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
- 2024.3
- 수록면
- 33 - 47 (15page)
- DOI
- 10.33162/JAR.2024.3.24.1.033
이용수
초록· 키워드
오류제보하기
Purpose: The detection of external and internal forces is important for the safety evaluation of mechanical structures. In this study, the results of using a multi-layer perceptron (MLP) and convolution neural network (CNN) to improve the detection reliability were analyzed.
Methods: Load data collected from structural analysis of a crawler crane was used for training the MLP and CNN. A brief procedure for setting the learning conditions and functions for the MLP and CNN was presented and verified with quantitative figures.
Results: The appropriate conditions for the functions used by the MLP and CNN for learning and recognizing the loads on the crawler crane were identified. Under these conditions, the maximum errors of the MLP and CNN were 0.24 and 1.45 tons, respectively. Thus, the MLP was more reliable at learning and recognizing the loads.
Conclusion: Based on the results, MLPs and CNNs can effectively detect loads on mechanical structures and systems. Thus, load-detection methods based on MLPs and CNNs could improve the safety evaluation of mechanical structures.
Methods: Load data collected from structural analysis of a crawler crane was used for training the MLP and CNN. A brief procedure for setting the learning conditions and functions for the MLP and CNN was presented and verified with quantitative figures.
Results: The appropriate conditions for the functions used by the MLP and CNN for learning and recognizing the loads on the crawler crane were identified. Under these conditions, the maximum errors of the MLP and CNN were 0.24 and 1.45 tons, respectively. Thus, the MLP was more reliable at learning and recognizing the loads.
Conclusion: Based on the results, MLPs and CNNs can effectively detect loads on mechanical structures and systems. Thus, load-detection methods based on MLPs and CNNs could improve the safety evaluation of mechanical structures.
목차
1. 서론
2. 인공 신경망의 적용 모델
3. 다층 신경망의 조건 설정
4. 합성곱 신경망의 조건 설정
5. 다층 신경망과 합성곱 신경망 학습의 결과
6. 결론
References
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