• 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.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.3
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• pp.122-130
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• 2021

A wind-speed estimation at the arbitrary elevations is key component for the design of the offshore wind energy structures and the computation of the wind-wave generation. However, the wind-speed estimation of the target elevation has been carried out by using the typical functions and their typical parameters, e.g., power and logarithmic functions because the available wind speed data is limited to the specific elevation, such as 2~3m, 10 m, and so on. In this study, the parameters of the vertical profile functions are estimated with optimal and analyzed the parameter ranges using the HeMOSU-1 platform wind data monitored at the eight different locations. The results show that the mean value of the exponent of the power function is 0.1, which is significantly lower than the typically recommended value, 0.14. The values of the exponent, the friction velocity, and the roughness parameters are in the ranges 0.0~0.3, 0~10 (m/s), and 0.0~1.0 (m), respectively. The parameter ranges differ from the typical ranges because the atmospheric stability condition is assumed as the neutral condition. To improve the estimation accuracy, the atmospheric condition should be considered, and a more general (non-linear) vertical profile functions should be introduced to fit the diverse profile patterns and parameters.

• The Journal of the Convergence on Culture Technology
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• v.4 no.3
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• pp.247-252
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• 2018

The problem of the environment is urgently coming to the world as a problem that humanity must solve. In particular, Korea is directly affected by air pollution and marine pollution due to its geopolitical position with China, and is also exposed to a great deal of pollution due to air, water, soil, and weather. In this situation, due to the disconnection between the management domain / service (system) related to the environment, the ability to quickly identify causes and cope with situations in the event of environmental pollution or disasters is weak, and duplication and investment are being faced. The development of a service platform for smart environment disaster management is designed to detect environmental disasters in an early stage through the management of smart environment disaster management at the national level, It will be a way to predict complex environmental disasters.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.395-398
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• 2017

The Smart Ship is considered one of the most discussed and novel topics in developing technological period. In this reason, the amount of running researches on it is evolving so fast. As a proof, the faced drawbacks such as the departure of ships, their safety, exchanging data, traffic and data monitoring system are being solved by presenting advanced technologies and innovations like Cloud, BigData, IoT and etc. Expanding the utilization of these technologies in the Marine world emphasizes not only the departure of the ships in the water but also they focus on solving the problems of the ships connected with the communication to the ports. In this paper, we present an IoT based smart reporting and mooring system for vessels and ports. In the proposed system, the ships automatically send all the data about themselves to the port and after getting the data, ports automatically send the information about possible spaces to moor for the ships using the sensors at the port. The intended system gives an amenity to minimize the time, effort and the cost while mooring the vessels.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.230-231
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• 2019

This study is a development on condition based maintenance(CBM) technology which is a core item of future autonomous ships. It is developing to design & installation of condition monitoring system and acquisition & processing of data from ongoing ships for fault prediction & prognosis of engine in operation. The ultimate goal of this study is to develop a predicts and decision support software for marine engine faults. To do this, the FMEA and fault tree analysis of the main engine should be accompanied by the analysis of classification of system, identification of the components, the type of faults, and the cause and phenomenon of the failure. Finally, the CBM system solution software could predict and diagnose the failure of main engine through integrated analysis for bid-data of ongoing ships and engineering knowledge. Through this study, it is possible to pro-actively cope with abnormal signals of engine and to manage efficiently, and as a result, expected that marine accident and ship operation loss during navigation will be prevented in advance.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.515-524
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• 2022

To effectively collect, manage, and share the maritime traffic information, it is necessary to identify the technology trends concerning this particular information and analyze its current status and problems. Therefore, this study observes the domestic and foreign technology trends involving maritime traffic information while analyzing and summarizing the current status and problems in collecting, managing, and sharing it. According to the data analysis, the problems in the collecting stage are difficulties in collecting visual information from long-distance radars, CCTVs, and cameras in areas outside the LTE network coverage. Notably, this explains the challenges in detecting smuggling ships entering the territorial waters through the exclusive economic zone (EEZ) in the early stage. The problems in the management stage include difficult reductions and expansions of maritime traffic information caused by the lack of flexibility in storage spaces mostly constructed by the maritime transportation system. Additionally, it is challenging to deal with system failure with system redundancy and backup as a countermeasure. Furthermore, the problems in the sharing stage show that it is difficult to share information with external operating organizations since the internal network is mainly used to share maritime transportation information. If at all through the government cloud via platforms such as LRIT and SASS, it often fails to effectively provide various S/W applications that help use maritime big data. Therefore, it is suggested that collecting equipment such as unmanned aerial vehicles and satellites should be constructed to expand collecting areas in the collecting stage. In the management and sharing stages, the introduction and construction of private clouds are suggested, considering the operational administration and information disclosure of each maritime transportation system. Through these efforts, an enhancement of the expertise and security of clouds is expected.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.6
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• pp.146-154
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• 2023

