• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.679-690
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• 2015

The need to remove the cause of traffic accidents by improving the engineering system for a vehicle and the road in order to minimize the accident hazard. This is likely to cause traffic accident continue to take a large and significant social cost and time to improve the reliability and efficiency of this generally poor road, thereby generating a lot of damage to the national traffic accident caused by improper environmental factors. In order to minimize damage from traffic accidents, the cause of accidents must be eliminated through technological improvements of vehicles and road systems. Generally, it is highly probable that traffic accident occurs more often on roads that lack safety measures, and can only be improved with tremendous time and costs. In particular, traffic accidents at intersections are on the rise due to inappropriate environmental factors, and are causing great losses for the nation as a whole. This study aims to present safety countermeasures against the cause of accidents by developing an intersection Traffic safety evaluation model. It will also diagnose vulnerable traffic points through BPA (Back -propagation algorithm) among artificial neural networks recently investigated in the area of artificial intelligence. Furthermore, it aims to pursue a more efficient traffic safety improvement project in terms of operating signalized intersections and establishing traffic safety policies. As a result of conducting this study, the mean square error approximate between the predicted values and actual measured values of traffic accidents derived from the BPA is estimated to be 3.89. It appeared that the BPA appeared to have excellent traffic safety evaluating abilities compared to the multiple regression model. In other words, The BPA can be effectively utilized in diagnosing and practical establishing transportation policy in the safety of actual signalized intersections.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.81-88
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• 2010

The analysis and synthesis of various types of hydrologic variables such as precipitation, surface runoff, and discharge are usually required in planning and management of water resources. These hydrologic variables are mostly represented using stochastic models. One of which is the autoregressive model, that gives promising results in time series modeling. This study is an application of this model, which aimed to determine the AR model that best represents the historical monthly streamflow of the two gauging stations, namely Andong Dam and Imha Dam, both located in the upper Nakdong River Basin. AR(3) model was found to be the best model for both gauging stations. Parameters of the determined order of AR model ($\phi_1$, $\phi_2$ and $\phi_3$) were also estimated. Using several diagnostic tests, the efficiency of the determined AR(3) model was tested. These tests indicated the accuracy of the determined AR(3) model.

• Proceedings of the KOR-KST Conference
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• 1998.10a
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• pp.265-274
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• 1998

