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Evaluation and Spatial-Temporal Difference Analysis of Urban Water Resource Utilization Efficiency Based on Two-Stage DEA Model

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2022年第5期9卷 1282-1296页

作者： Xie, Qiwei Ma, Hewen Zheng, Xiaolong Wang, Xiao Wang, Fei-Yue Beijing Univ Technol Res Base Beijing Modern Mfg Dev Beijing 100124 Peoples R China Hubei Univ Fac Math & Stat Wuhan 430062 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 100190 Peoples R China

In the present study, a two-stage data envelopment analysis (DEA) model and spatial econometric method were employed to evaluate and analyze the utilization efficiency of urban water resources and spatial-temporal differences in cities of China. The traditional DEA model was enhanced by adopting the Shannon entropy in the first stage. After selecting variables based on the previous step and the Bayes information criterion (BIC), redundant variables were removed. In the meanwhile, a comprehensive efficiency score (CES) was generated to rank the efficiency. Finally, spatial econometric analysis was applied to explore the spatial-temporal differences of urban water resource utilization efficiency. Results demonstrate that: 1) according to the calculations and analysis, communities should concentrate on increasing investment in equipment and technology that can help enhance water consumption efficiency, while overlooking some minor aspects, such as per capita gross domestic product (PCGDP);2) most cities have poor water resource utilization efficiency (low CES). However, both input and output have much room for the improvement;3) Lhasa, Beijing, Haikou, and Shanghai have high CES, indicating that the utilization efficiency of water resources is not entirely dependent on economic development;and 4) through performing the Lagrange multiplier (LM) test, the spatial error model (SEM) test is passed at the significant level of 5%. Moreover, the water resource utilization efficiency of a city may be enhanced with the economic development in neighboring cities.

关键词： Water resources Biological system modeling Urban areas Economics Econometrics Analytical models Water pollution Bayes information criterion (BIC) cities data envelopment analysis (DEA) Shannon entropy spatial econometric utilization efficiency of urban water resources

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LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGVs and USVs Based on Foundation Models and Scenarios Engineering

LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGV...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yonglin Tian Fei Lin Xinyuan Zhang Jingwei Ge Yutong Wang Xingyuan Dai Yisheng Lv Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems and State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China University of Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

Intelligent logistics with autonomous vehicles is promising to improve the efficiency of rapidly expanding delivery systems. However, complex environments, diverse packages, and irregular human interaction have imposed challenges to the application of unmanned delivery vehicles. Combining various forms of vehicles and developing knowledge-driven delivery services have been the key to further facilitating the upgrading of logistics systems. In this paper, we propose an integrated framework named LogisticsVISTA (Logistics on Vehicles with Intelligent systems for Transport Automation) comprised of UAVs (Unmanned Aerial Vehicles), UGVs (Unmanned Ground Vehicles), and USVs (Unmanned Surface Vehicles) to provide timely and flexible 3D delivery services. In LogisticsVISTA, we design Large language models (LLMs) and large multimodal models (LMMs) for task planning, active perception, autonomous decision, and human-machine interaction. Besides, virtual-real integrated parallel scenarios are constructed to provide the playing ground for embodied agents efficiently and flexibly.

关键词： Human computer interaction Training Three-dimensional displays Foundation models Transportation Transforms Real-time systems Resource management Logistics Testing

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An Edge Computing Architecture Based on Unikernel

An Edge Computing Architecture Based on Unikernel

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2022 Australian and New Zealand control Conference

作者： Chen, Shichao Xu, Ruijie Sun, Wenqiao Chinese Acad Sci State Key Lab Management & Control Complex Syst I Ctr Natl Railway Intelligent Transportat Syst Eng China Acad Railway Sci Corp Ltd Beijing Peoples R China Chinese Acad Sci Beijing Univ Chem Technol State Key Lab Management & Control Complex Syst I Coll Informat Sci & Technol Beijing Peoples R China China Acad Railway Sci Corp Ltd Transportat & Econ Res Inst Beijing Peoples R China China Acad Railway Sci Corp Ltd Ctr Natl Railway Intelligent Transportat Syst Eng Beijing Peoples R China

ISBN: (数字)9781665498876

ISBN: (纸本)9781665498876

In the era of big data, more and more applications of smart devices are computing-intensive, thus raising the strong demand for task offloading to cloud data centers. However, it gives rise to network delay and privacy data leak issues. Edge computing can effectively solve latency, bandwidth occupation and data privacy problems, but the deployment of applications are also limited by hardware architectures and resources, i.e., computing and storage resources. Therefore, the combination of virtualization technology and edge computing become important in order to realize the rapid deployment of intelligent application in an edge server or an edge node by virtualization technology. The traditional virtual machine (VM) is no longer suitable for resource-constrained devices. Container technique including Docker can effectively solve these problems, but it also depends on an operating system. Unikernel is the state-of-art virtualization technology. In this paper, we combine Unikernel with edge computing to explore its application in an edge computing system. An application architecture of edge computing based on Unikernel is proposed. It is suitable for application in edge computing.

