• Journal of the Korea Computer Industry Society
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• v.2 no.3
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• pp.377-388
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• 2001

Software metrics provide effective methods for characterizing software. Metrics have traditionally been composed through the definition of an equation, but this approach restricted within a full understanding of every interrelationships among the parameters. This paper use fuzzy logic system that is capable of uniformly approximating any nonlinear function and applying cognitive psychology theory. First of all, we extract multiple regression equation from the factors of 12 software complexity metrics collected from Java programs. We apply cognitive psychology theory in program volume factor, and then measure program volume complexity to execute fuzzy learning. This approach is sound, thus serving as the groundwork for further exploration into the analysis and design of software metrics.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.5
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• pp.345-351
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• 2015

Sounds of electrically powered vehicle components such as window lift system, roller blind, etc., are required to be more comfortable and not to irritate the people emotionally. In this paper, a study was conducted to identify the significant sound quality metric and compose the luxuriousness sound quality index of electrically powered vehicle roller blind which is part of vehicle sunroof system. Before conducting subjective evaluation, sound characteristics of roller blind was analyzed and set the target operating sound for subjective evaluation. Thus, transfer sound of roller blind which has the characteristics of sound modulation was used for subjective evaluation. Multiple linear regression analysis was carried out by chosen Zwicker's metrics which are pointed by comments of jurors. Loudness and sharpness related metrics are prime metrics in luxuriousness sound quality index we composed. Also, effect of roller blind assay when it is attached to real vehicle was identified to evaluate the validity of index.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.5
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• pp.429-435
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• 2007

The present study proposed a method fer simulating reflective boundary conditions in Boussinesq wave propagation model by lining lateral boundaries like breakwaters and seawalls with artificial sponge layers. In order to find out the reflective characteristics of sponge layers, 1D numerical experiments were performed varying the relative sponge width (sponge width/wave length). The results showed that the reflection coefficient can be effectively realized from no reflection to full reflection simply by adjusting the relative sponge width. Based on the results, a multiple regression formula was proposed to delineate the relationship among the reflection coefficient and other dimensionless variables. Finally, the reflective sponge layer was applied to a semi-infinite breakwater, demonstrating that it can also be successfully employed in 2D applications.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.683-688
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• 2012

Driver's feeling is variously affected by lots of components such as engine, frame, wheels, and seats during the operation of automobiles. The main objective of this research is to identify the correlation between subjective evaluation and vibration metrics that was set by ISO to investigate development of the car vibration quality index using multiple linear regressions (MLR). A previous research related with automotive vibration quality used the method of calculating acceleration values of the point of a seat, a seat back, foot as RMS for objective evaluation. The automotive comfort is determined by RMS values. In comparison with the previous research, this study includes not only the vibration metrics, but also subjective values by jury evaluation. By indentifying the correlation between subjective evaluation and vibration metrics, the automotive vibration quality index is developed through MLR. Based on the results of this study, the proposed the automotive vibration quality index which developed through MLR will be helpful to obtain objective and reliable automotive comfort values.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.109-125
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• 2004

In recent decades, microscopic simulation models have become powerful tools to analyze traffic flow on highways and to assist the investigation of level of service. The existing microscopic simulation models simulate an individual vehicle's speed based on a constant free-flow speed dominantly specified by users and driver's behavior models reflecting vehicle interactions, such as car following and lane changing. They set a single free-flow speed for a single vehicle on a given link and neglect to consider the effects of highway design elements to it in their internal simulation. Due to this, the existing models are limitted to provide with identical simulation results on both curved and tangent sections of highways. This paper presents a model developed to estimate the change of free-flow speeds based on highway design elements. Nine neural network models were trained based on the field data collected from seven different freeway curve sections and three different locations at each section to capture the percent changes of free-flow speeds: 100 m upstream of the point of curve (PC) and the middle of the curve. The model employing seven highway design elements as its input variables was selected as the best : radius of curve, length of curve, superelevation, the number of lanes, grade variations, and the approaching free-flow speed on 100 m upstream of PC. Tests showed that the free-flow speeds estimated by the proposed model were statistically identical to the ones from the field at 95% confidence level at each three different locations described above. The root mean square errors at the starting and the middle of curve section were 6.68 and 10.06, and the R-squares at these points were 0.77 and 0.65, respectively. It was concluded from the study that the proposed model would be one of the potential tools introducing the effects of highway design elements to free-flow speeds in simulation.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.17-17
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• 2023

