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Taxi Demand Prediction Using Parallel Multi-Task Learning Model

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 Long-Tail Regularization Method for Traffic Sign Recognition Based on Parallel Vision

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 957-961页

作者： Wang, Jiangong Wang, Xiao Shen, Tianyu Wang, Yutong Tian, Yonglin Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

The long-tail effect is prevalent in the actual world, and the resulting long-tail problem hinders the development of intelligent vehicles towards a more reliable and safe direction. In previous studies, the theory of Long-tail Regularization (LoTR) based on parallel vision was proposed, and further research and experiments on the long-tail problem of traffic sign recognition are conducted in this paper. Specifically, in the process of long-tail regularization for traffic sign recognition, a novel generative adversarial network is designed and improved to generate images. By feeding the category-specific image from standard gallery into the proposed network, it can generate a large number of virtual images with the same category. Finally, the generated virtual images are added to the original dataset to regularize the long tail, and then the recognition accuracy is significantly improved.

关键词： Parallel vision long tail traffic sign regularization

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning

arXiv

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arXiv 2024年

作者： Zhu, Yuanyang Wang, Zhi Zhu, Yuanheng Chen, Chunlin Zhao, Dongbin The Department of Control Science and Intelligent Engineering School of Management and Engineering Nanjing University Nanjing210093 China The Research Center for Novel Technology of Intelligent Equipment Nanjing University Nanjing210093 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

For on-policy reinforcement learning, discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without considering the inherent ordering between the discrete atomic actions, the explosion in the number of discrete actions can possess undesired properties and induce a higher variance for the policy gradient estimator. In this paper, we introduce a straightforward architecture that addresses this issue by constraining the discrete policy to be unimodal using Poisson probability distributions. This unimodal architecture can better leverage the continuity in the underlying continuous action space using explicit unimodal probability distributions. We conduct extensive experiments to show that the discrete policy with the unimodal probability distribution provides significantly faster convergence and higher performance for on-policy reinforcement learning algorithms in challenging control tasks, especially in highly complex tasks such as Humanoid. We provide theoretical analysis on the variance of the policy gradient estimator, which suggests that our attentively designed unimodal discrete policy can retain a lower variance and yield a stable learning process. © 2024, CC BY.

关键词： Contrastive Learning

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Design of a novel flexible assistive hand exoskeleton 37

Design of a novel flexible assistive hand exoskeleton

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Li, Guotao Cheng, Long Gao, Zhengrun Lu, Jilong Zhao, Yanjiang Institute of Automation State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China Fudan University The National Clinical Research Center for Aging and Medicine Shanghai China Harbin University of Science and Technology Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin150080 China Intelligent Machine Institute Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9781665465366

Hand paralysis caused by stroke, spinal cord injury, or neurological trauma has a significant impact on the independence and quality of life of the patients. The hand exoskeleton can provide hand assistance and improve the patient's grasp. In this article, a novel wearable flexible hand exoskeleton is proposed to assist users in activities of daily life. To create a simple and lightweight assistive hand exoskeleton, 3D printing is used to manufacture most of the components, and a remote drive system is designed to reduce the weight of the hand exoskeleton. A ball pair device and a slider device are used to ensure the freedom of the thumb and the adaptability of the hand exoskeleton to different hand sizes. Finally, the experiments are conducted to verify that the hand exoskeleton can achieve motion assistance. © 2022 IEEE.

关键词： 3D printing

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DeCASA in AgriVerse: Parallel Agriculture for Smart Villages in Metaverses

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2055-2062页

作者： Xiujuan Wang Mengzhen Kang Hequan Sun Philippe de Reffye Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Beijing Engineering Research Center of Intelligent Systems and Technology Beijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 Department of Chromosome Biology Max Planck Institute for Plant Breeding ResearchBeijing 100049China Faculty of Biology LMU MunichGroßhaderner Str.2Planegg-Martinsried D-82152Germany AMAP Univ MontpellierCIRADCNRSINRAEIRDMontpellier F-34398France IEEE Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

The demand for food is tremendously increasing with the growth of the world population,which necessitates the development of sustainable agriculture under the impact of various factors,such as climate *** fulfill this challenge,we are developing Metaverses for agriculture,referred to as Agri Verse,under our Decentralized complex Adaptive systems in Agriculture(De CASA)project,which is a digital world of smart villages created alongside the development of Decentralized Sciences(De Sci)and Decentralized Autonomous Organizations(DAO)for Cyber-Physical-Social systems(CPSSs).Additionally,we provide the architectures,operating modes and major applications of De CASA in *** achieving sustainable agriculture,a foundation model based on ACP theory and federated intelligence is ***,we discuss the challenges and opportunities.

