1 汇报安排

题 目：参加IEEE SMC 2015国际会议总结报告会

时 间：2015年10月19日（周一）上午9：30–10:30

地 点：机械学院第三会议室

报 告 人：博1319班 胡胜 学号：4113001065

指导教师：赵丽萍 教授

2参加国际会议信息

会议名称：2015 IEEE International Conference on Systems，Man，and Cybernetics (SMC 2015)

会议日期：October, 9-12, 2015

会议地点：Hong Kong，（HK）

会议简介：所参加的会议为2015 IEEE International Conference on Systems，Man，and Cybernetics (IEEE SMC 2015)，由Institute of Electrical and Electronics Engineers (IEEE)、IEEE Systems, Man and Cybernetics Society, City University of Hong Kong, The Hong Kong Polytechnic University, 以及K. C. Wong Education Foundation等多个单位组办。大会的主题主要涉及系统科学与工程、人机系统、控制论，主要包括分布式制造系统的自组织建模，多代理系统的智能控制，分布式自适应系统，控制系统的智能学习，系统建模与自动化控制，智能网络计算与应用，大数据，协作制造与供应链，产品质量管理，制造系统建模与动态控制等。

3参会论文信息

Title：A support vector machine based multi-kernel method for change point estimation on control chart

Author：Sheng Hu, Liping Zhao

Abstract—Despite the abnormal patterns recognition and mean shift size estimation of control chart signals could provide some evidence for statistical process diagnostics, it do not reveal the real time of the process changes, which is essential for identifying assignable causes and ultimately ensure stability of process. In this paper, a support vector machine based multi-kernel (MK-SVM) method for change point estimation on control chart is proposed. For this purpose, the moving window analysis is introduced to decompose the whole process sequence features into multiple time sub-sequences, and different types of kernel functions are combined together by using kernel method, which is mapped into a new feature space to form the multi-kernel function of SVM. Then each characteristic of the sub-sequences is regarded as a determined pattern to be recognized through the proposed model. We use the cross-validation method to search the optimized parameters of MK-SVM. Multiple sets of experiments are used to verify this method. Finally, a case study about the coating process of production lines is conducted to evaluate the performance of the proposed approach, results reveal that the proposed scheme is able to effectively estimating the time of change-point and outperform the commonly used approaches.

欢迎有兴趣的同学届时参加。