• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.335-345
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• 2020

This paper deals with the problem of estimating the log-transformed linear regression model to fit actual battle data from the Ardennes Campaign of World War II into the Lanchester model. The problem of determining a global solution for parameters and multicollinearity problems are identified and modified by examining the results of previous studies on data. The least squares method requires attention because a local solution can be found rather than a global solution if considering a specific constraint or a limited candidate group. The method of exploring this multicollinearity problem can be confirmed by a statistic known as a variance inflation factor. Therefore, the Lanchester model is simplified to avoid these problems, and the combat power attrition rate model was proposed which is statistically significant and easy to explain. When fitting the model, the dependence problem between the data has occurred due to autocorrelation. Matters that might be underestimated or overestimated were resolved by the Cochrane-Orcutt method as well as guaranteeing independence and normality.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.638-650
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• 2014

The objective of this study is to identify the causes of pedestrian volume path to the destination by investigating the influential levels of regional and planning features in the central area of Seoul. Regional characteristics can be classified from the result of the analysis and through the spatial characteristics of pedestrian volume. For global scale analysis, Ordinary Least Squares (OLS) regression is used for the degree of influence of each characteristics to pedestrian volume. For the local scale, Geographically Weighted Regression (GWR) is used to identify regional influential factors with consideration for spatial differences. The results of OLS indicate that boroughs with transportation facilities, commercial business districts, universities, and planning features with education research facilities and planning facilities have a positive effect on pedestrian volume path to the destination. Correspondingly, transportation hubs and congested areas, commercial and business centers, and university towns and research facilities in the Seoul central area can be identified through the results of GWR. The results of this study can provide information with relevance to existing plans and policies about the importance of regional characteristics and spatial heterogeneity effects on pedestrian volume, as well as significance in the establishment of regional development plans.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.4
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• pp.292-297
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• 2019

In this study, whole crop rice samples were used to develop near-infrared reflectance (NIR) equations to estimate six forage quality parameters: Moisture, crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), Ash and relative feed value (RFV). A population of 564 whole crop rice representing a wide range in chemical parameters was used in this study. Undried finely chopped whole crop rice samples were scanned at 1 nm intervals over the wavelength range 680-2500 nm and the optical data recorded as log 1/Reflectance (log 1/R). NIRS calibrations were developed by means of partial least-squares (PLS) regression. The correlation coefficients of cross-validation (R²_cv) and standard error of cross-validation (SECV) for whole crop rice calibration were 0.98 (SECV 1.81%) for moisture, 0.89 (SECV 0.50%) for CP, 0.86 (SECV 1.79%) for NDF, 0.89 (SECV 0.86%) for ash, and 0.84 (SECV 5.21%) for RFV on a dry matter (%), respectively. The NIRS calibration equations developed in this study will be useful in predicting whole crop rice quality for these six quality parameters.

• Agribusiness and Information Management
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• v.3 no.2
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• pp.43-62
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• 2011

The goal of this study is to determine, based on survey results, the underlying factors that affect the intention of the farmers who have not adopted the Hazard Analysis and Critical Control Points (HACCP) system for the rearing phase of pig production to adopt this system in the future. The research model for this study was con structed based on strategic contingency theory, the theory of the diffusion of innovation, and the technology acceptance model (TAM). Using structural equation modeling with partial least squares (PLS), this study analyzes the effects of the intensity of competition, the environmental uncertainty, the innovativeness and self-efficacy of the individual farmers, and the impact of the credibility of the Agricultural Technology Service Center (ATSC), which acts as the principal agent of technology dissemination and as a leader of change, on the perceived usefulness of technology and the farmers' intention to adopt the system. The results of the analysis are as follows. First, with regard to the underlying factors affecting the intention to adopt the new system, the intensity of competition within the industry and the institutional credibility of the ATSC were inferred to underlie the perceived usefulness. Second, institutional credibility has a positive impact on the perceived usefulness of the system, and the perceived usefulness, in turn, has a positive impact on the intention to adopt. The perceived ease of use also has a positive impact on the intention to adopt. Because the factor that has the biggest impact on the intention of a farm to adopt is the credibility of the ATSC, it is crucial for extension organizations, such as the ATSC, to make greater efforts to promote the expansion of the HACCP system. Because farmers feel that the implementation of the HACCP system is an instrumental strategy for coping with the high intensity of competition within the industry, they attempt to gain a competitive edge through the production of safe livestock products.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.37 no.2
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• pp.133-139
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• 2001