본 논문은 도로를 주행하는 차량의 지점속도에 대하여 단기간(short-term)으로 예측하는 네 가지의 모형들에 대한 개발 및 결과의 비교하고 평가했다. 사용된 기법들로는 다중회귀분석, 시계열분석(ARIMA), 인공 신경망, 칼만필터링 등이며, 모형의 구출을 위하여 다수의 독립변수 및 입력변수가 요구되는 다중회귀분석과 인공 신경망에서는 연속방정식에서 고려되는 변수들간의 단순상관계수 및 편상관계수의 계산을 통해서 입력변수가 설정이 되었으며, 시계열분석(ARIMA)과 칼만필터링 등 단일 입력 변수만을 요하는 모형에서는 바로 전 시간대와 현재시간대의간격동안 속도의 변화량을 입력변수로 설정하였다. 속도를 비롯해서 교통 데이터는 현장자료를 사용하였는데, 이는 서울의 한강 옆에 위치한 올림픽대로 중 한강대로에 위치한 검지기 3개를 통해서 천호동 방면으로 이동하는 교통류에 대해서 17시간 (00시~17시)동안 수집했다. 17시간 수집했는데 그중에 검지된 속도는 14km/h에서 98km/h까지 변하는 등, 수집된 자료에는 다양한 교통상태가 포함되어 있는데 이는 각 모형들의 정확한 예측력과 적응성을 평가하기 위함이었다. 각 모형은 예측하고자 하는 시점으로부터 1, 5, 10, 15분 후의 속도를 예측하는 것으로 총 4가지의 예측시간간격으로 각각 실험되었다. 결과는 전반적으로 신뢰성 있게 나왔으나 그중에서도 정확성면에서는 인공신경망과 칼만필터링이 우수했고 적응성면에서는 칼만필터리딩 탁월했다. 또한 1분 후의 속도를 예측하는 결과들은 모형들간에 거의 비슷한 정확도를 보여주었는데 이는 입력변수의 설정이 중요한 것임을 보여주는 것이라 판단된다. 있는 기법이다.적으로 세부적 차종분류로 접근한다.의 영향들을 고려함으로써 가로망 설계 과정에서 가로망의 상반된 역할인 이동성과 접근성의 비교가 가능한 보다 현실적인 가로망 설계 모형을 구축하고자 한다. 지금까지 소개된 가로망 설계모형들은 용량변화에 대한 설계변수의 형태에 따라 이산적 가로망 설계 모형과 연속적 가로망 설계모형으로 나뉘어지게 된다. 본 논문의 경우, 계산속도의 향상 측면에서는 연속적 가로망 설계 모형을 도입할 수 있지만, 이때 요구되는 도로용량이 이산적인 변수(차선 수)로 결정되어야만 신호제어 변수를 결정할 수 있기 때문에, 이산적 가로망 설계 모형이 사용된다. 하지만, 이산적 설계모형의 경우 조합최적화 문제이므로 정확한 최적해를 구하기 위해서는 상당한 시간이 소요되며, 경우에 따라서는 국부 최적해에 빠지게 된다. 이러한 문제를 극복하기 위해, 우선 이상적 모형의 근사화, 혹은 조합최적화문제를 위해 개발된 Simulated Annealing기법의 적용, 연속적 모형의 변수를 이산화하는 방법 등 다양한 모형들을 고려해 본 뒤, 적절한 모형을 적용할 것이다. 가로망 설계 모형에서 신호제어를 고려하기 위해서는 주어진 가로망에 대한 통행 배정과정에서 고려되는 통행시간을 링크통행시간과 교차로 지체시간을 동시에 고려해야 하는데, 이러한 문제의 해결을 위해서 최근 활발히 논의되고 있는 교차로에서의 신호제어에 대응하는 통행배정 모형을 도입하여 고려하고자 한다. 이를 위해서 지금까지 연구되어온 Global Solution Approach와 Iterative Approach를 비교, 검토한 뒤 모형에 보다 알맞은 방법을 선택한다. 차량의

• The Korean Journal of Financial Management
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• v.25 no.3
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• pp.33-78
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• 2008

This paper empirically examines whether firms engage in a dynamic adjustment process toward target capital structure and, whether there is a target capital structure or mean reverting using the partial adjustment model while allowing for costly adjustment. Also we investigate the empirical determinants of optimal target capital structure in long term equilibrium. As a result, our empirical model captures at least several important features of capital structure behavior for Korean listed firms. First, Korean firms pursue target capital structure and also there is mean reverting phenomenon. Second, Non-Chaebol and small firm in adjustment speed is faster than Chaebol and large firm. Third, even capital market restricts the adjustment speed interestingly. Fourth, Korean firms have target behavior according to a degree of observed gap. Fifth, Korean firms close about one-fourth of the gap between their actual and target debt ratios within one year and thence targeting behavior explains far more of the observed changes in capital structure than market timing or pecking order considerations. Sixth, capital market is significant in determining optimal capital structure.

• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.140-155
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• 1993

The characteristics of $I_{\lambda}$-optimality, one of the linear criteria suggested by Fedorov (1972) are investigated with respect to the D-and heteroscedastic G-optimality in case of non-constant variance function. Though having limited results obtained from simple models, we may conclude that $I_{\lambda}$-optimality is sometimes preferred to the heteroscedastic G-optimality suggested newly bv Wong and Cook (1992) in the sense that the experimenter's belief in weighting function exists in $I_{\lambda}$-optimality criterion, not to mention its computational simplicity.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.12
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• pp.565-572
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• 2017

