• Nutrition Research and Practice
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• v.11 no.2
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• pp.130-138
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• 2017

BACKGROUND/OBJECTIVES: Low consumption of fruit and vegetable is frequently viewed as an important contributor to obesity risk. With increasing childhood obesity and relatively low fruit and vegetable consumption among Thai children, there is a need to identify the determinants of the intake to promote fruit and vegetable consumption effectively. SUBJECTS/METHODS: This cross-sectional study was conducted at two conveniently selected primary schools in Nakhon Pathom. A total of 609 students (grade 4-6) completed questionnaires on personal and environmental factors. Adequate fruit and vegetable intakes were defined as a minimum of three servings of fruit or vegetable daily, and adequate total intake as at least 6 serves of fruit and vegetable daily. Data were analyzed using descriptive statistics, the chi-square test, and multiple logistic regression. RESULTS: The proportion of children with a sufficient fruit and/or vegetable intakes was low. Covariates of child's personal and environmental factors showed significant associations with sufficient intakes of fruit and/or vegetable (P < 0.05). Logistic regression analyses showed that the following factors were positively related to sufficient intake of vegetable; lower grade, a positive attitude toward vegetable, and fruit availability at home; and that greater maternal education, a positive child's attitude toward vegetable, and fruit availability at home were significantly associated with sufficient consumption of fruits and total fruit and vegetable intake. CONCLUSIONS: The present study showed that personal factors like attitude toward vegetables and socio-environmental factors, such as, greater availability of fruits were significantly associated with sufficient fruit and vegetable consumption. The importance of environmental and personal factors to successful nutrition highlights the importance of involving parents and schools.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.4
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• pp.505-514
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• 2015

This paper proposes a probabilistic generation assessment model of renewable energy generators(REGs) considering uncertainty of resources, mainly focused on Wind Turbine Generator(WTG) and Solar Cell Generator(SCG) which are dispersed widely in South Korea The proposed numerical analysis method assesses the one day-ahead generation by combining equivalent generation characteristics function and probabilistic distribution function of wind speed(WS) and solar radiation(SR) resources. The equivalent generation functions(EGFs) of the wind and solar farms are established by grouping a lot of the farms appropriately centered on Weather Measurement Station(WMS). First, the EGFs are assessed by using regression analysis method based on typical least square method from the recorded actual generation data and historical resources(WS and SR). Second, the generation of the REGs is assessed by adding the one day-ahead resources forecast, announced by WMS, to the EGFs which are formulated as third order degree polynomials using the regression analysis. Third, a Renewable Energy Generation Assessment System(REGAS) including D/B of recorded actual generation data and historical resources is developed using the model and algorithm predicting one day-ahead power output of renewable energy generators.

• KSBB Journal
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• v.21 no.1 s.96
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• pp.59-67
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• 2006

2D spectrofluorometer produces many spectral data during fermentation processes. The fluorescence spectra are analyzed using chemometric methods such as principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS). Analysis of the spectral data by PCA results in scores and loadings that are visualized in score-loading plots and used to monitor a few fermentation processes by S. cerevisae and recombinant E. coli. Two chemometric models were established to analyze the correlation between fluorescence spectra and process variables using PCR and PLS, and PLS was found to show slightly better calibration and prediction performance than PCR.

• Journal of Environmental Impact Assessment
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• v.23 no.1
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• pp.39-50
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• 2014

In recent years, the frequency and scale of the natural disasters are growing rapidly due to the global climate change. In case of the urban flooding, high-density of population and infrastructure has caused the more intensive damages. In this study, we analyzed the spatial characteristics of urban flooding damage factors using GWR(Geographically Weighted Regression) for effective disaster prevention and then, classified the causes of the flood damage by spatial characteristics. The damage factors applied consists of natural variables such as the poor drainage area, the distance from the river, elevation and slope, and anthropogenic variables such as the impervious surface area, urbanized area, and infrastructure area, which are selected by literature review. This study carried out the comparative analysis between OLS(Ordinary Least Square) and GWR model for identifying spatial non-stationarity and spatial autocorrelation, and in the results, GWR model has higher explanation power than OLS model. As a result, it appears that there are some differences between each of the flood damage areas depending on the variables. We conclude that the establishment of disaster prevention plan for urban flooding area should reflect the spatial characteristics of the damaged areas. This study provides an improved understandings of the causes of urban flood damages, which can be diverse according to their own spatial characteristics.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.995-1010
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• 2020

In this study, a multivariate analysis model of partial least square regression (PLSR) was developed to predict the moisture content of green peppers using hyperspectral imaging (HSI). In HSI, illumination is essential for high-quality image acquisition and directly affects the analytical performance of the visible near-infrared hyperspectral imaging (VIS/NIR-HSI) system. When green pepper images were acquired using a direct lighting system, the specular reflection from the surface of the objects and their intensities fluctuated with time. The images include artifacts on the surface of the materials, thereby increasing the variability of data and affecting the obtained accuracy by generating false-positive results. Therefore, images without glare on the surface of the green peppers were created using a polarization filter at the front of the camera lens and by exposing the polarizer sheet at the front of the lighting systems simultaneously. The results obtained from the PLSR analysis yielded a high determination coefficient of 0.89 value. The regression coefficients yielded by the best PLSR model were further developed for moisture content mapping in green peppers based on the selected wavelengths. Accordingly, the polarization filter helped achieve an uniform illumination and the removal of gloss and artifact glare from the green pepper images. These results demonstrate that the HSI technique with a polarized lighting system combined with chemometrics can be effectively used for high-throughput prediction of moisture content and image-based visualization.

