• Journal of Korean Society of Transportation
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• v.19 no.4
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• pp.7-18
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• 2001

This study represents a first attempt in Korea to develop the simultaneous freight supply-demand model which considers the relationship between freight supply and demand. As the existing study was limited in one area, or the supply and the demand was separated and assumed not to affect each other, this study take it into consideration the fact that the demand affects supply and simultaneously vice versa. This approach allows us to diagnose a policy carried on and helps us to make a resonable alternative for the effectiveness of freight transportation system. To find a relationship between them, we use a method of econometrics. a structural equation theory and two stage least-squares(2SLS) estimation technique, to get rid of bias which involves two successive applications of OLS. Based on the domestic freight data, this study consider as explanatory variables a number of population(P), industry(IN), the amount of production of the mining and manufacturing industries(MMI), the rate of the effectiveness of freight capacity(LE) and the distance of an empty carriage operation(VC). This study describes well the simultaneous process of freight supply-demand system in that the increase of VC from the decrease of VC raises the cargo capacity and cargo capacity also augments VC. By the way. it is analyzed that the increment of VC due to the increase of the cargo capacity is larger than the reduction of VC owing to the increase of the quantify of goods. Therefore an alternative policy is needed in a short and long run point of view. That is to say, to promote the effectiveness of the freight transportation system, a short term supply control and a long run logistic infrastructure are urgent based on the restoration of market economy by successive deregulation. So we are able to conclude that gradual deregulation is more desirable to build effective freight market.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.31 no.4
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• pp.307-325
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• 1995

IN order to gain a fundamental data for forecast or control of fish behavior and evaluated the feasibility of an application of the modeling technique to a field, in this paper a numerical model for describing the behavior of fishes in a water tank was presented. The parameters of the model were estimated by using the time-series data on the three-dimensional position of fishes and by applying the least squares algorithm. The estimated parameters were standardized to examine the variation of parameters according to the number of individuals and flow speed that the mean values of parameters were to be zero and their variances were to be one. The results obtained can be summarized as follows: (1) The standardized parameter $a^*$of propulsive force decreased according to increased the number of individuals and the flow speed. (2) The standardized parameter ${k_b}^*$ of interactive force increased according to increased the number of individuals, but decreased according to the flow speed. (3) The standardized parameter ${k_c}^*$ of schooling force increased according to │increased the number of individuals and the flow speed. (4) The standardized parameter │${k_w}^{+*}$│ of repulsive force against wall or bottom increased according to increased the number of individuals, but decreased according to the flow speed. (5) The standardized parameter │${k_w}^{-*}$│ of attractive force against wall or bottom was generally constant according to increased the number of individuals, but increased according to the flow speed. (6) The standardized parameter $\upsilon$ super(*) of damping force increased according to increased the number of individuals, but decreased according to the flow speed.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1244-1244
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from mastitic and healthy cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from mastitic and healthy cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference, thereby providing a useful means for spectroscopy-based clinic applications.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1042-1042
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from daily monitoring of two Japanese cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from two cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA md FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1510-1510
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• 2001

NIRS uses reflectance signals resulting from bending and stretching vibrations in chemical bonds between carbon, nitrogen, hydrogen, sulfur and oxygen. These reflectance signals are used to measure the concentration of major chemical composition and other descriptors of homogenized and freeze-dried whole broiler carcasses. Six strains of chicken were analyzed and the NIRS model predictions compared to reference data. The results of this comparison indicate that NIRS is a rapid tool for predicting dry matter (DM), fat, crude protein (CP) and ash content in the broiler carcass. Males and females of six commercial strain crosses of broiler chicken (Gallus domesticus) were used in this study (6$\times$2 factorial design). Each strain was grown to 16 weeks of age, and duplicate serial samples were taken for body composition analysis. Each whole carcass was pressure-cooked, homogenized, and a representative sample was freeze-dried. Body composition determined as follows: DM by oven dried method at 105$^{\circ}C$ for 3 hours, fat by Mojonnier diethyl ether extraction, CP by measuring nitrogen content using an auto-analyzer with Kjeldhal digest and ash by combustion in a muffle furnace for 24 hour at 55$0^{\circ}C$. These homogenized and freeze-dried carcass samples were then scanned with a Foss NIR Systems 6500 visible-NIR spectrophotometer (400-2500nm) (Foss NIR Systems, Silver Spring, MD., US) using Infra-Soft-International, ISI, WinISl software (ISI, Port Matilda, US). The NIRS spectra were analyzed using principal component (PC) analysis. This data was corrected for scatter using standard normal “Variate” and “Detrend” technique. The accuracy of the NIRS calibration equations developed using Partial Least Squares (PLS) for predicting major chemical composition and carcass descriptors- such as body mass (BM), bird dry matter and moisture content was tested using cross validation. Discrimination analysis was also used for sex and strain identification. According to Dr John Shenk, the creator of the ISI software, the calibration equations with the correlation coefficient, $R^2$, between reference data and NIRS predicted results of above 0.90 is excellent and between 0.70 to 0.89 is a good quantifying guideline. The excellent calibration equations for DM ($R^2$= 0.99), fat (0.98) and CP (0.92) and a good quantifying guideline equation for ash (0.80) were developed in this study. The results of cross validation statistics for carcass descriptors, body composition using reference methods, inter-correlation between carcass descriptors and NIRS calibration, and the results of discrimination analysis for sex and strain identification will also be presented in the poster. The NIRS predicted daily gain and calculated daily gain from this experiment, and true daily gain (using data from another experiment with closely related broiler chicken from each of the six strains) will also be discussed in the paper.