In order to obtain fundamental data for estimation of fisheries resource by echo sounder, we carried out the measuring of dorsal aspect Target strengths for rock bream and dusky spinefoot fishes that were caught much around the Jeju Island and in South Sea of Korea. The appropriate equations share the common form. TS=A+20 log L, where TS is the average dorsal aspect target strength in decibels, L is the fish total length in centimeters, and the coefficient A is determined by a least mean squares regression analysis. For rock bream, the result is TS=-72.97+20 log L and, for dusky spinefoot it is TS=-63.16+20 log L And, we have investigated the bearing range of maximum dorsal aspect target strength for all of rock bream and dusky spinefoot by the echo sounder with transducer of which frequency is 200kHz. They are $-12^\circ$-$-21^\circ$and $-1^\circ$--8 espectively, when the fishes is swimming down to the bottom. The maximum dorsal target strengths are -41.50dB at -18 or rock bream and -30.69dB at $-6^\circ$for dusky spinefoot.

• Analytical Science and Technology
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• v.11 no.3
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• pp.189-193
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• 1998

Binary alloys, $MoRu_3$ and $MoRh_3$, have been prepared using arc melting furnace. Mo and the noble metals Ru and Rh are the constituents of metallic insoluble residues, which were found in the early days of the post-irradiation studies on uranium oxide fuels. Detailed structural informations about these alloys have not been reported on JCPDS files of ICDD (International Centre for Diffraction Data). The results of X-ray diffraction study showed that the alloy was crystallized in hexagonal close-packing, well known as ${\varepsilon}$-phase. The X-ray diffraction patterns of these alloys matched well to that of $WRh_3$ with $P6_3/mmc$ of space group. The lattice parameters, a and c, were calculated using the least squares extrapolation. It was found from X-ray photoelectron spectroscopic measurements that Mo on the surface of the alloy was oxidized to Mo(6+), which could be removed by sputtering with Ar ions for approximately 15 minutes. The changes in binding energy of Mo, Ru, and Rh on the surface of the alloy were not observed. Magnetic susceptibility measurements resulted in the typical Pauli-paramagnetic behavior in the temperature range of 2 to 300 K.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.2
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• pp.143-151
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• 2003

A series of fishing experiments was carried out in the waters off the east coast of Korea from January, 2002 to March, 2003, using gill nets of different mesh sizes (m = 180, 210, 240, 270, and 300 ㎜) to determine the size selectivity of gill net for male snow crab Chionoecetes opilio. The maximum carapace length (RL) of each male snow crab caught in the fishing experiment was measured. The master curve of mesh selectivity was estimated by applying the extended Kitahara's method. Two kinds of functional models, quadratic function and cubic function were used to express logarithmic selectivity curve and were fitted to the data using the method of least squares. The obtained results were summarized as follows; 1. The cubic function of asymmetry was chosen to determine the selectivity curve of gill net for male snow crab from the model deviance comparison. 2. The result of size selectivity showed that the catch number of small male crab was getting decreased according to the increase of mesh size. 3. The optimum value (RL/m) was 0.549 and the RL/m was estimated to be 0.281, 0.296, and 0.356 when the retention probability were 0.2, 0.25 and 0.5, respectively.

• Journal of The Korean Society of Grassland and Forage Science
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• v.35 no.3
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• pp.257-263
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• 2015

This study was conducted to assess the potential of using NIRS to accurately determine the chemical composition and fermentation parameters in fresh coarse sorghum and sudangrass silage. Near Infrared Spectroscopy (NIRS) has been increasingly used as a rapid and accurate method to analyze the quality of cereals and dried animal forage. However, silage analysis by NIRS has a limitation in analyzing dried and ground samples in farm-scale applications because the fermentative products are lost during the drying process. Fresh coarse silage samples were scanned at 1 nm intervals over the wavelength range of 680~2500 nm, and the optical data were obtained as log 1/Reflectance (log 1/R). The spectral data were regressed, using partial least squares (PLS) multivariate analysis in conjunction with first and second order derivatization, with a scatter correction procedure (standard normal variate and detrend (SNV&D)) to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical constituents with a high degree of accuracy (i.e. the correlation coefficient of cross validation ($R^2{_{cv}}$) ranged from 0.86~0.96), except for crude ash which had an $R^2{_{cv}}$ of 0.68. Comparison of the mathematical treatments for raw spectra showed that the second-order derivatization procedure produced the best result for all the treatments, except for neutral detergent fiber (NDF). The best mathematical treatment for moisture, acid detergent fiber (ADF), crude protein (CP) and pH was 2,16,16 respectively while the best mathematical treatment for crude ash, lactic acid and total acid was 2,8,8 respectively. The calibrations of fermentation products produced poorer calibrations (RPD < 2.5) with acetic and butyric acid. The pH, lactic acid and total acids were predicted with considerable accuracy at $R^2{_{cv}}$ 0.72~0.77. This study indicated that NIRS calibrations based on fresh coarse sorghum and sudangrass silage spectra have the capability of assessing the forage quality control

