Cooperative Iteration Matching Method for Aligning Samples from Heterogeneous Industrial Datasets  

作　　者：LI Han SHI Guohong LIU Zhao ZHU Ping 李晗;史国宏;刘钊;朱平

机构地区：[1]School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China [2]Pan Asia Technical Automotive Center Co.,Ltd.,Shanghai 201201,China [3]School of Design,Shanghai Jiao Tong University,Shanghai 200240,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2025年第2期375-384,共10页上海交通大学学报(英文版)

基　　金：the Key National Natural Science Foundation of China(No.U1864211);the National Natural Science Foundation of China(No.11772191);the Natural Science Foundation of Shanghai(No.21ZR1431500)。

摘　　要：Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining samples do not correspond one-to-one correctly.Mismatched datasets caused by missing samples make the industrial data unavailable for further machine learning.In order to align the mismatched samples,this article presents a cooperative iteration matching method(CIMM)based on the modified dynamic time warping(DTW).The proposed method regards the sequentially accumulated industrial data as the time series.Mismatched samples are aligned by the DTW.In addition,dynamic constraints are applied to the warping distance of the DTW process to make the alignment more efficient.Then a series of models are trained with the cumulated samples iteratively.Several groups of numerical experiments on different missing patterns and missing locations are designed and analyzed to prove the effectiveness and the applicability of the proposed method.

关 键 词：dynamic time warping mismatched samples sample alignment industrial data data missing 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]