書誌事項
- タイトル別名
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- Vibrotactile Signal Generation by Deep Convolutional Generative Adversarial Network for Vibrotactile Displays
抄録
<p>To create intuitive tactile displays, collecting vibrotactile information is important, though, the collection procedure requires manual scanning of textures. Thus, a collection of vast information is difficult. However, by employing machine learning technology, there is a possibility to generate further virtual data from existing collected data. In this paper, we made a generation model of vibrotactile signals from the collected acceleration data by using Deep Convolutional Generative Adversarial Network (DCGAN). We held two kinds of experiments to evaluate the performance of our DCGAN model and we consider the result of experiments.</p>
収録刊行物
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- ロボティクス・メカトロニクス講演会講演概要集
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ロボティクス・メカトロニクス講演会講演概要集 2019 (0), 1P2-U04-, 2019
一般社団法人 日本機械学会
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詳細情報
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- CRID
- 1390846609787414016
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- NII論文ID
- 130007774559
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- ISSN
- 24243124
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可