• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1525-1525
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• 2001

Non-destructive determination of soluble solids(Brix) in harvested fruits using near infrared(hereafter, NIR) spectroscopy has been reported by many researchers. We have just reported on non-destructive estimation of Brix in harvested melons using a NIR Systems Model 6500 spectrophotometer(Ito et al., 2000). There is a melon cultivar that is difficult to judge the harvest time from the external appearance. If we can determine Brix in growing fruits non-destructively in the field, immature fruits will not be harvested. A portable m spectrophotometer for field use has been just developed by Kubota Corporation. The spectral data of growing melons were measured by the portable spectrophotometer. A commercial program was used for multiple linear regression analysis. Brix in growing melons could be estimated by a multiple regression equation calibrated with harvested melons. Absorbances of 906 and 874 nm were included as the independent variables in the multiple regression equation, and these wavelengths are key wavelengths for non-destructive Brix determination.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.855-868
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• 1998

For a random sample of size n from general linear model, $Y_i= heta(X_i)+varepsilon_i,;let Y_{in}$ denote the ith oder statistics of the Y sample values. The X-value associated with $Y_{in}$ is denoted by $X_{[in]}$ and is called the concomitant of ith order statistics. The estimator of the location of a maximum of a regression function, $ heta$($\chi$), was proposed by (equation omitted) and was found the convergence rate of it under certain weak assumptions on $ heta$. We will discuss the asymptotic distributions of both $ heta(X_{〔n-r+1〕}$) and (equation omitted) when r is fixed as nolongrightarrow$\infty$(i.e. extreme case) on the basis of the theorem of the concomitants of order statistics. And the will investigate the asymptotic behavior of Max｛$\theta$( $X_{〔n-r+1:n〕/}$ ), . , $\theta$( $X_{〔n:n〕}$)｝as an estimator for the peak of a regression function.

• Industrial Engineering and Management Systems
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• v.13 no.3
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• pp.289-296
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• 2014

With society becoming more advanced and complex, the required management engineering makes essential the development of human resources that can propose solutions for problems of new phenomena from a different perspective. As an example of such phenomena, we note a consumer electronics 'Eco-point' system campaign in this study. To mitigate global warming, revitalize the economy, and encourage the adoption of terrestrial digital compatible TVs, the consumer electronics Eco-point system campaign was implemented in May 2009 in Japan. In this study, we note a model which is constant term with exponential curve with notion of the growth phenomenon (Nakagiri and Kurita, Journal of the Operations Research Society of Japan, 2002). In our study, we call this model the 'differential equation model of the growth phenomenon.' This model represents a phenomenon with a hierarchical structure for capturing the properties of n species. In this study, we propose a new model which can represent not only the impact of largescale campaigns but also seasonal factors. Accordingly, we understand the phenomenon of fluctuation of sales of some products caused by large-scale campaigns and predict the fluctuation of sales. The final goal of this study is to develop human resources that can propose provision and solution for pre-consumption and reactionary decline in demand by understanding the impact of large-scale campaigns. As the first step of this goal, our objective is to propose a new regression method with different conventional perspective that can describe the fluctuation of sales caused by large-scale campaigns and show the possibility of new management engineering education.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.12
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• pp.467-476
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• 2016

As the energy saving issues become one of the important global agenda, the building simulation method is generally used to predict the inside energy usage to establish the power-saving strategies. To foretell an accurate energy usage of a building, proper and typical weather data are needed. For this reason, typical weather data are fundamental in building energy simulations and among the meteorological factors, the solar irradiation is the most important element. Therefore, preparing solar irradiation is a basic factor. However, there are few places where the horizontal solar radiation in domestic weather stations can be measured, so the prediction of the solar radiation is needed to arrive at typical weather data. In this paper, four solar radiation prediction models were analyzed in terms of their applicability for domestic weather conditions. A total of 12 regions were analyzed to compare the differences of solar irradiation between measurements and the prediction results. The applicability of the solar irradiation prediction model for a certain region was determined by the comparisons. The results were that the Zhang and Huang model showed the highest accuracy (Rad 0.87~0.80) in most of the analyzed regions. The Kasten model which utilizes a simple regression equation exhibited the second-highest accuracy. The Angstrom-Prescott model is easily used, also by employing a plain regression equation Lastly, the Winslow model which is known for predicting global horizontal solar irradiation at any climate regions uses a daily integration equation and showed a low accuracy regarding the domestic climate conditions in Korea.

