• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.968-974
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• 2010

We assessed the feasibility of discrete wavelet transform (DWT) applied for the spectral processing to enhance the estimation performance quality of soil organic matters using visible-near infrared spectra and mapped their distribution via block Kriging model. Continuum-removal and $1^{st}$ derivative transform as well as Haar and Daubechies DWT were used to enhance spectral variation in terms of soil organic matter contents and those spectra were put into the PLSR (Partial Least Squares Regression) model. Estimation results using raw reflectance and transformed spectra showed similar quality with $R^2$ > 0.6 and RPD> 1.5. These values mean the approximation prediction on soil organic matter contents. The poor performance of estimation using DWT spectra might be caused by coarser approximation of DWT which not enough to express spectral variation based on soil organic matter contents. The distribution maps of soil organic matter were drawn via a spatial information model, Kriging. Organic contents of soil samples made Gaussian distribution centered at around 20 g $kg^{-1}$ and the values in the map were distributed with similar patterns. The estimated organic matter contents had similar distribution to the measured values even though some parts of estimated value map showed slightly higher. If the estimation quality is improved more, estimation model and mapping using spectroscopy may be applied in global soil mapping, soil classification, and remote sensing data analysis as a rapid and cost-effective method.

• Journal of Animal Science and Technology
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• v.45 no.6
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• pp.885-890
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• 2003

This study was conducted to estimate the growth curve parameters of 253 heads of F2 population produced by inter-crossing F1 from Korean Native boars and Landrace sows, and to estimate the effects of Leptin receptor gene(LEPR) on their growth characteristics. Growth curve parameters were estimated from nonlinear regression using Gompertz model individually. Average mature weight and average maturing rate estimated were 179.69${\pm}$4.40kg and 0.3103${\pm}$0.0043, respectively. The effect of sex was insignificant for all the parameters estimated from Gempertz model(p〉.05), and the effect of calving group was significant for mature weight and maximum growth rate at inflection point (p〈.05). The effect of LEPR genotype were significant for all the growth curve parameters(p〈.05). According from the results of the least squares means of growth curve parameters by LEPR genotypes, mature weight and point of inflection were highest in genotype AA in which the maturing rate was the lowest, and were lowest in genotype DD in which maturing rate was the highest, reversely.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.63-70
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• 2015

The fierce competition of mobile messenger services (MMS) allows MMS providers to perform a variety of marketing campaigns and business activities to enhance user loyalty. The applied model in this study is based on Oliver's four-stage loyalty model for the formation processes of user loyalty about MMS. While social network formation and service quality are the key elements of cognitive loyalty, positive mood and negative mood are the key components of affective loyalty in the study. Conative loyalty is captured by commitment. The data of 249 KakaoTalk users at least five times for three months is empirically tested based on the research model using partial least squares. The analysis of test identifies that positive feeling and commitment significantly influences behavioral loyalty, whereas negative feeling plays a significant role in inhibiting behavioral loyalty. The findings of this study show that social network formation and service quality significantly affect only positive feeling. The analysis results reveal several insights that can help MMS managers understand the roles of cognitive, affective, conative, and behavioral loyalty in the MMS environment.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.spc
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• pp.108-114
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• 2008

Near infrared spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. The applicability of near infrared reflectance spectroscopic method was tested to determine the color value (L, a, b) of green tea. A total of 162 green tea calibration samples and 82 validation samples were used for NIRS equation development and validation, respectively. In the developed NIRS equation for analysis of the color value (L, a, b), the most accurate equation for L value was obtained at 2, 8, 6, 1 (2nd derivative, 8 nm gap, 6 points smoothing, and 1pointsecond smoothing), and for a, and b value were obtained at 1, 4, 4, 1 (1st derivative, 4 nm gap, 4points smoothing, and 1 point second smoothing) math treatment condition with SNVD (Standard Normal Variate and Detrend) scatter correction method and entire spectrum ($400{\sim}2,500\;nm$) by using MPLS (Modified Partial Least Squares) regression. Validation results of these NIRS equations showed very low bias (L: 0.005%, a: 0.003%, b: -0.013%) and standard error of prediction (SEP, L: 0.361%, a: 0.141%, b: 0.306%) as well as high coefficient of determination ($R^2$, L: 0.905, a: 0.986, b: 0.931). Therefore, these NIRS equations can be applicable and reliable for determination of color value (L, a, b) of green tea, and NIRS method could be used as a mass screening technique for breeding programs and quality control in the green tea industry.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.6
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• pp.522-528
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• 2009

This study focused on establishing prediction models using visible-near infrared spectrum to simultaneously detect multiple components of soils and enhancing the performance quality by suitably transformed input spectra and classification of soil spectral types for prediction model input. The continuum-removed spectra showed significant result for all cases in terms of soil properties and classified or bulk predictions. The prediction model using classified soil spectra at an absorption peak area around 500nm and 950nm efficiently indicating soil color showed slightly better performance. Especially, Ca and CEC were well estimated by the classified prediction model at $R^{2}$ > 0.8. For organic carbon, both classified and bulk prediction model had a good performance with $R^{2}$ > 0.8 and RPD> 2. This prediction model may be applied in global soil mapping, soil classification, and remote sensing data analysis.

