• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.2
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• pp.165-171
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• 2013

Due to the difficulty in considering dynamic load in the view point of a computer resource and computing time, it is common that external load is assumed as ideal static loads. However, structural analysis under static load cannot guarantee the safety of design of the structures under dynamic loadings. Recently, the systematic method to construct equivalent static load from the given dynamic load has been proposed. Previous study has calculated equivalent static load through the optimization procedure under displacement constraints. However, previously reported works to distribute equivalent static load were based on ad-hoc methods. Improper selection of equivalent static loading positions may results in unreliable prediction of structural design. The present study proposes the selection method of the proper locations of equivalent static loads to dynamically applied loads when we consider transient dynamic structural problems. Moreover, it is appropriate to take into account the stress constraint as well as displacement constraint condition for the safety design. But the previously reported studies of equivalent static load design methods considered only displacement constraint conditions but not stress constraint conditions. In the present study we consider not only displacement constraint but also stress constraint conditions. Through a few numerical examples, the efficiency and reliability of proposed scheme is verified by comparison of the equivalent stress between equivalent static loading and dynamic loading.

• Journal of the Korean GEO-environmental Society
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• v.13 no.11
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• pp.37-42
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• 2012

This study investigated the use of Geobacter lovleyi with TBOS(Tetrabutoxysilane) for TCE(Trichloroethylene) dechlorination. The TCE dechlorination by Geobacter lovleiy was mathematically described as the independent variables such as initial concentration of TCE, protein mass of Geobacter lovleyi and initial concentration of TOBS, and these were modeled by the use of response surface methodology(RSM). These experiments were carried out as a Box-Behnken Design(BBD) consisting of 15 experiments. The application of RSM yielded the following equation, which is empirical relationship for the dechlorination efficiency($Y_1$, %) of TCE and first order kinetic constant of TCE($Y_2,\;d^{-1}$) by independent variables in coded unit : $Y_1=-11.50X_1$(initial concentration of TCE) + $4.25X_2$(protein mass as Geobacter lovleyi injected mass) - $4.75X_3$(initial concentration of TBOS) - ${6.58X_1}^2$ - ${8.58X_2}^2$ + 93.67, $Y_2=-10.92X_1+5.06X_2-4.89X_3-{4.93X_3}^2-2.19X_1X_2+2.54X_1X_3-2.19X_2X_3+16.71$. In this case, the value of the adjusted determination coefficient(adjusted $R^2$= 0.975 and 0.934) were closed to 1, showing a high significance of the model. Statistical results showed the order of TCE dechlorination at experimental factors to be initial TCE concentration > initial TBOS concentration > protein mass, but the interaction effects were non-significant.

• Journal of Microbiology and Biotechnology
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• v.21 no.6
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• pp.637-645
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• 2011

Production of recombinant proteins by excretory expression has many advantages over intracellular expression in Escherichia coli. Hyperexpression of a secretory exoglucanase, Exg, of Cellulomonas fimi was previously shown to saturate the SecYEG pathway and result in dramatic cell death of E. coli. In this study, we demonstrated that overexpression of the PspA in the JM101(pM1VegGcexL-pspA) strain enhanced excretion of Exg to 1.65 U/ml using shake-flask cultivation, which was 80% higher than the highest yield previously obtained from the optimized JM101(pM1VegGcexL) strain. A much higher excreted Exg activity of 4.5 U/ml was further achieved with high cell density cultivation using rich media. Furthermore, we showed that the PspA overexpression strain enjoyed an elevated critical value (CV), which was defined as the largest quotient between the intracellular unprocessed precursor and its secreted mature counterpart that was still tolerable by the host cells prior to the onset of cell death, improving from the previously determined CV of 20/80 to the currently achieved CV of 45/55 for Exg. The results suggested that the PspA overexpression strain might tolerate a higher level of precursor Exg making use of the SecYEG pathway for secretion. The reduced lethal effect might be attributable to the overexpressed PspA, which was postulated to be able to reduce membrane depolarization and damage. Our findings introduce a novel strategy of the combined application of metabolic engineering and construct optimization to the attainment of the best possible E. coli producers for secretory/excretory production of recombinant proteins, using Exg as the model protein.

• Korean Journal of Environmental Biology
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• v.35 no.2
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• pp.207-214
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• 2017

There is little information about the seed longevity of wild plants, although seed bank storage is an important tool for biodiversity conservation. This study was conducted to predict the seed viability equation of Agastache rugosa. The A. rugosa seeds were stored at moisture contents ranging from 2.7 to 12.5%, and temperatures between 10 and $50^{\circ}C$. Viability data were fitted to the seed viability equation in a one step and two step approach. The A. rugosa seeds showed orthodox seed storage behaviour. The viability constants were $K_E=6.9297$, $C_W=4.2551$ $C_H=0.0329$, and $C_Q=0.00048$. The P85 of A. rugosa seeds was predicted to 152 years under standard seed bank conditions. The P85 predicted by seed viability equation can be used as basic information for optimization of seed storage processes.

