|
Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Estimation of Deterioration Levels of Transmission Towers via Deep Learning Maximizing Canonical Correlation between Heterogeneous Features
| Title: | Estimation of Deterioration Levels of Transmission Towers via Deep Learning Maximizing Canonical Correlation between Heterogeneous Features |
| Authors: | Maeda, Keisuke Browse this author | | Takahashi, Sho Browse this author →KAKEN DB | | Ogawa, Takahiro Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB |
| Keywords: | Deterioration level estimation | | deep extreme learning machine | | canonical correlation analysis |
| Issue Date: | Aug-2018 |
| Publisher: | IEEE |
| Journal Title: | IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING |
| Volume: | 12 |
| Issue: | 4 |
| Start Page: | 633 |
| End Page: | 644 |
| Publisher DOI: | 10.1109/JSTSP.2018.2849593 |
| Abstract: | This paper presents estimation of deterioration levels of transmission towers via deep learning maximizing the canonical correlation between heterogeneous features. In the proposed method, we newly construct a correlation-maximizing deep extreme learning machine based on a local receptive field (CMDELM-LRF). For accurate deterioration level estimation, it is necessary to obtain semantic information that effectively represents deterioration levels. However, since the amount of training data for transmission towers is small, it is difficult to perform feature transformation by using many hidden layers such as general deep learning methods. In CMDELM-LRF, one hidden layer, which maximizes the canonical correlation between visual features and text features obtained from inspection text data, is newly inserted. Specifically, by using projections obtained by maximizing the canonical correlation as weight parameters of the hidden layer, feature transformation for extracting semantic information is realized without designing many hidden layers. This is the main contribution of this paper. Consequently, CMDELM-LRF realizes accurate deterioration level estimation from a small amount of training data. |
| Rights: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/71406 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
|
Submitter: 前田 圭介
|
