A Study of Throughput Prediction using Convolutional Neural Network over Factory Environment

Yafei Hou, Kazuto Yano, Norisato Suga, Julian Webber, Eiji Nii, Toshihide Higashimori, Satoshi Denno, Yoshinori Suzuki

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

In this paper, using the time-series throughput data generated from a simulated factory scenario, we study throughput prediction using convolutional neural network (CNN). Different with image or numerical recognition using CNN, in which the distribution of the prediction target during training stage usually has the similar level, the distribution of the throughput data concentrates only on several values. This centralized distribution may degrade the prediction accuracy. Therefore, we will propose a new CNN prediction method employing target vectorization which can mitigate the centralization of distribution. This method makes training process of CNN hold more possibility and improves the prediction accuracy of the throughput.

本文言語English
ホスト出版物のタイトル23rd International Conference on Advanced Communication Technology
ホスト出版物のサブタイトルOn-Line Security in Pandemic Era!, ICACT 2021 - Proceeding
出版社Institute of Electrical and Electronics Engineers Inc.
ページ429-434
ページ数6
ISBN(電子版)9791188428069
DOI
出版ステータスPublished - 2021 2月 7
外部発表はい
イベント23rd International Conference on Advanced Communication Technology, ICACT 2021 - Virtual, PyeongChang, Korea, Republic of
継続期間: 2021 2月 72021 2月 10

出版物シリーズ

名前International Conference on Advanced Communication Technology, ICACT
2021-February
ISSN(印刷版)1738-9445

Conference

Conference23rd International Conference on Advanced Communication Technology, ICACT 2021
国/地域Korea, Republic of
CityVirtual, PyeongChang
Period21/2/721/2/10

ASJC Scopus subject areas

  • 電子工学および電気工学

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引用スタイル