| 研究課題/領域番号 |
21K17829
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| 研究機関 | 東京大学 |
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研究代表者 |
ルイ笠原 純ユネス 東京大学, 大学院工学系研究科(工学部), 特任講師 (20885412)
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| 研究期間 (年度) |
2021-04-01 – 2024-03-31
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| キーワード | Inspection / Domain adaptation / Clustering / Weak supervision |
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| 研究実績の概要 |
This research aims at the automation of the hammering test for the inspection of concrete structures. Supervised learning approaches have the
issue that when the concrete structure differs between training and deployment of the system, the performance is degraded (domain gap).
This year, efforts have been shifted towards approaches based on Deep Learning for outlier detection, namely Autoencoder-type approaches, which have been gaining attention in the field. The potential of combining such methods with the weak supervision framework are being currently investigated.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
Day-to-day operations were still greatly affected by restrictions related to COVID-19. Additionally, difficulties were encountered when attempting to
purchase sensors due to the shortage of electronic components.
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| 今後の研究の推進方策 |
Several pipelines for audio processing which Autoencoders have been established this year. In the next fiscal year, it is planned to expand those to include weak supervision and start tackling the domain adaptation issue.
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| 次年度使用額が生じた理由 |
Delay in research activities due to the pandemic and personal reasons. Remaining budget planned to be used for speeding up efforts in the next fiscal year.
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