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SSRN 2024年

作者： Gao, Hongbo Yang, Hanqing Zhu, Juping Su, Huiping Wang, Ning Xie, Liping Li, Keqiang Department of Automation University of Science and Technology of China Hefei230026 China Institute of Advanced Technology University of Science and Technology of China Hefei230088 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Chongqing College of Mobile Communication Chongqing400044 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing100084 China

Driving risk entropy, based on entropy law, is an innovative concept proposed for intelligent driving systems. The concept deals with the driving risks caused by the human-vehicle-road system from the driving information obtained;however, driving risks are difficult to assess due to many influencing factors and great uncertainty. The risk entropy model of vehicles is visualized by introducing the concept of basic isotropic risk elliptic line (BIREL). Further incorporating the concept of information entropy, risk entropy models for vehicle interaction and vulnerable participants are established, and the risk entropy model for roads is developed using raster coordinates. Ultimately, a three-dimensional driving risk entropy model is constructed based on the human-vehicle-road coupling model, with appropriate weights assigned to each parameter through the DDPG algorithm. Experimental results demonstrate the effectiveness of the driving risk entropy model. © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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The Multi-Blockchain Based Data Trading Markets with Novel Pricing Mechanisms

SSRN

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SSRN 2022年

作者： Li, Juanjuan Li, Junqing Wang, Xiao Qin, Rui Yuan, Yong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Mathematics Renmin University of China Beijing100872 China Macao Institute of Systems Engineering Macau University of Science and Technology 999078 China

Aiming to deal with the challenge of data security and data pricing in data trading markets, we propose the novel multi-blockchain-based framework and design the related pricing mechanisms. Specifically, data recording and trading are conducted separately in the Data Blockchain (DChain) and the Value Blockchain (VChain). Based on these blockchains, two-layer data trading markets are established to manage the initial data trading in the primary market and the data resale in the secondary market. Pricing mechanisms are proposed to protect the formulated data trading markets against strategic trading behaviors and balance the payoffs of both suppliers and users. In the regular data trading on VChain-S2D, two auction models are used to deal with users' strategic bidding in terms of the demand scale, where the incentive-compatible Vickrey-Clarke-Groves (VCG) model is dedicated to the low-demand trading scenario, while the nearly incentive-compatible Monopolistic Price (MP) model is for the high-demand trading scenario. In the temporary data trading on VChain-D2S, a reverse auction mechanism named Two-Stage Obscure Selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Moreover, experiments are carried out to validate our theoretical analysis. Our work is expected to enhance the data security and improve the trading efficiency. © 2022, The Authors. All rights reserved.

关键词： Blockchain

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S$^{\text{4}}$TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2023年第2期9卷 3220-3231页

作者： Xiao Wang Ke Tang Xingyuan Dai Jintao Xu Quancheng Du Rui Ai Yuxiao Wang Weihao Gu Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Haomo Technology Company Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S $^{\text{4}}$ TP) framework. Specifically, S $^{\text{4}}$ TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S $^{\text{4}}$ TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, S $^{\text{4}}$ TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S $^{\text{4}}$ TP in terms of safety and rationality. S $^{\text{4}}$ TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%.

关键词： Trajectory Safety Vehicles Trajectory planning Autonomous vehicles Risk management Behavioral sciences

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A Blockchain-based Educational Digital Assets Management System

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IFAC-PapersOnLine 2020年第5期53卷 47-52页

作者： Junqing Li Mo Lan Ying Tang Shichao Chen Fei-Yue Wang Wei Wei Shandong University of Science and Technology Qingdao China 266590 The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China 100190 Southwest University Chongqing China 400715 Beijing Achievelearn Education Technology Group Beijing China 100095 Electrical and Computer Engineering Rowan University Glassboro NJ 08003 USA Qingdao Academy of Intelligent Industries Qingdao China 266042 3E Smart Education Beijing China 100083

A variety of heterogeneous information is produced after the application of various emerging technologies in education, which is of great research value as educational assets. The traditional educational asset management system has emerged. In view of the disadvantages of traditional system centralization process, people begin to use decentralized blockchain technology for innovation. This paper proposes an educational digital asset management system architecture based on blockchain 3.0 technology system, which forms various data formed by students in and out of class learning process, courseware and experience data generated by teachers in the process of teaching into educational digital assets, and carries out the right confirmation and storage on the blockchain. The system can use a variety of heterogeneous data in digital assets to analyze the development status of school users, achieve the teaching purpose of teaching students according to their aptitude and different people.

关键词： blockchain blockchain system digital assets educational technology decentralized systems

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Parallel Hospital: ACP-Based Hospital Smart Operating System

Parallel Hospital: ACP-Based Hospital Smart Operating System

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Yongjun Wang Fei-Yue Wang Xiao Wang Zhongjun Guan Liwei Ouyang Jiangong Wang Lan Yan Wenbo Zheng Wenwen Zhang Beijing Tiantan Hospital Capital Medical University Beijing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Software Engineering Xi'an Jiaotong University Xi'an China

This article aims to propose a parallel hospital based on parallel medicine. The parallel hospital takes the parallel idea of virtual and real interaction as its core concept and uses parallel idea's artificial scenarios, computational experiments, and parallel execution (ACP) theory as the guiding theory to construct a new generation of intelligent hospital management system with virtual and real interaction. We conducted a case study of the parallel hospital practice case of Tiantan Hospital-Tiantan Smart Brain, so as to confirm the feasibility and scientificity of the proposed parallel hospital.

