과제정보
이 논문은 2023년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구 (No.RS-2023-00218913)와 해양수산부 재원으로 선박해양플랜트연구소의 기본사업인 "스마트 해양안전 및 기업지원을 위한 오픈플랫폼 기술개발"에 의해 수행되었습니다 (1525014880, PES4880).
참고문헌
- Abualigah, L. M., A. T. Khader, and M. A. Al-Betar(2016), Multi-objectives-based text clustering technique using K-mean algorithm. In 2016 7th International Conference on Computer Science and Information Technology (CSIT), IEEE, pp. 1-6.
- Ashari, I. F., E. D. Nugroho, R. Baraku, I. N. Yanda, and R. Liwardana(2023), Analysis of Elbow, Silhouette, Davies-Bouldin, Calinski-Harabasz, and Rand-Index Evaluation on K-Means Algorithm for Classifying Flood-Affected Areas in Jakarta. Journal of Applied Informatics and Computing, 7(1), 95-103. https://doi.org/10.30871/jaic.v7i1.4947
- Chen, P., Y. Huang, J. Mou, and P. Van Gelder(2018), Ship collision candidate detection method: A velocity obstacle approach. Ocean Engineering, 170, pp. 186-198. https://doi.org/10.1016/j.oceaneng.2018.10.023
- Cho, D. O., J. Y. Mok, and Y. U. Park(2002), The direction of development for the maritime safety tribunal system in Korea. Han'guk Haeyang Susan Kaebarwon.
- Choi, C. W., Y. N. Roh, D. S. Shin, H. M. Kim, and H. C. Park(2021), Identifying Risk Factors of Marine Accidents in Coastal Area by Marine Accident Types. Journal of the Korean Society of Transportation, 39(4), 540-554. https://doi.org/10.7470/jkst.2021.39.4.540
- Devlin, J., M. W. Chang, K. Lee, and K. Toutanova(2018), Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
- Fan, S., E. Blanco-Davis, Z. Yang, J. Zhang, and X. Yan (2020), Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network. Reliability Engineering & System Safety, 203, 107070.
- Faruqui, M., Y. Tsvetkov, P. Rastogi, and C. Dyer(2016), Problems with evaluation of word embeddings using word similarity tasks. ACL 2016, 30.
- Ham, J., Y. J. Choe, K. Park, I. Choi, and H. Soh(2020), KorNLI and KorSTS: New benchmark datasets for Korean natural language understanding. arXiv preprint arXiv:2004.03289.
- Han, Y. J.(2022), Development of risk leading indicators by sea area based on ship operation characteristics (Master's thesis). Pusan National University.
- He, A., C. Luo, X. Tian, and W. Zeng(2018), A twofold siamese network for real-time object tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4834-4843.
- Huang, Y., P. Van Gelder, and Y. Wen(2018), Velocity obstacle algorithms for collision prevention at sea. Ocean Engineering, 151, pp. 308-321. https://doi.org/10.1016/j.oceaneng.2018.01.001
- Jee, T. C., H. J. Lee, and Y. B. Lee(2007), Determining the number of Clusters in On-Line Document Clustering Algorithm. The KIPS Transactions: PartB, 14(7), 513-522. https://doi.org/10.3745/KIPSTB.2007.14-B.7.513
- Joshi, A., A. Kajale, J. Gadre, S.Deode, and R. Joshi(2023), L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi. In Science and Information Conference (pp. 1184-1199). Cham: Springer Nature Switzerland.
- Jung, C. H.(2018), A study on the improvement of safety by accidents analysis of fishing vessels, J. Fish. Mar. Sci. Educ, Vol. 30, pp. 179-186. https://doi.org/10.13000/JFMSE.2018.02.30.1.176
- Kadhim, A. I., Y. N. Cheah, and N. H. Ahamed(2014), Text document preprocessing and dimension reduction techniques for text document clustering. In 2014 4th international conference on artificial intelligence with applications in engineering and technology, IEEE, pp. 69-73.
- Kalra, V. and R. Aggarwal(2017), Importance of Text Data Preprocessing & Implementation in RapidMiner. ICITKM, 14, pp. 71-75.
- Kassambara, A.(2017), Practical guide to cluster analysis in R: Unsupervised machine learning, Vol. 1.
- Kim, G. and H. Kim(2011), Development of ship safety navigation supporting equipmentusing infrared LED. Journal of the Korea Institute of Information Technology, 9(2), pp. 27-32.
- Kim, S. -K. and J. -P. Kang(2011), A Study on the Relationships between the Casualties of Fishing Boats and Meteorological Factors (Doctoral dissertation).
- Kim, W. -S., Y. -K. Hyun, and Y. -W. Lee(2020), Risk factors of fisher on stow net fishing vessel using analysis of adjudication, Journal of the Korean Society of Fisheries and Ocean Technology, Vol. 56, pp. 155-162. https://doi.org/10.3796/KSFOT.2020.56.2.155
- Kodinariya, T. M. and P. R. Makwana(2013), Review on determining number of Cluster in K-Means Clustering. International Journal, 1(6), pp. 90-95.
