• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.173-185
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• 2015

This study aims to investigate the characteristics and types of car accidents and establish a prediction model by analyzing 456 car accidents having occurred in the 11 tunnels in Busan, through statistical analysis techniques. The results of this study can be summarized as below. As a result of analyzing the characteristics of car accidents, it was found that 64.9% of all the car accidents took place in the tunnels between 08:00 and 18:00, which was higher than 45.8 to 46.1% of the car accidents in common roads. As a result of analyzing the types of car accidents, the car-to-car accident type was the majority, and the sole-car accident type in the tunnels was relatively high, compared to that in common roads. Besides, people at the age between 21 and 40 were most involved in car accidents, and in the vehicle type of the first party to car accidents, trucks showed a high proportion, and in the cloud cover, rainy days or cloudy days showed a high proportion unlike clear days. As a result of analyzing the principal components of car accident influence factors, it was found that the first principal components were road, tunnel structure and traffic flow-related factors, the second principal components lighting facility and road structure-related factors, the third principal factors stand-by and lighting facility-related factors, the fourth principal components human and time series-related factors, the fifth principal components human-related factors, the sixth principal components vehicle and traffic flow-related factors, and the seventh principal components meteorological factors. As a result of classifying car accident spots, there were 5 optimized groups classified, and as a result of analyzing each group based on Quantification Theory Type I, it was found that the first group showed low explanation power for the prediction model, while the fourth group showed a middle explanation power and the second, third and fifth groups showed high explanation power for the prediction model. Out of all the items(principal components) over 0.2(a weak correlation) in the partial correlation coefficient absolute value of the prediction model, this study analyzed variables including road environment variables. As a result, main examination items were summarized as proper traffic flow processing, cross-section composition(the width of a road), tunnel structure(the length of a tunnel), the lineal of a road, ventilation facilities and lighting facilities.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.148-148
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• 2020

산업의 고도화가 진행됨에 따라 화학원료의 사용이 증가하고 있고 독성을 가진 화학물질이 하천으로 유입되는 사고가 빈번하게 발생하고 있다. 수환경으로 유입되는 유해화학물질은 주로 무색무취의 물질들로 사고가 발생하더라도 초기 발견이 어려워 어류폐사를 유발하거나 취수시설에서 용수로 취수되는 경우가 발생하기 때문에 이에 대한 대응책 마련이 필수적이다. 하천에 유입된 오염물질의 거동을 신속하게 예측하기 위해 1차원 오염물질 추적 모형이 활용되는데, Fickian 이송-분산 모형(Fickian Advection-dispersion equation model; FADE)이 주로 사용되고 있다. 하지만 FADE는 오염물질이 하천 저장대에서 지체되는 현상을 반영하지 못하기 때문에 농도곡선의 왜곡도를 구현하지 못하는 단점을 가지고 있다. 따라서 본 연구에서는 하천저장대모형(River Storage Model; RSM)을 개발하고 이를 국가하천인 감천에 적용하였다. 본 연구에서 개발한 RSM은 분산계수, 본류대 면적, 저장대 면적, 저장대 교환계수의 네 가지 매개변수를 통해서 하천의 물질 저장 및 교환 특성를 구현한 non-Fickian 모형으로서, 생화학반응, 휘발, 흡·탈착항을 추가하여 유해화학물질의 혼합 거동을 정확하게 모의할 수 있도록 개발하였다. 저장대 모형의 매개변수를 산정하기 위해서 하천 유량과 지형자료를 기반으로 HEC-RAS를 모의하여 계산된 수리특성을 입력변수로 사용하였다. 저수기, 평수기, 풍수기 유량을 기준으로 세 경우의 시나리오 모의를 수행하였는데, 5ton의 톨루엔이 김천산업단지에서 감천으로 유입된 경우 약 20km 하류에 위치한 취수장에서 톨루엔의 농도변화를 예측했다. 보존성 물질에 대한 모의 결과, 풍수기의 경우 저수기에 비해 유속이 크기 때문에 취수장에서 20.56시간 먼저 기준농도에 도달하고, 7.21시간 더 짧게 머무는 것으로 나타났다. 유해화학물질의 반응특성에 대한 민감도 분석을 시행한 결과, 생화학적 반감기가 18.98시간보다 길고, 옥탄올-물 분배계수가 2.267 이하인 물질은 생분해 및 흡·탈착 반응에 둔감한 것으로 나타났다. 1m 수심 기준 0.114m/s 이하 유속에서의 하천 수리조건에서는 화학물질의 휘발성을 무시할 수 있는 것으로 밝혀졌다.

