• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.1
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• pp.34-45
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• 2013

Due to its self-configuring nature, the tactical mobile ad hoc network is used for communications between tactical units and the command and control center (CCC) in battlefields, where communication infrastructure is not available. However, when a tactical unit moves far away from the CCC or there are geographical constraints, the data link between two communicating nodes can be broken, which results in an invalid data route from the tactical units to CCC. In order to address this problem, in this paper we propose a hierarchical connectivity maintenance scheme, namely ADLCoM (Autonomous Data Link Connectivity Maintenance). In ADLCoM, each tactical unit has one or more GW (gateway), which checks the status of data links between tactical units. If there is a possibility of link breakage, GWs request ground or aerial unmanned vehicles to become a relay for the data link. The simulation results, based on tactical scenarios, show that the proposed scheme can significantly improve the network performance with respect to data delivery ratio.

• The Journal of Engineering Geology
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• v.20 no.4
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• pp.371-380
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• 2010

A three-dimensional finite element analysis was performed to predict the location of a fault zone ahead of a tunnel face based on convergence displacement. Geometrical models for the numerical analysis were developed based on the possible geometric intersection between the fault zone and the tunnel. Fifteen fault models were generated from combinations of faults with five different strikes (at $15^{\circ}$ intervals) and three dips (vertical, $45^{\circ}$ and $-45^{\circ}$) relative to the tunnel route. The displacements on the crown and side walls were calculated and analyzed using a vector orientation approach. As a result, nine representative prediction charts were developed, showing location and orientation of the fault zone based on convergence displacement.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.6
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• pp.501-514
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• 2017

In the previous research, a study was carried out to estimate the actual performance such as the propeller Revolution Per Minute (RPM) and engine power of a Liquefied Natural Gas Carrier (LNGC) using the full-scale measurement data. After the predicted RPM and engine power were verified by comparing those with the measured values, the suggested method was regarded to be acceptable. However, there was a limitation to apply the method on the prediction of the RPM and engine power of a ship. Since the information of route, speed, and environmental conditions required for estimating the RPM and engine power is generally regarded as the intellectual property of a shipping company, it is difficult to secure the information on a shipyard. In this paper, the RPM and engine power of the 151K LNGC was estimated using the combination of Automatic Identification System (AIS) and European Centre for Medium-Range Weather Forecasts (ECMWF) database in order to replace the full-scale measurement. The simulation approach, which was suggested in the previous research, was identically applied to the prediction of RPM and engine power. After the results based on the AIS and ECMWF database were compared with those obtained from the full-scale measurement data, the feasibility was briefly reviewed.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.633-645
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• 2017

This study proposes a statistical procedure for analyzing container ship operation data that can help determine fuel consumption patterns. We first investigate the features that affect fuel consumption and develop the prediction model to find current fuel consumption. The ship data can be divided into two-type data. One set of operation data includes sea route, voyage information, longitudinal water speed, longitudinal ground speed, and wind, the other includes machinery data such as engine power, rpm, fuel consumption, temperature, and pressure. In this study, we separate the effects of external force on ships according to Beaufort Scale and apply a partial least squares regression to develop a prediction model.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.309-319
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• 2020

Billiards is a fun and popular sport, but both route planning and cueing prevent beginners from becoming skillful. A beginner in billiards requires constant concentration and training to reach the right level, but without the right motivating factor, it is easy to lose interests. This study aims to induce interest in billiards and accelerate learning by utilizing billiard path prediction and visualization on a highly immersive augmented reality platform that combines a stereo camera and a VR headset. For implementation, the placement of billiard balls is recognized through the OpenCV image processing program, and physics simulation, path search, and visualization are performed in Unity Engine. As a result, accurate path prediction can be achieved. This made it possible for beginners to reduce the psychological burden of planning the path, focus only on accurate cueing, and gradually increase their billiard proficiency by getting used to the path suggested by the algorithm for a long time. We confirm that the proposed AR billiards is remarkably effective as a learning assistant tool.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.159-167
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• 2009

The vehicles play a significant role in modern people's life as economy grows. The development of car navigation system(CNS) provides various convenience because it shows the driver where they are and how to get to the destination from the point of source. However, the existing map-based CNS does not consider any environments such as traffic congestion. Given the same starting point and destination, the system always provides the same route and the required time. This paper proposes a path planning method with traffic prediction by applying historical driving information to the Fuzzy theory and Bayesian update. Fuzzy theory classifies the historical driving information into groups of leaving time and speed rate, and the traffic condition of each time zone is calculated by Bayesian update. An ellipse area including starting and destination points is restricted in order to reduce the calculation time. The accuracy and practicality of the proposed scheme are verified by several experiments and comparisons with real navigation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.106-107
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• 2022

The ship type identification of the surrounding ship is important information for navigators and VTS officers since they can estimate the maneuverability and near-future route of the ships. However, it is more than frequent that the information is not provided due to transmission trouble and seafarers' unfamiliarity with AIS. Thus, this study suggests predicting ship types through the Random Forest classifier after preparing a training and test dataset that contains ship features and types. The AIS data for Ulsan coast in 2018 was used for this study. The method may provide the effect that many navigators and VTS officers discuss and share the experience of predicting ship types.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.15-25
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• 2017

In recent years, polar research has been focused on climate change, natural resources, and development of a new North Pole Route. Since 2010, the Korea Polar Research Institute has been collecting various in situ data from the Arctic/Antarctic oceans using ARAON, which is the first effort of Korea toward leading global polar research. As a part of these activities, a web-based GIS service was developed to collect in situ data and to standardize data formats. Visualizations of in situ measurements and thematic maps were also developed to improve both the quantitative and qualitative quality of polar ocean research, and to increase accessibility of polar oceanographic data. This system will ultimately share all of the data acquired from the Arctic/Antarctic oceans with international research groups.

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.334-343
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• 2023

Purpose: In this study, the cavity data found through ground cavity exploration was combined with underground facilities to derive a correlation, and the ground subsidence prediction map was verified based on the AI algorithm. Method: The study was conducted in three stages. The stage of data investigation and big data collection related to risk assessment. Data pre-processing steps for AI analysis. And it is the step of verifying the ground subsidence risk prediction map using the AI algorithm. Result: By analyzing the ground subsidence risk prediction map prepared, it was possible to confirm the distribution of risk grades in three stages of emergency, priority, and general for Busanjin-gu and Saha-gu. In addition, by arranging the predicted ground subsidence risk ratings for each section of the road route, it was confirmed that 3 out of 61 sections in Busanjin-gu and 7 out of 68 sections in Sahagu included roads with emergency ratings. Conclusion: Based on the verified ground subsidence risk prediction map, it is possible to provide citizens with a safe road environment by setting the exploration section according to the risk level and conducting investigation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.285-286
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• 2016

We aim to propose the prediction modeling method of ship's position with extracting ship's trajectory model through pattern recognition based on the data that are being collected in VTS centers at real time. Support Vector Machine algorithm was used for data modeling. The optimal parameters are calculated with k-fold cross validation and grid search. We expect that the proposed modeling method could support VTS operators' decision making in case of complex encountering traffic situations.