• Korean Journal of Food Science and Technology
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• v.41 no.1
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• pp.106-110
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• 2009

In order to reduce the bitter taste and improve the bioavailability of red ginseng extract(RGE), inclusion complexes (RGE-CD) of the extract with ${\alpha}-,\;{\beta}-,\;{\gamma}$-cyclodextrin were prepared and studied for their sensory quality and bioavailability compared to RGE. By complexation, the bitter taste-reducing efficacies of ${\alpha}$-CD and ${\beta}$-CD were much lower than that of ${\gamma}$-CD. In comparative sensory analysis for the bitter taste, RGE-${\gamma}$-CD10, prepared using 10%(w/w) of ${\gamma}$-CD, showed a score of 1.93(decreased by about 78%) compared to RGE as the control. In addition, in sensory analysis for flavor, RGE-${\gamma}$-CD10showed a score of 5.60. Upon increasing the amount of ${\gamma}$-CD to 15%(w/w) and 20%(w/w), respectively, the bitter taste of RGE-${\gamma}$-CD was removed and the flavor of RGE disappeared(scores of 2.67 and 1.67, respectively). Therefore RGE-${\gamma}$-CD10 was chosen as an optimum. The same dosages of RGE and RGE-${\gamma}$-CD10 were orally administered to SD(Sprague-Dawley) rats on a saponin basis, and the plasma concentrations of ginsenoside Rg1 and Rb1 were measured over time to estimate the average AUC(area under the plasma concentration versus time curve) of the ginsenosides. After the oral administration, there were no significant differences in the AUC values of the RGE and RGE-${\gamma}$-CD 10 groups for ginsenoside Rg1. However, AUC values for ginsenoside Rb1 were $25.8{\mu}g{\cdot}hr/mL$ in the RGE group and $81.5{\mu}g{\cdot}hr/mL$ in the RGE-${\gamma}$-CD 10 group, respectively. Therefore, the bioavailability of ginsenoside Rb1 in the RGE-${\gamma}$-CD 10 group was significantly higher by up to 315% compared with that in the RGE group(p = 0.0029). These results show that the bitter taste of RGE can be simultaneously removed by the complexation of RGE and ${\gamma}$-CD(RGE-${\gamma}$-CD) along with increased bioavailability.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.1
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• pp.50-57
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• 2016

This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation ($R^2{_{cv}}$) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, $R^2{_{cv}}$, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy ($R^2{_{cv}}$ 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.

• Journal of Energy Engineering
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• v.28 no.3
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• pp.65-79
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• 2019

The study attempted to estimate the optimal facility capacity by combining renewable energy sources that can be connected with gas CHP in industrial complexes. In particular, we reviewed industrial complexes subject to energy use plan from 2013 to 2016. Although the regional designation was excluded, Sejong industrial complex, which has a fuel usage of 38 thousand TOE annually and a high heat density of $92.6Gcal/km^2{\cdot}h$, was selected for research. And we analyzed the optimal operation model of CHP Hybrid System linking fuel cell and photovoltaic power generation using HOMER Pro, a renewable energy hybrid system economic analysis program. In addition, in order to improve the reliability of the research by analyzing not only the heat demand but also the heat demand patterns for the dominant sectors in the thermal energy, the main supply energy source of CHP, the economic benefits were added to compare the relative benefits. As a result, the total indirect heat demand of Sejong industrial complex under construction was 378,282 Gcal per year, of which paper industry accounted for 77.7%, which is 293,754 Gcal per year. For the entire industrial complex indirect heat demand, a single CHP has an optimal capacity of 30,000 kW. In this case, CHP shares 275,707 Gcal and 72.8% of heat production, while peak load boiler PLB shares 103,240 Gcal and 27.2%. In the CHP, fuel cell, and photovoltaic combinations, the optimum capacity is 30,000 kW, 5,000 kW, and 1,980 kW, respectively. At this time, CHP shared 275,940 Gcal, 72.8%, fuel cell 12,390 Gcal, 3.3%, and PLB 90,620 Gcal, 23.9%. The CHP capacity was not reduced because an uneconomical alternative was found that required excessive operation of the PLB for insufficient heat production resulting from the CHP capacity reduction. On the other hand, in terms of indirect heat demand for the paper industry, which is the dominant industry, the optimal capacity of CHP, fuel cell, and photovoltaic combination is 25,000 kW, 5,000 kW, and 2,000 kW. The heat production was analyzed to be CHP 225,053 Gcal, 76.5%, fuel cell 11,215 Gcal, 3.8%, PLB 58,012 Gcal, 19.7%. However, the economic analysis results of the current electricity market and gas market confirm that the return on investment is impossible. However, we confirmed that the CHP Hybrid System, which combines CHP, fuel cell, and solar power, can improve management conditions of about KRW 9.3 billion annually for a single CHP system.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.2
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• pp.114-120
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• 2019

