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
- 2022.10
- 수록면
- 295 - 302 (8page)
이용수
초록· 키워드
오류제보하기
The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.
목차
ABSTRACT
1. Introduction
2. Research Methodology
3. Data and Learning
4. Results of the Research
5. Conclusion
References
참고문헌 (19)
참고문헌 신청
Bertheussen Karolius, K., Cichowicz, J., & Vassalos, D. (2018). Risk-based positioning of flooding sensors to reduce prediction uncertainty of damage survivability. Proceedings of the 13th International Conference on the Stability of Ships and Ocean Vehicles, Kobe, Japan.
Calabrese, F., Corallo, A., Margherita, A., & Zizzari, A. A. (2012). A knowledge-based decision support system for shipboard damage control. Expert Systems with Applications, 39(9), 8204–8211. https://doi.org/10.1016/j.eswa.2012.01.146
DNV GL. (2017). Rules for classification ships - Part 3 Hull – Chapter 15 Stability. Det Norske Veritas.
Hu, L. F., Ma, K., & Ji, Z. S. (2013). AM–H method-based decision support system for flooding emergencies onboard warship. Ocean Engineering, 58, 192–200. https://doi.org/10.1016/j.oceaneng.2012.10.012
Hu, L. F., Tian, Z., Sun, Z., Zhang, Q., & Feng, B. (2015). Genetic algorithm-based counter-flooding decision model for damaged warship. International Shipbuilding Progress, 62(1–2), 1–15. https://doi.org/10.3233/ISP-140114
Calabrese, F., Corallo, A., Margherita, A., & Zizzari, A. A. (2012). A knowledge-based decision support system for shipboard damage control. Expert Systems with Applications, 39(9), 8204–8211. https://doi.org/10.1016/j.eswa.2012.01.146
DNV GL. (2017). Rules for classification ships - Part 3 Hull – Chapter 15 Stability. Det Norske Veritas.
Hu, L. F., Ma, K., & Ji, Z. S. (2013). AM–H method-based decision support system for flooding emergencies onboard warship. Ocean Engineering, 58, 192–200. https://doi.org/10.1016/j.oceaneng.2012.10.012
Hu, L. F., Tian, Z., Sun, Z., Zhang, Q., & Feng, B. (2015). Genetic algorithm-based counter-flooding decision model for damaged warship. International Shipbuilding Progress, 62(1–2), 1–15. https://doi.org/10.3233/ISP-140114
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