• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.11 s.114
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• pp.1120-1131
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• 2006

This paper describes the numerical analysis techniques using the Combined Integration/Matrix Method to calculate ground potential rise which can be occurred in the various grounding systems. Combined Integration/Matrix Method is used to reduce the error and computation time with the analytical integration equation and the proper segmentaion of earth embedded conductor. To do it, optimal segmentaion method for the buried conductors is presented through error analysis which is capable of applying the practical scaled various grounding systems. The optimum length of segmented element is much co-related with the buried depth of grounding electrode and the maximum length of buried electrode. As a result, less 3 precent errors was obtained by proposed model. The proposed model is applied to verify an effect of multi-grounding problems which was aroused much controversy with separated or common grounding between the high power grounding system and low power grounding system such as signal and telecommunication grounding.

• Geophysics and Geophysical Exploration
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• v.23 no.1
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• pp.38-49
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• 2020

Full-waveform inversion (FWI) is an optimization process of fitting observed and modeled data to reconstruct high-resolution subsurface physical models. In acoustic FWI (AFWI), pressure data acquired using a marine streamer has mainly been used to reconstruct the subsurface P-wave velocity models. With recent advances in marine seismic-acquisition techniques, acquiring multi-component data in marine environments have become increasingly common. Thus, AFWI strategies must be developed to effectively use marine multi-component data. Herein, we proposed an AFWI strategy using horizontal and vertical particle-acceleration data. By analyzing the modeled acoustic data and conducting sensitivity kernel analysis, we first investigated the characteristics of each data component using AFWI. Common-shot gathers show that direct, diving, and reflection waves appearing in the pressure data are separated in each component of the particle-acceleration data. Sensitivity kernel analyses show that the horizontal particle-acceleration wavefields typically contribute to the recovery of the long-wavelength structures in the shallow part of the model, and the vertical particle-acceleration wavefields are generally required to reconstruct long- and short-wavelength structures in the deep parts and over the whole area of a given model. Finally, we present a sequential-inversion strategy for using the particle-acceleration wavefields. We believe that this approach can be used to reconstruct a reasonable P-wave velocity model, even when the pressure data is not available.

• Journal of fish pathology
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• v.6 no.2
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• pp.111-122
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• 1993

The mechanism by which $V_H$ region gene segments is selected in B lymphocyte is not known. Moreover, evidence for both random and nonrandom expression of $V_H$ genes in matured B cells has been presented previously. In this report, the technique of in situ hybridization allowed us to analyze expressed $V_H$ gene families in normal B lymphocyte at the single cell level. The analysis of normal B cells in this study eliminated any posssible bias resulting from transformation protocols used previously and minimized limitation associated with sampling size. Therefore, an accurate measure of the functional and expressed $V_H$ gene repertoire in B lymphocyte could be made. One of the most important controls for the optimization of in situ hybridization is to establish probe concentration and washing stringency due to the degree of nucleotide sequence similarlity between different families which in some cases can be as high as 70%. When the radioactive $C{\mu}$ and $V_{H}J558$ RNA probes are tested on LPS-stimulated adult spleen cells, $2{\sim}4{\times}106cpm$/slide shows low background and reasonable frequency of specific positive cells. For the washing condition. 40~50% formamide at $54^{\circ}C$ is found to be optimum for the $C{\mu}$. $V_{H}S107$ and $V_{H}J558$ probes. The analyzed results clearly demonstrate that the level of each different $V_H$ gene family expression is dependent upon the complexity or size of that family. These findings are also extended to the level of $V_H$ gene family expression in separated bone marrow B cells depend upon the various stage of differentiation and conclude no preferential utilization of specific $V_H$ gene family. Thus, the utilization of VH gene segments in B lymphocyte of adult BALB/c mice is random and is not regulated or changed during the differentiation of B cells.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Microbiology and Biotechnology Letters
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• v.36 no.1
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• pp.61-66
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• 2008