This study was carried out to explore the accuracy of near infrared spectroscopy(NIRS) for the prediction of moisture content and chemical parameters on winter annual forage crops. A population of 2454 winter annual forages representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation($R^2$) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS calibration model to predict the moisture contents and chemical parameters had very high degree of accuracy except for barely. The $R^2$ and SECV for integrated winter annual forages calibration were 0.99(SECV 1.59%) for moisture, 0.89(SECV 1.15%) for acid detergent fiber, 0.86(SECV 1.43%) for neutral detergent fiber, 0.93(SECV 0.61%) for crude protein, 0.90(SECV 0.45%) for crude ash, and 0.82(SECV 3.76%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of winter annual forage for routine analysis method to evaluate the feed value.

• Journal of agriculture & life science
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• v.50 no.1
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• pp.105-115
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• 2016

This study was carried out to establish the optimum forest management plan for the Cryptomeria japonica plantations in southern inland and Jeju island in Korea. Sixty seven circular sample plots of 0.04ha were established and we surveyed vegetation structure and growth characteristics from three layers(upper, middle, and lower). As a result of cluster analysis obtained by importance values of each tree species, the community type of C. japonica stands were classified into C. japonica group(C1) and C. japonica-C obtusa group. C. obtusa community were also sbudivided into P. thunbergii-Q. serrata group(C2) and Q. serrata-C obtusa group(C3). In tree layers importance value(IV) of C. japonica were 97.2% in C1, 80.7% in C2, and 47.6% in C3 and in sub-tree layers IV were 8.9% in C1, 15.2% in C2, and 5.7% in C3. Especially in C3 there are bamboo species (Smilacina japonica var. lutecarpa and Pseudosasa japonica) it is necessary for us to control them. In shrub layers C. japonica were found in C1(9.2%) and C2(7.0%), but except for C3. In tree layer species diversity indices of each community ranged from the lowest 0.059 in C1 to the highest 0.548 in C3. Dominance ranged from 0.958 in C1 to 0.393 in C3 which may caused by interspecific competition. Current annual increment of diameter growth ranged from 7.01mm/yr to 8.04mm/yr. As a result of our study we recommend the application of proper thinning and pruning for C1 and C2.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Aquaculture
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• v.21 no.4
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• pp.331-338
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• 2008

Monthly changes in the gonadosomatic index (GSI) and hepatosomatic index (HSI) of wild river puffer Takifugu obscurus, and water quality environment in spawning area during breeding season were investigated from March 1995 to February 1996. Monthly changes in GSI and HSI of T. obscurus, that was cultured in low salinity, were calculated. The external morphology of the gonads, germ cell differentiation during gametogenesis and the reproductive cycle with the gonad developmental phases were investigated by histological analysis. The optimum water quality environment in Ganggyung, Choongcheongnam-do, where is spawning ground of wild T. obscurus, was $15-20^{\circ}C$ (water temperature) and 0 psu (salinity). Monthly changes in the GSI in females and males reached a maximum in May, and then rapidly decreased. Therefore, it is assumed that in the natural condition the spawning period of wild T. obscurus is May to June. In females and males, it showed a negative correlationship between the GSI and HSI. The external morphology of the gonads in female and male T. obscurus, that was cultured in low salinity, is composed of a pair of saccular structure. Based on monthly changes in the GSI, it is assumed that in female T. obscurus, that was cultured in low salinity, spawn from March through May. Therefore, it showed a negative correlationship between changes in the GSI and HSI. On the whole, in females and males, it showed a similar pattern between wild and cultured T. obscurus. The reproductive cycle with the gonad developmental phases can be classified into successive five stages in females: the early growing stage, late growing stage, mature stage, ripe and spent stage, and recovery and resting stage. In males, that can be divided into successive four stages: the growing stage, mature stage, ripe and spent stage, and recovery and resting stage. In case of wild T. obscurus, the spawning period has once a year, however, those cultured in the high water temperature ($20-27^{\circ}C$) - low salinity (under 3.3 psu) condition have reproductive characteristics having possibilities of discharge of eggs and sperms year-round as a multiple spawner.

