• Proceedings of the KSRS Conference
• /
• 2006.03a
• /
• pp.202-205
• /
• 2006

The objective of this study is to analyze the hazard-areas for landslide using GIS and RS. LRA (Logistic Regression Analysis) and AHP (Analytic Hierarchy Program) methods were used for evaluation of the hazard-areas by six topographic factors (slope, aspect, elevation, soil drain, soil depth, land use). These methods were applied to Anseong-si where frequent landslides were occurred mainly by the regional heavy rainfall. A landslide hazard-map of Anseong-si could describe into 7 hazard-grades. As results, LRA method was underestimated in higher grades areas, while AHP method was underestimated in lower grades areas. In order to evaluate the hazard-areas for landslides with accuracy, these results of each method were overlapped and the results of suggested method were compared with the historical landslide hazard records of KFRI (Korea Forest Research Institute).

• Journal of the Korean Society for Precision Engineering
• /
• v.26 no.1
• /
• pp.43-50
• /
• 2009

Semi dry cutting known as MQL (Minimum Quantity Lubrication) machining is widely spreaded into the machining shops nowadays. The objective of this research is to suggest how to derive optimum cutting conditions for the milling process in MQL machining. To reach these goals, a bunch of finish milling experiments was carried out while varying cutting speed, feed rate, oil quantity, depth of cut and so on with MQL. Then, response surface analysis was introduced for the variance analysis and the regression model with the experimental data. Finally, desirability function based on regression model was used to obtain optimal cutting parameters and verification experiment was done.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.10 no.2
• /
• pp.89-94
• /
• 2001

This paper presents a study of surface roughness prediction model by orthogonal design in turning operation. Regression analysis technique has been used to study the effects of the cutting parameters such as cutting speed, feed depth of cut, and nose radius on surface roughness. An effect of interaction between two parameters on surface roughness has also been investigated. The experiment has been conducted using coated tungsten carbide inserts without cutting fluid. The reliability of the surface roughness model as a function of the cutting parameters has been estimated. The results show that the experimental design used in turning process is a method to estimate the effects of cutting parameters on sur-face roughness.

• Korean journal of food and cookery science
• /
• v.20 no.4
• /
• pp.335-341
• /
• 2004

The purpose of this study was to measure the labor intensity of menus according to various cooking types. Through a literature review and in-depth interview, the attributes that affected the labor intensity were identified as the level of skill, amount of effort, degree of tiredness, time consumed, and machine usage. A survey was conducted in April, 2001 among cooks who Dew the entire process of cooking. There was a strong positive correlation between labor intensity and labor intensity attributes. Through regression analysis, a regression equation was obtained between labor intensity and labor intensity attributes. The labor intensity index calculated from this study showed the extent of labor intensity of menus. The result of this study could be used as basic data for foodservice manager to establish a menu planning and work schedule based on a scientific method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2000.04a
• /
• pp.519-524
• /
• 2000

This paper presents a study of surface roughness prediction model by experimental design in turning operation. Regression analysis technique has been used to study the effects of the cutting parameters such as cutting speed, feed and depth of cut on surface roughness. The experiment has been conducted using coated tungsten carbide inserts without cutting fluid. The reliability of the surface roughness model as a function of the cutting parameters has been estimated. The results show that the experimental design used in cutting process is a method to estimate the effects of cutting parameter on surface roughness.

• Journal of the Korean Society of Safety
• /
• v.24 no.1
• /
• pp.69-77
• /
• 2009

This paper reports the aging driver traffic accident severity modeling results. For the modeling, Poisson regression approach is applied using the data set obtained from the Korea Transportation Safety Authority's simulator-based driver aptitude test results. The test items include the estimations of moving objects' speed and stopping distance, drivers' multi-task capability, and kinetic depth perception and so on. The resulting model with the response variable of equivalent property damage only(EPDO) indicated that EPDO is significantly influenced by moving objects' speed estimation and drivers' multi-task capabilities. More interestingly, a comparison with the younger driver model revealed that the degradation of such capabilities may result in severer crashes for older drivers as suggested by the higher estimated parameters for the older driver model.

