检索结果-内蒙古大学图书馆

2022 Chinese Automation Congress, CAC 2022

作者： Hong, Weixin Wu, Huaiyu Shen, Zhen Xiong, Gang Hu, Bin Dong, Xisong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China

ISBN: (纸本)9781665465335

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly availab.e. © 2022 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Social Radars for Social Vision of intelligent Vehicles: A New Direction for Vehicle Research and Development

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON intelligent VEHICLES 2024年第3期9卷 4244-4248页

作者： Fan, Lili Wang, Xingxia Yang, Jing Liu, Yuhang Lv, Chen Yu, Hui Ma, Jiaqi Wang, Fei-Yue Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Jiangsu Ind Innovat Ctr Intelligent Equipment Co L Beijing 100081 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Nanyang 639798 Singapore Univ Portsmouth Sch Creat Technol Portsmouth 639798 England Univ Calif Los Angeles Dept Civil & Environm Engn Los Angeles CA 90024 USA Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Taipa 999078 Macau Peoples R China

The low-altitude economy is playing a crucial role in promoting economic development, strengthening social security, and serving international security, thus becoming an increasingly vital engine for global development. As an essential technological backbone and application carrier of the low-altitude economy, intelligent vehicles are not only active on land but also increasingly needed to actively participate in the air and water. The integration of social radars and social vision will enable intelligent vehicles to perceive complex scenarios and task demands from a human perspective, providing more efficient and safer services for the low-altitude economy. This presents an exciting prospect for the application of social radars and social vision, as well as their integration with intelligent vehicles for envisaged service scenarios in the low-altitude economy.

关键词： intelligent vehicles Radar Social networking (online) Psychology Fans Real-time systems Radar imaging Social radars social vision intelligent vehicles low-altitude economic

来源：评论

学校读者我要写书评

暂无评论

Real-Time Scheduling of Autonomous Mining Trucks via Flow Allocation-Accelerated Tabu Search

引用

IEEE TRANSACTIONS ON intelligent VEHICLES 2022年第3期7卷 466-479页

作者： Zhang, Xiaotong Guo, Ao Ai, Yunfeng Tian, Bin Chen, Long Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Univ Chinese Acad Sci Artificial Intelligence Dept Beijing 100049 Peoples R China Waytous Inc Qingdao 266109 Shandong 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 100049 Peoples R China Minist Emergency Management Key Lab Intelligent Min & Robot Beijing 100012 Peoples R China

Material transportation is an essential process in mining operations and accounts for a large portion of total energy consumption. With the development of automation technology, autonomous mining trucks have been deployed in open-pit mines to reduce transportation costs. Compared to manned mining trucks, autonomous trucks are more appropriate for automatic scheduling techniques due to their high informatization, high-precision perception, and sophisticated control. However, the existing truck scheduling research does not exploit autonomous trucks' strengths to perform more delicate control, but still takes a traditional approach toward autonomous trucks. In this paper, we propose a mixed-integer programming model for joint optimization of autonomous trucks trips and speeds to minimize energy consumption. To solve the proposed scheduling model, a novel tabu search algorithm is proposed, which consists of two parts: an improved flow allocation model with matching factor is formulated in the upper part to determine the optimal truck flow, and in the lower part a tabu search procedure guided with the optimal flow is developed. Based on the mathematical model and solution technique, we also propose a real-time scheduling system of autonomous trucks for the stochastic and dynamic mining environment. A coal mine in Inner Mongolia, China, for example, we verified the effectiveness of the proposed truck scheduling model and real-time scheduling approach. We demonstrate that the proposed allocation model effectively accelerates the tabu search procedure, and for short-term decisions, the proposed solution technique satisfies the computational requirements of the real-time scheduling system.

