• The Journal of Society for e-Business Studies
• /
• v.23 no.1
• /
• pp.47-63
• /
• 2018

This study suggested quantitative models to calculate a royalty rate as an important input factor of the relief from royalty method which has the characteristics of income approach method and market approach method that are generally used in the valuation of intangible assets. This study built a royalty rate regression model by referring to the patent infringement damages cases based on royalties, i.e., by using the royalty rates as a dependent variable and the patent indexes of the corresponding patent right as independent variables. Then, a logistic regression model was constructed by referring to inter-partes review cases of patent rights, i.e. by using not-unpatentable results as a dependent variable and the patent indexes of the corresponding patent right as independent variables. A final royalty rate was calculated by matching the royalty rate from the royalty rate regression model with a not-unpatentable probability from the logistic regression model. The suggested royalty rate was compared with the royalty rate obtained by the traditional methods to check its reliability.

• The Journal of Engineering Geology
• /
• v.14 no.2
• /
• pp.211-222
• /
• 2004

In this study, a probabilistic prediction model for debris flow occurrence was developed using a logistic regression analysis. The model can be applicable to metamorphic rocks and granite area. order to develop the prediction model, detailed field survey and laboratory soil tests were conducted both in the northern and the southern Gyeonggi province and in Sangju, Gyeongbuk province, Korea. The seven landslide triggering factors were selected by a logistic regression analysis as well as several basic statistical analyses. The seven factors consist of two topographic factors and five geological and geotechnical factors. The model assigns a weight value to each selected factor. The verification results reveal that the model has 90.74% of prediction accuracy. Therefore, it is possible to predict landslide occurrence in a probabilistic and quantitative manner.

• The Journal of Engineering Geology
• /
• v.32 no.4
• /
• pp.585-596
• /
• 2022

This study determined major factors influencing landslide occurrence along a forest road near Sangsan village, Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea. Within a 2 km radius of the study area, landslides occur intensively during periods of heavy rainfall (August 2020). This makes study of the area advantageous, as it allows examination of the influence of only geological and tomographic factors while excluding the effects of rainfall and vegetation. Data for 82 locations (37 experiencing landslides and 45 not) were obtained from geological surveys, laboratory tests, and geo-spatial analysis. After some data preprocessing (e.g., error filtering, minimum-maximum normalization, and multicollinearity), structural equation model (SEM) and logistic regression (LR) analyses were conducted. These showed the regolith thickness, porosity, and saturated unit weight to be the factors most influential of landslide risk in the study area. The sums of the influence magnitudes of these factors are 71% in SEM and 83% in LR.

• The Journal of Korean Association of Computer Education
• /
• v.16 no.2
• /
• pp.39-48
• /
• 2013

This study investigates the predictive model of the adolescents' computer entertainment behavior using neural network with the KYPS data (3449 in the junior high school; 1725 boys and 1724 girls). This study compares the results of neural network(model 1) to the logistic regression model and neural network(model 2) with the exact same variables used in logistic regression. The results reveal that the prediction of neural network model 1 is the highest among three models and with gender, computer use time, family income, the number of close friends, the number of misdeed friends, individual study time, self-control, private education time, leisure time, self-belief, stress, adaptation to school, and study related worries, the neural network model 1 predicts the computer entertainment behavior more efficiently. These results suggest that the neural network could be used for diagnosing and adjusting the adolescents' computer entertainment behavior.

• Journal of Korean Society of Transportation
• /
• v.24 no.2 s.88
• /
• pp.139-145
• /
• 2006

This paper develops pedestrian fatality models capable of producing the probability of pedestrian fatality in collision between vehicles and pedestrians. Probabilistic neural network (PNN) and binary logistic regression (BLR) ave employed in modeling pedestrian fatality pedestrian age, vehicle type, and collision speed obtained from reconstructing collected accidents are used as independent variables in fatality models. One of the nice features of this study is that an iterative sampling technique is used to construct various training and test datasets for the purpose of better performance comparison Statistical comparison considering the variation of model Performances is conducted. The results show that the PNN-based fatality model outperforms the BLR-based model. The models developed in this study that allow us to predict the pedestrian fatality would be useful tools for supporting the derivation of various safety Policies and technologies to enhance Pedestrian safety.

