• Journal of Biosystems Engineering
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• 제12권2호
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• pp.38-43
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• 1987

An experimental study was conducted to develop a thin-layer rewetting equation of short grain rough rice of Akihikari variety. Four thin-layer rewetting equations were experimentally determined from $25^{\circ}C$ to $45^{\circ}C$ and 70%RH to 85%RH conditions. Diffusion, Henderson, Page, and Thompson equations widely used as thin-layer drying equations were selected. Experimental data were fitted to these equations using linear regression analysis except diffusion equation. The diffusivity in the diffusion equation was determined by optimization method. Four equations were highly significant. In order to compare the goodness of fit of each equation, the error mean square of each equawas calculated. The diffusion model was not a very good model because the error mean square was very large. The other three models showed the same level or error mean square and could predict satisfactorily the rewetting rate or short grain rough rice.

• Communications for Statistical Applications and Methods
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• 제26권6호
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• pp.575-582
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• 2019

The problem of selecting variables in the presence of outliers is considered. Variable selection and outlier detection are not separable problems because each observation affects the fitted regression equation differently and has a different influence on each variable. We suggest a simultaneous method for variable selection and outlier detection in a linear regression model. The suggested procedure uses a sequential method to detect outliers and uses all possible subset regressions for model selections. A simplified version of the procedure is also proposed to reduce the computational burden. The procedures are compared to other variable selection methods using real data sets known to contain outliers. Examples show that the proposed procedures are effective and superior to robust algorithms in selecting the best model.

• Journal of Biosystems Engineering
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• 제18권3호
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• pp.220-229
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• 1993

A dimensional analysis was carried out to investigate if model agricultural radial tire can predict the tractive performance of prototype tires. Experimental data was analyzed to prove the results of dimensional analysis. The results was summerized as follows ; 1. When the model and prototype tires are tested under the same soil conditions, inflation pressure, slip and dynamic load, traction coefficient ratio between two tires depend on the geometry of two tires. 2. According to the regression analysis of the experimental data, traction equation parameters of the prototype tires can be predicted from the that of model tire 3. Predicted traction coefficient of prototype tire, calculated from the traction equation paramters, showed good correlation with that of experimental results. Thus it was possible to predict net and gross traction of prototype tire from the model traction equation parameters.

• 한국농공학회지
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• 제26권2호
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• pp.106-124
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• 1984

Monthly streanflow of watersheds is one of the most important elements for the planning, design, and management of water resources development projects, e.g., determination of storage requirement of reservoirs and control of release-water in lowflow rivers. Modeling of longterm runoff is theoretically based on water-balance analysis for a certain time interval. The effect of the casual factors of rainfall, evaporation, and soil-moisture storage on streamflow might be explained by multiple regression analysis. Using the basic concepts of water-balance and regression analysis, it was possible to develop a generalized model called the Regionalized Regression Model for Monthly Streamflow in Korean Watersheds. Based on model verification, it is felt that the model can be reliably applied to any proposed station in Korean watersheds to estimate monthly streamflow for the planning, design, and management of water resources development projects, especially those involving irrigation. Modeling processes and properties are summarized as follows; 1. From a simplified equation of water-balance on a watershed a regression model for monthly streamflow using the variables of rainfall, pan evaporation, and previous-month streamflow was formulated. 2. The hydrologic response of a watershed was represented lumpedly, qualitatively, and deductively using the regression coefficients of the water-balance regression model. 3. Regionalization was carried out to classify 33 watersheds on the basis of similarity through cluster analysis and resulted in 4 regional groups. 4. Prediction equations for the regional coefficients were derived from the stepwise regression analysis of watershed characteristics. It was also possible to explain geographic influences on streamflow through those prediction equations. 5. A model requiring the simple input of the data for rainfall, pan evaporation, and geographic factors was developed to estimate monthly streamflow at ungaged stations. The results of evaluating the performance of the model generally satisfactory.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 춘계 학술발표회
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• pp.1043-1052
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• 2009

To derive easily the coefficient of permeability from several other soil properties, the estimation model of coefficient of permeability was proposed using linear regression analysis. The coefficient of permeability is one of the major factors to evaluate the soil characteristics. The study area is located in Kangwon-do Pyeongchang-gun Jinbu-Myeon. Soil samples of 45 spots were taken from the study area and various soil tests were carried out in laboratory. After selecting the soil factor influenced by the coefficient of permeability through the correlation analysis, the estimation model of coefficient of permeability was developed using the linear regression analysis between the selected soil factor and the coefficient of permeability from permeability test. Also, the estimation model of coefficient of permeability was compared with the results from permeability test and empirical equation, and the suitability of proposed model was proved. As the result of correlation analysis between various soil factors and the coefficient of permeability using SPSS(statistical package for the social sciences), the largest influence factor of coefficient of permeability were the effective grain size, porosity and dry unit weight. The coefficient of permeability calculated from the proposed model was similar to that resulted from permeability test. Therefore, the proposed model can be used in case of estimating the coefficient of permeability at the same soil condition like study area.

