• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.12
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• pp.1700-1709
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• 2007

The purpose of this study was to examine the relationships between thermo-physiological factors and the insensible loss of body weight(IL) of resting women wearing seasonal comfortable clothing. Air temperature was maintained at a mean of 22.5, 24.7, and 16.8 for spring/fall, summer and winter, respectively. We selected a total of 26 clothing ensembles(8 ensembles for spring/fall, 7 ensembles for summer, and 11 ensembles for winter). The results showed that 1) IL was $19{\pm}5g{\cdot}m^{-2}{\cdot}hr$ for spring/fall environment, $21{\pm}5g{\cdot}m^{-2}{\cdot}hr$ for summer, $18{\pm}6{\cdot}m^{-2}{\cdot}hr$ for winter(p<.001). 2) Insensible water loss through respiratory passage(IWR) showed the reverse tendency to IL. IWR was $6{\pm}1g{\cdot}m^{-2}{\cdot}hr$ for winter and $5{\pm}1g{\cdot}m^{-2}{\cdot}hr$ for summer. This difference was significant(p<.001). 3) The proportion of IWR out of whole insensible water loss(IW), had a mean of the mean 28% for summer and 38% for winter(p<.001). 4) In comfort, the heat loss by IW out of heat production had a mean of 25% for spring/fall, 27% for summer, and 23% for winter. 5) There was a weak negative correlation between It and clothing insulation/body surface area covered by clothing. 6) There were significant correlations between IL and air temperature$(T_a)$, air humidity$(H_a)$, energy metabolism, ventilation, mean skin temperature $\={T}_{sk})$ and clothing microclimate humidity$(H_{clo})$. However, the coefficients were less than 0.5. In conclusion, there were weak relationships between the IL and thermo-physiological factors. However, when subjects rested in thermal comfort, the IL was maintained in a narrow range even though the clothing insulation and air temperature were diverse.

• Journal of Intelligence and Information Systems
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• v.16 no.1
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• pp.93-116
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• 2010

Ensemble approach is applied to the detection modeling of illegal cash accommodation (ICA) that is the well-known type of fraudulent usages of credit cards in far east nations and has not been addressed in the academic literatures. The performance of fraud detection model (FDM) suffers from the imbalanced data problem, which can be remedied to some extent using an ensemble of many classifiers. It is generally accepted that ensembles of classifiers produce better accuracy than a single classifier provided there is diversity in the ensemble. Furthermore, recent researches reveal that it may be better to ensemble some selected classifiers instead of all of the classifiers at hand. For the effective detection of ICA, we adopt ensemble size reduction technique that prunes the ensemble of all classifiers using accuracy and diversity measures. The diversity in ensemble manifests itself as disagreement or ambiguity among members. Data imbalance intrinsic to FDM affects our approach for ICA detection in two ways. First, we suggest the training procedure with over-sampling methods to obtain diverse training data sets. Second, we use some variants of accuracy and diversity measures that focus on fraud class. We also dynamically calculate the diversity measure-Forward Addition and Backward Elimination. In our experiments, Neural Networks, Decision Trees and Logit Regressions are the base models as the ensemble members and the performance of homogeneous ensembles are compared with that of heterogeneous ensembles. The experimental results show that the reduced size ensemble is as accurate on average over the data-sets tested as the non-pruned version, which provides benefits in terms of its application efficiency and reduced complexity of the ensemble.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.4
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• pp.703-713
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• 2021

