• Journal of agriculture & life science
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• v.53 no.3
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• pp.147-157
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• 2019

In this study, we tried to find out the most appropriate pre-processing method and to verify the feasibility of developing a low-price sensing system for predicting the hardy kiwis sugar content based on VNIRS and subsequent spectral analysis. A total of 495 hardy kiwi samples were collected from three farms in Muju, Jeollabukdo, South Korea. The samples were scanned with a spectrophotometer in the range of 730-2300 nm with 1 nm spectral sampling interval. The measured data were arbitrarily separated into calibration and validation data for sugar content prediction. Partial least squares (PLS) regression was performed using various combinations of pre-processing methods. When the latent variable (LV) was 8 with the pre-processing combination of standard normal variate (SNV) and orthogonal signal correction (OSC), the highest R2 values of calibration and validation were 0.78 and 0.84, respectively. The possibility of predicting the sugar content of hardy kiwi was also examined at spectral sampling intervals of 6 and 10 nm in the narrower spectral range from 730 nm to 1200 nm for a low-price optical sensing system. The prediction performance had promising results with R2 values of 0.84 and 0.80 for 6 and 10 nm, respectively. Future studies will aim to develop a low-price optical sensing system with a combination of optical components such as photodiodes, light-emitting diodes (LEDs) and/or lamps, and to locate a more reliable prediction model by including meteorological data, soil data, and different varieties of hardy kiwi plants.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.501-514
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• 2023

When analyzing high dimensional data such as text data, if we input all the variables as explanatory variables, statistical learning procedures may suffer from over-fitting problems. Furthermore, computational efficiency can deteriorate with a large number of variables. Dimensionality reduction techniques such as feature selection or feature extraction are useful for dealing with these problems. The sparse principal component analysis (SPCA) is one of the regularized least squares methods which employs an elastic net-type objective function. The SPCA can be used to remove insignificant principal components and identify important variables from noisy observations. In this study, we propose a dimension reduction procedure for text data based on the SPCA. Applying the proposed procedure to real data, we find that the reduced feature set maintains sufficient information in text data while the size of the feature set is reduced by removing redundant variables. As a result, the proposed procedure can improve classification accuracy and computational efficiency, especially for some classifiers such as the k-nearest neighbors algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.367-380
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• 2011

This paper has focused on deriving a GPS based geodetic network adjustment strategy to continuously determine coordinate sets of the national geodetic control points. After domestic literature review on the topic and overseas case studies about countries that recently reformed their geodetic infrastructure have been carried out, a simplified geodetic network consisting of two layers, namely GPS active and passive network, has been proposed to maximize effectiveness of the network adjustment through reducing the number of the passive points. Furthermore, a GPS data processing and network adjustment procedure has been derived to support the continuous management scheme. While a scheme for the active layer adopts a sequential least squares adjustment based on a multi-baseline, that of the passive layer employs a multi-session adjustment technique with respect to 3-dimensional baseline vectors. Finally, experimental adjustment against a network comprising 24 active and 6,900 passive stations has been performed to demonstrate the efficiency and the effectiveness of the proposed method.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.5
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• pp.641-644
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• 2000

In vitro true digestibility of cup-plant (Silphium perfoliatum L.) is higher than other alternative forages and comparative to alfalfa (Medicago sativa L.) even at the high neutral detergent fiber (NDF) concentration. This study was conducted to determine whether the digestion kinetic parameters of cup-plant could explain high in vitro true digestibility of cup-plant at the several NDF levels. Cup-plant and alfalfa were both collected in Arlington and Lancaster, Wisconsin to meet the NDF content within 40 to 50% range. The collected samples were incubated with rumen juice to investigate the digestion kinetics at 3, 6, 9, 14, 20, 28, 36, 48, and 72 h. Kinetics was estimated by the model $R=D_0\;e-k(t-L)+U$ where R is residue remaining at time t, and $D_0$ is digestible fraction, k is digestion rate constant, L is discrete lag time, and U is indigestible fraction. Parameters of the model were estimated by the direct nonlinear least squares (DNLS) method. Digestion rate and potential extent of digestion were not statistically different in either forage. However, alfalfa had shorter lag time (p<0.05). The indigestible fraction increased with maturation in alfalfa and in cup-plant (p<0.05). The ratio of indigestible fraction to acid detergent lignin (ADL) was higher in cup-plant than in alfalfa (p<0.05). From the results, alfalfa is probably digested more rapidly than cup-plant, however, cup-plant maintains higher digestibility with maturation due to a relatively slower increase of indigestible fraction in NDF.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.5
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• pp.399-408
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• 2006

Railroads have been developed as public transportation and made a great contribution to national economic growth. But after the mid-20th century, its qualities have been getting low because of focusing on the investment and development of cars and airplanes. Its role which is getting the excellent merits on the mass transportation, rapid transit, safety, state period, energy efficiency, and prevention of environmental pollution has been reconsidered. Elements of horizontal alignment are needed in the case where the existing railroad lines should be improved or moved. If its design drawing was lost or damaged, it is impossible to recover. It is not easy to repair for it as disasters. We must understand an existing railroad line to bring a function included a basic geography situation. In this study, we acquire, analyze, and process the digital images of the railroad and then reappear shape of three dimension. And we expect to be utilized to construct the facility information by extracting the alignment elements of existing railroad lines reversely.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.747-755
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• 2015

