본문 바로가기
  • Home

Deep Learning Based User Safety Profiling Using User Feature Information Modeling

  • Journal of Software Assessment and Valuation
  • Abbr : JSAV
  • 2021, 17(2), pp.143-150
  • DOI : 10.29056/jsav.2021.12.15
  • Publisher : Korea Software Assessment and Valuation Society
  • Research Area : Engineering > Computer Science
  • Received : November 30, 2021
  • Accepted : December 20, 2021
  • Published : December 31, 2021

Kye Kyung Kim 1

1한국전자통신연구원

Accredited

ABSTRACT

There is a need for an artificial intelligent technology that can reduce various types of safety accidents by analyzing the risk factors that cause safety accidents in industrial site. In this paper, user safety profiling methods are proposed that can prevent safety accidents in advance by specifying and modeling user information data related to safety accidents. User information data is classified into normal and abnormal conditions through deep learning based artificial intelligence analysis. As a result of verifying user safety profiling technology using more than 10 types of industrial field data, 93.6% of user safety profiling accuracy was obtained.

Citation status

* References for papers published after 2022 are currently being built.