• Korea Trade Review
• /
• v.46 no.2
• /
• pp.281-299
• /
• 2021

This paper analyzes the feasibility of using machine learning methods to forecast the employment. The machine learning methods, such as decision tree, artificial neural network, and ensemble models such as random forest and gradient boosting regression tree were used to forecast the employment in Busan regional economy. The following were the main findings of the comparison of their predictive abilities. First, the forecasting power of machine learning methods can predict the employment well. Second, the forecasting values for the employment by decision tree models appeared somewhat differently according to the depth of decision trees. Third, the predictive power of artificial neural network model, however, does not show the high predictive power. Fourth, the ensemble models such as random forest and gradient boosting regression tree model show the higher predictive power. Thus, since the machine learning method can accurately predict the employment, we need to improve the accuracy of forecasting employment with the use of machine learning methods.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.30 no.11 s.158
• /
• pp.1550-1559
• /
• 2006

The purpose of this study is to measure middle aged men's bodies around the ages of thirty-five through filly-five, whose age-group undergoes drastic changes on their body shapes. Further, body shapes are grouped based on the measurements, and pattern making methods are presented in order to effectively make up for the undesirable body features of middle-aged men. In order to develop patterns fit for middle aged men's body shapes, the regression which reflects physical characteristics was applied for the experimental shirts pattern, instead of comparative patterns which applies the same equation. For the two body types classified above(Bend-forward Type and Lean-back Type), correlation among the factors necessary for pattern design was considered and the regression was formularized accordingly. To validate the regression, the same amount of ease as in the comparative patterns was applied to the chest girth, and reasonable ease to the armhole depth and neck girth. In this way, experimental shirts patterns fit for the body types were developed. After the modification through the wearing test, the experimental shirts pattern was completed as herein.

• Computers and Concrete
• /
• v.13 no.1
• /
• pp.17-30
• /
• 2014

This research presents the effect of various ground pozzolanic materials in blended cement concrete on the strength and chloride penetration resistance. An experimental investigation dealing with concrete incorporating ground fly ash (GFA), ground bottom ash (GBA) and ground rice husk ash (GRHA). The concretes were mixed by replacing each pozzolan to Ordinary Portland cement at levels of 0%, 10%, 20% and 40% by weight of binder. Three different water to cement ratios (0.35, 0.48 and 0.62) were used and type F superplasticizer was added to keep the required slump. Compressive strength and chloride permeability were determined at the ages of 28, 60, and 90 days. Furthermore, using this experimental database, linear and nonlinear multiple regression techniques were developed to construct a mathematical model of chloride permeability in concretes. Experimental results indicated that the incorporation of GFA, GBA and GRHA as a partial cement replacement significantly improved compressive strength and chloride penetration resistance. The chloride penetration of blended concrete continuously decreases with an increase in pozzolan content up to 40% of cement replacement and yields the highest reduction in the chloride permeability. Compressive strength of concretes incorporating with these pozzolans was obviously higher than those of the control concretes at all ages. In addition, the nonlinear technique gives a higher degree of accuracy than the linear regression based on statistical parameters and provides fairly reasonable absolute fraction of variance ($R^2$) of 0.974 and 0.960 for the charge passed and chloride penetration depth, respectively.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.49 no.3
• /
• pp.63-68
• /
• 2007

The LRA(Logistic Regression Analysis) conducts a quantitative analysis by collecting a lot of samples and the AHP(Analytic Hierarchy Program) makes use of expert decision influenced by subjective judgment to a certain degree. This study is to suggest a combination method in mapping landslide hazard by giving equal weight for the result of LRA and AHP. Topographic factors(slope, aspect, elevation), soil dram, soil depth and land use were adopted to classify landslide hazard areas. The three methods(LRA, AHP, the combined approach) was applied to a $520km^2$ region located in the middle of South Korea which have occurred 39 landslides during 1999 and 2003. The suggested method showed 58.9% matching rate for the real landslide sites comparing with the classified areas of high-risk landslide While LRA and AHP Showed 46.1% and 48.7% matching rates respectively. Further studies are recommended to find the optimal combining weight of LRA and AHP with more landslide data.