Acknowledgement
이 논문(혹은 프로젝트, 연구)은 2022년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. 2020R1A2C1102294).
References
- Wang, Y. H., Ou, Y., Deng, X. D., Zhao, L. R., & Zhang, C. Y. (2019, June). The Ship Collision Accidents Based on Logistic Regression and Big Data. In 2019 Chinese Control And Decision Conference (CCDC) (pp. 4438-4440). IEEE.
- Z.J. Liu and Z.L. Wu, "Human factor data mining based on ship collision accident investigation report [J]", China Navigation, vol. 2, no. 59, pp. 1-6, 2004.
- Li, G., Weng, J., & Hou, Z. (2021). Impact analysis of external factors on human errors using the ARBN method based on small-sample ship collision records. Ocean Engineering, 236, 109533.
- 김상수, 정재용, 하원재, 송두현, & 박진수. (2000). 선박충돌 사고의 원인 조사 및 분석방법에 관한 연구'. 한국해양항만학회지, 24(1).
- Fujii, Y., & Tanaka, K. (1971). Traffic Capacity. Journal of Navigation, 24(4), 543-552. doi:10.1017/S0373463300022384
- Hansen, M., Jensen, T., Lehn-Schioler, T., Melchild, K., Rasmussen, F., & Ennemark, F. (2013). Empirical Ship Domain based on AIS Data. Journal of Navigation, 66(6), 931-940. doi:10.1017/S0373463313000489
- Zhang, L., & Meng, Q. (2019). Probabilistic ship domain with applications to ship collision risk assessment. Ocean Engineering, 186, 106130.
- Chen, M., Shi, X., Zhang, Y., Wu, D., & Guizani, M. (2017). Deep feature learning for medical image analysis with convolutional autoencoder neural network. IEEE Transactions on Big Data, 7(4), 750-758.
- Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 international conference on engineering and technology (ICET) (pp. 1-6). Ieee.
- 허명회, & 이용구. (2004). K-평균 군집화의 재현성 평가 및 옹용.
- Xiao, F., Ligteringen, H., Van Gulijk, C., & Ale, B. (2015). Comparison study on AIS data of ship traffic behavior. Ocean Engineering, 95, 84-93. https://doi.org/10.1016/j.oceaneng.2014.11.020
- Choo, H. S., & Kim, D. S. (2013). Tide and Tidal Currents Around the Archipelago on the Southwestern Waters of the South Sea, Korea. Journal of the Korean Society of Marine Environment & Safety, 19(6), 582-596. https://doi.org/10.7837/kosomes.2013.19.6.582