2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第6页

On Disturbance Rejection of Piezo-actuated Nanopositioner 压电驱动纳米逆激器抗扰性能研究

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8515914

Wei Wei, Pengfei Xia, Min Zuo

引用次数: 1

Moving Object Real-time Detection and Tracking Method Based on Improved Gaussian Mixture Model 基于改进高斯混合模型的运动目标实时检测与跟踪方法

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8515905

Shanliang Zhu, Xin Gao, Haoyu Wang, Guangwei Xu, Qiuling Xie, Shuguo Yang

引用次数: 3

Resilient Consensus with Switching Networks and Double-Integrator Agents 交换网络和双积分器代理的弹性共识

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516075

Jinbo Huang, Yiming Wu, Liping Chang, Xiongxiong He, Sheng Li

引用次数: 2

Power Management of Battery Energy Storage System Using Model Free Adaptive Control 基于无模型自适应控制的电池储能系统电源管理

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516079

Weiming Zhang, Dezhi Xu, X. Lou, Wenxu Yan, Weilin Yang

引用次数: 4

Unknown Input and Measurement Noise Estimations for Switched Nonlinear Systems 开关非线性系统的未知输入和测量噪声估计

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8515957

F. Zhu, Jiancheng Zhang, Fengning Wang, S. Guo

引用次数: 0

VISSIM Parameter Calibration Based on Traffic Characteristics Distribution at Signalized Intersections 基于信号交叉口交通特征分布的VISSIM参数标定

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8515913

N. Li, Yujie Sun

{"title":"VISSIM Parameter Calibration Based on Traffic Characteristics Distribution at Signalized Intersections","authors":"N. Li, Yujie Sun","doi":"10.1109/DDCLS.2018.8515913","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515913","url":null,"abstract":"In order to increase the accuracy of traffic simulation and better reproduce the real traffic condition at signalized intersections, this paper proposed a parameter calibration method based on the traffic distribution rules at signalized intersections. First, after qualitatively analyzing the traffic condition at signalized intersections based on dynamic traffic features, this paper selected the key parameters that need to be calibrated. Then, regarding the selected key parameters, this paper first designed and implemented the collecting method. Then filtered and analyzed the data, and acquired the distribution pattern of each key parameter at signalized intersection. Finally, in order to validate the calibration process based on vehicle types through simulation, this paper chose travel time and number of stops as validation parameters. The results showed that there had been a great increase in the accuracy after calibration. The maximum inaccuracy among all evaluation parameters was 14.6%, which indicated that the calibration process based on traffic characteristics distribution at signalized intersections was effective.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"97 1","pages":"150-153"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84826288","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: 2

Energy Saving and Management of the Industrial Process Based on An Improved DEA Cross-model 基于改进DEA交叉模型的工业过程节能与管理

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8515973

Zhiqiang Geng, Ju Bai, Qunxiong Zhu, Yuan Xu, Yangming Han

{"title":"Energy Saving and Management of the Industrial Process Based on An Improved DEA Cross-model","authors":"Zhiqiang Geng, Ju Bai, Qunxiong Zhu, Yuan Xu, Yangming Han","doi":"10.1109/DDCLS.2018.8515973","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515973","url":null,"abstract":"Data envelopment analysis (DEA) has been commonly used in the energy saving of enterprise plants. Nevertheless, when the traditional DEA model analyzes the effectiveness of decision-making units (DMUs), over 1/3 of the DMUs’ efficiency values are 1, so the traditional DEA model cannot distinguish the cons and pros of the DMUs. And although the DEA cross-model(DEACM) is able to differentiate the cons as well as pros of the effective DMUs, it can’t obtain the improvement direction of the ineffective DMUs. Therefore, an energy saving and management method based on an improved DEACM, which can use the higher efficiency distinction to identify the efficiency state of the DMUs, is proposed in this paper. Meanwhile, the improvement direction of the ineffective DMU can be found by the self-evaluation of the improved DEACM. Finally, the improved DEACM is utilized to save and manage the energy configuration of the PTA solvent system in the industrial process. The experimental results reveal that the practicality and effectiveness of the proposed method are verified, and in addition, the efficiency discrimination is well. Moreover, the proposed model can find the direction of the quantitative targets of energy saving to improve the energy efficiency of PTA production.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"36 1","pages":"154-159"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85015178","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: 1

Subordinate based Cluster Center Identification in Density Peak Clustering 密度峰值聚类中基于从属的聚类中心识别

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516003

Jian Hou, Aihua Zhang, Lv Chengcong, E. Xu

{"title":"Subordinate based Cluster Center Identification in Density Peak Clustering","authors":"Jian Hou, Aihua Zhang, Lv Chengcong, E. Xu","doi":"10.1109/DDCLS.2018.8516003","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516003","url":null,"abstract":"Recently, a clustering algorithm is proposed by treating local density peaks as cluster centers. This algorithm proposes to describe the data to be clustered with local density and the distance of one data to the nearest data of larger local density. This description highlights the uniqueness of cluster centers and is utilized to determine cluster centers. With the assumption that one data and the nearest data of larger local density are in the same cluster, the non-center data are assigned labels efficiently. By studying the clustering process of this algorithm in depth, we find that the local density is not very effective in highlighting the uniqueness of cluster centers. As a result, this algorithm is dependent on the parameters in local density calculation. We discuss this problem and find that it is the role of density peaks, but not the absolute local density, that highlights the uniqueness of cluster centers. Based on this observation, we introduce the concept of subordinate and use the amount of subordinates to replace the local density in cluster center identification. Together with a new density kernel, this new criterion is shown to be effective in experiments and comparisons.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"106 1","pages":"551-554"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88028363","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

A New Measure of Dynamic Similarity for Nonlinear Systems based on Gap Metric and Deterministic Learning Theory 基于间隙度量和确定性学习理论的非线性系统动态相似度度量

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516002

Danfeng Chen, Cong Wang, Wenbo Zhu

引用次数: 0

High-Order Distributed Consensus in Multi-Agent Networks 多智能体网络中的高阶分布式一致性

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8515999

Zunshui Cheng, Tiansun Wang, Youming Xin

引用次数: 1