Traditional method for time series analysis, autoregressive integrated moving average (ARIMA) allows to mine significant patterns from the past observations using autocorrelation and to forecast future sequences. However, Korean baseball games do not have regular intervals to analyze relationship among the past attendance observations. To address this issue, we propose artificial neural network (ANN) based attendance prediction model using various measures including performance, team characteristics and social influences. We optimized ANNs using grid search to construct optimal model for regression problem. The evaluation shows that the optimal and ensemble model outperform the baseline model, linear regression model.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.335-351
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• 2011

In order to resolve data scarcity problem related to crisis, Oh and Kim (2007) proposed to use stability oriented approach which focuses a base period of financial market, fits asymptotic stationary autoregressive model to the base period and then compares the fitted model with the current market situation. Based on such approach, they developed financial market instability index. However, since neural network, their major tool, depends on the base period too heavily, their instability index tends to suffer from inaccuracy. In this study, we consider linear asymptotic stationary autoregressive model and neural network to fit the base period and produce two instability indexes independently. Then the two indexes are combined into one integrated instability index via newly proposed combining method. It turns out that the combined instability performs reliably well.

• Journal of Navigation and Port Research
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• v.41 no.4
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• pp.181-188
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• 2017

In the existing ship sales market, prices determined based on the prices of similar ship types that recently traded. ince the 2008 financial crisis, ship prices have fluctuated, and ship price criteria have become ever more necessary to the imminent value of the ship. Therefore, this research used the hedonic price model to estimate imminent values of ships. In this study, the influence on ship prices was analyzed by the value of each characteristic and an estimated functional formula was. Out of the four models suggested by the hedonic price model, an optimal model was selected with variance inflation factors and a stepwise selection. For this, the influence of determinants of ship prices was analyzed based on actually traded ships and characteristic data. The selected model s the Log-Line model; as a result of regression analysis, eight variables, including DWT, Age, Market Value, Short-Term Charter, Long-Term Charter, Enbloc, Special Survey Due and Builder were to affect the ship price model. This model is expected to be useful for objective and balanced ship price evaluation.

• Economic and Environmental Geology
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• v.44 no.2
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• pp.135-152
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• 2011

Nitrate concentrations in springs at the southern slope of Jeju Island were predicted using multiple linear regression (MLR) of spatial variables including hydrogeological parameters and land use characteristics. Springs showed wide range of nitrate concentrations from <0.02 to 86 mg/L with a mean of 20 mg/L. Spatial variables were generated for the circular buffer when the optimal buffer radius was assigned as 400 m. Selected regression models were tested using the p values and Durbin-Watson statistics. Explanatory variables were selected using the adjusted $R^2$, Cp (total squared error) and AIC (Akaike's Information Criterion), and significance. In addition, mutual linear relations between variables were also considered. Small portion of springs, usually <10% of total samples, were identified as outliers indicating limitations of MLR using circular buffers. Adjusted $R^2$ of the proposed models was improved from 0.75 to 0.87 when outliers were eliminated. In particular, the areal proportion of natural area had the greatest influence on the nitrate concentrations in springs. Among anthropogenic land uses, the influence of nitrate contamination is diminishing in the following order of orchard, residential area, and dry farmland. It is apparent quality of springs in the study area is likely to be controlled by land uses instead of hydrogeological parameters. Most of all, it is worth highlighting that the contamination susceptibility of springs is highly sensitive to nearby land uses, in particular, orchard.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1137-1147
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• 2022

River and groundwater stages are the main elements in the hydrologic cycle. They are spatially correlated and can be used to evaluate hydrological and agricultural drought. Stochastic simulation is often performed independently on hydrological variables that are spatiotemporally correlated. In this setting, interdependency across mutual variables may not be maintained. This study proposes the Bayesian vector autoregression model (VAR) to capture the interdependency between multiple variables over time. VAR models systematically consider the lagged stages of each variable and the lagged values of the other variables. Further, an autoregressive model (AR) was built and compared with the VAR model. It was confirmed that the VAR model was more effective in reproducing observed interdependency (or cross-correlation) between river and ground stages, while the AR generally underestimated that of the observed.