• Journal of The Korean Society of Grassland and Forage Science
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• v.37 no.4
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• pp.350-357
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• 2017

Near infrared spectroscopy (NIRS) is a rapid and accurate method for analyzing the quality of cereals, and dried animal forage. However, one limitation of this method is its inability to measure fermentation parameters in dried and ground samples because they are volatile, and therefore, respectively lost during the drying process. In order to overcome this limitation, in this study, fresh coarse haylage was used to test the potential of NIRS to accurately determine chemical composition and fermentation parameters. Fresh coarse Italian ryegrass haylage samples were scanned at 1 nm intervals over a wavelength range of 680 to 2500 nm, and optical data were recorded as log 1/reflectance. Spectral data, together with first- and second-order derivatives, were analyzed using partial least squares (PLS) multivariate regressions; scatter correction procedures (standard normal variate and detrend) were used in order to reduce the effect of extraneous noise. Optimum calibrations were selected based on their low standard error of cross validation (SECV) values. Further, ratio of performance deviation, obtained by dividing the standard deviation of reference values by SECV values, was used to evaluate the reliability of predictive models. Our results showed that the NIRS method can predict chemical constituents accurately (correlation coefficient of cross validation, $R_{cv}^2$, ranged from 0.76 to 0.97); the exception to this result was crude ash ($R_{cv}^2=0.49$ and RPD = 2.09). Comparison of mathematical treatments for raw spectra showed that second-order derivatives yielded better predictions than first-order derivatives. The best mathematical treatment for DM, ADF, and NDF, respectively was 2, 16, 16, whereas the best mathematical treatment for CP and crude ash, respectively was 2, 8, 8. The calibration models for fermentation parameters had low predictive accuracy for acetic, propionic, and butyric acids (RPD < 2.5). However, pH, and lactic and total acids were predicted with considerable accuracy ($R_{cv}^2$ 0.73 to 0.78; RPD values exceeded 2.5), and the best mathematical treatment for them was 1, 8, 8. Our findings show that, when fresh haylage is used, NIRS-based calibrations are reliable for the prediction of haylage characteristics, and therefore useful for the assessment of the forage quality.

• Analytical Science and Technology
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• v.23 no.6
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• pp.537-543
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• 2010

Dopamine (DA) is an important neurotransmitter molecule of catecholamines. Its deficiency could lead to brain disorder such as Parkinson's disease and schizophrenia. Therefore, it is necessary to establish a suitable analytical technique with sensitivity and simplicity. A competitive enzyme-linked immunosorbent assay for DA has been optimized and characterized. Assay sensitivity is controlled by two factors in competitive immunoassay. One is a nature and concentration of competitor, and the other is those of binder, antibody. Thus, optimization was performed: BSA-DA conjugate and antibody-avidin conjugate were prepared by dual heterobifunctional coupling method using SATA and SMCC. Assay condition was optimized with $6.66\;{\mu}gmL^{-1}$ of BSA-DA and $4.17{\times}10^{-10}\;M$ of antibody-avidin conjugate. A dose-response curve was constructed, and a limit of detection and a dynamic range for DA were accomplished to $2.3{\times}10^{-2}\;{\mu}g\;mL^{-1}$ and four orders of magnitude ($1.0{\times}10^{-7}\;M$ to $1.0{\times}10^{-3}\;M$), respectively. Calibration curve was constructed on dynamic range and least-squares regression of this data gave the following relationship: absorbance = -0.1098 log[DA]+0.0353 ($R^2$ = 0.9956).