• Journal of Radiation Protection and Research
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• v.38 no.2
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• pp.60-67
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• 2013

A diffusion coefficient is an important parameter in the prediction of atmospheric dispersion using a Gaussian plume model, and its modelling approach varies. In this study, dispersion coefficients recommended by the U. S. Nuclear Regulatory Commission's (U. S. NRC's) regulatory guide and the Canadian Nuclear Safety Commission's (CNSC's) regulatory guide, and used in probabilistic accident consequence analysis codes MACCS and MACCS2 have been investigated. Based on the atmospheric dispersion model for a hypothetical accidental release recommended by the U. S. NRC, its influence to atmospheric dispersion factor was discussed. It was found that diffusion coefficients are basically predicted from a Pasquill- Gifford curve, but various curve fitting equations are recommended or used. A lateral dispersion coefficient is corrected with consideration for the additional spread due to plume meandering in all models, however its modelling approach showed a distinctive difference. Moreover, a vertical dispersion coefficient is corrected with consideration for the additional plume spread due to surface roughness in all models, except for the U. S. NRC's recommendation. For a specified surface roughness, the atmospheric dispersion factors showed differences up to approximately 4 times depending on the modelling approach of a dispersion coefficient. For the same model, the atmospheric dispersion factors showed differences by 2 to 3 times depending on surface roughness.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.662-674
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• 2014

In this study, accident frequency prediction models were constructed by collecting variables such as geometric structures, safety facilities, traffic volume and weather conditions, land use, highway design-satisfaction criteria along 780km (4,372 sections) of 4 lane-highways over 8 areas. As for models, a fixed parameter model and a random parameter model were employed. In the random parameter model, some influences were reversed as the range was expressed based on specific probability in the case of no fixed coefficients. In the fixed parameter model, the influences of independent variables on accident frequency were interpreted by using one coefficient, but in the random parameter model, more various interpretations were took place. In particular, curve radius, securement of shoulder lane, vertical grade design criteria satisfaction showed both positive and negative influence, according to specific probability. This means that there could be a reverse effect depending on the behavioral characteristics of drivers and the characteristics of highway sections. Rather, they influence the increase of accident frequency through the all sections.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1353-1355
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• 1996

This paper presents classification of high impedance fault pattern using linear prediction coefficients. A feature of neutral phase current is extracted by the linear predictive coding. This feature is classified into faults by a multilayer perceptron neural network. Neural network successfully classifies test data into three faults and one normal state.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.155-166
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• 2002

Since 1990s. there has been an increasing number of traffic accidents at intersection. which requires more urgent measures to insure safety on intersection. This study set out to analyze the road conditions, traffic conditions and traffic operation conditions on signalized intersection. to identify the elements that would impose obstructions in safety, and to develop a traffic accident prediction model to evaluate the safety of an intersection using the cop relation between the elements and an accident. In addition, the focus was made on suggesting appropriate traffic safety policies by dealing with the danger elements in advance and on enhancing the safety on the intersection in developing a traffic accident prediction model fir a signalized intersection. The data for the study was collected at an intersection located in Wonju city from January to December 2001. It consisted of the number of accidents, the road conditions, the traffic conditions, and the traffic operation conditions at the intersection. The collected data was first statistically analyzed and then the results identified the elements that had close correlations with accidents. They included the area pattern, the use of land, the bus stopping activities, the parking and stopping activities on the road, the total volume, the turning volume, the number of lanes, the width of the road, the intersection area, the cycle, the sight distance, and the turning radius. These elements were used in the second correlation analysis. The significant level was 95% or higher in all of them. There were few correlations between independent variables. The variables that affected the accident rate were the number of lanes, the turning radius, the sight distance and the cycle, which were used to develop a traffic accident prediction model formula considering their distribution. The model formula was compared with a general linear regression model in accuracy. In addition, the statistics of domestic accidents were investigated to analyze the distribution of the accidents and to classify intersections according to the risk level. Finally, the results were applied to the Spearman-rank correlation coefficient to see if the model was appropriate. As a result, the coefficient of determination was highly significant with the value of 0.985 and the ranks among the intersections according to the risk level were appropriate too. The actual number of accidents and the predicted ones were compared in terms of the risk level and they were about the same in the risk level for 80% of the intersections.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.75-75
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• 2015

