인공신경망 모델을 이용한 항만 다운타임 예측

DC Field Value Language
dc.contributor.author 이진학 -
dc.contributor.author 류경호 -
dc.contributor.author 정원무 -
dc.contributor.author 백원대 -
dc.date.accessioned 2020-07-16T04:33:09Z -
dc.date.available 2020-07-16T04:33:09Z -
dc.date.created 2020-02-11 -
dc.date.issued 2014-06-18 -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/26165 -
dc.description.abstract In this study, a downtime forecasting technique for Pohang New Harbour, which is located near the inmost part of pocket-type bay in the east coast of Korea and where downtime due to swell-like waves frequently occur, was developed using the artificial neural networks model as the non-parametric estimation model. For this purpose, a neural networks model was constructed using wide-area wave observational data from ocean wave buoys and observational data from bay entrance and several points in harbour from September 2008 onward. The back-propagation neural networks model, having excellent generalization capability and being widely employed in various engineering problems including weather forecasting, was used as the neural networks model. Wide-area observational data was used as input data, while in-harbour observational data was used as output data. Currently, results are being compared using various neural network structures, and it is expected that the optimum neural networks model would be constructed through this comparative study. Moreover, using such an optimally constructed artificial neural networks model, it is also expected that the forecasting system can be constructed and operated more efficiently. artificial neural networks model as the non-parametric estimation model. For this purpose, a neural networks model was constructed using wide-area wave observational data from ocean wave buoys and observational data from bay entrance and several points in harbour from September 2008 onward. The back-propagation neural networks model, having excellent generalization capability and being widely employed in various engineering problems including weather forecasting, was used as the neural networks model. Wide-area observational data was used as input data, while in-harbour observational data was used as output data. Currently, results are being compared using various neural network structures, and it is expected that the optimum neural networks model would be constructed through this comparative study. Moreover, using such an optimally constructed artificial neural networks model, it is also expected that the forecasting system can be constructed and operated more efficiently. -
dc.description.uri 1 -
dc.language English -
dc.publisher ASCE -
dc.relation.isPartOf Proceedings of the 34th International Conference on Coastal Engineering (ICCE 2014) -
dc.title 인공신경망 모델을 이용한 항만 다운타임 예측 -
dc.title.alternative Downtime Forecasting Technique in Harbour Using Artificial Neural Netwroks Model -
dc.type Conference -
dc.citation.conferencePlace KO -
dc.citation.endPage 1 -
dc.citation.startPage 1 -
dc.citation.title Proceedings of the 34th International Conference on Coastal Engineering (ICCE 2014) -
dc.contributor.alternativeName 이진학 -
dc.contributor.alternativeName 류경호 -
dc.contributor.alternativeName 정원무 -
dc.contributor.alternativeName 백원대 -
dc.identifier.bibliographicCitation Proceedings of the 34th International Conference on Coastal Engineering (ICCE 2014), pp.1 -
dc.description.journalClass 1 -
Appears in Collections:
Marine Industry Research Division > Ocean Space Development & Energy Research Department > 2. Conference Papers
Marine Industry Research Division > Maritime ICT & Mobility Research Department > 2. Conference Papers
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