• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.1-8
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• 2005

Permanent Deformation is one of the most important load-related pavement distresses in asphalt pavements. The Korean Pavement Design Guide currently being developed adopted the mechanistic-empirical approach and needed the pavement distress prediction models. This study intends to develop the model for prediction of permanent deformation in the asphalt layer and estimate the pavement performance. The objectives of this paper are to figure out the factors affecting the permanent deformation and then develop the permanent deformation prediction model for asphalt mixtures. The repeated triaxial load test was Performed on the 19mm dense graded asphalt mixture with variation of temperature and air void. Results from the laboratory tests showed that temperature and air void in asphalt mixtures have significantly influenced on the factors in prediction model. The permanent deformation prediction model for 19m dense grade asphalt mixtures has been developed using the multiple regression approach and validated the proposed permanent deformation prediction model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3A
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• pp.227-234
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• 2011

Recently, a high strength concrete of more than 40 MPa has been increasingly used in practice. However, use of the high strength concrete may influence on design parameters, particularly stress distribution. This is very true since the current everyday practice employs equivalent rectangular stress distribution that is derived from normal strength concrete. Subsequently, the stress distribution seems to be reevaluated and then a new distribution with new parameters needs to be suggested for the high strength concrete. For this purpose, linear and multiple regression analyses have been carried out in term of using experimental data for the high strength concrete of 40 to 80 MPa available in literatures. Accordingly, new parameters associated with the stress distribution have been proposed and employed for the design of flexural and compressive members. Comparative design examples indicate that designs with new parameters reduce section dimensions compared to those with the current code parameters for concrete strengths of 40 to 70 MPa. In particular, for compressive members, design with new parameters exhibit conservative compressive force compared to those with the current code parameters.

• Journal of the Economic Geographical Society of Korea
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• v.10 no.4
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• pp.427-443
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• 2007

The purpose of this study is to investigate spacio-temporal structures of traffic flows on the subway network in the Metropolitan Seoul, and the relationships between topological structures of traffic flows and land use patterns. In particular we analyze in the topological structures of traffic flows on the subway network in time dimension as well as in spatial dimension. For the purpose, this study utilizes data mining techniques to the one day T-card transaction data of the last four years, which has developed for exploring the characteristics of traffic flows from large scale trip-transaction databases. The topological structures of traffic flows on the subway network has changed considerably during the last four years. The volumes of traffic flows, the travel time and stops per trip have increased until 2006 and decreased again in 2007. The results are visualized by utilizing GIS and analyzed, and thus the spatial patterns of traffic flows are analyzed. The spatial distribution patterns of trip origins and destinations show substantial differences among time zones during a day. We analyze the relationships between traffic flows at subway stops and the geographical variables reflecting land use around them. We obtain 6 log-linear functions from stepwise multiple regression analysis. We test multicollinearity among the variables and autocollelation for the residuals.

• Journal of Animal Environmental Science
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• v.7 no.3
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• pp.183-190
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• 2001

The objective of this research was to design and construct an image processing system to measure easily and accurately cow's weight. The image processing system was built for a dairy cattle to be measured and estimated it's weight using camera and personal computer. The pixel numbers, which was derived from the image processing system, were counted to estimate the weight of a dairy cattle. They were utilized various was for finding the relationships between pixel numbers and it's real weight. Based on the results of this research the following conclusions were made: 1. It's weight could be estimated by using pixel numbers, which was captured from top and side cameras to measure it. The correlations with tea-view pixel numbers, side-view pixel numbers, superficial area pixel numbers and the volume pixel numbers were 0.909, 0.939, 0.944 and 0.965. 2. 50 cattle was used to execute an experiment with the image processing system, but average errors were big to make out the good relationship between cow's weight and pixel numbers. In order measure accurately a cattle weight, cattle weight, cattle groups would be divided by the age of cattle and further study should be carried out to be based on the results of this research. 3. The average time it took to perform the image processing to be measure it was 10 seconds, but it took 10 minutes for cattle to enter for measuring it's weight into the weighting system.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.414-414
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• 2017

