Generation of Unusual Plasma Discharge Video by Generative Adversarial Network-Reference-Cited by-同舟云学术

Generation of Unusual Plasma Discharge Video by Generative Adversarial Network

Published:2022-09-30 Issue:1 Volume:10 Page:1-24
ISSN:2166-7160
Container-title:International Journal of Software Innovation
language:ng
Short-container-title:

Author:

Ngan Tran Vo Khanh¹,Hochin Teruhisa¹,Nomiya Hiroki¹,Nakanishi Hideya²^ORCID,Shoji Mamoru²

Affiliation:

1. Kyoto Institute of Technology, Japan

2. National Institute for Fusion Science, Japan

Abstract

In nuclear fusion experiments in large helical device (LHD), a lot of videos containing the images of plasma discharge are recorded. An observation of the recorded images of plasma light emission can lead to a new discovery or help to optimize the operational parameters for the experiment. An unusual plasma discharge, which may cause damage to the device, is expected to be foreseen through a prediction method. Due to the shortage of videos having such unusual emissions, the generation of more videos having similar phenomenon is required. However, video generation is a very challenging issue as the videos should have not only similarity in features in the real one but also a plausibility in frame-by-frame transition, especially in the case of plasma discharges. Thus, this paper proposes a method to generate a video containing plasma light emission using generative adversarial network (GAN). It has been confirmed that the proposed generative model can produce a new video having plasma light emission with a very smooth frame transition.

Publisher

IGI Global

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Computer Science Applications,Software

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