• Structural Engineering and Mechanics
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• 제60권4호
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• pp.655-665
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• 2016

This paper aims to develop models to accurately predict the behavior of fresh concrete exposed to vibration using artificial neural networks (ANNs) model and regression model (RM). For this purpose, behavior of a full scale precast concrete mold was investigated experimentally and numerically. Experiment was performed under vibration with the use of a computer-based data acquisition system. Transducers were used to measure time-dependent lateral displacements at some points on mold while both mold is empty and full of fresh concrete. Modeling of empty and full mold was made using both ANNs and RM. For the modeling of ANNs: Experimental data were divided randomly into two parts. One of them was used for training of the ANNs and the remaining part was used for testing the ANNs. For the modeling of RM: Sinusoidal regression model equation was determined and the predicted data was compared with measured data. Finally, both models were compared with each other. The comparisons of both models show that the measured and testing results are compatible. Regression analysis is a traditional method that can be used for modeling with simple methods. However, this study also showed that ANN modeling can be used as an alternative method for behavior of fresh concrete exposed to vibration in precast concrete structures.

• 한국안전학회지
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• 제24권2호
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• pp.76-82
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• 2009

The NIOSH lifting equation has been used as a dominant tool in evaluating the hazard levels of lifting tasks. Although it provides two different ways for each simple and complex lifting task, the NIOSH simple lifting equation is almost used for not only simple tasks but also complex tasks. However, most of lifting tasks in industries are in the form of complex lifting. Therefore some errors occur inevitably in the evaluation of complex lifting tasks. Among complex lifting tasks, a multi-stacking task is the most popular in lifting tasks. To compensate the error in the evaluation of multi-stacking tasks by using the NIOSH simple lifting equation, a set of calculations for finding LIs(Lifting Indices) was performed for the systematically varying multi-stacking tasks. Then a regression model which finds the equivalent height in simple lifting task for multi-stacking task was established. By using this model, multi-stacking tasks can be evaluated with less error. To validate this model, some real multi-stacking tasks were evaluated as examples.

• Journal of the Korean Statistical Society
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• 제29권2호
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• pp.201-217
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• 2000

The method of generalized estimating equations (GEE) has become very popular for the analysis of longitudinal data. We extend this work to the use of M-estimators; the resultant regression estimates are robust to heavy tailed errors and to outliers. The proposed method does not require correct specification of the dependence structure between observation, and allows for heterogeneity of the error. However, an estimate of the dependence structure may be incorporated, and if it is correct this guarantees a higher efficiency for the regression estimators. A goodness-of-fit test for checking the adequacy of the assumed M-estimation regression model is also provided. Simulation studies are conducted to show the finite-sample performance of the new methods. The proposed methods are applied to a real-life data set.

• 한국환경과학회지
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• 제11권7호
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• pp.687-696
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• 2002

Air quality monitoring data and meteorology data which had collected from 1995. 1. to 1999. 2. in six areas of Daegu, Manchondong, Bokhyundong, Deamyungdong, Samdukdong, Leehyundong and Nowondong, were investigated to determine the distribution and characteristic of ozone. A equation of multiple regression was suggested after time series analysis of contribution factor and meteorology factor were investigated during the day which had high concentration of ozone. The results show the following; First, 63.6% of high ozone concentration days, more than 60 ppb of ozone concentration, were in May, June and September. The percentage of each area showed that; Manchondong 14.4%, Bokhyundong 15.4%, Deamyungdong 15.6%, Samdukdong 15.6%, Leehyundong 17.3% and Nowondong 21.6%. Second, correlation coefficients of ozone, $SO_2$, TSP, $NO_2$ and CO showed negative relationship; the results were respectively -0.229, -0.074, -0.387, -0.190(p<0.01), and humidity were -0.677. but temperature, amount of radiation and wind speed had positive relationship; the results were respectively 0.515, 0.509, 0.400(p<0.01). Third, $R^2$ of equation of multiple regression at each area showed that; Nowondong 45.4%, Lee hyundong 77.9%, Samdukdong 69.9%, Daemyungdong 78.8%, Manchondong 88.6%, Bokhyundong 77.6%. Including 1 hour prior ozone concentration, $R^2$ of each area was significantly increased; Nowondong 75.2%, Leehyundong 89.3%, Samdukdong 86.4%, Daemyungdong 88.6%, Manchondong 88.6%, Bokhyundong 88.0%. Using equation of multiple regression, There were some different $R^2$ between predicted value and observed value; Nowondong 48%, Leehyundong 77.5%, Samdukdong 58%, Daemyungdong 73.4%, Manchondong 77.7%, Bokhyundong 75.1%. $R^2$ of model including 1 hour prior ozone concentration was higher than equation of current day; Nowondong 82.5%, Leehyundong 88.3%, Samdukdong 80.7%, Daemyungdong 82.4%, Manchondong 87.6%, Bokhyundong 88.5%.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.129-133
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• 2023

Classification or prediction problem is how to solve it using a specific feature to obtain the predicted class. A wheat seeds specifications 4 3 classes of seeds will be used in a prediction process. A multi linear regression will be built, and a prediction error ratio will be calculated. To enhance the prediction ratio an ANN model will be built and trained. The obtained results will be examined to show how to make a prediction tool capable to compute a predicted class number very close to the target class number.