토양수분은 지면환경에서 일어나는 수문 및 에너지 순환을 이해하는 데 있어 중요한 기상인자이다. 토양수분 현장관측은 땅속에 매설된 센서에 의해 상당히 정확하게 이루어지만, 관측점 수가 충분치 않아 공간적 연속성을 확보하지 못하는 어려움이 존재한다. 이에 광역적 및 연속적 관측이 가능한 마이크로파 위성센서가 토양수분 정보 획득을 위한 보조수단으로서 그 중요성이 부각되고 있다. 마이크로파 위성센서는 구름 등 기상조건의 제약을 받지 않으며, 1978년 이래 현재까지 여러 위성에 의해 25 km 및 10 km 해상도의 전지구 토양수분자료가 생산되어 왔다. 마이크로파 센서를 이용한 토양수분자료는 동일지점에 대하여 하루 2회 정도 산출되므로 적절한 시간분해능을 가지지만, 공간해상도가 최고 10 km로서 지역규모의 수문분석에 적용하기에는 충분치 않다. 이러한 토양수분자료의 공간해상도 문제 해결을 위하여 다양한 지면환경요소를 활용한 통계적 다운스케일링이 대안으로 제시되었다. 최근의 선행연구들은 대부분 방정식을 이용한 결합모형을 통해 통계적 다운스케일링을 수행하였는데, 회귀식과 같은 선형결합뿐 아니라 신경망이나 기계학습 등의 비선형결합에서도, 불가피하게 발생할 수밖에 없는 잔차(residual)로 인하여 다운스케일링 전후의 공간분포 패턴이 달라져버리는 문제를 안고 있었다. 회귀분석에 잔차의 공간내삽을 결합시킨 회귀크리깅(regression kriging)은 잔차보정을 통해 이러한 문제를 해결함으로써 다운스케일링 전후의 공간분포 일관성을 보장하는 기법이다. 이 연구에서는 회귀크리깅을 이용하여 일자별 AMSR2(Advanced Microwave Scanning Radiometer 2) 토양수분 자료를 10 km에서 1 km 해상도로 다운스케일링하고, 다운스케일링 전후의 자료패턴 일관성을 평가한다. 지면온도(LST), 지면온도상승률(RR), 식생온도건조지수(TVDI)는 일자별로 DB를 구축하였고, 식생지수(NDVI), 수분지수(NDWI), 지면알베도(SA)는 8일 간격으로 DB를 구축하였다. 이러한 8일 간격의 자료를 일자별로 변환하기 위하여 큐빅스플라인(cubic spline)을 이용하여 시계열내삽을 수행하였다. 또한 상이한 공간해상도의 자료는 최근린법을 이용하여 다운스케일링 목표해상도인 1 km에 맞도록 변환하였다. 우선 저해상도 스케일에서 추정치를 산출하기 위해서는 저해상도 픽셀별로 이에 해당하는 복수의 고해상도 픽셀을 평균화하여 대응시켜야 하며, 이를 통해 6개의 설명변수(LST, RR, TVDI, NDVI, NDWI, SA)와 AMSR2 토양수분을 반응변수로 하는 다중회귀식을 도출하였다. 이식을 고해상도 스케일의 설명변수들에 적용하면 고해상도 토양수분 추정치가 산출되는데, 이때 추정치와 원자료의 차이에 해당하는 잔차에 대한 보정이 필요하다. 저해상도 스케일로 존재하는 잔차를 크리깅 공간내삽을 통해 고해상도로 변환한 후 이를 고해상도 추정치에 부가해주는 방식으로 잔차보정이 이루어짐으로써, 다운스케일링 전후의 자료패턴 일관성이 유지되는(r>0.95) 공간상세화된 토양수분 자료를 생산할 수 있다.

• Journal of Convergence for Information Technology
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• v.7 no.2
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• pp.111-117
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• 2017

Background/Objectives: The purpose of this study is to investigate the effects of eating habits and self-efficacy on health promotion behavior and to improve the quality of life in each life cycle. Methods/Statistical analysis: This study is a narrative research study for nursing students at university B in C city. The data were analyzed by SPSSWIN 18.0, t-test, ANOVA and bonferroni post test, Pearson correlation coefficient calculation and multiple regression analysis. Findings: Health promotion behaviors were positively correlated with eating habits and self-efficacy. Multiple regression analysis showed that 53.8% of the parents, caregivers, health status, eating habits, and self - efficacy of the nursing students showed the health promotion behaviors of the nursing students. The higher the self-efficacy(t=6.06, p<.001), the better the health-promoting behaviors(t=3.28, p=.001). Application/mprovements: Development of programs for strengthening health education and self-efficacy is required for proper eating habits and health promotion.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.770-778
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• 2023