关键词： Mathematical models Fuels Autonomous vehicles Real-time systems Dynamic scheduling Load modeling Transportation Autonomous truck heuristic algorithm open-pit mine real-time scheduling

来源：评论

学校读者我要写书评

暂无评论

Parallel Societies: A Computing Perspective of Social Digital Twins and Virtual-Real Interactions

引用

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2020年第1期7卷 2-7页

作者： Wang, Fei-Yue Qin, Rui Li, Juanjuan Yuan, Yong Wang, Xiao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

Welcome to the first issue of the IEEE Transactions on Computational Social systems (TCSS) of 2020, and Happy New Year to You! We would like to take this opportunity to express our sincere thanks to our editors, reviewers, authors, and readers for your great support and effort devoted to the TCSS, along with our best wish and hope that everyone has a happy, healthy, and fruitful 2020.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SWDPM: A Social Welfare-Optimized Data Pricing Mechanism

arXiv

引用

arXiv 2023年

作者： Yu, Yi Yao, Shengyue Wang, Fei-Yue Li, Juanjuan Lin, Yilun Urban Computing Lab Shanghai AI Laboratory Shanghai China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Macau Institute of Systems Engineering Macau University of Science and Technology China

Data trading has been hindered by privacy concerns associated with user-owned data and the infinite reproducibility of data, making it challenging for data owners to retain exclusive rights over their data once it has been disclosed. Traditional data pricing models relied on uniform pricing or subscription-based models. However, with the development of Privacy-Preserving Computing techniques, the market can now protect the privacy and complete transactions using progressively disclosed information, which creates a technical foundation for generating greater social welfare through data usage. In this study, we propose a novel approach to modeling multi-round data trading with progressively disclosed information using a matchmaking-based Markov Decision Process (MDP) and introduce a Social Welfare-optimized Data Pricing Mechanism (SWDPM) to find optimal pricing strategies. To the best of our knowledge, this is the first study to model multi-round data trading with progressively disclosed information. Numerical experiments demonstrate that the SWDPM can increase social welfare 3 times by up to 54% in trading feasibility, 43% in trading efficiency, and 25% in trading fairness by encouraging better matching of demand and price negotiation among traders. Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Design of a curled hyper-redundant manipulator and the motion control with tip-following algorithm

Design of a curled hyper-redundant manipulator and the motio...

引用

第35届中国控制与决策会议

作者： Aoshun Zhang En Li Feng Zhang Rui Guo Mingrui Luo Yuwei Zhang School of Artificial Intelligence University of Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Engineering Laboratory for Intelligent Industrial Vision CAS State Grid Intelligent Technology Co.Ltd. State Grid Shandong Electric Power Company Jinan Power Supply Company

In order to meet the operational needs in some narrow environments,the hyper-redundant snake-shaped manipulator has received extensive attention and has been widely studied due to its compact shape and multiple degrees of *** present,the research of hyper-redundant manipulator mainly focuses on the straight push *** the limitation of working space,this paper designed a hyper-redundant manipulator delivered by a curled *** to the characteristics of turntable delivery,a tip-following algorithm based on curve fitting and a tip-following strategy based on terminal posture record are ***,with the algorithm applied to the physical robot we made,we successfully realized the tipfollowing motion control of the hyper-redundant manipulator.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Visual Pencil: Design of Portable Human-Computer Interaction Based on 2D Visual Tracking 17

Visual Pencil: Design of Portable Human-Computer Interaction...

引用

17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Tong, Ru Wang, Tianzhu Yu, Junzhi Institute of Automation Cas State Key Lab Management and Control for Complex Systems Beijing100190 China

ISBN: (纸本)9781728164151

Convenient and efficient human-computer interaction has always been the goal pursued by researchers. In this paper, we present a vision-based handwriting interaction device for non-touch screens called Visual Pencil. Unlike the conventional interactive method that captures the user's gestures using a motionless visual sensor, Visual Pencil directly mounts the visual sensor in the device. More specifically, the visual data are processed by the feature detection algorithm, and the movement of the pen tip in a 2D plane is calculated, so as to control the cursor and achieve the purpose of interaction. We further define performance indicators for evaluating the design and select more reasonable parameters through specific experiments to achieve the best interaction performance. The experiments verify that the proposed design is able to fulfill the desired operations. The significance of this design lies in that it integrates the functions of the mouse and smart pen, thus removing the requirement of more auxiliary equipment. This work sheds light on the interaction with non-touch screen computers. © 2020 IEEE.

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

In-Situ 3D Printing Monitoring in Dynamic Environments via Self-Supervised Deep Neural Network Adaptation 20

In-Situ 3D Printing Monitoring in Dynamic Environments via S...

