of Traditional Chinese Medicine the Eighth Affiliated Hospital of Sun Yat-Sen UniversityShenzhenGuangdong Province518033China Xiyuan Hospital National Cardiovascular Clinical Medical Research Center of Traditional MedicineBeijing100091China Graduate School of Beijing University of Chinese Medicine Beijing100091China Cardiovascular Department the Second Affiliated HospitalShanxi University of Chinese MedicineTaiyuan030002China Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing100090China Cadre 2 Ward Xinjiang Uygur Autonomous Region Institute of Traditional Chinese MedicineUrumqi830000China Academy of Integrative Medicine Fujian Univrsity of Traditional Chinese MedicineFuzhou350122China

Objective:To assess the efficacy of Qingda Granule(QDG)in ameliorating hypertensioninduced cardiac damage and investigate the underlying mechanisms ***:Twenty spontaneously hypertensive rats(SHRs)were used to develope a hypertension-induced cardiac damage *** 10Wistar Kyoto(WKY)rats were used as normotension *** were administrated intragastrically QDG[0.9 g/(kg·d)]or an equivalent volume of pure water for 8 *** pressure,histopathological changes,cardiac function,levels of oxidative stress and inflammatory response markers were ***,to gain insights into the potential mechanisms underlying the protective effects of QDG against hypertensioninduced cardiac injury,a network pharmacology study was *** results were validated by Western blot,radioimmunoassay immunohistochemistry and quantitative polymerase chain reaction,***:The administration of QDG resulted in a significant decrease in blood pressure levels in SHRs(P<0.01).Histological examinations,including hematoxylin-eosin staining and Masson trichrome staining revealed that QDG effectively attenuated hypertension-induced cardiac ***,echocardiography demonstrated that QDG improved hypertension-associated cardiac ***-linked immunosorbent assay and colorimetric method indicated that QDG significantly reduced oxidative stress and inflammatory response levels in both myocardial tissue and serum(P<0.01).Conclusion:Both network pharmacology and experimental investigations confirmed that QDG exerted its beneficial effects in decreasing hypertensioninduced cardiac damage by regulating the angiotensin converting enzyme(ACE)/angiotensinⅡ(AngⅡ)/AngⅡreceptor type 1 axis and ACE/AngⅡ/AngⅡreceptor type 2 axis.

关键词： blood pressure cardiac damage hypertension Qingda Granule renin-angiotensin system

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