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Sparse online kernelized actor-critic Learning in reproducing kernel Hilbert space

ARTIFICIAL INTELLIGENCE REVIEW 2022年第1期55卷 23-58页

作者： Yang, Yongliang Zhu, Hufei Zhang, Qichao Zhao, Bo Li, Zhenning Wunsch, Donald C. Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Univ Macau State Key Lab Internet Things Smart City Taipa 59193 Macao Peoples R China Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65401 USA

In this paper, we develop a novel non-parametric online actor-critic reinforcement learning (RL) algorithm to solve optimal regulation problems for a class of continuous-time affine nonlinear dynamical systems. To deal with the value function approximation (VFA) with inherent nonlinear and unknown structure, a reproducing kernel Hilbert space (RKHS)-based kernelized method is designed through online sparsification, where the dictionary size is fixed and consists of updated elements. In addition, the linear independence check condition, i.e., an online criteria, is designed to determine whether the online data should be inserted into the dictionary. The RHKS-based kernelized VFA has a variable structure in accordance with the online data collection, which is different from classical parametric VFA methods with a fixed structure. Furthermore, we develop a sparse online kernelized actor-critic learning RL method to learn the unknown optimal value function and the optimal control policy in an adaptive fashion. The convergence of the presented kernelized actor-critic learning method to the optimum is provided. The boundedness of the closed-loop signals during the online learning phase can be guaranteed. Finally, a simulation example is conducted to demonstrate the effectiveness of the presented kernelized actor-critic learning algorithm.

关键词： Reproducing kernel Hilbert space Actor-critic learning Value function approximation Online sparsification Non-parametric learning

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iMAPeM: A New Paradigm for Implementing Intelligent Mining With Humans in the Loop

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2024年第12期54卷 7536-7547页

作者： Ai, Yunfeng Teng, Siyu Yang, Qingyuan Wu, Ye Li, Yuchen Gao, Yu Meng, Feng Yang, Shengli Tian, Bin Chen, Long Wang, Fei-Yue Univ Chinese Acad Sci Artificial Intelligence Dept Beijing 100049 Peoples R China Waytous Inc Qingdao 266109 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Guoneng Baorishile Energy Co Ltd Hulunbuir 021000 Peoples R China CHN Energy Investment Grp Co Ltd Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100045 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

Since the 1990s, the automated process within open-pit mines has been rapidly advanced. However, mineral transportation, as the most costly and dangerous production process, has not witnessed efficient achievements. Adverse weather conditions and complex work environments are two critical bottlenecks that impede the development and deployment of autonomous transportation of open-pit mines. To alleviate this issue, this research proposes a novel paradigm, named IMAPeM, designed to enable safe and efficient autonomous transportation with humans in the loop. IMAPeM includes three categories of miners: 1) biological miners;2) digital miners;and 3) robotic miners, as well as three operational modes: 1) autonomous model (AM);2) parallel model (PM);and 3) expert/emergency model (EM). IMAPeM employs these miners and modes depend on the complexity of the task, optimizing the utilization of digital and robotic miners while reducing the workload for biological miners. In addition, we developed the YUGONG system based on IMAPeM. The empirical implementation demonstrates the exceptional performance of the YUGONG system in autonomous transportation across diverse open-pit mines. This system contributes to the advancement of sustainable mining practices, which also carries profound significance for achieving long-term environmentally responsible mining operations.

关键词： Autonomous transportation intelligent mining parallel driving operating system parallel systems theory

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Adaptively temporal graph convolution model for epidemic prediction of multiple age groups

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Fundamental Research 2022年第2期2卷 311-320页

作者： Yuejiao Wang Dajun Daniel Zeng Qingpeng Zhang Pengfei Zhao Xiaoli Wang Quanyi Wang Yin Luo Zhidong Cao The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina Shenzhen Artificial Intelligence and Data Science Institute(Longhua) ShenzhenChina School of Data Science City University of Hong Kong-Hong Kong SARChina Institute for Infectious Disease and Endemic Disease Control Beijing Center for Disease Prevention and Control BeijingChina

Introduction:Multivariate time series prediction of infectious diseases is significant to public health,and the deep learning method has attracted increasing attention in this research *** and methods:An adaptively temporal graph convolution(ATGCN)model,which leams the contact patterns of multiple age groups in a graph-based approach,was proposed for COVID-19 and influenza *** compared ATGCN with autoregressive models,deep sequence learning models,and experience-based ATGCN models in short-term and long-term prediction ***:Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term(12.5%and 10%improvements on RMSE)and longterm(12.4%and 5%improvements on RMSE)prediction *** the RMSE of ATGCN predictions fluctuated least in different age groups of COVID-19(0.029±0.003)and influenza(0.059±0.008).Compared with the Ones-ATGCN model or the Pre-ATGCN model,the ATGCN model was more robust in performance,with RMSE of 0.0293 and 0.06 on two datasets when horizon is ***:Our research indicates a broad application prospect of deep learning in the field of infectious disease *** characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature ***:The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups,indicating its great potentials for exploring the implicit interactions of multivariate variables.