• Cartoon and Animation Studies
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• s.37
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• pp.143-158
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• 2014

In the modeling processes of 3D computer animation, methods to build optimal work conditions to realize real forms for more efficient works have been advanced. Digital sculpting software, published in 1999, ZBrush has been positioned as an essential factor in character model work requiring of realistic descriptions through different manufacturing methods from previous modeling work processes and easy shape realization. Their functional areas are expanding. So, in this production case paper, as a method to product more optimized animation character models, the efficiency of production method linking digital sculpting software (Z-Brush) and animation production software (Maya) was deliberated and its consequences and implications are suggested. To this end, first the technical features of polygon modeling and Retopology were reviewed. Second, based on it, the efficiency of animation character modeling work processes through step linking ZBrush and Maya suggested in this paper was analyzed. Third, based on the features drawn before, in order to prove the hypothesis on modeling optimization method suggested in this paper, the production process of character Dumvee from a short animation film, 'Cula & Mina' was analyzed as an example. Through this study, it was found that technical approach easiness and high level of completion could be realized through two software linked work processes. This study is considered to be a reference for optimizing production process of related industries or modeling-related classes by deliberating different modeling process linked systems.

• Journal of Korea Water Resources Association
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• v.42 no.8
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• pp.643-657
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• 2009

The major reason to construct large dams is to store surplus water during rainy seasons and utilize it for water supply in dry seasons. Reservoir storage has to meet a pre-defined target to satisfy water demands and cope with a dry season when the availability of water resources are limited temporally as well as spatially. In this study, a Hedging rule that reduces total reservoir outflow as drought starts is applied to alleviate severe water shortages. Five stages for reducing outflow based on the current reservoir storage are proposed as the Hedging rule. The objective function is to minimize the total discrepancies between the target and actual reservoir storage, water supply and demand, and required minimum river discharge and actual river flow. Mixed Integer Linear Programming (MILP) is used to develop a multi-reservoir operation system with the Hedging rule. The developed system is applied for the Han River basin that includes four multi-purpose dams and one water supplying reservoir. One of the fours dams is primarily for power generation. Ten-day-based runoff from subbasins and water demand in 2003 and water supply plan to water users from the reservoirs are used from "Long Term Comprehensive Plan for Water Resources in Korea" and "Practical Handbook of Dam Operation in Korea", respectively. The model was optimized by GAMS/CPLEX which is LP/MIP solver using a branch-and-cut algorithm. As results, 99.99% of municipal demand, 99.91% of agricultural demand and 100.00% of minimum river discharge were satisfied and, at the same time, dam storage compared to the storage efficiency increased 10.04% which is a real operation data in 2003.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Food Science and Preservation
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• v.15 no.6
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• pp.832-839
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• 2008

The stock that is the first step for preparation of soups and purees links to the taste of food. Many types of vegetable have been used in stocks, but this study focused on stocks prepared with sweet pumpkin. The stock preparation conditions including the weight of sweet pumpkin, the water volume, and the boiling time at $97^{\circ}C$ were optimized by response surface methodology. The quality characteristics of the resulting stock were investigated. The color, flavor, taste and overall acceptability were dependent parameters. A model equation was proposed with regard to the sweet pumpkin weight, water volume, and boiling time at $97^{\circ}C$. A sweet pumpkin weight of 357.9 to 403.0 g, a water volume of 689.8 to 768.5 mL, and a boiling time of 9.9 to 10.3 min at $97^{\circ}C$ were found to be the optimal stock preparation conditions. The quality characteristics of the sweet pumpkin stock prepared under the optimized conditions were pH 6.64, total acidity 0.18%, soluble solids $2.39\;^{\circ}Brix$, color value (L, 99.07 a, -2.43 b, 11.82), total polyphenol 280.75 mg/L, and electron donating ability 21.32%.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.77-90
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• 2018

Software Defect Prediction (SDP) is a field of study that identifies defective modules. With insufficient local data, a company can exploit Cross-Project Defect Prediction (CPDP), a way to build a classifier using dataset collected from other companies. Most machine learning algorithms for SDP have used more than one parameter that significantly affects prediction performance depending on different values. The objective of this study is to propose a parameter selection technique to enhance the performance of CPDP. Using a Harmony Search algorithm (HS), our approach tunes parameters of cost-sensitive boosting, a method to tackle class imbalance causing the difficulty of prediction. According to distributional characteristics, parameter ranges and constraint rules between parameters are defined and applied to HS. The proposed approach is compared with three CPDP methods and a Within-Project Defect Prediction (WPDP) method over fifteen target projects. The experimental results indicate that the proposed model outperforms the other CPDP methods in the context of class imbalance. Unlike the previous researches showing high probability of false alarm or low probability of detection, our approach provides acceptable high PD and low PF while providing high overall performance. It also provides similar performance compared with WPDP.