• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1203-1213
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• 2018

In Arctic and Antarctic ocean, remote sensing is the most effective observation for environmental changes due to the inaccessibility of the regions. Even though satellite, UAV (Unmanned Aerial Vehical) are well known remote sensing platforms, and research vessel also used for automatic measurement on the regions, varied environment of Polar regions require time series and wide coverage of data. Especially, in high latitude, apply an optical satellite remote sensing is not easy due to low sun altitude. In this paper, we introduce an operation of hyper-spectrometer (HyperSAS/Satlantic inc.) which is mounted on Ice Breaker Research Vessel ARAON of Korea Polar Research Institute since 2010, to acquire an above water reflectance atomatically through every research cruise on Arctic and Antarctic ocean and transit both regions. In addition to, auxiliary data for the remotely acquired data, in situ water sampling were also obtained. The above water reflectance and in situ water sampling data are continuously acquired since 2010 will contribute to improve an Ocean Color algorithm in the high latitude and help to understand ocean reflectances over from high latitude through low latitude. Preliminary result from above water reflectance showed characteristics of Arctic ocean and Antarctic Ocean and used to develop algorithms for estimating various ocean factors such as chlorophyll and suspended sediment.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.421-426
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• 2018

With regard to LED lighting devices which have currently been commercialized, LED operating sequences are being sold in a fixed state. In such a state, the external environmental factors are not taken into consideration as only the illumination environment application is considered. Currently, it is difficult to create an optimal lighting environment which can adapt to changes in external environmental factors in the ship. Therefore, it was concluded that there is a need to input the external environment value so that the optimal illumination value can be reflected in real time in order to adapt more organically and actively to the change of external environmental factors. In this paper, we used a microprocessor as an integrated management system for environmental data that changes in real time according to existing external environmental factors. In addition, a controller capable of lighting control of RGB LED module by combining fuzzy inference system. For this, a fuzzy control algorithm is designed and a fuzzy control system is constructed. The distance and the illuminance value from the external environment element are input to the sensor, and these values are converted to the optimum illumination value through the fuzzy control algorithm, and are expressed through the dimming control of the RGB LED module and the practical effectiveness of the fuzzy control system is confirmed.

• Journal of Navigation and Port Research
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• v.43 no.3
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• pp.186-196
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• 2019

Berthing energy is majorly influenced by the berthing velocity. It is necessary to design an appropriate berthing velocity for each pier, since excessive berthing velocity can cause berthing accident causing damage to the ship and pier. In this study, as a statistical approach for berthing velocity, the probability distributions suitable for the berthing velocities were confirmed using the K-S test, the A-D test and the Q-Q plot. As a result, the frequency distribution of the berthing velocity was found to be suitable using the Weibull distribution as well as the lognormal distribution. Additionally, the predicted values obtained through estimation of the berthing velocity using the concept of probability of exceedance in this study is proposed as a reference of design berthing velocity. It can be observed that the design berthing velocity is set to be somewhat low so that it does not practically match with the reality. This study and its results can be expected to contribute to the development of a proper design velocity calculation method.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.289-296
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• 2022

The purpose of this study was to investigate the inter-correlation between crude oil prices and Dry Bulk Freight rates. Eco-friendly shipping fuels has being actively developed to reduce carbon emission. However, carbon neutrality will take longer than anticipated in terms of the present development process. Because of OVID-19 and the Russian invasion of Ukraine, crude oil price fluctuation has been exacerbated. So we must examine the impact on Dry Bulk Freight rates the oil prices have had, because oil prices play a major role in shipping fuels. By using the VAR (Vector Autoregressive) model with monthly data of crude oil prices (Brent, Dubai and WTI) and Dry Bulk Freight rates (BDI, BCI and (BP I) 2008.10~2022.02, the empirical analysis documents that the oil prices have an impact on Dry bulk Freight rates. From the analysis of the forecast error variance decomposition, WTI has the largest explanatory relationship with the BDI and Dubai ranks seoond, Brent ranks third. In conclusion, WTI and Dubai have the largest impact on the BDI, while there are some differences according to the ship-type.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.266-274
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• 2021