The proteins in whey are separated and used as food additives. The remains (mainly lactose) are spray-dried to produce sweet whey powder, which is widely used as an additive for animal feed. Sweet whey powder is also used as a carbon source for the production of valuable products such as polysaccharides. Glucose, fructose, galactose, and sucrose as asupplemental carbon source were evaluated for the production of PS-7 from Azotobacter indicus var. myxogenes L3 grown on whey based MSM media. Productions of PS-7 with 2% (w/v) fructose and sucrose were 2.05 and 2.31g/L, respectively. The highest production of PS-7 was 2.82g/L when 2% (w/v) glucose was used as the carbon source. Galactose showed low production of PS-7 among the carbon sources tested. The effects of various carbon sources addition to whey based MSM medium showed that glucose could be the best candidate for the enhancement of PS-7 production using whey based MSM medium. To evaluate the effect of glucose addition to whey based media on PS-7 production, fermentations with whey and glucose mixture (whey 1, 2, 3%; whey 1% + glucose 1%, whey 1% + glucose 2% and glucose 2%, w/v) were carried out. Significant enhancement of PS-7 production with addition of 1% (w/v) and 2% (w/v) glucose in 1% (w/v) whey media was observed. The PS-7 concentration of 2% glucose added whey lactose based medium was higher than that of 1% glucose addition, however, the product yield $Y_{p/s}$ was higher in 1% glucose added whey lactose based MSM medium. Therefore, the optimal condition for the PS-7 production from the Azotobacter indicus var.myxogenes L3, was 1% glucose addition to 1% whey lactose MSM medium.

• Korean Journal of Food Science and Technology
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• v.39 no.4
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• pp.438-446
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• 2007

The fermented soybean product, cheonggukjang, is favored by many people, partly due to its bio-functional ingredients. Since the fermentation process of cheonggukjang is mediated by enzymes, including proteases, produced by microbes, analysis of the proteome profile changes in cheonggukjang during fermentation would provide us with valuable information for fermentation optimization, as well as a better understanding of the formation mechanisms of the bio-functional substances. The soluble proteins from cheonggukjang were prepared by a phenol/chloroform extraction method, in order to remove interfering molecules for high resolution 2-D gel analysis. Proteomic analysis of the cheonggukjang different fermentation periods suggested that most of the soluble soy proteins were degraded into smaller forms within 20hr, and many microbial proteins, such as mucilage proteins, dominated the soluble protein fraction. The proteomic profile of cheonggukjang was very different from natto, in terms of the 2-D gel protein profile. Among the separated protein spots on the 2-D gels, 50 proteins from each gel were analyzed by MALDI-TOF MS and PMF for protein identification. Due to database limitations with regard to soy proteins and microbial proteins, identification of the changed proteins during fermentation was restricted to 9 proteins for cheonggukjang and 15 for natto. From de novo sequencing of the proteins by a tandem MS/MS, as well as by database searches using BLASTP, a limited number of proteins were identified with low reliability. However, the 2-D gel analysis of proteins, including protein preparation methods, remains a valuable tool to analyze complex mixtures of proteins entirely. Also, for intensive mass spectrometric analysis, it is also advisable to focus on a few of the interestingly changed proteins in cheonggukjang.

• Journal of the Korean Chemical Society
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• v.63 no.4
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• pp.246-252
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• 2019

In the system of GC-OTC/FID (Gas chromatography-Open Tubular Column/Flame Ionization Detector), DMSO (Dimethyl sulfide) solvent was used to separate the polar solvents (Alcohols). In this system DMSO was eluted later than the separated polar solvents. At this system to calculate chromatographic factors [adjusted retention time ($t_R^{\prime}=t_R-t_O$), capacity factor{$k^{\prime}=(t_R-t_O)/t_O$} and separation factor {${\alpha}=(t_{R2}-t_O)/(t_{R1}-t_O)$}], dead time($t_O$) is necessary, but the method to calculate it has not been reported yet. Therefore, we have tried to develop $t_O$. To calculate $t_O$, we conversed DMSO retention time (DMSO $t_R$) to logarithm ($f(x)={\log}\;t_{R(DMSO)}{\rightarrow}t_O$, $t_O={\log}$ 9.551=0.980). To confirm the optimization of the developed method, we compared with $CH_4\;t_R$ and ${\ln}\;t_{R(DMSO)}$. Both of the values calculated by $CH_4\;t_R$ and ${\ln}\;t_{R(DMSO)}$ were not suitable in the calculation k' and ${\alpha}$. The developed method in this study{${\log}\;t_{R(DMSO)}$} has satisfied both of the values k' criteria (1${\alpha}(1<{\alpha}<2)$. The developed calculation method in this study was easy and convenient, therefore it can be expected to be applied to these similar systems.