• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.91-91
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• 2020

IPCC (Intergovernmental Panel on Climate Change) 5차 평가보고서에 의하면 최근 배출 온실가스의 양은 관측 이래 최고 수준이며 온실가스로 인한 기후변화는 인간계와 자연계에 광범위한 영향을 주고 있다고 보고하였다. 기후변화의 영향은 국제적으로 빙하 감소, 사막화, 해수면 상승 등 뚜렷하게 나타나고 있다. 이러한 기후변화에 대응하기 위해 온실가스 완화 정책과 동시에 새로운 기후변화 환경에 적응하는 것이 필요하다. 기후변화 적응이란 현재 나타나고 있거나 미래에 나타날 것으로 예상되는 기후변화의 파급효과와 영향에 대응할 수 있도록 하는 모든 행동이며 이를 위해서는 기후변화 영향분석이 수반되어야 한다. MOTIVE 연구단에서는 기후변화 적응대책 수립의 지원을 목표로 7개 부문(건강, 물관리, 농업, 산림, 생태, 해양, 수산)에서 "한국형 통합평가 모형"을 개발하고 있다. 각 부문에서 개발하는 프로세스 모델은 시스템에 대한 지식을 가진 상황에서 사용하면 신뢰할 수 있는 예측 결과를 얻을 수 있지만, 부문별 통합을 통한 영향 분석 시 타 분야에 대한 지식이 수반되어야 하는 어려움을 가진다. 이를 위해 본 연구에서는 시스템 내의 물리적 프로세스에 대한 요구 없이 입출력 데이터만을 이용하여 결과를 신속하게 추정하는 데이터 모델링(기계학습)을 이용하였다. 데이터 모델링을 위한 데이터는 다양한 자연 현상에 대한 BANPOL(수질 프로세스 모델) 분석을 통한 자료를 이용하여 학습 자료를 구축하였다. 즉, 데이터 모델링은 BANPOL 모델을 대리하는 메타모델이며, 낙동강 표준유역에 대한 유량 및 수질을 높은 상관성으로 추정하였다. 원 모델보다 정확도는 낮을 수 있으나 메타모델의 개발을 통한 웹 시스템을 개발하여 비전문가의 구동 및 신속한 기후 시나리오를 적용할 수 있는 환경을 개발하였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.22-35
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• 2011

Incheon Bay has a suitable condition for tidal power generation due to the high tidal range by topographical effect. Therefore a study on the technology development for tidal energy utilization has been promoted since 2006. It is needed to deduce optimal alternatives to determine the suitable location of facilities for tidal power generation and to reduce the environmental damage from development. In order to carry out efficiently this mission, spatial information system is essential to manage and use various spacial elements related to the development and conservation. In this study, for the development of tidal energy, spatial data could be defined as three kinds of dataset. Fundamental dataset is defined as spatial data such as tide, tidal current, wave, erosion and sedimentation. Framework dataset is composed of topographical map, facility map and bathymetry. The reference dataset is composed of marine ecology and environment having the characteristics of thematic map. This study is mainly aimed at establishing methodology of conceptual spatial data modeling classifying as essential data model and optional data model through the definition of the components of spatial data.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.275-276
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• 2022

VTS operators instruct ships to wait for entry and departure to sail in one-way to prevent ship collision accidents in ports with narrow routes. Currently, the instructions are not based on scientific and statistical data. As a result, there is a significant deviation depending on the individual capability of the VTS operators. Accordingly, this study built a 1d-convolutional neural network model by collecting ship and weather data to predict the exact travel time for ship entry/departure waiting for instructions in the port. It was confirmed that the proposed model was improved by more than 4.5% compared to other ensemble machine learning models. Through this study, it is possible to predict the time required to enter and depart a vessel in various situations, so it is expected that the VTS operators will help provide accurate information to the vessel and determine the waiting order.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.51-52
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• 2019

In the past, waves, which are the main external forces acting on marine and coastal structures, have been dealt with only in terms of safety. Recently, various studies have been conducted to define the characteristics of waves in a specific area in order to respond to the increasing demand for diversified marine activities such as ocean energy development and marine leisure sports. Although the characteristics of waves are specific to the site, the available spectrum model proposed in previous studies are limited. In this study, we analyzed the shape of the ocean wave spectrum by comparing it with the standard spectrum model.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.4
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• pp.365-372
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• 2024

The South Korean marine industry is emerging as a significant market, driven by the growing popularity of various water leisure activities, including sailing. This trend suggests a rising demand for sailing yachts. Consequently, since 2022, the design and development of a 28ft sailing yacht have been ongoing, supported by the government and the Ministry of Oceans and Fisheries, to promote yachting culture in South Korea. The Velocity Prediction Program (VPP) analysis was conducted using WinDesign during the preliminary design stage to evaluate performance and determine design parameters. The hydrodynamic model used for this vessel is based on regression methods developed from years of experience in naval architecture and yacht research at the Wolfson Unit, providing reliable estimates for most modern yachts. However, owing to the lack of specific hydrodynamic data from towing tank tests or CFD numerical analysis, verification of the hydrodynamic model has faced some challenges. Additionally, an incomplete weight estimate resulted in variable VCG values, potentially affecting stability and overall performance. The optimal boat speed for this vessel was determined at true wind speeds (TWS) of 4, 8, 12, 16, and 20 knots, using both the jib (up to 120° TWA) and the spinnaker (from 80° TWA). The optimized speed of the yacht was found to be comparable to that of international similar-class yachts.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.10a
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• pp.230-232
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• 2014

해양 분야에서 최근 활발한 논의가 이루어지고 있는, 해상에서 이루어지는 여러 가지 활동에 필요한 정보를 적시에 제공함으로써 사용자의 의사결정을 지원하는 서비스 체계로서의 e-navigation에 대해 살펴본다. 그리고 e-navigation 환경에서 해상과 육상의 다양한 데이터를 통합, 활용한 정보 서비스를 제공함에 있어 S-100의 역할에 대해 논의한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.276-283
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• 2018

Climate changes consistently cause coastal accidents such as coastal flooding, so the studies on monitoring the marine environments are progressing to prevent and reduce the damage from coastal accidents. In this paper, we propose a new method to predict the sea level which can be applied to coastal monitoring systems to observe the variation of sea level and warn about the dangers. Existing sea level models are very complicated and need a lot of tidal data, so they are not proper for real-time prediction systems. On the other hand, the proposed algorithm is very simple but precise in short period such as one or two hours since we use the measured data from the sensor. The proposed method uses Kalman filter algorithm for harmonic analysis and double exponential smoothing for additional error correction. It is shown by experimental results that the proposed method is simple but predicts the sea level accurately.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.72-78
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• 2019

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.222-224
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• 2019

자율운항선박의 도입을 위해서는 선박 항해 장비 및 시스템들에 대한 국제표준화가 필수적이며, 선박의 항해 및 무선통신 장비들에 대한 표준을 제정하는 국제전기연합(IEC)의 표준화 활동은 매우 중요하다. 본 논문에서는 자율운항선박에서 다루게 될 수 있는 사이버보안 표준을 비롯하여 선박 장비 및 시스템에 대한 표준현황을 다루고 있다. 또한, 해사서비스의 디지털화를 위한 고려요소인 해사공통정보모델과 이를 위한 이내비게이션 서비스 표준 동향도 다루고 있다.