• Journal of Korea Technology Innovation Society
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• v.19 no.1
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• pp.137-160
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• 2016

This study assesses the structure relationships between resource, product and process innovation capability, technology commercialization competence and performance of firms in Daedeok Innopolis. In particular, this study attempts to analyze technology commercialization competence that may be influenced by product and process capabilities. The development of the research model is based on the literature of resource-based view and the empirical studies of technology commercialization competence. The survey of 109 firms was conducted from January 5, 2015 through February 4, 2015. We investigate the role of a firm's technology commercialization competence in determining its performance by performing Partial Least Squares analysis. The results indicate that a firm's human resources and intangible resources lead to a higher level of its product and process innovation capabilities. This study, however, finds that a firm's tangible resources do not have significant effects on its product and process innovation capabilities. And the study finds evidence that firm's product and process capabilities have positive effects on its technology commercialization competence. The study also finds that a firm's technology commercialization competence is a driving force behind its performance, showing that its technology commercialization competence positively involves its performance. In addition the study finds that technology commercialization competence mediates the relationship between a firm's innovation capability and performance, indicating that the technology commercialization used as mediating variable positively affects its innovation performance.

• Journal of the Korean Geographical Society
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• v.51 no.4
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• pp.509-523
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• 2016

The soil prediction can provide quantitative soil information for sustainable mountainous ecosystem management. Visible near infrared spectroscopy, one of soil prediction methods, has been applied to predict several soil properties with effective costs, rapid and nondesctructive analysis, and satisfactory accuracy. Spectral preprocessing is a essential procedure to correct noisy spectra for visible near infrared spectroscopy. However, there are no attempts to evaluate various spectral preprocessing methods. We tested 5 different pretreatments, namely continuum removal, Savitzky-Golay filter, discrete wavelet transform, 1st derivative, and 2nd derivative to predict soil carbon(C) and nitrogen(N). Partial least squares regression was used for the prediction method. The total of 153 soil samples was split into 122 samples for calibration and 31 samples for validation. In the all range, absorption was increased with increasing C contents. Specifically, the visible region (650nm and 700nm) showed high values of the correlation coefficient with soil C and N contents. For spectral preprocessing methods, continuum removal had the highest prediction accuracy(Root Mean Square Error) for C(9.53mg/g) and N(0.79mg/g). Therefore, continuum removal was selected as the best preprocessing method. Additionally, there were no distinct differences between Savitzky-Golay filter and discrete wavelet transform for visual assessment and the methods showed similar validation results. According to the results, we also recommended Savitzky-Golay filter that is a simple pre-treatment with continuum removal.

• Journal of Distribution Science
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• v.14 no.2
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• pp.135-144
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• 2016

Purpose - The purpose of this paper is to empirically investigate the moderating effects of social capital on antecedents factors path of export performance in the materials and components SMEs(Small and Medium-sized Enterprises) of Busan and Kyungnam region. In case of materials and components SMEs, they are always trying to achieve business performance including export sales and market share, but it is difficult for them to increase performance due to the limitation of inner & tangible resources. Therefore intangible asset such as technology capability and its antecedents factors which are technology innovation and learning orientation are getting more important to SMEs. In addition, it is supposed that social capital such as local network including distribution channel in overseas market plays an essential role to enhance export performance. Accordingly, the main goal of this study is to find out the relationship between export performance and antecedents factors and the validity of social capital as a moderating valuable. Research design, data, and methodology - Technology innovation, learning orientation and technology capability have been used as antecedents factors for export performance and social capital such as network diversity and intensity has been used for moderating effects analysis. In order to select these valuables mentioned above, this study examined the existing researches on a basis of Resources Based View, Organizational Learning Theory and Social Capital theory. To achieve the objective of this paper, 7 hypotheses including the moderating effects have been proposed with 6 potential variables measured by 24 questions. The survey was carried out from December 2014 to March 2015 and 137 samples out of total 175 were selected for the analysis. PLS(Partial Least Squares) has been used for the methodology of empirical analysis for both antecedents factors path and moderating effects. Results - Research findings are as follows. First, technology innovation has a significant impact on learning orientation, learning orientation has a positive effect on the technology capability and technology capability also has a significant impact on export performance. Therefore 3 valuables are proved as antecedents factors of export performance. Second, the social capital(both network diversity and intensity) plays a moderating role with learning orientation to technology capability. However, there is no moderating effects between both of social capital and technology capability regarding export performance. Conclusions - According to path analysis results, it is suggested that the materials and components SMEs should raise technology innovation and learning orientation in order to improve technology capability and export performance. Meantime, the moderating effect analysis shows that SMEs should consider local network diversity and intensity along with learning orientation to add up technology capability. But local network diversity and intensity does not work systematically with technology capability for export performance and it means that SMEs should find the appropriate local partners for the purpose of establishing concrete distribution channels based on marketing perspective, not for improving technology capability.