关键词： Parallel Agriculture management and control AgriVerse Agriculture CPSS ACP DAO-Based Platform Precision Agriculture

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3-D Electromagnetic Position Estimation System Using High-Magnetic-Permeability Metal for Continuum Medical Robots

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 2581-2588页

作者： Wang, Heng Wang, Shuangyi Liu, Hongbin Rhode, Kawal Hou, Zeng-Guang Rajamani, Rajesh South China Univ Technol Sch Intelligent Engn Guangzhou 511442 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Kings Coll London St Thomas Hosp Sch Biomed Engn & Imaging Sci London SE1 7EH England Univ Minnesota Dept Mech Engn Minneapolis MN 55455 USA

In this letter, a new 3-D electromagnetic position sensing method is proposed for localization of continuum medical robots. An electromagnet and magnetic sensors are placed outside the human body while only a piece of passive mu-metal with high magnetic permeability is attached to the robot moving inside the body, resulting in a wireless non-contacting position estimation system. The mu-metal gets easily magnetized by the electromagnet and thus exerts position-dependent influence on the external magnetic field, which is measured for position estimation using a particle filter. An alternating magnetic field from the electromagnet is used and hence disturbances from nearby ferromagnetic objects can be rejected. The 3-D position estimation system is evaluated on a flexible trans-esophageal robot for ultrasound imaging with motions of insertion and maneuver. Experiments show that the mean position estimation error is about 5 mm and the system is robust in the presence of magnetic disturbances from a ferromagnetic object. This new wireless and robust 3-D position estimation system is demonstrated to have the potential to localize a continuum medical robot, which can enable autonomous navigation of the robot.

关键词： Electromagnetic tracking 3D position estimation high-magnetic-permeability metal continuum medical robot

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A two-step method for 4-pin form-closure gripper with grasping force optimization

A two-step method for 4-pin form-closure gripper with graspi...

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作者： Li, Rui Li, Yinlin Su, Xiaojie School of Automation Chongqing University Chongqing400040 China State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Grasp planning and the grasping force optimization are challenging issues associated with the dexterous robotic hand grasping problem. To ensure a safe, fast, and stable grasp for robots, careful determination of the grasping points and the grasping forces is required. The former creates an open question because of the availability of multiple solutions. The latter is formulated as a problem for minimizing an objective function subject to form-closure constraints and balance constraints of external forces. In this work, we propose a novel approach for the grasping force optimization of the 4-pin environmental constraint-based form-closure grasp using dynamic neural networks. To find a form-closure grasp, we propose an environmental constraint-based algorithm and convert it to a problem of finding the local minima of a constraint region, resulting in an easy determination of the grasping points with the guarantee of grasping safety. To satisfy the grasping force limit of the contact points, we solve the grasping force optimization problem with friction constraint, which maximize the efficiency of the applied force and power of the dexterous hand. The proposed approach is evaluated with 4-pin gripper, and the result demonstrates that we can plan a safe, fast, and stable grasp from any initial state to a form-closure grasping state. © 2021 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Grippers

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Parallel crop planning based on price forecast

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INTERNATIONAL JOURNAL OF intelligent systems 2022年第8期37卷 4772-4793页

作者： Fan, Menghan Kang, Mengzhen Wang, Xiujuan Hua, Jing He, Chaoxing Wang, Fei-Yue 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 Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Qingdao AgriTech Co Ltd Qingdao Peoples R China Chinese Acad Agr Sci Inst Vegetables & Flowers Beijing Peoples R China

Agrifood system actors operate within diverse sociocultural, economic, and biophysical settings. For growers, crop planning, usually a yearly business plan, is a key decision to make on when, what, and how many to plant. It is a challenging task as it deals with multiple constraints in volatile economic and/or climate environment. Most crop planning models have difficulty in adapting to changing situation. In this study, a parallel system of crop planning composed of the artificial system, computational experiment, and parallel execution is proposed. The farmers are described as agents, and the decision is made based on the heuristic searching of optimal plan;the adaption of plan is triggered autonomously given strong environment changes. Focus is given to economic environment, which is indicated as product price. In a case study, the economic environment of the artificial system is built based on the monthly and weekly price information for 13 products during 7 years. The computational experiment provides the initial cropping plan and harvest time, with social and ecological constraints. Result shows that the cropping plan can further adapt to price variation. This flexible cropping plan system can strengthen the capability of cooperatives serving small-scale farmers.

关键词： agent-based modeling crop planning parallel agricultural management price prediction

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Valuing Equity-Linked Guaranteed Minimum Death Benefits with European-Style Asian Payoffs Under the Regime-Switching Diffusion Process with Jump

SSRN

引用

SSRN 2022年

作者： Wang, Yayun Liu, Shengda Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing400065 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

In this paper, we consider a novel and efficient method to price equity-linked guaranteed minimumdeath benefits (GMDB) with European-style geometric Asian and arithmetic Asian payoffs. In thesituation of continuous transition of market economy, we assume that the underlying asset price processfollows regime-switching Ĺevy model and use complex Fourier series (CFS) expansion method to derivethe approximate value of GMDB products. The error analysis shows an exponential convergence rateunder geometric Asian and arithmetic Asian payoffs. Furthermore, the CFS approach is comparedwith other Fourier transform methods and Monte Carlo simulation, the numerical experiment resultsillustrate our method has a distinct advantage over other methods in both efficiency and accuracy. © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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