WEKO3
アイテム
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Detection method of unlearned pattern using support vector machine in damage classification based on deep neural network
https://nied-repo.bosai.go.jp/records/4087
https://nied-repo.bosai.go.jp/records/40874039215c-b7b3-420a-a246-6dfc1a82bed3
Item type | researchmap(1) | |||||||||||
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公開日 | 2023-03-30 | |||||||||||
タイトル | ||||||||||||
言語 | en | |||||||||||
タイトル | Detection method of unlearned pattern using support vector machine in damage classification based on deep neural network | |||||||||||
言語 | ||||||||||||
言語 | eng | |||||||||||
著者 |
Masayuki Kohiyama
× Masayuki Kohiyama
× Kazuya Oka
× Takuzo Yamashita
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抄録 | ||||||||||||
内容記述タイプ | Other | |||||||||||
内容記述 | Deep neural networks (DNNs) are a powerful tool for structural health monitoring because they can automatically identify features that are useful for classifying and recognizing damage patterns of a target structure with high accuracy. However, it can misclassify input data of an unlearned damage pattern as any of the learned damage patterns. To address this shortcoming, this paper presents a method to detect unlearned damage patterns by using the collective decision of support vector machines (SVMs). SVMs are constructed using feature vectors from training data, which are stored in the output layer of a DNN. To validate the proposed method, we used two different datasets, one containing experimental data of a steel frame structure and the other containing simulated and experimental data of a wooden house. In both cases, it correctly identified data of both learned and unlearned damage patterns. The proposed method can enhance the effectiveness of structural health monitoring (SHM). In addition, because it does not employ SHM-specific characteristics, it can be used in various pattern recognition applications, such as image and audio processing. | |||||||||||
言語 | en | |||||||||||
書誌情報 |
en : STRUCTURAL CONTROL & HEALTH MONITORING 巻 27, 号 8 |
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出版者 | ||||||||||||
言語 | en | |||||||||||
出版者 | JOHN WILEY & SONS LTD | |||||||||||
ISSN | ||||||||||||
収録物識別子タイプ | EISSN | |||||||||||
収録物識別子 | 1545-2263 | |||||||||||
DOI | ||||||||||||
関連識別子 | 10.1002/stc.2552 |