Reconstruction Method for Missing Measurement Data Based on Wasserstein Generative Adversarial Network-Reference-Cited by-同舟云学术

Reconstruction Method for Missing Measurement Data Based on Wasserstein Generative Adversarial Network

Published:2021-03-20 Issue:2 Volume:25 Page:195-203
ISSN:1883-8014
Container-title:Journal of Advanced Computational Intelligence and Intelligent Informatics
language:en
Short-container-title:JACIII

Author:

Zhang Changfan, ,Chen Hongrun,He Jing,Yang Haonan

Abstract

Focusing on the issue of missing measurement data caused by complex and changeable working conditions during the operation of high-speed trains, in this paper, a framework for the reconstruction of missing measurement data based on a generative adversarial network is proposed. Suitable parameters were set for each frame. Discrete measurement data are taken as the input of the frame for preprocessing the data dimensionality. The convolutional neural network then learns the correlation between different characteristic values of each device in an unsupervised pattern and constrains and improves the reconstruction accuracy by taking advantage of the context similarity of authenticity. It was determined experimentally that when there are different extents of missing measurement data, the model described in the present paper can still maintain a high reconstruction accuracy. In addition, the reconstruction data also conform well to the distribution law of the measurement data.

Funder

National Natural Science Foundation of China

Project of Hunan Provincial Department of Education

Excellent Youth Research Project of the Education Department of Hunan Province

Hunan Provincial Natural Science Foundation of China

Publisher

Fuji Technology Press Ltd.

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction

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