引用

IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Wang, Fang Xiong, Gang Fang, Qihang Shen, Zhen Wang, Di Wan, Li Dong, Xisong Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Dongguan 523808 Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Guangdong Peoples R China Beijing Ten Dimens Technol Co Ltd Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9798350358513;9798350358520

3D printing technology has rapidly advanced, yet the variability in deployment environments poses significant challenges for monitoring systems, because the dynamic deployment environments such as changes in lighting and monitoring camera status, can severely impact the effectiveness and robustness of monitoring systems. To tackle this problem in monitoring system, this paper introduces a novel approach for deep neural network (DNN) adaptation to the dynamic environments through self-supervised learning and applies it to during in-situ monitoring. Specifically, we introduce a self-supervised learning strategy that leverages the auxiliary reconstruction task during in-situ monitoring, subsequently applying self-supervised fine-tuning to classification tasks with a new imbalanced-aware classification loss. Our methodology was rigorously evaluated using a real-world dataset for 3D printing defect detection. The experimental outcomes affirm the robustness of our approach, showcasing a higher defect detection accuracy rate than baselines. This substantially mitigates the adverse effects associated with printing defects, thereby increasing the reliability and quality of 3D printing processes.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Learning control for Air Conditioning systems via Human Expressions

引用

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第8期68卷 7662-7671页

作者： Wei, Qinglai Li, Tao Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

In this article, a deep reinforcement learning method is developed to solve air conditioning control problems through human expressions. The main contribution of this article is to design a deep reinforcement learning method for air conditioning control problems with human expressions as the input for the first time. The method aims to eliminate human sleepiness and improve people's work efficiency as much as possible. First, the air conditioning system and deep reinforcement learning methods are introduced. Second, the image processing algorithm for human expressions is described. Third, the deep Q-network method is designed to obtain the optimal control policy for air conditioning systems. Finally, simulation results are given to illustrate the present method that can effectively eliminate sleepiness and improve the work environment of people.

关键词： Adaptive dynamic programming air conditioning control deep learning (DL) deep Q-network (DQN) human expressions optimal control reinforcement learning (RL) Q-learning

来源：评论

学校读者我要写书评

暂无评论

Autonomous Mobile Robot Navigation using Adaptive Neuro Fuzzy Inference System

Autonomous Mobile Robot Navigation using Adaptive Neuro Fuzz...

引用

2022 International Conference on Innovations and Development of Information Technologies and Robotics, IDITR 2022

作者： Haider, Muhammad Husnain Ali, Hub Khan, Abdullah Aman Zheng, Hao Bhutta, M. Usman Maqbool Usman, Shaban Zhi, Pengpeng Wang, Zhonglai University of Electronic Science and Technology of China Chengdu China Yangtze Delta Region Institute Huzhou China Casia State Key Laboratory for Management & Control of Complex Systems Beijing China Sichuan Artificial Intelligence Research Institute Yibin China The Chinese University of Hong Kong Robotics Lab Mae Hong Kong

ISBN: (纸本)9781665409322

Navigation of autonomous robots in unknown and cluttered environments lies among the marked trends in robotics. Unlike animals and humans, the collision-free movement of a robot is challenging and requires processing complex information. An autonomous robot needs to cope with a large amount of uncertainty while navigating. The previous methods have limitations, such as lacking obstacle avoidance behaviour, having a large number of governing rules, designing a separate controller for each navigation and obstacle avoidance, not considering the robot's dynamics, computationally expensive training, and poor performance in a cluttered environment. This paper proposes a method that comprises a single adaptive neuro fuzzy inference system (ANFIS) based controller with 16 rules compared to hundred of rules used by previous methods to address such problems. Our method takes heading angle along with distance sensors data as input. AU the inputs are fuzzified into linguistic variables such as near-far and left-right. Additionally, a fuzzy inference system (FIS) is designed and trained using the generated dataset for optimum performance of ANFIS. The proposed method efficiently provides collision-free navigation of the mobile robot in densely cluttered environments. Comprehensive experiments are performed to prove the robustness and potency of the proposed ANFIS controller. Moreover, the performance of the proposed method is compared with various previous methods. The results of these comparisons indicate our proposed method's superiority in finding a near-optimal path. © 2022 IEEE.

关键词： Navigation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：