지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.12
- 수록면
- 435 - 443 (9page)
- DOI
- 10.5143/JESK.2019.38.6.435
이용수
초록· 키워드
오류제보하기
Objective: The aim of this study is to investigate whether an artificial intelligence technique can be successfully applied to safety culture survey data classification.
Background: Without help of artificial intelligence technique, classifying safety culture level from safety culture scores collected from a large-scale survey would require a lot of experts" time and effort.
Method: Two convolution layers and 1 pooling layer was used as the middle layer of the artificial neural network to design a classifier for safety culture level. 1045 safety culture survey data collected from power plant workers used to train and validate the classifier.
Results: After 40 epochs" training the classifier approached near 95% precision in classifying "safe" and "need to be improved" classes of safety culture level. Precision, recall and F1-score for the test data set showed over 95% of accuracy performance.
Conclusion: An artificial intelligence technique using such as convolution neural network can help classification of safety culture survey data.
Application: The safety culture level classifier using deeper neural network and big survey data might improve the performance and might substitute expert interviewers for safety culture evaluation.
Background: Without help of artificial intelligence technique, classifying safety culture level from safety culture scores collected from a large-scale survey would require a lot of experts" time and effort.
Method: Two convolution layers and 1 pooling layer was used as the middle layer of the artificial neural network to design a classifier for safety culture level. 1045 safety culture survey data collected from power plant workers used to train and validate the classifier.
Results: After 40 epochs" training the classifier approached near 95% precision in classifying "safe" and "need to be improved" classes of safety culture level. Precision, recall and F1-score for the test data set showed over 95% of accuracy performance.
Conclusion: An artificial intelligence technique using such as convolution neural network can help classification of safety culture survey data.
Application: The safety culture level classifier using deeper neural network and big survey data might improve the performance and might substitute expert interviewers for safety culture evaluation.
목차
1. Introduction
2. Method
3. Results and Conclusions
References
참고문헌 (14)
참고문헌 신청
Ball, P. W. / 1998 / Approaches to Safety Culture Enhancement / British Nuclear Fuels, Ltd
Geron, A. / 2017 / Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems / O'Leilly
Hale, A. / 1998 / Occupational Injury: Risk Prevention, and Intervention / Taylor & Francis :129~166
Health and Safety Executive (HSE) / 2005 / A Review of the Safety Culture and Safety Climate Literature for the Development of the Safety Culture Toolkit
김범수 / 2016 / A Methodology for Establishment of Safety Culture in Industry through Global Company Practices / 대한인간공학회지 35(2):85~95
Geron, A. / 2017 / Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems / O'Leilly
Hale, A. / 1998 / Occupational Injury: Risk Prevention, and Intervention / Taylor & Francis :129~166
Health and Safety Executive (HSE) / 2005 / A Review of the Safety Culture and Safety Climate Literature for the Development of the Safety Culture Toolkit
김범수 / 2016 / A Methodology for Establishment of Safety Culture in Industry through Global Company Practices / 대한인간공학회지 35(2):85~95
함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
강화학습알고리즘을 이용한 안전문화평가
대한인간공학회지
2022 .04
-
A Methodology for Establishment of Safety Culture in Industry through Global Company Practices
대한인간공학회지
2016 .04
-
Safety-II 개념에 기반한 성공사례 분석과 안전문화 모범관행의 도출
대한인간공학회지
2020 .08
-
동일업종 그룹 계열사의 사업장 규모 및 작업형태별 안전문화 특성연구
한국안전학회지
2017 .01
-
조직의 안전행동에 따른 연구원의 안전의식 수준 및 안전문화 향상정도에 관한 연구
한국안전학회지
2015 .01
-
안전문화 향상 및 사고 예방을 위한 안전문화 측정시스템 구축
한국가스학회 학술대회논문집
2022 .05
-
Suggestions for More Reliable Measurement of Korean Nuclear Power Industry Safety Culture
대한인간공학회지
2016 .04
-
Measuring Safety Culture to Promote Aviation Safety Culture
대한인간공학회지
2016 .04
-
국내 철도운영기관 철도안전문화수준 조사
한국도시철도학회논문집
2015 .09
-
안전패러다임 전환에(Safety-Ⅱ) 따른 기업안전문화 평가 기법연구
대한인간공학회 학술대회논문집
2020 .10
-
안전프로그램(Safety Program) 및 안전문화(Safety Culture)에 대한 조사
한국항공운항학회지
2019 .01
-
Development Requirements of Indicators to monitor Nuclear Safety Culture
대한인간공학회 학술대회논문집
2015 .10
-
Current Status and Issues of Nuclear Safety Culture
대한인간공학회지
2016 .08
-
Machine learning methods for predicting the outcome of safety culture attributes
대한인간공학회 학술대회논문집
2022 .10
-
An Research Into The Reactive Safety Action Program for Promoting Aviation Safety Culture
대한인간공학회지
2016 .06
-
원자력 안전문화의 정량화 방법론 개발
한국안전학회지
2015 .01
-
안전문화 평가방안 연구
한국안전학회지
2016 .01
-
Convolution Neural Network Front-end / Back-end Design
대한전자공학회 학술대회
2020 .11
-
간호사의 환자안전문화인식이 환자안전간호활동에 미치는 영향：안전통제감의 조절 및 매개효과
대한설비관리학회지
2022 .06
-
비순차 데이터 입력에 대한 Convolution Neural Network 분류기 적합성 평가
대한전기학회 학술대회 논문집
2022 .07
이 논문의 저자 정보
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2020-530-000339874