References
- Breiman, L.(2001), Random forests, Machine learning, Vol. 45, No. 1, pp. 5-32. https://doi.org/10.1023/A:1010933404324
- Breiman, L., J. H. Friedman, R. Olshen, and C. J. Stone (1984), Classification and Regression Trees, Wordsworth.
- Brolsma, J. U.(1977), On Fender Design and Berthing Velocities, Proc. International Navigation Congress, Section II, Subject 4, pp. 87-100.
- Cho, I. S., J. W. Cho, and S. W. Lee(2018), A Basic Study on the Measured Data Analysis of Berthing Velocity of Ships, Journal of Coastal Disaster Prevention, Vol. 5, No. 2, pp. 61-71. https://doi.org/10.20481/kscdp.2018.5.2.61
- Diersen, S., E. J. Lee, D. Spears, P. Chen, and L. Wang (2011), Classification of seismic windows using artificial neural networks, Procedia computer science, Vol. 4, pp. 1572-1581. https://doi.org/10.1016/j.procs.2011.04.170
- Han, J., J. Pei, and M. Kamber(2011), Data Mining: Concepts and Techniques, Elsevier.
- Harris, D. and S. Harris(2007), Digital design and computer architecture, Morgan Kaufmann.
- Hastie, T., R. Tibshirani, and J. Friedman(2009), The elements of statistical learning: data mining, inference, and prediction, Springer Science & Business Media.
- Jun, S. Y., Y. M. Kim, B. G. Woo, and H. Chung(2008), A Systematic Approach to Decide Maximum Berthing Ship Size Coupled with Berth Design Criteria, Journal of the Korean Society of Marine Environment & Safety, Vol. 14, No. 1, pp. 45-54.
- Kanal, L. N.(2003), Perceptron, Encyclopedia of Computer Science, pp. 1383-1385.
- Kim, M. K., J. H. Kim, and H. Yang(2019), Gyroscope Signal Denoising of Ship's Autopilot using Kalman Filter and Multi-Layer Perceptron, Journal of the Korean Society of Marine Environment & Safety, Vol. 25, No. 6, pp. 809-818. https://doi.org/10.7837/kosomes.2019.25.6.809
- Kohavi, R.(1995), A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai, Vol. 14, No. 2, pp. 1137-1145.
- Lee, S. W., J. W. Cho, and I. S. Cho(2019), Estimation of Berthing Velocity Using Probability Distribution Characteristics in Tanker Terminal. Journal of Navigation and Port Research, Vol. 43, No. 3, pp. 186-196. https://doi.org/10.5394/KINPR.2019.43.3.186
- Ministry of oceans and fisheries(2017), Harbor and Fishery Design Criteria.
- PIANC(2020), Berthing Velocity Analysis of Seagoing Vessels over 30,000DWT, Working group 145 of the MARITIME NAVIGATION COMMISSION.
- Rosenblatt, F.(1962). Principles of Neurodynamics, Spartan Books.
- Roubos, A., L. Groenewegen, and D. J. Peters(2017), Berthing velocity of large seagoing vessels in the port of Rotterdam. Marine Structures, Vol. 51, pp. 202-219. https://doi.org/10.1016/j.marstruc.2016.10.011
- Shalev-Shwartz, S. and S. Ben-David(2014), Understanding machine learning: From theory to algorithms, Cambridge university press.
- Tukey, J. W.(1977), Exploratory Data Analysis, Addison-Wesley Pub. Co.
- Zheng, A. and A. Casari(2018), Feature Engineering for Machine Learning: Principles and Techniques for Data Scientist, O'Reilly Media Inc.
Cited by
- Development of Machine Learning Strategy for Predicting the Risk Range of Ship’s Berthing Velocity vol.8, pp.5, 2020, https://doi.org/10.3390/jmse8050376
- 폐경 여성에서 트리기반 머신러닝 모델로부터 골다공증 예측 vol.43, pp.6, 2020, https://doi.org/10.17946/jrst.2020.43.6.495
- An AIS Data-Driven Approach to Analyze the Pattern of Ship Trajectories in Ports Using the DBSCAN Algorithm vol.11, pp.2, 2021, https://doi.org/10.3390/app11020799
- Data-Driven Analysis for Safe Ship Operation in Ports Using Quantile Regression Based on Generalized Additive Models and Deep Neural Network vol.21, pp.24, 2020, https://doi.org/10.3390/s21248254
- Analysis of Trends in Mega-Sized Container Ships Using the K-Means Clustering Algorithm vol.12, pp.4, 2020, https://doi.org/10.3390/app12042115