• Journal of Forest and Environmental Science
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• v.24 no.3
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• pp.155-158
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• 2008

Site index is the essential tool for forest management to estimate the productivity of forest land Generally, site index equation is developed and used by relationship between stand age and dominant tree heights. However, there is a limit to use the site index equation in the application of variable ages, environmental influence, and estimation of site index for unstocked land. Therefore, it was attempted to develop a new site index equations based on various environmental factors including site and topographical variables. This study was conducted to develop regional site index equations based on the relationship between site index and soil factors for Pinus densiflora. Environmental factors that obtained from GIS application, were selected by stepwise-regression. Site index Equation was estimated by multiple regression from selected factors. Four environmental factors were selected in the final site index equations by stepwise regression. It was observed that coefficients of determination for site index equations were ranged from 0.34 which seem to be relatively low but good enough for estimation of forest stand productivity. The site index equations developed in this study were also verified to be useful by three evaluation statistics such as model's estimation bias, model's precision and mean square error type of measure.

• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.28-33
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• 2020

Recently, the overseas construction market has been actively promoted for about 10 years, and overseas dam construction has been continuously performed. For the economic and safe construction of the dam, it is important to prepare the main dam construction plan considering the design frequency of the diversion tunnel and the cofferdam. In this respect, the prediction of river level during the rainy season is significant. Since most of the overseas dam construction sites are located in areas with poor infrastructure, the most efficient and economic method to predict the water level in dam construction is to use the upstream water level. In this study, a linear regression model, which is one of the simplest statistical methods, was proposed and examined to predict the downstream level from the upstream level. The Pyeongchang River basin, which has the characteristics of the upper stream (mountain stream), was selected as the target site and the observed water level in Pyeongchang and Panwoon gaging station were used. A regression equation was developed using the water level data set from August 22th to 27th, 2017, and its applicability was tested using the water level data set from August 28th to September 1st, 2018. The dependent variable was selected as the "level difference between two stations," and the independent variable was selected as "the level of water level in Pyeongchang station two hours ago" and the "water level change rate in Pyeongchang station (m/hr)". In addition, the accuracy of the developed equation was checked by using the regression statistics of Root Mean Square Error (RMSE), Adjusted Coefficient of Determination (ACD), and Nach Sutcliffe efficiency Coefficient (NSEC). As a result, the statistical value of the linear regression model was very high, so the downstream water level prediction using the upstream water level was examined in a highly reliable way. In addition, the results of the application of the water level change rate (m/hr) to the regression equation show that although the increase of the statistical value is not large, it is effective to reduce the water level error in the rapid level rise section. Accordingly, this is a significant advantage in estimating the evacuation water level during main dam construction to secure safety in construction site.

• Asian Journal of Turfgrass Science
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• v.23 no.1
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• pp.77-90
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• 2009