• Journal of Food Hygiene and Safety
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• v.22 no.4
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• pp.257-267
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• 2007

The possibility of rapid non-destructive qualitative and quantitative analysis of vegetable oils such as perilla, com, soybean and rapaseed oils in sesame oils was evaluated. A calibration equation calculated by MPLS(Modified Partial Least Squares) regression technique was developed and coefficients of determination for perilla oil, com oil, soybean oil and rapaseed oil contents were 0.9992, 0.9694, 0.9795 and 0.9790 respectively. According to the data obtained from validation study, $R^2$ of contents of perilla, com, soybean, rapaseed oils were 0.997, 0.848, 0.957 and 0.968, and SEP of content of them 0.747, 5.069, 3.063 and 3.000 by MPLS respectively. The results indicate that the NIRS procedure can potentially be used as a non-destructive analysis method for the rapid and simple measurement of sesame oil mixed with other vegetable oils. The detection limits of the NIRS for perilla oil, com oil, soybean oil and rapaseed oil were presumed as 2%, $15{\sim}20%,\;15{\sim}20%$ and 10%, respectively.

• Journal of Korean Society of Transportation
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• v.18 no.4
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• pp.117-126
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• 2000

Conventional methods for collecting origin-destination trips have been mainly relied on the surveys of home or roadside interview. However, the methods tend to be costly, labor intensive and time disruptive to the trip makers, thus the methods are not considered suitable for Planning applications such as routing guidance, arterial management and information Provision, as the parts of deployments in Intelligent Transport Systems Motivated by the problems, more economic ways to estimate origin-destination trip tables have been studied since the late 1970s. Some of them, which have been estimating O-D table from link traffic counts are generally Entropy maximizing, Maximum likelihood, Generalized least squares(GLS), and Bayesian inference estimation etc. In the Paper, with user equilibrium constraint we formulate GLS problem for estimating O-D trips and develop a solution a1gorithm by using Genetic Algorithm, which has been known as a g1oba1 searching technique. For the purpose of evaluating the method, we apply it to Seoul inner ringroad and compare it with gradient method proposed by Spiess(1990). From the resu1ts we fond that the method developed in the Paper is superior to other.

• 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).

• Korean Journal of Plant Resources
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• v.27 no.4
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• pp.286-292
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• 2014

Near infrared reflectance spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. This study was conducted to measure baicalin, baicalein, and wogonin contents in Scutellaria baicalensis by using NIRS system. Total 63 samples previously were analyzed by HPLC, which showed baicalin, baicalein, and wogonin contents ranging 4.56 to 13.59%, 0.28 to 5.54%, and 0.50 to 1.63% with an average of 9.66%, 2.09% and 0.52%, respectively. Each sample was scanned by NIRS and calculated for calibration and validation equation. A calibration equation calculated by modified partial least squares(MPLS) regression technique was developed in which the coefficient of determination for baicalin, baicalein, and wogonin content was 0.958, 0.944, and 0.709, respectively. Each calibration equation was applied to validation set that was performed with the remaining samples not included in the calibration set, which showed high positive correlation both in baicalin and baicalein content file. In case of wogonin, the prediction model was needed more accuracy because of low $R^2$ value in validation set. These results demonstrate that the developed NIRS equation can be practically used as a rapid screening method for quantification of baicalin and baicalein contents in Scutellaria baicalensis.

• Journal of Food Hygiene and Safety
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• v.21 no.4
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• pp.204-212
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• 2006

This study was conducted to investigate the possibility of rapid and non-des tructive evalution of AV (Acid Value), IV (Iodine Value) and fatty acids in sesame oils. The samples were scanned over the range $400\sim2500nm$ using transmittance spectrum of NIRS(Near-infrared spectroscopy). A calibration equation calculated by MPLS regression technique was developed and correlation coefficient of determination for AV, IV, palmitic acid, stearic acid, linoleic acid and linolenic acid content were 0.9907, 0.9677, 0.9527, 0.9210, 0.9829, 0.9736 and 0.9709 respectively. The validation model for measuring the AV content had R of 0.989, SEP of 0.058 and IV content had R of 0.944, SEP of 0.562 and palmitic acid content had R of 0.924, SEP of 0.194 and stearic acid content had R of 0.717, SEP of 0.168 and oleic acid content had R of 0.989, SEP of 0.221 and linoleic acid content had R of 0.967, SEP of 0.297 and linolenic acid content had R of 0.853, SEP of 0.480 by MPLS. The obtained results indicate that the NIRS procedure can potentially be used as a non-destructive analysis method for the purpose of rapid and simple measurement of AV, IV and fatty acids in sesame oils.