2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第3页

Distributed Optimal Coordination Control for Continuous-Time Nonlinear Multi-Agent Systems With Input Constraints 具有输入约束的连续时间非线性多智能体系统的分布式最优协调控制

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275176

Y. Deng, Jun Xiao, Qinglai Wei

引用次数: 2

A Fast-Convergence Method of Monte Carlo Counterfactual Regret Minimization for Imperfect Information Dynamic Games 不完全信息动态博弈的蒙特卡罗反事实后悔最小化快速收敛方法

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275075

Xiaoyang Hu, Li Xia, Jun Yang, Qianchuan Zhao

引用次数: 0

Solving Imperfect Information Poker Games Using Monte Carlo Search and POMDP Models 用蒙特卡罗搜索和POMDP模型求解不完全信息扑克博弈

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275053

Jian Yao, Ze-yu Zhang, Li Xia, Jun Yang, Qianchuan Zhao

{"title":"Solving Imperfect Information Poker Games Using Monte Carlo Search and POMDP Models","authors":"Jian Yao, Ze-yu Zhang, Li Xia, Jun Yang, Qianchuan Zhao","doi":"10.1109/DDCLS49620.2020.9275053","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275053","url":null,"abstract":"Recent advances achieved in the field of reinforcement learning have led AI algorithms capable of beating world champions in some perfect information games like Chess and Go. However, the AI approach to imperfect information games (such as Poker) is much more difficult because the complexities in estimating hidden information and behaviors of opponents may become extremely challenging. Since Markov Decision Process (MDP) is the underlying mathematical model of reinforcement learning with perfect information games, Partially Observable Markov Decision Process (POMDP) deserves research attention for studying the games with imperfect information. In this paper, we study a 16-cards Rhode Island Hold’em poker game and present a POMDP model to formulate this imperfect information extensive game. Based on the POMDP model, we use Bayesian approach to estimate the opponent’s hand and transform the original problem to several perfect information games. Furthermore, to handle the challenge of explosively huge storage space and computation burdens, we develop a Monte Carlo optimization algorithm to estimate the action values of the POMDP model. Finally, we conduct numerical experiments in the Rhode Island Hold’em poker game to demonstrate the effectiveness of our approach.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133544361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Calculation Method of Carbon Emission in Production Process for Optimization of Polyester Low Elastic Yarn Process 涤纶低弹纱工艺优化生产过程碳排放计算方法

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275279

Ning Li, Jingfeng Shao

{"title":"Calculation Method of Carbon Emission in Production Process for Optimization of Polyester Low Elastic Yarn Process","authors":"Ning Li, Jingfeng Shao","doi":"10.1109/DDCLS49620.2020.9275279","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275279","url":null,"abstract":"Aiming at the problems of many parameters and energy consumption fluctuation in the production process of polyester low elastic yarn, firstly, the mechanism of energy consumption fluctuation and coupling of various parameters was analyzed. Based on the carbon footprint theory, an energy consumption analysis model of polyester low elastic yarn process was constructed around the production process of polyester low elastic yarn. Furthermore, the carbon footprint accounting was carried out for the process flow, and then the carbon emission and key process parameters were fitted, on the basis of the function relationship between the parameters, a process optimization model of polyester low elastic yarn based on the combination of signal-to-noise ratio orthogonal test and comprehensive weighting VIKOR method was constructed. Finally, through the experimental verification and analysis, the results show that the model reduces the carbon emission of the production process by 4.58% compared with the initial state by optimizing the key process parameters of polyester low elastic yarn production. On the premise of reducing energy consumption, the quality of polyester low elastic yarn has been improved.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132440745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven Adaptive Cooperative Control for Urban Traffic Signal Timing in Multi-intersections 城市多路口交通信号配时的数据驱动自适应协同控制

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275110

Honghai Ji, Hao Liu, Shida Liu, Li Wang, Lingling Fan

引用次数: 1

Adaptive Dynamic Programming Method for Optimal Battery Management of Battery Electric Vehicle 纯电动汽车电池最优管理的自适应动态规划方法

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275259

Jiguang Xue, Chunsheng Yan, Danxue Wang, Jun Wang, Jun Wu, Zehua Liao

引用次数: 7

A New Compound Fault Diagnosis Method for Gearbox Based on Convolutional Neural Network 基于卷积神经网络的齿轮箱复合故障诊断新方法

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275264

Mingxuan Xia, Zehui Mao, Rui Zhang, B. Jiang, Muheng Wei

{"title":"A New Compound Fault Diagnosis Method for Gearbox Based on Convolutional Neural Network","authors":"Mingxuan Xia, Zehui Mao, Rui Zhang, B. Jiang, Muheng Wei","doi":"10.1109/DDCLS49620.2020.9275264","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275264","url":null,"abstract":"This paper focus on the fault diagnosis problem for the compound faults of rotating machine, in which the rolling bearing and the sun gear faults simultaneously occurred are considered as the compound fault. Considering the traditional compound fault diagnosis methods usually utilize the manual fault features extraction, which are mainly dependent on engineering experience, we propose a compound fault diagnosis method named multi-sensor based convolutional neural network (MCNN). For vibration signals of compound faults, the different transmission paths and the positions of the sensors means one part of the embedded single faults may have higher energy. The vibration signals collected from three sensors at different positions can help guarantee the completeness of the characteristics of the compound fault. Then, the multi-sensor signals are combined together and fused by the convolutional operation of the convolutional neural network (CNN) model. The CNN model, which can automatically extract features from the vibration signals and achieve classification, is used for fault extraction and fault recognition. The experiments are presented on the physical platform of power transmission, and the proposed fault diagnosis method can be verified with the satisfied performance.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115909726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Deep Learning Method for Rolling Bearing Fault Diagnosis through Heterogeneous Data 基于异构数据的滚动轴承故障深度学习诊断方法

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275189

Wei Zhou, Yandong Hou

引用次数: 0

Sliding-Mode Control of Dynamic Wireless Charging EV System 电动汽车动态无线充电系统滑模控制

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275270

Yayan Kang, Yang Song, Cheng Peng, Ling Deng

引用次数: 0

Event-Triggered Consensus Output Tracking Strategy for Multiagent Systems Utilizing Model-Free Adaptive Control 基于无模型自适应控制的多智能体系统事件触发一致性输出跟踪策略

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275167

Weizhao Song, Jian Feng

引用次数: 1