• 한국IT서비스학회지
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• 제17권4호
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• pp.139-150
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• 2018

This paper investigated the human distribution in subway station areas in Seoul, using geotweets and subway ridership data. Eight stations were selected from the districts of Gangnam and Gangbuk. Geotweets located within a 600-meter radius of the central coordinates of each station were extracted, and distances between the center of station and each tweet location were calculated. Donut-shaped dimension and pie-shaped dimension were generated, using the Haversine distance formula and the Azimuth angle formula respectively. By combining the two dimensions, Dartboard-shaped space division is created. Popular places within the subway station areas identified from this research are almost the same as the current well-known popular places, and this is an important case showing that people send tweets from various places where they engage in daily activities. We expect this study can be a methodological guideline for social scientists who use spatio-temporal or GPS data for their research.

• Communications for Statistical Applications and Methods
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• 제22권3호
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• pp.241-253
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• 2015

In this paper we predict the track of typhoons using a Bayesian principal component regression model based on wind field data. Data is obtained at each time point and we applied the Bayesian principal component regression model to conduct the track prediction based on the time point. Based on regression model, we applied to variable selection prior and two kinds of prior distribution; normal and Laplace distribution. We show prediction results based on Bayesian Model Averaging (BMA) estimator and Median Probability Model (MPM) estimator. We analysis 8 typhoons in 2006 using data obtained from previous 6 years (2000-2005). We compare our prediction results with a moving-nest typhoon model (MTM) proposed by the Korea Meteorological Administration. We posit that is possible to predict the track of a typhoon accurately using only a statistical model and without a dynamical model.

• 대한조선학회논문집
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• 제59권5호
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• pp.314-322
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• 2022

During the shipbuilding process, many blocks are moved between shipyard workshops by block carrying vehicles called a transporter. Because block logistics management is one of the essential factors in enhancing productivity, it is necessary to manage block information with the transporter that moves it. Currently, because a large amount of data per day are collected from sensors attached to blocks and transporters via IoT infrastructure installed in shipyards, automated methods are needed to analyze them. Therefore, in this study, we developed an algorithm that can automatically match the transporter and the working block based on the GPS sensor data. By comparing the distance between the transporter and the blocks calculated from the Haversine formula, the block is found which is moved by the transporter. In this process, since the time of the measured data of moving objects is different, the time standard for calculating the distance must be determined. The developed algorithm was verified using actual data provided by the shipyard, and the correct result was confirmed with the distance based on the moving time of the transporter.

• 한국융합학회논문지
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• 제11권7호
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• pp.169-180
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• 2020

본 연구는 국가 간 이동이 발생한 지오트윗 사용자를 이동거리평균과 이동거리표준편차에 따라 그룹화하여, 국가 간 이동과 자국 내 이동에서 나타나는 특징을 발견하고 연구의 의의를 찾고자 하였다. 데이터마트를 구축 후 국가 간 이동과 자국 내 이동이 발생한 지오트윗을 분리하였고, 해버사인공식을 이용해 사용자의 이동거리를 측정하였다. 국가 간 이동 집단에서는 동일한 언어를 사용하며 생활방식이 비슷한 국가 사이에서 많이 이동하였고, 자국 내 이동에서는 인프라가 잘 구축된 선진국 위주의 국가에서 많은 이동이 발생하였다. 본 연구는 사용자별 이동거리를 계산하여 공통된 특징을 도출하고자 하였으며, 사용자의 이동거리 특성에 따라 그룹화하였다. 본 연구에서 분석한 21개국은 국가별 경제력이나 나이, 직업 등에서 차이가 커 많은 제반 사항이 고려되어야 정밀한 분석이 가능할 것이다. 향후에는 현실적인 사항을 추가한 연구가 진행되어야 할 것이다.