• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.08a
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• pp.54-63
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• 2004

There are two principal routes for the Asia-North America containerized cargo, that of Asia-West Coast and Asia-East Coast. On the West Coast, the Asia-Los Angeles, dominate the commerce, whereas on the Asia-East Coast it's the Panama Canal. Each of these routes has different characteristics. All are similar in that each is the door to the commerce of containerized cargo originating in Asia; each combines maritime and overland transportation; each has important intermodal connections and is able to distribute cargo throughout the West and East Coasts of the United States. Each route also has its port of preference that has the necessary infrastructure, equipment and intermodal connections. For example, in the case of the Port of Los Angeles, in spite of some of its advantages, it has several serious problems due to the interminable containerized cargo traffic that must be solved rapidly and satisfactorily in order to progress. In this paper, we would like to show the problems of two main routes.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.243-245
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• 2019

In the VTS, the predictions of vessel mobility and situation awareness of maritime environment are basic function. In recent years, pilotage information is an essential aware element of VTS personnel for vessel traffic management. So, we designed the structure of pilotage information service with VTS and tested in real environment. In the future, similar pilotage information can be used as a useful VTS service.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.554-558
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• 2002

In this paper, we analysed the technical characteristics of a universal shipborne automatic identification system using self-organizing time division multiple access in the VHF marine mobile band. IMO's Marine Safety Committee approved revision of chapter V of the Safety of Life at Sea(SOLAS) Convention in 73rd meeting. According to this revision, AIS will become a nandatary carriage requirement by 01 July 2002. AIS is a broadcast system, operating in the VHF marine band. It is capable of sending infomation such as would be beneficial to the safety of navigation and the identification and monitoring of maritime traffic. It is absolutely necessary to analyse the related international and domestic specifications for the AIS implementation and installation.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.459-473
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• 2007

In this paper, we suggest a spatial regression model to predict AADT. Although Euclidian distances between one monitoring site and its neighboring sites were usually used in the many analysis, we consider the shortest travel path between monitoring sites to predict AADT for unmonitoring site using spatial regression model. We used universal Kriging method for prediction and found that the overall predictive capability of the spatial regression model based on shortest travel path is better than that of the model based on multiple regression by cross validation.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.491-494
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• 2019

The results from the analysis of recent security breach cases of industrial control systems revealed that most of them were caused by the employees of a partner company who had been managing the control system. For this reason, the majority of the current company security management systems have been developed focusing on their performances. Despite such effort, many hacking attempts against a major company, public institution or financial institution are still attempted by the partner company or outsourced employees. Thus, the institutions or organizations that manage Industrial Control Systems (ICSs) associated with major national infrastructures involving traffic, water resources, energy, etc. are putting emphasis on their security management as the role of those partners is increasingly becoming important as outsourcing security task has become a common practice. However, in reality, it is also a fact that this is the point where security is most vulnerable and various security management plans have been continuously studied and proposed. A system that enhances the security level of a partner company with a Virtual Desktop Infrastructure (VDI) has been developed in this study through research on the past performances of partner companies stationed at various types of industrial control infrastructures and its performance outcomes were statistically compiled to propose an appropriate model for the current ICSs by comparing vulnerabilities, measures taken and their results before and after adopting the VDI.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.243-250
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• 2019

In this paper, we propose a system for processing and analyzing drone image information which can be applied variously in disasters-security situation. The proposed system stores the images acquired from the drones in the server, and performs image processing and analysis according to various scenarios. According to each mission, deep-learning method is used to construct an image analysis system in the images acquired by the drone. Experiments confirm that it can be applied to traffic volume measurement, suspect and vehicle tracking, survivor identification and maritime missions.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.69-84
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• 2021

Awareness of boats is a main issue in areas of fishery management, illegal fishing, and maritime traffic, etc. For the awareness, Automatic Identification System (AIS) and Vessel-Pass System (V-PASS) have been widely used to collect the boat-related information. However, only using these systems makes it difficult to collect the accurate information. Recently, satellite-based data has been increasingly used as a cooperative system. In 2015, U.S. National Oceanic and Atmospheric Administration (NOAA) developed a boat detection algorithm using Visible Infrared Imaging Radiometer Suite (VIIRS) Day & Night Band (DNB) data. Although the detections have been widely utilized in many publications, it is difficult to estimate the night-time fishing boats immediately. Particularly, it is difficult to estimate the threshold due to the lunar irradiation effect. This effect must be corrected to apply a single specific threshold. In this study, the moon phase was considered as the main frequency of this effect. Considering the moon phase, relational expressions are derived and then used as offsets for relative correction. After the correction, it shows a significant reduction in the standard deviation of the threshold compared to the threshold of NOAA. Through the correction, this study can set a constant threshold every day without determination of different thresholds. In conclusion, this study can achieve the detection applying the single specific threshold regardless of the moon phase.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.263-269
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• 1998

The forecasting of container cargo volumes should be estimated correctly because it has a key roles on the establishment of port development planning, and the decision of port operating system. Container cargo volumes have a dynamic characteristics which was changed by effect of competitive ports. Accordingly forecasting was needed overall approach about competitive port's development, alternation and information. But, until now, traffic forecasting was not executed according to competitive situation, and that was accomplished at the point of unit port. Generally, considering the competition situation, simulation method was desirable at forecasting because system's scale was increased, and the influence power was intensified. In this paper, considering this situation, the objectives can be outlined as follows. 1) Structural model constructs by System dynamics method. 2) Structural simulation model develops according to modelling of competitive situation by expended SD method which included HEP(Hierarchical Fuzzy Process) And actually, effectiveness was verified according to proposed model to major port in northeast asia.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.79-88
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• 2009

The objective of this study is to develop the new vehicle classification algorithm and minimize classification errors. The existing vehicle classification algorithm collects data from loop and piezo sensors according to the specification("Vehicle classification guide for traffic volume survey" 2006) given by the Ministry of Land, Transport and Maritime Affairs. The new vehicle classification system collects the vehicle length, distance between axles, axle type, wheel-base and tire type to minimize classification error. The main difference of new system is the "Wandering" sensor which is capable of measuring the wheel-base and tire type(single or dual). The wandering sensor obtains the wheel-base and tire type by detecting both left and right tire imprint. Verification tests were completed with the total traffic volume of 762,420 vehicles in a month for the new vehicle classification algorithm. Among them, 47 vehicles(0.006%) were not classified within 12 vehicle types. This results proves very high level of classification accuracy for the new system. Using the new vehicle classification algorithm will improve the accuracy and it can be broadly applicable to the road planning, design, and management. It can also upgrade the level of traffic research for the road and transportation infrastructure.