Near Infrared Reflectance Spectroscopy(NIRS) is a quick, accurate, and non-destructive method to measure multiple nutrient components in plant leaves. This study was to acquire a liner regression equation by evaluating the nutrient contents of 'CY2' creeping bentgrass rapidly and accurately using NIRS. In particular, nitrogen fertility is a primary element to keep maintaining good quality of turfgrass. Nitrogen, moisture, carbohydrate, and starch were assessed and analyzed from 'CY2' creeping bentgrass clippings. A linear regression equation was obtained from accessing NIRS values from NIR spectrophotometer(NIR system, Model XDS, XM-1100 series, FOSS, Sweden) programmed with WinISI III project manager v1.50e and ISIscan(R) (Infrasoft International) and calibrated with laboratory values via chemical analysis from an authorized institute. The equation was formulated as MPLS(modified partial least squares) analyzing laboratory values and mathematically pre-treated spectra. The accuracy of the acquired equation was confirmed with SEP(standard error of prediction), which indicated as correlation coefficient($r^2$) and prediction error of sample unacquainted, followed by the verification of model equation of real values and these monitoring results. As results of monitoring, $r^2$ of nitrogen, moisture, and carbohydrate in 'CY2' creeping bentgrass was 0.840, 0.904, and 0.944, respectively. SEP was 0.066, 1.868, and 0.601, respectively. After outlier treatment, $r^2$ was 0.892, 0.925, and 0.971, while SEP was 0.052, 1.577, and 0.394, respectively, which totally showed a high correlation. However, $r^2$ of starch was 0.464, which appeared a low correlation. Thereof, the verified equation appearing higher $r^2$ of nitrogen, moisture, and carbohydrate showed its higher accuracy of prediction model, which finally could be put into practical use for turf management system.

• Journal of Environmental Science International
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• v.12 no.10
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• pp.1109-1116
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• 2003

The genetic algorithm is investigated fer parameters estimation of SED (storage - effective drainage) model from the Wi-stream watershed in Nakdong river basin. In the practical application of model, as a number of watershed parameters do not measure directly, it is desirable to make a good estimation from the known rainfall and runoff data. For the estimation of parameters of the SED model using the genetic algorithm, parameters of Green-Ampt equation(SM, K$\_$s/) for the estimation of an effective rainfall and initial storage(y$\_$in/) used in SED model are obtained a regression equation with 5, 10, 20 days antecedent precipitation. And as a consequence of computation, the parameters were obtained to satisfy the proposed objective function. From the comparison of observed and computed hydrographs, it shows a good agreement in the shape and the rising limb, peak, falling limb of hydrograph, so the SED model using the genetic algorithm shows a suitable model for runoff analysis in river basin.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.1
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• pp.49-54
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• 2016

The wind power plants were installed in many places because of the low climate changing effects since 2000. Generally, the wind power plants located in the seaside and the mountainous area and the heights of the windmills are about 40 m~140 m above the ground level. So the noises emitted from the wind power plants propagate far away compared with other environment noise sources like trains and cars noise. Because of these reasons, the noise emitted from the wind power plant is easy to cause the additional social problems like as noise complaints. Under the situation, the ministry of environment has established the guideline to evaluate the environmental effects for the wind power plant. According to the guideline, the noise of the wind power plant has to meet 55 dB(A) at daytime and 45 dB(A) at night in the residential area, which is regulated in the noise and vibration management law. But, it is difficult to estimate the noise emitted from the wind power plant because of the absence of the prediction model of the wind power plant noise. Therefore, the noise prediction model for wind power plants using the regression analysis method is developed in this study. For the development of the model, the sound pressure levels of the wind power plants in Jeju island are measured and the correlations between the sound pressure levels are analyzed. Finally, the prediction equation of the wind power plant noise using by regression analysis method derived. The prediction equation for the wind power plant noise proposed in this study can be useful to evaluate the environmental effects in any wind power plant development district.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.89-98
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• 2017

This study analyzed empirically the same data through SPSS statistics(regression analysis) and AMOS program(structural equation model) used for cause and effect analysis. The result of empirical analysis of moderation effect was as follows. Meanwhile, SPSS statistics(regression analysis) did not pictured moderation effect in the categorical data(sex) and continous data(satisfaction of consunting), AMOS program(structural equation model) pictured partial moderation effect about the effecting of consultant's capability and attitude on the consulting repurchase within 10% level of significant. Eventually, This study showed that AMOS program and SPSS statistics used different methology in moderation effect, thus the different outcomes appeared although using the same data.