국내의 부지특성 및 감시 관련 규정은 원자력 안전위원회 고시 제2021-16호 제4조(세부지침)와 원자력 안전위원회 고시 제2021-17호 제16조에 의거하고, 국외는 국제원자력기구(IAEA: International Atomic Energy Agency)에서 안전기준을 제시하고 있다(IAEA, 2011). 따라서 국내 중·저준위 방사성폐기물 처분시설은 2006년부터 광역 지질을 포함한 부지 지질/지형, 기상, 수문, 수리지질, 인문사회 등을 망라한 조사를 시행하여 부지 현황에 대한 분석 및 안정성 평가를 수행한다. 부지감시의 수문·지구화학 분야에서는 현장 수질 측정 6항목과 실내 분석 26항목을 감시하고 있으나, 본 연구는 이 중 9개 항목(EC, Na, K, Ca, Mg, SiO₂, Cl, SO₄, HCO₃)을 선정하여 분석하였다. 연구 목적은 물시료 분석자료의 주성분-다중선형회귀-군집 분석과 Piper Diagram 분석결과로부터 해수와 담수(지하수)와의 특성분석 및 해수 영향을 분석하는 것이다. 현장 부지내 지하수 7개 관정(GM-1, 2, 4, 5, 6, 7, 8)과 해수 2개 지점(Sea-1, 2)을 대상으로 통계학적 주성분 분석결과, 대부분의 지하수는2개~4개의 요인으로 구분되고, 해수와의 유사성을 해석하기 위해 확인한 관정은 GM-5, GM-6, GM-1 지점으로 분류되었다. 상기와 같이 해수의 영향을 확인하기 위해 해수 2개 지점과 동일한 군집으로 분류되는 지하수는 GM-5 관정으로 확인되었고, 해안선에서 가까운 GM-5 관정과 같이 유사한 거리에 분포한 지하수 GM-1, 2, 4 관정은 2개 혹은 3개의 최적 군집으로 분류하였을 때도 GM-5와는 다른 특성을 보여주었다. 이는 해안과 인접하더라도 수질은 다른 지질학적 특성(지형, 기상, 단열대 등)에 따라 영향받았음을 지시한다.

• Food Science of Animal Resources
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• v.33 no.6
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• pp.796-805
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• 2013

This study was conducted to investigate the relationship between quality factors and freshness or palatability of Hanwoo beef according to storage condition. The drip loss, cooking loss, volatile basic nitrogen (VBN), thiobarbituric acid reactive substance (TBARS), total viable counts (TVC) and sensorial characteristics of Hanwoo beef (raw and cooked) were investigated during storage for 36 d at 0 and $10^{\circ}C$. The drip loss, cooking loss, VBN, and TBARS were increased during storage period. The correlation between these factors and freshness was shown to be highly significant at both $10^{\circ}C$ than $0^{\circ}C$. Especially, correlation of between the cooking loss and freshness of Hanwoo beef showed high significance (p<0.01) at higher storage temperature. The correlation coefficient between factors such as VBN, cooking loss, and TVC and palatability were decreased with increased storage temperature. As a statistical analysis result, a multiple regression equation of $Y_1=10.768-0.706X_1$ (Drip loss) with $R^2=0.87$ was obtained for freshness evaluation of Hanwoo beef. Also, multiple regression with drip loss ($X_1$) and TVC ($X_5$) increased the coefficient of determination for sensorial palatability ($Y_2$) to $R^2=0.95$ with a regression equation of $Y_2=9.702-0.438X_1(Drip\;loss)-0.232X_5(TVC)$.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.548-556
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• 2019

The purpose of this study is to analyze the impact of dietary lifestyles and demographic characteristics on the Brand image of restaurants with Nutritional labeling to provide basic marketing data for establishing differentiated Brand image strategies for restaurant businesses. To that end, the SPSS21.0 (ver.) program, frequency analysis, descriptive statistics, factor analysis, reliability analysis, correlation analysis, and multiple linear regression analysis were conducted to verify the hypothesis. As a result, the Brand image of restaurants with Nutritional labeling improved as the metropolitan area sought safety, non-capital area sought taste, males sought health, and females sought safety. In terms of age, it was analyzed that as more people in their 20s sought taste, those their 30s and 40s sought safety, and both married and unmarried people sought safety, the Brand image of restaurants with Nutritional labeling improved. In other words, it could be seen that people with Dietary lifestyles who pursued health and safety had positive images of restaurants with Nutritional labeling regardless of residential area, age, gender, marital status, or whether they had children.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.