• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.337-348
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• 2016

An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.

• Atmosphere
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• v.20 no.3
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• pp.287-306
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• 2010

An ensemble Kalman filter (EnKF) with Weather Research and Forecasting (WRF) Model is applied for Typhoon Wukong (200610) to investigate the performance of ensemble forecasts depending on experimental configurations of the EnKF. In addition, the ensemble sensitivity analysis is applied to the forecast and analysis ensembles generated in EnKF, to investigate the possibility of using the ensemble sensitivity analysis as the adaptive observation guidance. Various experimental configurations are tested by changing model error, ensemble size, assimilation time window, covariance relaxation, and covariance localization in EnKF. First of all, experiments using different physical parameterization scheme for each ensemble member show less root mean square error compared to those using single physics for all the forecast ensemble members, which implies that considering the model error is beneficial to get better forecasts. A larger number of ensembles are also beneficial than a smaller number of ensembles. For the assimilation time window, the experiment using less frequent window shows better results than that using more frequent window, which is associated with the availability of observational data in this study. Therefore, incorporating model error, larger ensemble size, and less frequent assimilation window into the EnKF is beneficial to get better prediction of Typhoon Wukong (200610). The covariance relaxation and localization are relatively less beneficial to the forecasts compared to those factors mentioned above. The ensemble sensitivity analysis shows that the sensitive regions for adaptive observations can be determined by the sensitivity of the forecast measure of interest to the initial ensembles. In addition, the sensitivities calculated by the ensemble sensitivity analysis can be explained by dynamical relationships established among wind, temperature, and pressure.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.6
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• pp.763-770
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• 2002

The objective of the study was to investigate skin temperature responses of Hanbok when it was worn. Two healthy females(average 21 years, 155cm and 60kg were exposed to a climatic chamber(Room Temp. $21{\pm}1^{\circ}C,\;52{\pm}2%R.H.$, 0.15m/s). During the experiment, rectal temperature, skin temperature of 9 areas, clothing microclimate, subjective sensation were measured. Chima and Jogory to be made of silk nobang(SN) or Ramie were worn for summer. Polyester(P) Chima and Jogori(R) could be wort for spring and autumn. For winter, silk Chima, Jogori(S) and Durumagi(D) were commonly worn. Rectal temperature was high in order of naked(N), R, SN, P, S, D. However Mean skin temperature was reversely high in order of D, S, SN, R, P, naked. In naked, skin temperature was high in order of head, trunk upper extremity and lower extremity. But on wearing of Hanbok, it was the highest at the chest except head regardless of kinds of clothing ensembles. Skin temperature of upper arm was secondly highest on wearing the silk ensemble and the Durumagi ensemble, but skin temperature of buttock was secondly highest on wearing the silk nobang ensemble and the ramie ensemble. Skin temperature on wearing the silk ensemble was generally higher than those on other clothing ensembles. Local and mean skin temperatures on wearing the silk ensemble and the Durumagj ensemble were generally higher than on other clothing ensembles. Heat resistance of the fabric might have affected on the local skin temperature.

• Journal of the Korean Society of Costume
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• v.60 no.3
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• pp.44-55
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• 2010

This study was intended to develop some lounge wear ensembles of emotional design by using an eco-friendly material, Kenaf fabrics in line with the contemporary trend of valuing health and environment. With market survey on commercially available indoor clothing ensembles in the market, the designs of lounge wear ensembles being on sale via on/off-line routes were analyzed. Provided by Korea High Tech Textile Research Institute, Kenaf fabrics were employed to make 4 pieces of lounge wear for women (cardigan, T-shirt, slacks, vest) and 4 ones for toddlers. In addition, some housewives in their thirties or forties who lived in Seoul were asked to respond to a series of questionnaires concerning the prototypes made directly in order to evaluate consumer satisfaction with them. Although the consumer awareness of Kenaf fabrics is very low as far, this attempt to present the designs of lounge wear made of Kenaf to young housewives who have a lot of concern for and purchase experiences of eco-friendly materials is meaningful in the light of the possibility to popularize Kenaf which is not familiar as a textile material yet.

