• Journal of Navigation and Port Research
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• v.38 no.2
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• pp.97-103
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• 2014

In these days, the world has been increasing navy forces such as aircraft carriers and high-tech destroyers etc. and the importance of submarines is being emphasized. Therefore, accurate values of the derivatives in equations of motion are required to control motion of the submarines. Hydrodynamic derivatives were measured by the vertical planar motion mechanism(VPMM) model test. VPMM equipment gave pure heave and pitch motion respectively to the submarine model and the forces and moments were acquired by load cells. As a result, the hydrodynamic derivatives of the submarine are provided through the Fourier analysis of the forces and moments in this paper.

• 한국지형공간정보학회:학술대회논문집
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• 2002.03a
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• pp.74-78
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• 2002

본 연구에서는 Greenland의 Sondrestrom 지역에 존재하는 빙하의 운동을 관측하기 위해 두 장의 ERS-1 SAR 영상을 이용한 SAU Interferometry(InSAR) 기법을 적용하였다. 본 연구에서 사용한 지역은 영상 좌편의 암석 지역과 영상 우편의 빙하 지역으로 구성되어 있기 때문에 복잡한 위상차를 보이며, 두 지역의 경계선 지역에서는 자료의 상관도(coherence)가 떨어지기 때문에 절대위상 복원(phase unwrapping) 수행시 시작점(seed point) 위치의 선정이 매우 중요한 사항이다. 또한 대상 지역에 대한 정확한 기준점의 확보가 어렵기 때문에 기선길이(baseline) 추정시 대상지역의 수치고도모형을 이용하여 많은 수의 기준점을 추출하여 기선길이를 추정하였다. 그 결과로 위성의 경사거리 방향에 대한 빙하의 속도 성분을 추출할 수 있었다.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.513-523
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• 2016

Fitting a mixture of multivariate skew normal distribution (MSNMix) with multiple skewness parameter vectors via EM algorithm often requires a highly expensive computational cost to calculate the moments and probabilities of multivariate truncated normal distribution in E-step. Subsequently, it is common to fit an asymmetric data set with MSNMix with a simple skewness parameter vector since it allows us to compute them in E-step in an univariate manner that guarantees a cheap computational cost. However, the adaptation of a simple skewness parameter is unrealistic in many situations. This paper proposes an approximate estimation for the MSNMix with multiple skewness parameter vectors that also allows us to treat them in an univariate manner. We additionally provide some experiments to show its effectiveness.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.567-575
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• 2016

With the popularity of smartphone, user's location information is of great interest as mobile apps based on the location information are increasing. In this paper, we are interested in analyzing user's speed data based on the location information. It is not uncommon to observe locations with great measurement errors, removing them is necessary. The distribution of speed can be considered as a mixture model in accordance with transportation means. We identify a tail part as a component of a mixture model and fit a simple parametric model to the tail part of the speed distribution.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.4
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• pp.23-29
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• 2008

This research proposes two sub-models and one integrated model for the classification of safety in curve section of road, where the fatal-rate is relatively higher in accidents. The first sub-model calculates the accident-rate by safety-index that is based on the road geometries. The second decides the safety of curve section by the speed difference between before and in the curve. Finally, the integrated model of two sub-modules can classify the safety of curve section of road.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.159-168
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• 2008

Since the term structure of interest rates (TSIR) has longitudinal data, we should consider as input variables both time left to maturity and time simultaneously to get a more useful and more efficient function estimation. However, since the resulting data set becomes very large, we need to develop a fast and reliable estimation method for large data set. Furthermore, it tends to overestimate TSIR because data are correlated. To solve these problems we propose a mixture of weighted least squares support vector machines. We recognize that the estimate is well smoothed and well explains effects of the third stock market crash in USA through applying the proposed method to the US Treasury bonds data.

• International Journal of Highway Engineering
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• v.9 no.4
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• pp.21-32
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• 2007

The linear traffic model(Vertical queueing model) that is adopted widely in traffic flow estimation assumes that all vehicles have the identical motion before joining a queue at the stop-line. Thus, a queue is supposed to form vertically not horizontally. Due to the simplicity of this model, the departure time of the leading vehicle is assumed to coincide with the start of effective green time. Thus, the delay estimates given by the Vertical queueing model is not always realistic. This paper explores a microscopic traffic model(a Kinematic Car-following model at Signalised intersections: a KCS traffic model) based on the one dimensional Kinematic equations in physics. A comparative evaluation in delay and sensitivity of delay difference between the KCS traffic model and the previously known Vertical queueing model is presented. The results show that the delay estimate in the Vertical queueing model is always greater than or equal to the KCS traffic model; however, the sensitivity of delay in the KCS traffic model is greater than the Vertical queueing model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.1
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• pp.85-93
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• 2015

Previous studies have estimated crash prediction models with the fixed effect model which assumes the fixed value of coefficients without considering characteristics of each intersections. However the fixed effect model would estimate under estimation of the standard error resulted in over estimation of t-value. In order to overcome these shortcomings, the random effect model can be used with considering heterogeneity of AADT, geometric information and unobserved factors. In this study, data collections from 89 intersections in Daejeon and estimates of crash prediction models were conducted using the random and fixed effect negative binomial regression model for comparison and analysis of two models. As a result of model estimates, AADT, speed limits, number of lanes, exclusive right turn pockets and front traffic signal were found to be significant. For comparing statistical significance of two models, the random effect model could be better statistical significance with -1537.802 of log-likelihood at convergence comparing with -1691.327 for the fixed effect model. Also likelihood ration value was computed as 0.279 for the random effect model and 0.207 for the fixed effect model. This mean that the random effect model can be improved for statistical significance of models comparing with the fixed effect model.

• Journal of Korean Port Research
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• v.14 no.1
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• pp.66-75
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• 2000

From the traffic analysis, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results were obtained : ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy. ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period showed 10mph decrease when compared with the 24hours'average speed, but the speed did not show a big difference in the afternoon peak period. ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge section. ⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.111-121
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• 1999

From the traffic analyses, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results obtained: ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy.ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period shown 10mph decrease when compared with the 24hours' average speed, but the speed did not show a big difference in the afternoon peak period.ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge sectionⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.