• Journal of Soil and Groundwater Environment
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• v.13 no.1
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• pp.92-100
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• 2008

Experimental studies were conducted to identify the active agents for reductive dechlorination of TCE in cement/Fe(II) systems focusing on cement components such as CaO, $Fe_2O_3$, and $Al_2O_3$. A hematite that was used to simulate an $Fe_2O_3$ component in cement was found to have degradation efficiencies (k = 0.641 $day^{-1}$) equivalent to that of cement/Fe(II) systems in the presence of CaO/Fe(II), only when it contained an aluminum impurity$(Al_2O_3)$. When the effect of $Al_2O_3$ content of hematite/CaO/$Al_2O_3$/Fe(II) system was tested, the mole ratio of $Al_2O_3$ to CaO affected the rate of TCE degradation with an optimum ratio around 1 : 10 that resulted in a rate constant of 0.895 $day^{-1}$. In the SEM images of hematite/CaO/$Al_2O_3$/Fe(II) systems, acicular crystals were also found that were also observed in cement/Fe(II) systems. Thus it was suspected that these crystals were reactive reductants and that they might be goethite or ettringite that are known to have acicular structures. An EDS element map analysis revealed that these crystals were not goethite crystals. A subsequent experiment that tested reactivities of compounds formed during the ettringite synthesis showed that ettringite and minerals associated with ettringite formation are not reactive reductants. These observations conclude that a mineral containing CaO and $Al_2O_3$ with a acicular structure could be a major reactive reductant of cement/Fe(II) systems.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.59 no.1
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• pp.101-108
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• 2014

This study was conducted to investigate the effect of nitrogen rates on the growth characteristics and yield of maize in agricultural fields with the stream. This indicates the necessity and optimal level of nitrous fertilization to examine the possibilities of quantity enhancement. Plant height and ear height of maize were not significantly different among the nitrogen rates. Stem diameter and leaf area index increased in the nitrogen treatment compared to untreated control. Changes of photosynthetic rate in maize leaves depending on nitrogen treatments increased as much as nitrogen rates were increased up to the highest level, 36 kg per 10a. NDF and ADF content levels of maize were investigated with different nitrogen rates regardless of treatments. In the case of NDF, it showed a tendency to decrease after 8 days of tasseling date. ADF had also decreased after 15 days of tasseling date. Nitrogen uptake of maize leaves with different nitrogen rates showed the highest level, $4.9g\;kg^{-1}$ with 36 kg per 10a on the tasseling date. Ear length and 100-kernel weight, there were no significant differences according to yield and the components with different nitrogen rates. Ear diameter and kernel number, nitrogen rates of 18 kg and 36 kg were increased compared to nitrogen rate of 9 kg per 10a and untreated control. The pericarps in 9 kg nitrogen rate and control were thicker than those of 18 kg and 36 kg treatment. The yield, 18 kg, 36 kg, and 9 kg treatments were increased by 10.96%, 9.27%, and 3.31%, compared to control. The component analysis on maize kernel with different nitrogen rates, starch showed no significant differences among treatments. Total sugar in 18 kg nitrogen treatment represented the highest content level, 6.37%. In addition, Amylopectin in 18 kg treatment showed the highest content level of 90.38%. However, amylose in 18 kg treatment showed the lowest level, 9.62% which drew a conclusion that waxy of 18 kg treatment is considered to be the strongest one. From the results described above, nitrous fertilization is essential to grow maize in agricultural fields with the stream. The optimum level of nitrous fertilization is considered 18 kg per 10a.

• Journal of Mushroom
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• v.14 no.2
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• pp.59-63
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• 2016

A new commercial strain of oyster mushroom (was developed by hyphal anastomosis, and was improved byhybridization between a monokaryotic strain derived from Pleurotus ostreatus ASI 0635 (Gonji 7ho) and a dikaryotic strain derived from P. ostreatus ASI 0666 (Mongdol). The optimum temperatures for mycelial growth and fruiting body development were $25{\sim}30^{\circ}C$ and $12{\sim}18^{\circ}C$, respectively. When PDA (potato dextrose agar medium) and MCM (mushroom complete medium) were compared, mycelial growth was faster in MCM. Similar results were observed with the control strain P. ostreatus ASI 2504 (Suhan 1ho). Analysis of the genetic characteristics of the new cultivar ('Gosol') showed a different DNA profile from that of the control ASI 2504 strain, when RAPD (raurpDNA) primers URP1, 2, 3, and 7 were used. Fruiting body production per bottle was approximately116 g based on a production performance test. In addition, yields from a farm field trial were stably achieved in an inadequate production enviro. The color of the pileus was blackish gray, and the stipe was long and thick. Therefore, we expect that this new strain will satisfy consumer demand for high quality mushrooms.