关键词： unikernel docker virtualization technology edge computing

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Singular perturbation-based saturated adaptive control for underactuated Euler-Lagrange systems

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ISA TRANSACTIONS 2022年 119卷 74-80页

作者： Sun, Tairen Zhang, Xuexin Yang, Hongjun Pan, Yongping Jiangsu Univ Sch Elect Informat & Engn Zhenjiang 212013 Jiangsu Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

This paper proposes a saturated smooth adaptive controller for regulating a certain type of underactuated Euler-Lagrange systems (UELSs) with modeling uncertainties and control saturations based on a singular perturbation approach. Compared with relevent literature, the advantages of the proposed controller include: (1) it renders the UELS semiglobally asymptotically track the desired position without the violation of control input constraints;(2) high-order derivatives of positions are not required in its implementation. The Hoppensteadt's Theorem is employed to show that the proposed saturated controller renders the UELS semiglobally asymptotically stable about the desired set point with the satisfaction of control input constraints. The control effectiveness is validated by simulations on a two-link compliant robot arm. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Underactuated system Euler-Lagrange system Compliant robot Nonlinear control Adaptive control control saturation Output regulation

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Mixture Correntropy-Based Kernel Extreme Learning Machines

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2022年第2期33卷 811-825页

作者： Zheng, Yunfei Chen, Badong Wang, Shiyuan Wang, Weiqun Qin, Wei Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Xidian Univ Sch Life Sci & Technol Xian 710071 Peoples R China

Kernel-based extreme learning machine (KELM), as a natural extension of ELM to kernel learning, has achieved outstanding performance in addressing various regression and classification problems. Compared with the basic ELM, KELM has a better generalization ability owing to no needs of the number of hidden nodes given beforehand and random projection mechanism. Since KELM is derived under the minimum mean square error (MMSE) criterion for the Gaussian assumption of noise, its performance may deteriorate under the non-Gaussian cases, seriously. To improve the robustness of KELM, this article proposes a mixture correntropy-based KELM (MC-KELM), which adopts the recently proposed maximum mixture correntropy criterion as the optimization criterion, instead of using the MMSE criterion. In addition, an online sequential version of MC-KELM (MCOS-KELM) is developed to deal with the case that the data arrive sequentially (one-by-one or chunk-by-chunk). Experimental results on regression and classification data sets are reported to validate the performance superiorities of the new methods.

关键词： Kernel Optimization Learning systems Robustness Support vector machines Mean square error methods Extreme learning machine (ELM) kernel method mixture correntropy online learning

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Constrained Tensor Representation Learning for Multi-View Semi-Supervised Subspace Clustering

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IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 3920-3933页

作者： Tang, Yongqiang Xie, Yuan Zhang, Chenyang Zhang, Wensheng Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 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 101408 Peoples R China

Multi-view subspace clustering is an effective method to partition data into their corresponding categories. Nevertheless, existing multi-view subspace clustering approaches generally operate in a purely unsupervised manner, while ignoring the valuable weakly supervised information that can be readily obtained in many practical applications. In this paper, we consider the weakly supervised form of sample pair constraints, and devote to promoting the performance of multi-view subspace clustering with the aid of such prior knowledge. To achieve this goal, inspired by the intrinsic block diagonal structure of ideal low-rank representation (LRR), we propose a novel regularization to integrate must-link, cannot-link and normalization constraints into a unified formulation. The proposed regularization can be regarded as a general description for sample pairwise constraints, and thus provides a flexible framework for multi-view semi-supervised subspace clustering task. Furthermore, we devise a contrained tensor representation learning (CTRL) model that takes advantage of our proposed regularization to facilitate the learning of the desired representation tensor. An efficient optimization algorithm based on alternating direction minimization strategy is carefully designed to solve the proposed CTRL model. Extensive experiments on eight challenging real-world datasets are conducted, and the results validate the effectiveness of our designed pairwise constraints regularization, as well as the superiority of the proposed CTRL model.

关键词： Representation learning Tensors Correlation Clustering algorithms Minimization Task analysis Optimization Multi-view learning pairwise constraint semi-supervised clustering subspace learning tensor singular value decomposition (t-SVD)

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Bridging the Micro and Macro: Calibration of Agent-Based Model Using Mean-Field Dynamics

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第11期52卷 11397-11406页

作者： Ye, Peijun Chen, Yuanyuan Zhu, Fenghua Lv, Yisheng Lu, Wanze Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Univ Technol Sch Artificial Intelligence & Automat Beijing 100124 Peoples R China Qingdao Acad Intelligent Ind Parallel Intelligence Res Ctr Qingdao 266109 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

Calibration of agent-based models (ABM) is an essential stage when they are applied to reproduce the actual behaviors of distributed systems. Unlike traditional methods that suffer from the repeated trial and error and slow convergence of iteration, this article proposes a new ABM calibration approach by establishing a link between agent microbehavioral parameters and systemic macro-observations. With the assumption that the agent behavior can be formulated as a high-order Markovian process, the new approach starts with a search for an optimal transfer probability through a macrostate transfer equation. Then, each agent's microparameter values are computed using mean-field approximation, where his complex dependencies with others are approximated by an expected aggregate state. To compress the agent state space, principal component analysis is also introduced to avoid high dimensions of the macrostate transfer equation. The proposed method is validated in two scenarios: 1) population evolution and 2) urban travel demand analysis. Experimental results demonstrate that compared with the machine-learning surrogate and evolutionary optimization, our method can achieve higher accuracies with much lower computational complexities.