The purpose of this study was to analyze the factors on behavioral intention in u-health system using Health Promotion Model(HPM) by Pender [11]. This study used PLS-SEM(Partial Least Squares-Structural Equation Modeling) method for verification of the proposed model and conducted the analysis with sample data of 216 respondents. As a result of the analysis, a path from individual experience to personal health and also, path from self-efficacy to behavioral intention had the highest influence in the research model. Also, it was confirmed that the only factor that affect health promotion behavior was 'self-efficacy' variable in u-health system. However, in the future time, additional research is needed in that this study has small sample data and needs more clear definition on u-health system.

• Journal of Biosystems Engineering
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• v.42 no.4
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• pp.301-313
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• 2017

Purpose: This study was conducted to discriminate between normal hulled barley and Fusarium (Fusarium asiaticum and Fusarium graminearum) infected hulled barley by using the near-infrared spectroscopy (NIRS) technique. Methods: Fusarium asiaticum and Fusarium graminearum were artificially inoculated in hulled barley and the reflectance spectrum of the barley spike was obtained by using a near-infrared spectral sensor with wavelength band in the range 1,175-2,170 nm. After obtaining the spectrum of the specimen, the hulled barley was cultivated in a greenhouse and visually inspected for infections. Results: From a partial least squares discriminant analysis (PLS-DA) prediction model developed from the raw spectrum data of the hulled barley, the discrimination accuracy for the normal and infected hulled barley was 99.82% (563/564) and 100% (672/672), respectively. Conclusions: NIRS is effective as a quick and nondestructive method to detect whether hulled barley has been infected with Fusarium. Further, it expected that NIRS will be able to detect Fusarium infections in other grains as well.

• Journal of Animal Environmental Science
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• v.20 no.3
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• pp.117-124
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• 2014

The objectives were to assess the use of near-infrared reflectance spectroscopy (NIRS) as a tool for estimating nutrient compositions of corn kernel, and to apply an NIRS-based indium gallium arsenide array detector to the system for collecting spectra and analyzing calibration equations using equipments designed for field application. Partial Least Squares Regression (PLSR) was employed to develop calibration equations based on representative data sets. The kennard-stone algorithm was applied to induce a calibration set and a validation set. As a result, the method for structuring a calibration set significantly affected prediction accuracy. The prediction of chemical composition of corn kernel resulted in the following (kennard-stone algorithm: relative) moisture ($R^2=0.82$, RMSEP=0.183), crude protein ($R^2=0.80$, RMSEP=0.142), crude fat ($R^2=0.84$, RMSEP=0.098), crude fiber ($R^2=0.74$, RMSEP=0.098), and crude ash ($R^2=0.81$, RMSEP=0.048). Result of this experiment showed the potential of NIRS to predict the chemical composition of corn kernel.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.3
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• pp.315-318
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• 2006

Associations were analysed between polymorphisms localized in intron 3 of the growth hormone gene (GH-MspI) and milk production traits of 543 China Holstein cows. A PCR-RFLP method was used for identification of genotypes. The following frequencies of genotypes and alleles were found: 0.77, 0.21 and 0.02 for +/+, +/- and -/-, respectively, and 0.87 and 0.13 for $GH^+$ and $GH^-$, respectively. Significant differences between herds were observed in the frequencies of both genotypes and alleles. The results of least squares analysis showed that in all three lactation phases the GH +/+ cows yielded most milk (p<0.01 for lactation I and p<0.05 for lactations II and III), whereas +/- cows showed higher milk fat content than +/+ individuals (p<0.05 for lactation I and II, and p<0.01 for lactation III). The +/+ cows yielded more fat than +/- individuals (p<0.01 only in lactation I). The +/+ cows yielded more milk protein than +/- individuals (p<0.01 for lactation I, II, and III). The +/+ cows produced milk of higher protein content than that of +/- individuals (p<0.05 only in lactation II). Based on these results, we conclude that the +/+ of GH locus should be the favored genotype in China Holstein cow breeds for use in marker-assisted selection programmes.

• Environmental and Resource Economics Review
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• v.21 no.1
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• pp.157-173
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• 2012

It has been argued that investment in basic treatment facilities could have both a direct improvement effect and an indirect diversion effect on water quality. The reason why the investment in basic treatment facilities could have a negative diversion effect is that the investment in treatment facilities could affect a budget-constrained regulatory agency's choice in a way that would perversely encourage the regulated firms' emissions, giving a negative result in terms of water quality. We have reviewed the Korean experience and tested if the treatment facilities have improved water quality since 1991. Using a two-stage least-squares method we have shown that building treatment facilities has contributed to improving the water quality even with consideration of the negative effect through reduced enforcement effort. The model and results draw attention to the importance of optimally balancing efforts to build wastewater treatment facilities with efforts to set and enforce regulatory standards.