关键词： Training Hospitals Operating systems Digital twin Conferences

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The Multi-Blockchain Based Data Trading Markets with Novel Pricing Mechanisms

SSRN

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SSRN 2022年

关键词： Blockchain

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Motion trajectory design for underactuated three-link gymnast robot

Motion trajectory design for underactuated three-link gymnas...

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Asian control Conference

作者： Ancai Zhang Jinhua She Jianlong Qiu Chengdong Yang Lan Zhou School of Automation and Electrical Engineering Linyi University Linyi Shandong China Key Laboratory of Complex Systems and Intelligent Computing Universities of Shandong Linyi Shandong China School of Automation China University of Geosciences Wuhan Hubei China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan School of Information Science and Engineering Linyi University Linyi Shandong China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan Hunan China

ISBN: (纸本)9781509015740;9781509015733

This paper concerns the motion trajectory design for an underactuated three-link robot (UTGR). First, we design a controller for the UTGR system based on the system's dynamic structure characteristics. And then, the properties of closed-loop system are analyzed. After that, a motion trajectory of the UTGR is designed based on the analyzed properties and an inverted trajectory idea. The designed trajectory starts from the downward position and ends at the upward position. Tracking of this trajectory can enable the stabilization control objective of UTGR from the downward position to the upward position to be achieved. A numerical example demonstrates the effectiveness of our proposed theoretical results.

关键词： Trajectory Robots Closed loop systems Mathematical model Mechanical systems Tracking

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Procedure Recognition by Knowledge-Driven Segmentation in Robotic-Assisted Vitreoretinal Surgery

Procedure Recognition by Knowledge-Driven Segmentation in Ro...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Zhen Li Yawen Deng Qiang Ye Weihong Yu Haoxiang Qi Yaliang Liu Zhangguo Yu Gui-Bin Bian School of Electronic and Information Engineering Tongji University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China School of Mechatronic Engineering Beijing Institute of Technology Beijing China Department of Ophthalmology Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Key Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Internal limiting membrane (ILM) peeling is a vital vitreoretinal surgery procedure. However, due to the thickness of just 1-2 micrometers and the intricacies associated with its varying density and adhesion, the difficulty of manipulation exceeds the physiological limits of human perception and operation. Surgical robot is characterized by high precision and stability. However, navigating intricate intraocular environments and handling minuscule high-precision areas remain enormous challenges. These include issues of uneven lighting, field-of-view loss, and motion blur. This paper proposed a perception method named ‘Multimodal Surgical Process Recognition based on Domain Knowledge and Segmentation (MSPR-DKS),’ designed to address these challenges and provide input for the precise control of robots. Moreover, a comprehensive dataset focused on ILM peeling during macular hole surgeries was established. Experimental results underscore the efficacy of this approach, with segmentation accuracies exceeding 99.27% for instruments and macular holes and an average accuracy of 98.97% in recognizing surgical processes. This study paves the way for leveraging domain knowledge and image segmentation to improve robot-assisted manipulation of soft tissues in ophthalmology.

关键词： Image segmentation Visualization Accuracy Medical robotics Motion segmentation Surgery Lighting

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Toward parallel intelligence: an interdisciplinary solution for complex systems

arXiv

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arXiv 2023年

作者： Zhao, Yong Zhu, Zhengqiu Chen, Bin Qiu, Sihang Huang, Jincai Lu, Xin Yang, Weiyi Ai, Chuan Huang, Kuihua He, Cheng Jin, Yucheng Liu, Zhong Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Changsha410073 China Hunan Institute of Advanced Technology Changsha410073 China Shanghai Institute of Infectious Disease and Biosecurity Shanghai200032 China Neuherberg Germany Department of Computer Science Hong Kong Baptist University 999077 Hong Kong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling, analysis, management, and control. To meet these demands, the parallel systems method rooted in Artificial systems, Computational experiments, and Parallel execution (ACP) approach has been developed. The method cultivates a cycle, termed parallel intelligence, which iteratively creates data, acquires knowledge, and refines the actual system. Over the past two decades, the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines, offering versatile interdisciplinary solutions for complex systems across diverse fields. This review explores the origins and fundamental concepts of the parallel systems method, showcasing its accomplishments as a diverse array of parallel technologies and applications, while also prognosticating potential challenges. We posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation. © 2023, CC BY-NC-ND.

关键词： Iterative methods

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Fusionplanner: A Multi-Task Motion Planner for Mining Trucks Using Multisensor Fusion Method

SSRN

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SSRN 2023年

作者： Teng, Siyu Chen, Long Li, Luxi Li, Yuchen Hu, Xuein Li, Lingxi Ai, Yunfeng Department of Computer Science Hong Kong Baptist University Hong Kong The School of Artificial Intelligence Hubei University Wuhan China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Waytous Inc. Haidian Distinct Beijing China Indianapolis United States School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research, including a simulation platform, a testing benchmark, and a trustworthy and robust motion planner. Firstly, we introduce the Parallel Mining Simulator (PMS), a high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables to management and control of open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches suitable for transportation roads. The purpose of MiningNav is to evaluate the trustworthiness and robustness of algorithms in open-pit mines. Finally, we propose the first multi-task motion planning algorithm, called FusionPlanner, for the mining truck by the multisensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. We validate the performance of our planner using MiningNav in PMS, and empirical results demonstrate a significant reduction in the number of collisions and takeovers of FusionPlanner, thereby improving overall trustworthiness and robustness. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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