• Journal of The Korean Society of Grassland and Forage Science
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• v.26 no.1
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• pp.45-52
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• 2006

This study was carried out to explore the accuracy of Near Infrared Reflectance Spectroscopy (NIRS) fer the prediction of digestibility and energy value of corn silages. The spectral data were regressed against a range of digestibility and energy parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with first and second order derivatization, with scatter correction procedure(SNV-Detrend) to reduce the effect of extraneous noise. Calibration models for NIRS measurements gave multivariate correlation coefficients of determination$(R^2)$ and standard errors of cross validation of 0.92(SECV 1.73), 0.91(SECV 1.13) and 0.93(SECV 1.74) for in vitro dry matter digestibility(IVDMD), in vitro true digestibility(IVTD), and cellulase dry matter digestibility(CDMD), respectively. The standard error of prediction(SEP) and the multiple correlation coefficient of validation$(R^2v)$ on the validation set(n=39) was used in comparing the prediction accuracy. The SEP value was 0.30(TDN), 0.01(NEL), and 0.01(ME). The relative ability of NIRS to predict digestibility and energy value was very good for CDMD, total digestible nutrients(TDN), net energy fer lactation(NEL) and metabolizable energy(ME). This paper shows the potential of NIRS to predict the digestibility and energy value of con silage as a routine method in feeding programmes and for giving advice to farmers.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1620-1620
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• 2001

Forages, both grazed and conserved, provide the basis of ruminant production systems throughout the world. More than 90 per cent of the feed energy consumed by herbivorous animals world - wide were provided by forages. With such world - wide dependence on forages, the economic and nutritional necessity of been able to characterize them in a meaningful way is vital. The characterization of forages for productive animals is becoming important for several reasons. Relative to conventional laboratory procedures, Near Infrared Reflectance Spectroscopy (NIRS) offers advantages of simplicity, speed, reduced chemical waste, and more cost-effective prediction of product functionality. NIR spectroscopy represents a radical departure from conventional analytical methods, in that entire sample of forage is characterized in terms of its absorption properties in the near infrared region, rather than separate subsamples being treated with various chemicals to isolate specific components. This forces the analyst to abandon his/her traditional narrow focus on the sample (one analyte at a time) and to take a broader view of the relationship between components within the sample and between the sample and the population from which it comes. forage is usually analysed by NIRS in dry and ground presentation. Initial success of NIRS analysis of coarse forages suggest a need to better understand the potential for analysis of minimally processed samples. Preparation costs and possible compositional alterations could be reduced by samples presented to the instrument in undried and unground conditions. NIRS has gained widespread acceptance for the analysis of forage quality constituents on dry material, however little attention has been given to the use of NIRS for chemical determinations on undried and unground forages. Relatively few works reported the use of NIRS to determine quality parameters on undried materials, most of them on both grass and corn silage. Only two works have been found on the determination of quality parameters on fresh forages. The objectives of this paper were (1) to evaluate the use of NIRS for determination of nitrogen and moisture on undried and unground forage samples and (2) to explore two mathematical treatments and two NIR regions to predict chemical parameters on fresh forage. Four hundred forage samples (n: 400) were analysed in a NIRS 6500 instrument (NIR Systems, PA, USA) in reflectance mode. Two mathematical treatments were applied: 1,4,4,1 and 2,5,5,2. Predictive equations were developed using modified partial least squares (MPLS) with internal cross - validation. Coefficient of determination in calibration (${R^2}_{CAL}$) and standard error in cross-validation (SECV) for moisture were 0.92 (12.4) and 0.92 (12.4) for 1,4,4,1 and 2,5,5,2 respectively, on g $kg^{-1}$ dry weight. For crude protein NIRS calibration statistics yield a (${R^2}_{CAL}$) and (SECV) of 0.85 (19.8) and 0.85 (19.6) for 1,4,4,1 and 2,5,5,2 respectively, on a dry weight. It was concluded that NIRS is a suitable method to predict moisture and nitrogen on fresh forage without samples preparation.