In this study, the peak wave period T_p and mean wave period T₀₂ and T_{m-1, 0}, which are major parameters for classifying ocean characteristics, were calculated using water surface elevation data observed from the second west coast oceanographic and meteorological observation tower. In addition, the ratio of abnormal data, correlation analysis, and optimal probability density function were estimated. In the case of T_p among the calculated representative periods, the proportion of abnormal data was 5.73% and 0.67% at each point, and T₀₂ was 4.35% and 0.01%. T_{m-1, 0} was found to be 2.82% and 0.03%. Meanwhile, as a result of analyzing the relationship between T₀₂ and T_p, the relationship was calculated to be 0.53 and 0.63 for each point. The relationship between T_{m-1, 0} and T_p was 1.15 and 1.32, respectively, and T₀₂, T_{m-1, 0} was 1.18 and 1.22. As a result of estimating the optimal probability density function of the calculated representative period, T_p followed the 'Log-normal' and 'Normal' distributions at each point, and T₀₂ was 'Gamma', 'Normal' distribution and T_{m-1, 0} showed that 'Log-normal' and 'Normal' distribution were dominant, respectively. It is decided that these results can be used as basic data for wave analysis conducted on the west coast.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.437-445
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• 2018

IMO (International Maritime Organization) has been strengthening the regulations of ship emission gas such as sulfur oxides (SOX), nitrogen oxides (NOX) and carbon dioxides (CO2) to protect the marine environment. Especially, ECA (Emission Control Area) has been set and operated in the USA and US. As a countermeasure against these environmental regulations, the demand for environmentally, friendly and highly efficient vessels has led to a growing interest in technology related research with respect to electric propulsion systems capable of reducing exhaust gas. Container ships were excluded from the application coverage of the electric propulsion systems for reasons of operation at economical speed. However, in the future, the need for electric propulsion system is expected to rise, because it is easy to monitor and control so that it can be an applicate to smart ship which are represented by fourth industrial revolution technology. In this study, research was carried out to design a generator and battery capacity through the load analysis of the 6,800TEU container ship to apply the electric propulsion system of the container ship. A capacity design based on the load analysis has an advantage that the generator can be operated in a high efficiency section through the load distribution control using the battery.

• Journal of the Korean earth science society
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• v.45 no.1
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• pp.48-71
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• 2024

Typhoons are representative oceanic and atmospheric phenomena that cause interactions within the Earth's system with diverse influences. In recent decades, the typhoons have tended to strengthen due to rapidly changing climate. The 2022 revised science curriculum emphasizes the importance of teaching-learning activities using advanced science and technology to cultivate digital literacy as a citizen of the future society. Therefore, it is necessary to solve the temporal and spatial limitations of textbook illustrations and to develop effective instructional materials using global-scale big data covered in the field of earth science. In this study, according to the procedure of the PDIE (Preparation, Development, Implementation, Evaluation) model, the inquiry activity data was developed to visualize the track of the typhoon using the image data of GK-2A. In the preparatory stage, the 2015 and 2022 revised curriculum and the contents of the inquiry activities of the current textbooks were analyzed. In the development stage, inquiry activities were organized into a series of processes that can collect, process, visualize, and analyze observational data, and a GUI (Graphic User Interface)-based visualization program that can derive results with a simple operation was created. In the implementation and evaluation stage, classes were conducted with students, and classes using code and GUI programs were conducted respectively to compare the characteristics of each activity and confirm its applicability in the school field. The class materials presented in this study enable exploratory activities using actual observation data without professional programming knowledge which is expected to contribute to students' understanding and digital literacy in the field of earth science.