This study was to investigate the thermal insulation of clothing ensembles, including padded jackets with two different filling types. Thermal insulation of the ensemble was measured using a thermal manikin in four conditions (10℃, 30% RH and 20℃, 50% RH with an air velocity of less than 0.15 m·s^-1 and 1.5 m·s^-1). Ten males participated at 10℃ and 30% RH with an air velocity of less than 0.15 m·s^-1 and 1.5 m·s^-1. The results showed that the polyester ensemble was warmer than a goose down ensemble in 0.15 m·s^-1 conditions and the goose down ensemble had greater thermal insulation than the polyester ensemble at an air velocity of 1.5 m·s^-1. Thermal insulation was reduced 5-7% when temperature decreased 10℃ and reduced 40-50% when air velocity reached 1.5 m·s^-1 for both ensembles. Thermal insulation of the ensemble in human trials decreased more than a thermal manikin at 10℃, 30% RH with an air velocity of 1.5 m·s^-1. Lower temperatures and human trials were effective in identifying the properties of the thermal insulation by filling types even though there were restrictions on the general application because of two types of a clothing ensemble.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.156-159
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• 2008

Proper orthogonal decomposition is a statistical pattern analysis technique for finding the dominant components, called the proper orthogonal modes, in ensembles of spatially distributed data. We present recent ideas based on proper orthogonal decomposition (POD) and detailed experiments that yield new perspectives into the microscale structures. The linearized modeling technique based on POD is very useful to show the principal characteristics of the complex dynamic responses.

• Journal of information and communication convergence engineering
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• v.8 no.4
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• pp.370-376
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. However, its performance can be degraded due to multicollinearity problem where multiple classifiers of an ensemble are highly correlated with. This paper proposes genetic algorithm-based optimization techniques of SVM ensemble to solve multicollinearity problem. Empirical results with bankruptcy prediction on Korea firms indicate that the proposed optimization techniques can improve the performance of SVM ensemble.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2018.06a
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• pp.88-88
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• 2018

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.11
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• pp.723-729
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• 2014

In this paper, we derive the average repair bandwidth and/or read cost for arbitrary repairable linear code ensembles. The repair bandwidth and read cost are the required amount of data and access number of nodes to restore a failed node, respectively. Here, the repairable linear code ensemble is given by such parameters as the number k of data symbols, the number m of parity symbols, and their degree distributions. We further assume that the code is systematic, and no other constraint is assumed, except possibly that the exact repair could be done by the parity check-sum relation with fully connected n=k+m storages. This enables one to apply the result of this paper directly to any randomly constructed codes with the above parameters, such as linear fountain codes. The final expression of the average repair read cost shows that it is highly dependent on the degree distribution of parity symbols, and also the values n and k.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.203-211
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• 2016

The genetic code is a key to bio-informatics and to a science of biological self-organizing on the whole. Modern science faces the necessity of understanding and systematically explaining mysterious features of ensembles of molecular structures of the genetic code. This paper is devoted to symmetrical analysis for genetic systems. Mathematical theories of noise-immunity coding and discrete signal processing are based on Jacket matrix methods of representation and analysis of information. Both of the RNA and Jacket Matrix property also have the Element(Block) - wise Inverse Matrices. These matrix methods, which are connected closely with relations of symmetry, are borrowed for a matrix analysis of ensembles of molecular elements of the genetic code. This method is presented for its simplicity and the clarity with which it decomposes a Jacket Matrix in terms of the genetic RNA Codon.

• Ocean and Polar Research
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• v.30 no.2
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• pp.129-140
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• 2008

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.209-220
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• 2011

Input uncertainty is one of the major sources of uncertainty in hydrologic modeling. In this paper, first, three alternate rainfall inputs generated by different interpolation schemes were used to see the impact on a distributed watershed model. Later, the residuals of precipitation interpolations were tested as a source of ensemble streamflow generation in two river basins in the U.S. Using the Monte Carlo parameter search, the relationship between input and parameter uncertainty was also categorized to see sensitivity of the parameters to input differences. This analysis is useful not only to find the parameters that need more attention but also to transfer parameters calibrated for station measurement to the simulation using different inputs such as downscaled data from weather generator outputs. Input ensembles that preserves local statistical characteristics are used to generate streamflow ensembles hindcast, and showed that the ensemble sets are capturing the observed steamflow properly. This procedure is especially important to consider input uncertainties in the simulation of streamflow forecast.