오염원과 취수장이 동일 구간 내에 공존하는 국내하천의 특성상, 하천 평면 내에서 오염물의 거동 및 혼합 특성을 보다 정확하게 해석하기 위해서는 2차원 이송-분산 모형의 적용이 필요하다. 이를 위해서는 2차원 모형의 주요 매개변수인 종분산계수와 횡분산계수의 적절한 입력이 매우 중요하다. 하지만 국내외적으로 횡분산계수에 대한 연구는 많이 진행된 반면, 현재까지 종분산계수에 대한 연구는 충분히 이루어지지 않은 실정이다. 분산계수를 결정하는 방법에는 실측된 농도 자료의 유무에 따라 크게 두 가지로 분류된다. 실측된 농도 자료가 없는 경우, 이론식이나 경험식을 이용하는 방법이 있다. 반면에 추적자 실험 등을 수행하여 실측된 농도 자료가 있는 경우, 모멘트법 또는 추적법을 적용하여 농도-시간 분포 곡선으로부터 분산계수를 계산하는 것이다. 모멘트법은 임의 지점에서 농도의 횡분포를 통해 얻을 수 있는 2차 모멘트의 종방향 변화율이 횡분산계수와 비례한다는 원리를 이용한 것이며, 추적법은 상류부의 관측된 농도를 입력자료로 하여 하류부의 농도를 계산한 후 계산된 농도와 실측된 하류부 농도의 비교를 통해 분산계수를 산정하는 방법이다. 본 연구에서는 불규칙한 단면 형상을 가지는 자연하천에서의 2차원 종 횡분산계수를 산정하기 위해서 Baek & Seo(2010)가 제안한 2차원 유관추적법(2D Stream-tube Routing Procedure)을 적용하였다. 본 연구에서는 국내 자연하천 중 다양한 사행형태를 갖으며 수질오염 사고의 위험이 높은 구간을 선정하고, 추적자로서 Rhodamine WT를 이용하여 현장실험을 수행하였다. 실험에서 수집된 수리량 및 농도자료로부터 추적자의 2차원적 거동을 분석하였으며, 2차원 유관추적법을 적용하여 종분산계수를 산정하였다. 그 결과 하폭 대 수심비(W/H)와 마찰손실관련 무차원변수(U/U*)의 증가에 따라 종분산계수가 증가됨을 확인 할 수 있었다. 본 연구에서 산출된 종분산계수와 선행 연구에서 수집된 자료를 이용하여 추정식을 개발하였다. 차원해석을 통해 무차원 종분산계수에 영향을 미치는 무차원 인자를 선별하고 회귀분석을 이용하여 종분산계수 추정식을 유도하였다. 추정식을 이용하여 산정한 종분산계수의 범위는 Elder (1959)가 제안한 이론값보다 약 10배 정도로 크게 나타났다. 혼합 특성이 밝혀지지 않은 자연하천에 2차원 확산모형을 적용하고자 할 때 본 연구에서 개발된 추정식으로부터 계산된 종분산계수를 사용할 수 있을 것이다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.620-631
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• 2022

This paper shows the numerical prediction of the change in oil outflow rate according to the orifice shape of a damaged ship by using the computational fluid dynamics (CFD) analysis method. It also provides discharge coefficients for various orifice shapes to be used in theoretical prediction approaches. The oil outflow from the model ship was analyzed using a multiphase flow method under the condition that the Froude and Reynolds number similitudes were satisfied. The present numerical results were verified by comparing them with the available experimental data. Along with the aspect ratio of the orifice and the wall thickness of the cargo tank, the effects of the orifice shapes defined by mathematical figures on the oil outflow were investigated. To consider more realistic situations, the investigation of the ef ect of the crushed iron plate around the damaged part was also included. The numerical results confirmed the change in oil outflow time for various shapes of the damaged part of the oil tank, and discharge coefficients that quantify the viscous effects of those orifice shapes were extracted. To verify the predicted discharge coefficients, they were applied to an oil spill estimation equation. As a result, a good agreement between the CFD and theoretical results was obtained.

• Nuclear Engineering and Technology
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• v.25 no.1
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• pp.102-109
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• 1993

A PC-computer program RADAP has been developed in this study to perform a quick real-time analysis of dose assessment following an accident in a nuclear facility. RADAP uses an interactive LKagrangian puff model in simulating the transport and diffusion of radioactive plume in the atmosphere. For real-time analysis, RADAP treats one or multiple puffs of ground-level releases, simultaneously. It is assumed to maintain a Gaussian distribution within the puff and the diffusion coefficients are computed using the USNRC's normal sigma curve method. The program, however, does not consider the spatial variations but the temporal variations in wind conditions. Whole body and thyroid doses for 3$\times$31 grid are directed to output files, and they are also displayed through computer graphics on VGA or EGA color monitor. The results show that RADAP can be an excellent tool for quick estimation of accidental doses.