• Journal of Korea Technology Innovation Society
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• v.20 no.1
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• pp.62-80
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• 2017

The purpose of this paper is to explore the relationship between industrial regulations and the R&D activities of firms by analysing the case of manufacturing enterprises in Korea. The sample is gathered from the 2012 Korean Innovation Survey data of Korean Institute of Science & Technology Evaluation and Planning and merged with Korean Regulation Index data of Korean Institute of Public Administration. The Ordinary Least Square (OLS) as well as 2 Stage Least Square (2SLS) regression results show that the impact of the level of the manufacturing field's regulation on firms' R&D activities or inputs may be both positive and negative, depending on the size of the firms. The analysis results suggest that regulatory policy makers need to formulate and implement R&D programs that consider the different effects of industrial regulations on large enterprises or Small and Medium sized Enterprises (SMEs).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1036-1043
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• 2014

The installation of PV system to the power distribution system is being increased as one of solutions for environmental pollution and energy crisis. Because the output efficiency of PV system is getting decreased because of the aging phenomenon and several operation obstacles, the technology development of output prediction and state diagnosis of PV modules are required in order to improve operation performance of PV modules. The conventional methods for output prediction by considering various parameters and standard test condition values of PV modules may have difficult and complex computation procedure and also their prediction values may produce large error. To overcome these problems, this paper proposes an optimal prediction algorithm and state diagnosis algorithm of PV modules by using least square methods of linear regression analysis. In addition, this paper presents a state diagnosis evaluation system of PV modules based on the proposed optimal algorithms of PV modules. From the simulation results of proposed evaluation system, it is confirmed that the proposed algorithms is a practical tool for state diagnosis of PV modules.

• Journal of the Korean Chemical Society
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• v.53 no.6
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• pp.717-726
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• 2009

Multivariate models were developed for the simultaneous spectrophotometric determination of copper (II), nickel (II) and zinc (II) in water with 1-(2-thiazolylazo)-2-naphthol as chromogenic reagent in the presence of Triton X-100. To overcome the drawback of spectral interferences, principal component regression (PCR) and partial least square (PLS) multivariate calibration approaches were applied. Performances were validated with several test sets, and their results were then compared. In general, no significant difference in analytical performance between PLS and PCR models. The root mean square error of prediction (RMSEP) using three components for $Cu^{2+}$, $Ni^{2+}$ and $Zn^{2+}$ were 0.018, 0.010, 0.011 ppm, respectively. Figures of merit such as sensitivity, analytical sensitivity, limit of detection (LOD) were also estimated. High reliability was achieved when the proposed procedure was applied to simultaneous determination of $Cu^{2+}$, $Ni^{2+}$ and $Zn^{2+}$ in synthetic mixture and tap water.

• Korean Journal of Soil Science and Fertilizer
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• v.37 no.4
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• pp.259-265
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• 2004

A quicker method was developed for foliar analysis in diagnosis of nitrogen in apple trees based on multivariate calibration procedure using partial least squares regression (PLSR) and principal component regression (PCR) to establish the relationship between reflectance spectra in the near infrared region and nitrogen content of fresh- and dry-leaf. Several spectral pre-processing methods such as smoothing, mean normalization, multiplicative scatter correction (MSC) and derivatives were used to improve the robustness and performance of the calibration models. Norris first derivative with a seven point segment and a gap of six points on MSC gave the best result of partial least squares-1 PLS-1) model for dry-leaf samples with root mean square error of prediction (RMSEP) equal to $0.699g\;kg^{-1}$, and that the Savitzky-Golay first derivate with a seven point convolution and a quadratic polynomial on MSC gave the best results of PLS-1 model for fresh-samples with RMSEP of $1.202g\;kg^{-1}$. The best PCR model was obtained with Savitzky-Golay first derivative using a seven point convolution and a quadratic polynomial on mean normalization for dry leaf samples with RMSEP of $0.553g\;kg^{-1}$, and obtained with the Savitzky-Golay first derivate using a seven point convolution and a quadratic polynomial for fresh samples with RMSEP of $1.047g\;kg^{-1}$. The results indicate that nitrogen can be determined by the near infrared reflectance (NIR) technology for fresh- and dry-leaf of apple.

• Journal of the Korean Geographical Society
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• v.49 no.5
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• pp.690-704
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• 2014

Single family houses are the traditional & typical type of house in human history. But there had been little attention to single family houses in Korea so that there was little studies on single family houses. This study aimed to analyse price determinants of single family houses in Seoul, using Quantile Regression Analysis(QRA). Because single family houses has large levels of price, quantile regression analysis is more proper than Ordinary Least Square(OLS). The Results of analysis showed that, land coverage ratio, zoning, passed years, basement floor, hight of land, shape of land were important factors to single family houses price. The scale of effect of land coverage ratio to single family houses price was different to price levels of single family houses. And basement floor affected more negative effects to middle price, location and zoning had positive effects to high price single family houses. The degree of influence of determinants of single family houses price was deferent by region, KangBuk and KangNam. In KangNam, land coverage ratio and accessibilities were more important in low price single family houses, green zone and more far way is affected positive effects on single family houses price. In Kangbuk, land coverage ratio affects similar effects on single family houses price.