关键词： Calibration Computational modeling Mathematical model Machine learning Aggregates Optimization Bayes methods Agent-based model (ABM) calibration Markovian process

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Extended state Observer-Based controller With Model Predictive Governor for 3-D Trajectory Tracking of Underactuated Underwater Vehicles

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第9期17卷 6114-6124页

作者： Kong, Shihan Sun, Jinlin Qiu, Changlin 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 Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Peking Univ State Key Lab Turbulence & Complex Syst Dept Adv Mfg & Robot BIC ESATColl Engn Beijing 100871 Peoples R China

In this article, an extended state observer (ESO)-based controller with a model predictive governor is designed for 3-D trajectory tracking of underactuated underwater vehicles. The proposed control scheme takes three primary challenges including underactuated property, velocity constraint, and lumped disturbance into consideration. With respect to the model predictive governor, an underactuated kinematic tracking error model is utilized to produce reference velocities. Meanwhile, a heading angle compensation mechanism is utilized to avoid the steady tracking errors resulting from dynamics coupling of the vehicle. Besides, an ESO is designed to estimate the lumped disturbances and unmeasured velocity states. Based on the ESO, a kinetic controller is offered to accomplish the precise velocity tracking only in virtue of the position and orientation information. Note that this article details both the design process of the control scheme and rigorous theoretical analysis. Eventually, simulation and experimental results demonstrate the feasibility and superiority of the proposed method. Notably, this work lays the foundation for the underactuated trajectory tracking control in complicated and turbulent underwater environments.

关键词： Underwater vehicles Trajectory tracking Predictive models Three-dimensional displays Observers Solid modeling Kinetic theory 3-D trajectory tracking extended state observer (ESO) model predictive control (MPC) robust control underactuated underwater vehicle

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Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and Videos

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2021年第2期31卷 594-607页

作者： Chen, Xingyu Yu, Junzhi Kong, Shihan Wu, Zhengxing Wen, Li 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 Peking Univ State Key Lab Turbulence & Complex Syst Dept Mech & Engn Sci BIC ESATColl Engn Beijing 100871 Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China

Object detection has been vigorously investigated for years but fast accurate detection for real-world scenes remains a very challenging problem. Overcoming drawbacks of single-stage detectors, we take aim at precisely detecting objects for static and temporal scenes in real time. Firstly, as a dual refinement mechanism, a novel anchor-offset detection is designed, which includes an anchor refinement, a feature location refinement, and a deformable detection head. This new detection mode is able to simultaneously perform two-step regression and capture accurate object features. Based on the anchor-offset detection, a dual refinement network (DRNet) is developed for high-performance static detection, where a multi-deformable head is further designed to leverage contextual information for describing objects. As for temporal detection in videos, temporal refinement networks (TRNet) and temporal dual refinement networks (TDRNet) are developed by propagating the refinement information across time. We also propose a soft refinement strategy to temporally match object motion with the previous refinement. Our proposed methods are evaluated on PASCAL VOC, COCO, and ImageNet VID datasets. Extensive comparisons on static and temporal detection verify the superiority of DRNet, TRNet, and TDRNet. Consequently, our developed approaches run in a fairly fast speed, and in the meantime achieve a significantly enhanced detection accuracy, i.e., 84.4% mAP on VOC 2007, 83.6% mAP on VOC 2012, 69.4% mAP on VID 2017, and 42.4% AP on COCO. Ultimately, producing encouraging results, our methods are applied to online underwater object detection and grasping with an autonomous system. Codes are publicly available at https://***/SeanChenxy/TDRN.

关键词： Feature extraction Head Object detection Videos Detectors Real-time systems Task analysis Object detection neural networks computer vision deep learning

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Traffic Signal control Based on Reinforcement Learning and Fuzzy Neural Network 25

Traffic Signal Control Based on Reinforcement Learning and F...

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IEEE 25th International Conference on Intelligent Transportation systems (ITSC)

作者： Zhao, Hongxia Chen, Songhang Zhu, Fenghua Tang, Haina Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Fujian Inst Res Struct Matter Fujian Prov Key Lab Intelligent Identificat & Con Fuzhou 350002 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

For traffic signal control of intersections in cities, a new controller based on reinforcement learning and fuzzy neural network is proposed in this paper. The fuzzy neural network has the advantages of both fuzzy control and neural network, and overcome the former's lack of self-learning and generalization ability, and the latter's lack of understandability. Meanwhile, the reinforcement learning can make the controller improve itself on line continually by the simple feedback of environment. The result of computational experiments shows that the proposed traffic signal control algorithm can achieve a more effective optimization control.

关键词： Fuzzy control Adaptation models Urban areas Neural networks Green products Transportation Reinforcement learning

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