It is important to increase the productivity of every shipyard. Visualizing the actual work status during any industrial activity is essential. Work observation as one of the methods of industrial engineering has been applied in various fields in shipyards in Japan to increase productivity. However, current work observation requires both time and labor, and in some cases, shipyards hesitate to implement work observation. The aim of this study was to develop a methodology that uses deep neural networks to reduce the disadvantages of current work observation approaches while identifying work tasks and the accuracy of this observation.
|出版ステータス||出版済み - 1 1 2019|
|イベント||SNAME Maritime Convention 2019, SMC 2019 - Tacoma, 米国|
継続期間: 10 30 2019 → 11 1 2019
|会議||SNAME Maritime Convention 2019, SMC 2019|
|Period||10/30/19 → 11/1/19|
All Science Journal Classification (ASJC) codes