Ship officers are subject to excessive job stress, which has negative physical and psychological impacts and may adversely affect the smooth supply and demand of human resources. In this study, a mobile web application was developed as a tool for systematic job stress measurement and management of officers and verified through quality evaluation. Requirement analysis was performed by ship officers and staff in charge of human resources of shipping companies, and the results were reflected in the application configuration step. The application was designed according to the waterfall model, which is a traditional software development method, and functions were implemented using JSP and Spring Framework. Performance evaluation on the user interface, confirmed that proper input and output results were implemented, and the respondent results and the database were configured in the administrator interface. The results of evaluation questionnaires for quality evaluation of the interface based on ISO/IEC 9126-2 metric were significant 4.60 for the user interface and 4.65 for the administrator interface in a 5-point scale. In the future, it is necessary to conduct follow-up research on the development of data analysis system through utilization of the collected big-data sets.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.303-312
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• 2022

Bridges across waterways act as interference factors, that reduce the navigable water area from the perspective of navigation safety. To analyze the safety navigational width of ships navigating bridges across waterways, the optimal traffic distribution based on AIS data was investigated, and ships were classified according to size through k-means clustering. As a result of the goodness-of-fit analysis of the clustered data, the lognormal distribution was found to be close to the optimal distribution for Incheon Bridge and Busan Harbor Bridge. Also, the normal distributions for Mokpo Bridge and Machang Bridge were analyzed. Based on the lognormal and normal distribution, the analysis results assumed that the safe passage range of the vessel was 95% of the confidence interval, As a result, regarding the Incheon Bridge, the difference between the normal distribution and the lognormal distribution was the largest, at 64m to 98m. The minimum difference was 10m, which was revealed for Machang Bridge. Accordingly, regarding Incheon Bridge, it was analyzed that it is more appropriate to present a safety width of traffic by assuming a lognormal distribution, rather than suggesting a safety navigation width by assuming a normal distribution. Regarding other bridges, it was analyzed that similar results could be obtained using any of the two distributions, because of the similarity in width between the normal and lognormal distributions. Based on the above results, it is judged that if a safe navigational range is presented, it will contribute to the safe operation of ships as well as the prevention of accidents.

• Journal of Navigation and Port Research
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• v.47 no.3
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• pp.167-173
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• 2023

The manufacturing industry is the backbone of the Korean economy. Among them, the petrochemical industry is a strategic growth industry, which makes a profit through reexports based on eminent technology in South Korea which imports all of its crude oil. South Korea imports whole amount of crude oil, which is the raw material for many manufacturing industries, by sea transportation. Therefore, it must respond swiftly to a highly volatile tanker freight market. This study aimed to make an early warning model of crude oil shipping market using a signal approach. The crisis of crude oil shipping market is defined by BDTI. The overall leading index is made of 38 factors from macro economy, financial data, and shipping market data. Only leading correlation factors were chosen to be used for the overall leading index. The overall leading index had the highest correlation coefficient factor of 0.499 two months ago. It showed a significant correlation coefficient five months ago. The QPS value was 0.13, which was found to have high accuracy for crisis prediction. Furthermore, unlike other previous time series forecasting model studies, this study quantitatively approached the time lag between economic crisis and the crisis of the tanker ship market, providing workers and policy makers in the shipping industry with an framework for strategies that could effectively deal with the crisis.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.327-328
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• 2022

The recent spread of the corona pandemic and a temporary surge in demand for consumer goods have resulted in an increase in port cargo volume, and the resulting port congestion is coupled with a shortage of labor in the port, exacerbating the global supply chain chaos. Supply chain disruptions will increase logistics costs and ultimately increase global inflationary pressures. In this situation, the role of the port, which is the nodal point between land and sea, is gradually becoming more important. And fully automated ports that are operated unmanned are evaluated as being able to respond stably and flexibly by reducing operational risks in situations such as COVID-19. Therefore, this study compared the operational performance of fully automated and non-fully automated terminals within the same port before and after the corona outbreak, and analyzed the fully automated terminal was stable in actual operation. As a result of the analysis, the fully automated terminal showed stable operating efficiency in all aspects of operational performance compared to the non-fully automated terminal even under severe port congestion due to COVID-19.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.