• International Journal of Highway Engineering
• /
• v.13 no.2
• /
• pp.49-56
• /
• 2011

Understanding various casual factors affecting the occurrence of freeway traffic crash is a backbone of deriving effective countermeasures. The first step toward understanding such factors is to identify crash risks on freeways. Unlike existing studies, this study focused on the unsafe vehicle maneuvering that can be detected by in-vehicle sensors. The recent advancement of sensor technologies allows us to gather and analyze detailed microscopic events leading to crash occurrence such as the abrupt change in acceleration. This study used an accelerometer to capture the unsafe events. A set of candidate variables representing unsafe events were derived from analyzing acceleration data obtained by the accelerometer. Then, the crash risk was modeled by the binary logistic regression technique. The probabilistic outcome of crash risk can be provided by the proposed model. An application of the methodology assessing crash risk was presented, and further research items for the successful field implementation were also discussed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.3
• /
• pp.313-321
• /
• 2012

Korea has been continuously affected by landslides, as 70% of the land is covered by mountains and most of annual rainfall concentrates between June and September. Recently, abrupt climate change affects the increase of landslide occurrence. Gangwon region is especially suffered by landslide damages, because the most of the part is mountainous, steep, and having shallow soil. In this study, a landslide risk assessment model was developed by applying logistic regression to the various data of Duksan-ri, Inje-eup, Inje-gun, Gangwon-do, which has suffered massive landslide triggered by heavy rain in July 2006. The information collected from field investigation and aerial photos right after the landslide of study area were stored in GIS DB for analysis. Slope gradient entered in two ways-as categorical variable and as linear variable. Error matrix for each case was made, and developed model showed the classification accuracy of 81.4% and 81.9%, respectively.

• Journal of Korean Society of Transportation
• /
• v.22 no.1 s.72
• /
• pp.33-42
• /
• 2004

지하철역별 수요는 개통 후 경과 연도에 따라서 S자 형태로 증가한다. 즉 개통 초기에는 잠재되어 있던 지하철 수요가 시간의 경과에 따라 계속적으로 증가하다가, 개통 후 10$\sim$13년 정도가 경과하면 최대를 나타낸 후 거의 정체하는 현상을 보인다. 그러나 지금까지 지하철 수요를 추정하기 위해서 이용되었던 4단계 모형은 이러한 지하철 수요의 증가 추세를 반영할 수 없기 때문에 실제 수요와 많은 차이를 보였다. 따라서 본 연구에서는 이러한 문제를 해결해 보고자 서울시 지하철 2$\sim$8호선의 실제 수요를 토대로 지하철역별 수요, 특히 순수한 승차인원을 추정하는 모형을 개발하였다. 모형에 적용되는 함수식은 실제 지하철역별 수요와 가장 유사한 형태를 보이고 있는 로지스틱 함수식을 이용하였다. 또한 각각의 지하철역별로 나타나는 상이한 특성은 카테고리로 분류하여 모형에 반영하였다. 카테고리는 토지이용도, 사회경제활동의 규모, 그리고 지하철역의 특성에 따라 분류하였다. 각 카테고리별 특성을 대표하는 독립 변수로 인구 종사자수, 학생수와 개통 후 경과 연도 등을 선정하였다. 그 결과 카테고리별로 추정된 지하철역별 수요는 통계적으로 매우 유의한 것으로 나타났다. 본 연구는 지하철역별로 승차하는 순수한 수요를 보다 정확하게 추정하기 위한 모형을 개발하는 것이 주된 목적이다. 반면에 본 모형을 이용하여 지하철역별 하차 수요 및 횐승 수요를 추정하는 것은 어렵다. 따라서 기존에 지하철 수요를 추정하는 데에 가장 많이 사용된 4단계 모형과 접목하여야 하며, 이에 대한 방안도 본 연구에서 제시하였다.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.5
• /
• pp.1021-1026
• /
• 2017

Mixture experiments are commonly encountered in many fields including food, chemical and pharmaceutical industries. In mixture experiments, measured response depends on the proportions of the components present in the mixture and not on the amount of the mixture. Statistical analysis of the data from mixture experiments has mainly focused on a continuous response variable. In the example of quantal response data in mixture experiments, however, the tumor incidence data have been analyzed in Chen et al. (1996) to study the effects of 3 dietary components on the expression of mammary gland tumor. In this paper, we compared the logistic regression models with linear predictors such as second degree Scheffe polynomial model, Becker model and Akay model in terms of classification accuracy.

• Communications for Statistical Applications and Methods
• /
• v.15 no.6
• /
• pp.859-873
• /
• 2008

Job satisfaction impacts on the turnover intention of employee, which affects the turnover behavior. This paper concerns with the impact of job satisfaction on the turn over behavior. Since turnover intention is highly correlated with job satisfaction, salary, employment status and etc, we should pay careful attention for modelling of those variables as independent variables and the turnover behavior as a dependent variable in the empirical study for the impact of factors on turnover behavior. We detect significant variables which effect the turnover behavior using 2-stage logistic regression inserting the turnover intention, an independent variable, with the chance estimates derived from the instrumental variables in Graduates Occupational Mobility Survey.