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Mathematical Problems in Engineering
Volume 2013, Article ID 136241, 10 pages
Research Article

An Improved Generalized-Trend-Diffusion-Based Data Imputation for Steel Industry

School of Control Sciences and Engineering, Dalian University of Technology, Dalian 116023, China

Received 5 January 2013; Accepted 20 February 2013

Academic Editor: Jun Zhao

Copyright © 2013 Ying Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Integrality and validity of industrial data are the fundamental factors in the domain of data-driven modeling. Aiming at the data missing problem of gas flow in steel industry, an improved Generalized-Trend-Diffusion (iGTD) algorithm is proposed in this study, where in particular it considers the sort of problem with data properties of consecutively missing and small samples. And, the imputation accuracy can be greatly increased by the proposed Gaussian membership-based GTD which expands the useful knowledge of data samples. In addition, the imputation order is further discussed to enhance the sequential forecasting accuracy of gas flow. To verify the effectiveness of the proposed method, a series of experiments that consists of three categories of data features in the gas system is presented, and the results indicate that this method is comprehensively better for the imputation of the periodical-like data and the time-series-like data.