• ETRI Journal
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• v.42 no.6
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• pp.886-898
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• 2020

Unusual data patterns or outliers can be generated because of human errors, incorrect measurements, or malicious activities. Detecting outliers is a difficult task that requires complex ensembles. An ideal outlier detection ensemble should consider the strengths of individual base detectors while carefully combining their outputs to create a strong overall ensemble and achieve unbiased accuracy with minimal variance. Selecting and combining the outputs of dissimilar base learners is a challenging task. This paper proposes a model that utilizes heterogeneous base learners. It adaptively boosts the outcomes of preceding learners in the first phase by assigning weights and identifying high-performing learners based on their local domains, and then carefully fuses their outcomes in the second phase to improve overall accuracy. Experimental results from 10 benchmark datasets are used to train and test the proposed model. To investigate its accuracy in terms of separating outliers from inliers, the proposed model is tested and evaluated using accuracy metrics. The analyzed data are presented as crosstabs and percentages, followed by a descriptive method for synthesis and interpretation.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.6 s.165
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• pp.966-973
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• 2007

The purpose of this study was to examine the correlation between the combination of women's clothing by season and thermal insulation using a thermal manikin. A total of 34 kinds of clothing ensembles were selected based on previous studies(8 types for spring/fall, 7 types for summer, and 19 types for winter). The results were as follows: The thermal insulation of clothing ensembles($I_{cle-total}$) ranged from $0.34{\sim}0.60clo$ for spring/fall, $0.16{\sim}0.37clo$ for summer, and $0.89{\sim}1.35clo$ for winter. The correlation coefficient between the thermal insulation of clothing ensembles and thermal insulation accumulated by the individual garments composing of the clothing ensembles($I_{cle-summed}$) was 0.982(p<0.001). The correlation coefficient between the thermal insulation of clothing ensembles and total clothing layers for the upper body part was 0.750 (p<0.001), for the total clothing weight was 0.978(p<0.001), and for the covering area was 0.776(p<0.001). In conclusion, $I_{cle-total}$ showed higher relationships to the $I_{cle-summed}$ and total clothing weight than to the total clothing layers or surface area covered by clothing.

• Fashion & Textile Research Journal
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• v.3 no.5
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• pp.479-485
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• 2001

This study was to determine the effect of garment closures and layering systems on insulation, using a thermal movable manikin in cold weather conditions. The insulation values of ensembles with opened and closed features were measured, and those of four different layered clothing ensembles were tested while standing and while walking. Our research indicated that when there was an opening involved in design the system, insulation decreased; even a zip-out lining in the armpit affected little. If a light weight jacket and pants are put on over a fleece shirt and pants instead polyester underwear, the amount of insulation increase was 0.43 clo.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.617-625
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1147-1151
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• 2009

In this study, we address the problem of producing probability forecasts of summer seasonal rainfall, on the basis of Hindcast experiments from a ensemble of GCMs(cwb, gcps, gdaps, metri, msc_gem, msc_gm2, msc_gm3, msc_sef and ncep). An advanced Hierarchical Bayesian weighting scheme is developed and used to combine nine GCMs seasonal hindcast ensembles. Hindcast period is 23 years from 1981 to 2003. The simplest approach for combining GCM forecasts is to weight each model equally, and this approach is referred to as pooled ensemble. This study proposes a more complex approach which weights the models spatially and seasonally based on past model performance for rainfall. The Bayesian approach to multi-model combination of GCMs determines the relative weights of each GCM with climatology as the prior. The weights are chosen to maximize the likelihood score of the posterior probabilities. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.543-559
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• 2017

Tree-based regression and classification ensembles form a standard part of the data-science toolkit. Many commonly used methods take an algorithmic view, proposing greedy methods for constructing decision trees; examples include the classification and regression trees algorithm, boosted decision trees, and random forests. Recent history has seen a surge of interest in Bayesian techniques for constructing decision tree ensembles, with these methods frequently outperforming their algorithmic counterparts. The goal of this article is to survey the landscape surrounding Bayesian decision tree methods, and to discuss recent modeling and computational developments. We provide connections between Bayesian tree-based methods and existing machine learning techniques, and outline several recent theoretical developments establishing frequentist consistency and rates of convergence for the posterior distribution. The methodology we present is applicable for a wide variety of statistical tasks including regression, classification, modeling of count data, and many others. We illustrate the methodology on both simulated and real datasets.