关键词： Graph convolution model Infectious disease prediction Multiple age group Multivariate time series Public health

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A Learning Method for Feature Correspondence with Outliers 26

A Learning Method for Feature Correspondence with Outliers

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26th International Conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Yang, Xu Zeng, Shao-Feng Han, Yu Lu, Yu-Chen Liu, Zhi-Yong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Feature correspondence is an important topic in many computer vision or robot vision tasks. Different from traditional optimization based matching method, in the last two years, researchers are finally able to solve the matching process in a learning manner. As a representative method, SuperGlue achieves superior performance in many real-world tasks, but it still has problems in dealing with outlier features. Targeting at the outlier problem, this paper improves SuperGlue by introducing a deep learning based feature correspondence method, which consists of the pruned attentional graph neural network and the improved matching layer for the outlier problem. Experiments on real world images validate the effectiveness of the proposed method.

关键词： Learning systems Deep learning Computer vision Robot vision systems Graph neural networks Pattern recognition Task analysis

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A Time-Series Augmentation Method Based on Empirical Mode Decomposition and Integrated LSTM Neural Network 44

A Time-Series Augmentation Method Based on Empirical Mode De...

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44th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Li, Chenguang Yang, Hongjun Cheng, Long Huang, Fubiao 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

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Adequate patients' data have always been critical for disease assessment. However, large amounts of patient data are often difficult to collect, especially when patients are required to complete a series of assessment movements. For example, assessing the hand motor function of stroke patients or Parkinson's patients requires patients to complete a series of evaluation movements, and it is often difficult for patients to complete each group of actions multiple times, resulting in a small amount of data. To solve the problem of insufficient data quantity, this study proposes a data augmentation method based on empirical mode decomposition and integrated long short-term memory neural network (EMD-ILSTM). The method mainly consists of two parts: one is to decompose the raw signal by the method of EMD, and the other is to use LSTM for data augmentation of the decomposed signal. Then, the method is tested on the public dataset named Ninaweb, and the test results show that the classification accuracy can be improved by 5.2% by using the augmented data for classification tasks. Finally, clinical trials are conducted to verify that after dimensionality reduction, the augmented data and raw data have smaller intra-class distances and larger inter-class distances, indicating that data augmentation is effective.

关键词： Dimensionality reduction Empirical mode decomposition Stroke (medical condition) Clinical trials Biology Task analysis Biological neural networks

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Communication-Efficient Federated Edge Learning for NR-U-Based IIoT Networks

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IEEE INTERNET OF THINGS JOURNAL 2021年第14期9卷 12450-12459页

作者： Chen, Qimei Xu, Xiaoxia You, Zehua Jiang, Hao Zhang, Jun Wang, Fei-Yue Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

As a key infrastructural technology, Industrial Internet of Things (IIoT) and its related techniques have emerged in the age of Industrial Internet. Among them, an increasing popular and attractive federated edge learning (FEL) mechanism, which performs data analysis and inference at the edge devices distributedly, and aggregates local FEL units at a centralized controller, is introduced to meet the stringent data privacy and low-latency requirements for high-stake IIoT devices. Due to the bandwidth limitation, only parts of the IIoT devices can be selected to transmit their local FEL models to the centralized controller at each learning step. However, the centralized controller prefers to collect all the local FEL models to generate the global FL model since each IIoT device has a differential data set. Existing works mainly focus on selecting an appropriate subset of IIoT devices through advanced scheduling mechanisms without extending the resource bandwidth. However, the new radio in unlicensed spectrum (NR-U) technology in the 5G network opens up new possibilities for FEL since it is a privately owned network with fruitful bandwidth resources. We thus propose a novel communication-efficient FEL mechanism for NR-U-based IIoT networks, which aims to select data importance IIoT devices for local training under relatively sufficient unlicensed resources. The objective function is formulated as a tradeoff between total FEL data importance and the transmission latency via joint learning, device selection, and resource management scheduling, which is a mixed-integer nonlinear programming (MINLP). To deal with this problem, an alternating direction method of multipliers and block coordinate update (ADMM-BCU) algorithm with low computational complexity has been used. Closed-form expressions for both optimal device selection and resource management are derived, which highlighted significant insights. Numerical results demonstrate the algorithmic advantages and structural

关键词： Industrial Internet of Things Wireless fidelity Servers Resource management Data models Wireless communication Data privacy Communication efficiency federated edge learning (FEL) Industrial Internet of Things (IIoT) new radio in unlicensed spectrum (NR-U) resource management user selection

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Parallel System-Based Predictive control for Traffic Signals in Large-Scale Road Networks 26

Parallel System-Based Predictive Control for Traffic Signals...