Purpose: In order to reduce forklift industrial accidents, it is necessary to classify them into institutional, technical, and educational factors and conduct research on whether each factor affects disaster reduction. Method: Descriptive statistical analysis, validity analysis, reliability analysis, and multiple regression analysis were conducted using SPSS 18 program based on an offline questionnaire based on a 5-point Likert scale. Result: As a result of multiple regression analysis, it was found that institutional, technical, and educational factors, which are independent variables for disaster reduction, explain about 62.5% of the variance in disaster prevention, which is the dependent variable. The regression model verification was found to be statistically significant with F=118.775 and significance probability p<0.01. Conclusion: First, there is a need to prevent disasters by including electric forklifts weighing less than 3 tons in the inspection system. Second, there is a need to make it mandatory to install front and rear cameras and forklift line beams to prevent forklift collision disasters. Third, there is a need to conduct special training related to forklifts every year, and drivers and nearby workers need to be included in the special training for forklifts.

• Journal of Wetlands Research
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• v.18 no.3
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• pp.301-312
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• 2016

This study has performed comparative analysis on characteristics of contaminated 35 tributaries on seasonal variation/point discharge load/pollutant distribution of water quality factors(8) in order to understand the effect of the watershed in Nakdong River Basin. As a results, the water quality of $BOD_5$(Biochemical Oxygen Demand), Chl-a(Chlorophyll a) and Fecal E. Coli shows II grade at tributaries of more than 50% without COD(Chemical Oxygen Demand), TP(Total Phosphate), TOC(Total Oxygen Carbon) and TN(Total Nitrogen) factors. The specific discharge(Q) were occupied about 54.4% (19 sites) as $0.05m^3/sec/km^2$ value. Among these results, the contaminant level of Dalseocheon, Hyeonjicheon, Seokkyocheon 1, Uriyeongcheon and Dasancheon was also high, which has to consider a discharged pollutant load(kg/day). The 35 major tributaries of Nakdong River were included in 7 mid-watershed, such as Nakdong Waegwan, Geumho River, Nakdong Goryung, Nakdong Changnyung, Nam River, Nakdong Milyang, Nakdong River Hagueon. Especially, the discharged pollutant load of Nam River and Geumho River also was high according to the amount of discharge such as Kachang dam, Gongsan dam and Nam river dam. Seasonal difference of the water quality factors such as $BOD_5$, TN, SS and Q was observed largely, on the other hand the TP and Chl-a was not. This is guessed due to the precipitation effect of site, biological and physicochemical degradation properties of pollutant and etc. The co-relationship between the seasonal difference and water quality factors was observed using a Pearson correlation coefficients. Besides, the Multiple Regression analysis using a Stepwise Regression method was conducted to understand the effect between seasonal difference and water quality factors/regression equations. As a result, the Multiple Regression analysis was adapted in the spring, summer and autumn without the winter, which was observed high at spring, summer and autumn in the order COD/TP, Chl-a/TOC, TOC/COD/$BOD_5$ water quality factors, respectively.

• Korean Journal of Soil Science and Fertilizer
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• v.7 no.4
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• pp.215-220
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• 1974

The relation between yield or total dry matter yield and nitrogen, phosphorus, potassium or silica uptake was investigated according to simple or multiple correlation coefficients, and multiple regression equations. 1. Simple correlation coefficient was always higher with total dry matter yield than with grain yield and highest with N in no nitrogen (0-6-8) or no fertilizer (0-0-0) plot, with P in no phosphorus plot (10-0-8) but lowest with K in no potassium plot (10-6-0). 2. Multiple correlation coefficient was always higher than simple correlation and the same is true with including Si as one more variation. There was clear trend that multiple correlation coefficient was highest in no fertilizer plot and lowest in no potassium plot. 3. Simple correlation coefficient with P was higher in the warm year in which P uptake and fertilizer-P use efficiency were higher while it with K was higher in the cool year in which K uptake and fertilizer-K use efficiency were higher. Nitrogen and silicate followed potassiuum. But partial regression coefficients of N. P. K and Si with yield were mostly significant only in the warm year. 4. Partial regression coefficient of K was negative in many cases with yield but significant positive value with total dry matter yield. 5. Partial regression coefficients of N. P and K were decreased when Si was included and the decrease was great in P. 6. With the increase of nitrogen fertilizer level partial regression coefficient was increased in N but decreased in P, and no consistency in K or Si. 7. According to single or multiple correlation coefficients and partial regression coefficients the contribution of nutrient to grain yield appears to be in the order of N > Si > P > K and to total dry matter yield in the order of N > K > Si > P, indicating that N is the main factor and others are closely related to each other throughout to N. The superiority of N was also proved by frequency pattern of relative yield.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.