• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.3
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• pp.1279-1287
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• 2014

The Korean government establishes a strategy to develop the Busan New Port as a world ranking two transit-oriented port. This paper aims at presenting an efficient inter-terminal transport (ITT) system in the Busan New Port as a method of achieving the government strategy. First, it presents results of long term forecast for the inter-terminal transportation volume in the port. Second, it proposes two systems to treat ITT in the port; Double stack Multiple Trailer System (DMTS) and Rail-based transportation system. The implementation methods in the port are introduced in detail for the both systems, and the required number of the systems and costs are calculated for implementation of both the systems. B/C for DMTS is analyzed to 3.7, moreover unit-fare per [$ton{\cdot}km$] can is lowered to 67% against current fare. DMTS is shown to highly potential for efficient ITT in the port.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.13-21
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• 1996

The necessity and usefulness of higher-order neural networks have been well-known since early days of neurocomputing. However the explosive number of terms has hampered the design and training of such networks. In this paper we present an evolutionary learning method for efficiently constructing problem-specific higher-order neural models. The crux of the method is the neural tree representation employing both sigma and pi units, in combination with the use of an MDL-based fitness function for learning minimal models. We provide experimental results in classification and prediction problems which demonstrate the effectiveness of the method. I. Introduction topology employs one hidden layer with full connectivity between neighboring layers. This structure has One of the most popular neural network models been very successful for many applications. However, used for supervised learning applications has been the they have some weaknesses. For instance, the fully mutilayer feedforward network. A commonly adopted connected structure is not necessarily a good topology unless the task contains a good predictor for the full *d*dWs %BH%W* input space.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.16 no.2
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• pp.108-119
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• 2004

The breakwater extension at Anmok Harbor has resulted in erosional stresses along the wide range of shorelines immediately south of the harbor. In this study, therefore, the downdrift affects caused by the breakwater extension are investigated through both analytical and numerical approaches. In addition, this study stresses the need of monitoring and analysis system for the effective integrated coastal zone management and shows through the case study of Anmok Harbor how the numerical experiments are accomplished for the coastal zone management. The numerical model system, which predicts the seabed changes obtained from the difference between the rates of sediment pickup and settling due to gravity, is combined with the wave, wave-induced currents, and suspended sediment transport models. A new relationship between the near-bed concentration and the depth-mean concentration, which is required in estimating the settling rates. is presented by analyzing the vertical structure of concentration.

• The Journal of the Korea Contents Association
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• v.11 no.1
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• pp.65-71
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• 2011

In this paper, the RFID / USN-based ad-hoc network structure is presented for efficient operation of a container terminal yard. Communication between fixed or mobile devices in the container terminal yard is accomplished through the ad-hoc node, to collect the status information of a container in real time. Any outside shipper of the container as well as central server allows to share the status information of a container through ad-hoc communication. In addition, to predict the maximum wireless transmission range of nodes by RFID tag position in the yard, LAODV (Location based AODV) routing protocol is proposed. The validity is proved by performance evaluation via computer simulation.

• Journal of Navigation and Port Research
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• v.32 no.10
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• pp.837-844
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• 2008

In an automated container terminal, various factors affect the operation of container handling equipment such as quay cranes and AGVs, and thus calculating the exact operation time is nearly infeasible. This uncertainty makes it difficult to dispatch AGVs well. In this paper, we propose a simulation-based AGV dispatching algorithm When dispatching an AGV to an operation, the proposed algorithm conducts multiple stochastic simulation for the succeeding AGV operations for the predetermined period to collect stochastic samples of the result of the dispatching. In the stochastic simulation, the uncertainty of crane operations is represented as a simple probability distribution and the operation time of a crane is determined according to this. A dispatching option is evaluated by the total delay time of quay cranes which is estimated by averaging the quay crane delay of each simulation In order to collect a sufficient number of samples that guarantee the credibility of the evaluation, we devised a high-speed simulator that simulates AGV operation The effectiveness of the proposed algorithm is validated by simulation experiments.

• Environmental and Resource Economics Review
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• v.23 no.1
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• pp.1-19
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• 2014

Weather seems to influence industries in a variety of ways. On a day-to-day basis, it is the most volatile external factor influencing consumer and market behavior. And, because weather is constantly changing, industries must deal with a continuously shifting array of opportunities and risks. This study aims to examine how climate and weather changes and information, as external environmental factors, have affected the Korean industries, particularly marine shipping and logistics. To find out the economic value of marine weather information, we use measurable results of VVOS(Vessel and Voyage Optimization Services) in the ocean shipping, which the marine weather software tool can save fuel costs up to 4%. When the fuel saving is same as VVOS's performance, the saving of Korean flag ship is estimated about 62 billion won and the saving of total flag ship is estimated about 519 billion won. However, coastal shipping companies have been struggling with the heavy weather factors, such as wave height, wave period and wind. Major findings are that wind and wave height have a significant negative effect on cargo transport, while wave period has a significant positive effect on cargo transport. And to conclude, when we use efficiently the marine weather information, we can increase cargo transport and save fuel costs etc.

• Journal of Navigation and Port Research
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• v.44 no.4
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• pp.354-361
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• 2020

The term "articles for ship" is a general term for all relevant mechanical accessories (SPARE) and consumable materials (STORE) commonly used in ships. Ships commonly are at sea, so it is difficult to respond rapidly to the demand for them in an emergency situation. In particular, it is more difficult to determine the boarding location of tramper ships as it is more difficult to predict the next sailing route in advance. The purpose of this study was to identify the important factors to be considered in determining the boarding location of tramper ships through a survey of each ship owner and ship management company. This valuable information on the proposed supply procedures for each country and port, would be an efficient way to supply articles for ships.

• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.343-350
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• 2003

The range of applications of artificial neural networks(Am) in many branches of geotechnical engineering is growing rapidly. This study was undertaken to develop an analysis model representing ultrasonically enhanced soil flushing by the use of ANN. Input data for the model-development were obtained by laboratory study, and used for training and verification. Analyses involved various ranges of momentum, loaming rate, activation function, hidden layer, and nodes. Results of the analyses were used to obtain the optimum conditions for establishing and verifying the model. The coefficient of correlation between the measured and the predicted data using the developed model was relatively high. It shows potential application of ANN to ultrasonically enhanced soil flushing which is not easy to build up a mathematical model.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.395-405
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• 2018

Seakeeping safety module in Korean e-Navigation system is one of the ship remote monitoring services that is employed to ensure the safety of ships by monitoring the ship's real time performance and providing a warning in advance when the abnormal conditions are encountered in seakeeping performance. In general, seakeeping performance has been evaluated by simulating ship motion analysis under specific conditions for its design. However, due to restriction of computation time, it is not realistic to perform simulations to evaluate seakeeping performance under real-time operation conditions. This study aims to introduce a reasonable and faster method to predict a ship's roll motion which is one of the factors used to evaluate a ship's seakeeping performance by using a machine learning-based surrogate model. Through the application of various learning techniques and sampling conditions on training data, it was observed that the difference of roll motion between a given surrogate model and motion analysis was within 1%. Therefore, it can be concluded that this method can be useful to evaluate the seakeeping performance of a ship in real-time operation.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1047-1056
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• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.