- Korean Maritime Safety Tribunal(2007), Busan Regional Maritime Safety Tribunal Decision 2007-036.
- Korean Maritime Safety Tribunal(2008), Busan Regional Maritime Safety Tribunal Decision 2008-002.
- Korean Maritime Safety Tribunal(2009a), Busan Regional Maritime Safety Tribunal Decision 2009-032.
- Korean Maritime Safety Tribunal(2009b), Busan Regional Maritime Safety Tribunal Decision 2009-039.
- Korean Maritime Safety Tribunal(2009c), Busan Regional Maritime Safety Tribunal Decision 2009-059.
- Korean Maritime Safety Tribunal(2010a), Busan Regional Maritime Safety Tribunal Decision 2010-020.
- Korean Maritime Safety Tribunal(2010b), Busan Regional Maritime Safety Tribunal Decision 2010-062.
- Korean Maritime Safety Tribunal(2012), Busan Regional Maritime Safety Tribunal Decision 2012-046.
- Korean Maritime Safety Tribunal(2016a), Busan Regional Maritime Safety Tribunal Decision 2016-052.
- Korean Maritime Safety Tribunal(2016b), Busan Regional Maritime Safety Tribunal Decision 2016-061.
- Korean Maritime Safety Tribunal(2017a), Busan Regional Maritime Safety Tribunal Decision 2017-054: Summary of the Collision Case between Fishing Vessels Deukyongho and Buyeongho.
- Korean Maritime Safety Tribunal(2017b), Busan Regional Maritime Safety Tribunal Decision 2017-058: Summary of the Collision Case between Fishing Vessel Geoseongho and Hansungho.
- Korean Maritime Safety Tribunal(2017c), Busan Regional Maritime Safety Tribunal Decision 2017-069: Summary.
- Korean Maritime Safety Tribunal(2018a), Busan Regional Maritime Safety Tribunal Decision 2018-021: Summary.
- Korean Maritime Safety Tribunal(2018b), Busan Regional Maritime Safety Tribunal Decision 2018-069: Summary.
- Korean Maritime Safety Tribunal(2019a), Busan Regional Maritime Safety Tribunal Decision 2019-007: Summary.
- Korean Maritime Safety Tribunal(2019b), Busan Regional Maritime Safety Tribunal Decision 2019-018: Summary.
- Korean Maritime Safety Tribunal(2020a), Busan Regional Maritime Safety Tribunal Decision 2020-008: Summary.
- Korean Maritime Safety Tribunal(2020b), Busan Regional Maritime Safety Tribunal Decision 2020-024: Summary.
- Korean Maritime Safety Tribunal(2020c), Busan Regional Maritime Safety Tribunal Decision 2020-086: Summary.
- Korean Maritime Safety Tribunal(2021), Busan Regional Maritime Safety Tribunal Decision 2021-024: Summary.
- KMST. (2022). Marine accident statistics and casebook, 12.
- Lee, J. S., B. K. Lee, and I. S. Cho(2019), Text Mining Analysis Technique on ECDIS Accident Report. Journal of the Korean Society of Marine Environment & Safety, 25(4), 405-412. https://doi.org/10.7837/kosomes.2019.25.4.405
- Liu, Y., M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov(2019), Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
- Madhulatha, T. S.(2012), An overview on clustering methods. arXiv preprint arXiv:1205.1117.
- Park, H., M. A. Cheon, Y. Namgung, H. Yoon, M. S. Choi, J. G. Kim, and J. H. Kim(2020), Classification of vessel accidents according to word and sentence embedding. Proceedings of the Korean Institute of Information Scientists and Engineers Conference, 413-415.
- Park, S. -A. and D. -J. Park(2023), A study on the analysis of marine accidents on fishing ships using accident cause data, Journal of Korean Navigation and Port Research, Vol. 47-1, pp. 1-9.
- Reimers, N. and I. Gurevych(2019), Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084.
- Soni, N. and A. Ganatra(2012), Categorization of several clustering algorithms from different perspective: a review. International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, No. 8, pp. 63-68.
- Tan, P. -N., M. Steinbach, and V. Kumar(2005), Introduction to Data Mining, Addison-Wesley, ISBN 0-321-32136-7, Chapter 8, page 500.
- Tirunagari, S., N. Poh, D. Windridge, A.Iorliam, N. Suki, and A. T. Ho(2015), Detection of face spoofing using visual dynamics. IEEE transactions on information forensics and security, 10(4), pp. 762-777. https://doi.org/10.1109/TIFS.2015.2406533
- Vijaymeena, M. K. and K. Kavitha(2016), A survey on similarity measures in text mining. Machine Learning and Applications: An International Journal, 3(2), pp. 19-28. https://doi.org/10.5121/mlaij.2016.3103
- Wolfram Research(2007), CosineDistance - Wolfram Language & System Documentation Center, wolfram.com.
- Xie, J. and S. Jiang(2010), A simple and fast algorithm for global k-means clustering. 2010 Second International Workshop on Education Technology and Computer Science.