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Training Autoencoder using Three Different Reversed Color Models for Anomaly Detection
http://hdl.handle.net/10228/00008261
http://hdl.handle.net/10228/000082610dff7fa9-c700-420b-bed5-b9a565c86f46
名前 / ファイル | ライセンス | アクション |
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jrnal.k.200512.008.pdf (1.4 MB)
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Item type | 学術雑誌論文 = Journal Article(1) | |||||||||||
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公開日 | 2021-05-20 | |||||||||||
資源タイプ | ||||||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
資源タイプ | journal article | |||||||||||
タイトル | ||||||||||||
タイトル | Training Autoencoder using Three Different Reversed Color Models for Anomaly Detection | |||||||||||
その他のタイトル | ||||||||||||
その他のタイトル | Training autoencoder using three different reversed color models for anomaly detection | |||||||||||
言語 | ||||||||||||
言語 | eng | |||||||||||
著者 |
Al aama, Obada
× Al aama, Obada× 田向, 権
WEKO
6059
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抄録 | ||||||||||||
内容記述タイプ | Abstract | |||||||||||
内容記述 | Autoencoders (AEs) have been applied in several applications such as anomaly detectors and object recognition systems. However, although the recent neural networks have relatively high accuracy but sometimes false detection may occur. This paper introduces AE as an anomaly detector. The proposed AE is trained using both normal and anomalous data based on convolutional neural network with three different color models Hue Saturation Value (HSV), Red Green Blue (RGB), and our own model (TUV). As a result, the trained AE reconstruct the normal images without change, whereas the anomalous image would be reconstructed reversely. The training and testing of the AE in case of RGB, HSV, and TUV color models were demonstrated and Cifar-10 dataset had been used for the evaluation process. It can be noticed that HSV color model has been more effective and achievable as an anomaly detector rather than other color models based on Z- and F-test analyses. | |||||||||||
書誌情報 |
Journal of Robotics, Networking and Artificial Life 巻 7, 号 1, p. 35-40, 発行日 2020-05-20 |
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出版社 | ||||||||||||
出版者 | Atlantis Press | |||||||||||
DOI | ||||||||||||
関連タイプ | isIdenticalTo | |||||||||||
識別子タイプ | DOI | |||||||||||
関連識別子 | https://doi.org/10.2991/jrnal.k.200512.008 | |||||||||||
日本十進分類法 | ||||||||||||
主題Scheme | NDC | |||||||||||
主題 | 548 | |||||||||||
ISSN | ||||||||||||
収録物識別子タイプ | ISSN | |||||||||||
収録物識別子 | 2405-9021 | |||||||||||
ISSN | ||||||||||||
収録物識別子タイプ | ISSN | |||||||||||
収録物識別子 | 2352-6386 | |||||||||||
著作権関連情報 | ||||||||||||
権利情報 | Copyright (c) 2020 The Authors. Published by Atlantis Press SARL.This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/). | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | Convolutional neural network | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | autoencoder | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | anomaly detection | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | color models | |||||||||||
出版タイプ | ||||||||||||
出版タイプ | VoR | |||||||||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
査読の有無 | ||||||||||||
値 | yes | |||||||||||
研究者情報 | ||||||||||||
https://hyokadb02.jimu.kyutech.ac.jp/html/100000641_ja.html | ||||||||||||
論文ID(連携) | ||||||||||||
10360422 | ||||||||||||
連携ID | ||||||||||||
8844 | ||||||||||||
著者別名 | ||||||||||||
姓名 | Al aama, O. | |||||||||||
著者別名 | ||||||||||||
姓名 | Tamukoh, Hakaru | |||||||||||
言語 | en | |||||||||||
姓名 | 田向, 権 | |||||||||||
言語 | ja | |||||||||||
姓名 | タムコウ, ハカル | |||||||||||
言語 | ja-Kana |