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IEEE 26th International Conference on Intelligent Transportation systems (ITSC)

作者： Dai, Xingyuan Zhang, Yan Tang, Yiqing Chen, Hongrui Sun, Jiaming Cong, Fei Lu, Yanan Lv, Yisheng Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China China Telecom Digital City Technol Co Ltd Xiongan 071700 Peoples R China China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China

ISBN: (纸本)9798350399462

This paper proposes a parallel system-based predictive control (PPC) method to address the problem of active traffic signal control in large-scale urban road networks. The method leverages simulated artificial transportation systems to infer the short-term future operating states of the real transportation system. During the inference process, an efficient predictive learning-based multi-agent reinforcement learning (RL) algorithm is employed to optimize the cooperative control policies. The optimized policies are then deployed to the real transportation system at fixed intervals to adapt to the real-time and dynamic traffic flow. Experimental results demonstrate that PPC outperforms traditional traffic control methods and some multi-agent RL benchmarks in large-scale road network control scenarios with nearly two hundred intersections, showcasing superior generalization capabilities.

关键词： Roads and streets

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Two-Stage Co-Optimization for Utility-Social systems With Social-Aware P2P Trading

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第4期10卷 1875-1887页

作者： Zhao, Pengfei Li, Shuangqi Hu, Paul Jen-Hwa Cao, Zhidong Gu, Chenghong Yan, Xiaohe Huo, Da Hernando-Gil, Ignacio Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100864 Peoples R China Univ Bath Dept Elect & Elect Engn Bath BA2 7AY Avon England Beijing Inst Technol Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China Univ Utah David Eccles Sch Business Salt Lake City UT 84112 USA North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Cranfield Univ Sch Water Energy & Environm Bedford MK43 0AL England Univ Bordeaux ESTIA Inst Technol F-64210 Bidart France

Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model's effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water-energy-social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.

关键词： Water heating Optimization Costs Cogeneration Water resources Resistance heating Peer-to-peer computing Distributionally robust optimization (DRO) peer-to-peer (P2P) trading smart city social-aware management water-energy-social nexus

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Game-theoretical explorations of the mesoscale flow structure and regime transitions in bubble columns

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Particuology 2020年第1期18卷 100-108页

作者： Shuo Yuan Yifen Mu Xiaoping Guan Zhixin Liu Jianhua Chen Ning Yang Lin Zhang Key Lab of Systems and Control Institute of Systems SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China

Understanding the mesoscale structure and regime transition in bubble columns is of great significance for reactor design and *** on the energy-minimization multiscale(EMMS)model,a noncooperative game model with constraints is proposed to investigate the structural properties of gas-liquid systems in which small and large bubbles are chosen as players and the energy consumption form the objective *** conservation equations of the system can be regarded as the constraints of the *** the formulated noncooperative game model,the concept of the generalized Nash equilibrium(GNE)is used to characterize the *** algorithm is developed to numerically compute the GNE and some important structural parameters in the *** numerical results show the existence of the GNE for all values of the superficial gas velocity *** Ug varies,the trends in the state variables can be observed and the critical point of Ug *** overall trend of the flow regime transition agrees with the original EMMS model and experimental results,although the GNE calculation also reveals different single-bubble dominant mechanisms with increasing Ug.

关键词： Bubble column Mesoscale Regime transition Game theory Generalized Nash equilibrium EMMS model

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Development, Calibration, and Image Processing of Underwater Structured Light Vision System: A Survey

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷

作者： Fan, Junfeng Wang, Xuan Zhou, Chao Ou, Yaming Jing, Fengshui Hou, Zengguang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China China Univ Min & Technol Sch Mech Elect & Informat Engn Dept Mech Engn Beijing 100083 Peoples R China

The underwater structured light vision system has the characteristics of a large amount of information, high measurement accuracy, and good robustness, which has attracted the attention of researchers and is widely used in the underwater 3-D measurement. In this article, the state-of-the-art techniques of the underwater structured light vision system are comprehensively summarized, which provides valuable guidance for relevant researchers. First, underwater structured light vision systems are classified and introduced, and their advantages and disadvantages are analyzed. Then, the calibration methods of the underwater structured light vision system are introduced, including underwater camera calibration, underwater laser calibration, and underwater projector calibration. Besides, the image processing methods of the underwater structured light vision system are analyzed, including laser structured light image processing and coded structured light image processing. Moreover, the applications of the underwater structured light vision system in underwater archeology, seabed mapping, and underwater structure inspection are introduced. On this basis, the research directions of the underwater structured light vision system are prospected.

关键词： Machine vision Measurement by laser beam Three-dimensional displays Cameras Sea measurements Calibration Image processing Image processing system calibration system development underwater structured light vision system

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