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| Abstract | Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship’s operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship’s velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study. |
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| Publication Date | 2021-08-12 |
| Role | Corresponding Author |
| Category | SCIE |
| Impact Factor | 2.473 |
June-Beom Lee, Myung-Il Roh, Ki-Su Kim, “Prediction of Ship Power Based on Variation in Deep Feed-forward Neural Network”, International Journal of Naval Architecture and Ocean Engineering, Vol. 13, pp. 641-649, 2021.08.12
https://doi.org/10.1016/j.ijnaoe.2021.08.001
Min-Chul Kong, Myung-Il Roh, Ki-Su Kim, Ho-Gyun Park, Jong-Oh Kim, "Variable Indexing Method in Rule Documents for Ship Design Using Extraction of PDF Elements", Journal of Computational Design and Engineering, Vol. 9, No. 6, 2022.11.16
Jin-Hyeok Kim, Myung-Il Roh et al., "Prediction of the Superiority of the Hydrodynamic Performance of Hull Forms Using Deep Learning", International Journal of Naval Architecture and Ocean Engineering, Vol. 49, pp. 100490.1-15, pp. 2022.11.01
Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, "Automation of Crane Control for Block Lifting Based on Deep Reinforcement Learning", Journal of Computational Design and Engineering, Vol. 9, No. 4, pp. 1430- 1448, 2022.07.23
Min-Chul Kong, Myung-Il Roh, Ki-Su Kim, Jeongyoul Lee, Jongoh Kim, Gapheon Lee, "Object Detection Method for Ship Safety Plans Using Deep Learning", Ocean Engineering, 2022.02.15
Ki-Su Kim, Myung-Il Roh, Seung-Min Lee, "Quasi-static Flooding Analysis Method of a Damaged Ship Considering Oil Spill and Cargo Load", Journal of Ship Production and Design, 2022.02.10
Won-Jae Lee, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Yeong-Min Cho, Nam-Sun Son, “Detection and Tracking for the Awareness of Surroundings of a Ship Based on Deep Learning”, Journal of Computational Design and Engineering, Vo. 8, No. 5
June-Beom Lee, Myung-Il Roh, Ki-Su Kim, “Prediction of Ship Power Based on Variation in Deep Feed-forward Neural Network”, International Journal of Naval Architecture and Ocean Engineering, 2021.08.07
Hye-Won Lee, Myung-Il Roh, Ki-Su Kim, “Ship Route Planning in Arctic Ocean Based on POLARIS”, Ocean Engineering, Vol. 234, pp. 109297.1-14, 2021.08.15
Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Donghun Yu, “Deep Reinforcement Learning-based Collision Avoidance for an Autonomous Ship”, Ocean Engineering, Vol. 234, pp. 1-20, 2021.08.15
Beom-Soo Kim, Min-Jae Oh, Jae-Hoon Lee, Yonghwan Kim, Myung-Il Roh, "Study on Hull Optimization Process Considering Operational Efficiency in Waves", Processes, Vol. 9, No. 5, pp. 898.1-21, 2021.05.19
Min-Jae Oh, Myung-Il Roh, Sung-Woo Park, Do-Hyun Chun, Myeong-Jo Son, Jeong-Youl Lee, “Operational Analysis of Container Ships by Using Maritime Big Data”, Journal of Marine Science and Engineering, Vol. 9, No. 4, pp. 438: 1-21, 2021.04.18
남권우, 노명일, 이혜원, 이원재, "자율 운항 선박을 위한 딥 러닝 기반 선박 이미지 분류 방법", 한국CDE학회 논문집, Vol. 26, No. 2, pp. 144-153, 2021.06.01
Jisang Ha, Myung-Il Roh, Hye-Won Lee, “Quantitative Calculation Method of the Collision Risk for Collision Avoidance in Ship Navigation Using the CPA and Ship Domain”, Journal of Computational Design and Engineering, Vol. 8, No. 3, 2021.06.01
Seung-Min Lee, Jong-Hyeok Lee, Myung-Il Roh, Ki-Su Kim, Seung-Ho Ham, Hye-Won Lee, "An Optimization Model of Tugboat Operation for Conveying a Large Surface Vessel", Journal of Computational Design and Engineering, Vol. 8, No. 2, 2021.04.01
Hye-Won Lee, Myung-Il Roh, Seung-Ho Ham, "Underactuated Crane Control for the Automation of Block Erection in Shipbuilding", Automation in Construction, Vol. 124, pp. 1-28, 2021.04.01
Hye-Won Lee, Joo-Hyun Woo, Myung-Il Roh, Seung-Ho Ham, et al., "Integrated Simulation of Virtual Prototypes and Control Algorithms of Unmanned Surface Vehicles Based on a Robot Operating System", Journal of Marine Science and Technology (Taiwan)
김기수, 노명일, 정양재, 나갑주, "해기상 예측 및 가시화를 위한 응용 프로그램", 한국CDE학회 논문집, Vol. 25, No. 4, pp. 434-444, 2020.12.01
Jisang Ha, Myung-Il Roh, Seung-Ho Ham, Sung-Jun Lee, “Optimum Design of the Underwater Discharge System Based on Surrogate Modeling”, Journal of Marine Science and Technology (Taiwan), Vol. 29, No. 3, pp. 338-353, 2021.06.01
Ki-Su Kim, June-Beom Lee, Myung-Il Roh, Ki-Min Han, Gap-Heon Lee, "Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM", Journal of Marine Science and Engineering, Vol. 8, No. 10, pp. 805:1-24, 2020.10.14
Ki-Su Kim, Myung-Il Roh, “ISO 15016:2015-based Method for Estimating the Fuel Oil Consumption of a Ship”, Journal of Marine Science and Engineering, Vol. 8, No. 10, pp. 791:1-18, 2020.10.12
