A method for extracting the work status in shipyard using deep neural networks

研究成果: 会議への寄与タイプ論文

抄録

It is necessary to increase the productivity in every shipyard. To visualize the actual work status in any industrial activity, the observation of the work being performed is essential. However, current work observation requires both time and labor and some cases, shipyards hesitate to implement work observation. The aim of this study is to develop a new work observation method by use of deep neural networks to reduce the disadvantages of current work observation approaches while improving the accuracy of work identification.

元の言語英語
出版物ステータス出版済み - 1 1 2018
イベント2018 SNAME Maritime Convention, SMC 2018 - Providence, 米国
継続期間: 10 24 201810 27 2018

会議

会議2018 SNAME Maritime Convention, SMC 2018
米国
Providence
期間10/24/1810/27/18

Fingerprint

Shipyards
Productivity
Personnel
Deep neural networks

All Science Journal Classification (ASJC) codes

  • Engineering(all)

これを引用

Tanaka, T., & Shinoda, T. (2018). A method for extracting the work status in shipyard using deep neural networks. 論文発表場所 2018 SNAME Maritime Convention, SMC 2018, Providence, 米国.

A method for extracting the work status in shipyard using deep neural networks. / Tanaka, Takashi; Shinoda, Takeshi.

2018. 論文発表場所 2018 SNAME Maritime Convention, SMC 2018, Providence, 米国.

研究成果: 会議への寄与タイプ論文

Tanaka, T & Shinoda, T 2018, 'A method for extracting the work status in shipyard using deep neural networks' 論文発表場所 2018 SNAME Maritime Convention, SMC 2018, Providence, 米国, 10/24/18 - 10/27/18, .
Tanaka T, Shinoda T. A method for extracting the work status in shipyard using deep neural networks. 2018. 論文発表場所 2018 SNAME Maritime Convention, SMC 2018, Providence, 米国.
Tanaka, Takashi ; Shinoda, Takeshi. / A method for extracting the work status in shipyard using deep neural networks. 論文発表場所 2018 SNAME Maritime Convention, SMC 2018, Providence, 米国.
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