• Journal of the Korean housing association
• /
• v.24 no.6
• /
• pp.141-150
• /
• 2013

Hanok is Korea's traditional housing, which is build detached unit. Most of the People who live in the environment of residential high-rise apartments likes new residential environment, and pursue eco-friendly homes, health homes, especially traditional Hanok was reassure potential. In urban context, resident think more compact land use in Hanok also, because Hanok is dissatified in compact land use, and it should be build as more economic aspect. The purpose of this study is to propose a typology which traditional Hanok also can be build higher land use and traditional values as a modern housing type; First of all, clustered Hanok is formed by traditional houses and interior spaces in modern house., and its types are configured by lifestyle of modern and image element of traditional Hanok. This kinds of clustering Hanok can be seen from historical city, but the trends is a minority of the housing type and form. Now, the modern clustering Hanok, even though handful of cases, appears as sustainable housing type, its possibilities as a new housing should be more detailed researches. A elements of Modern cluster Hanok discused in layout, plan, envelopment of house, structure, roofs, and the coordination of the element can be so much diverse.

• Nuclear Engineering and Technology
• /
• v.52 no.7
• /
• pp.1429-1435
• /
• 2020

Applying an accurate parametric prediction model to identify abnormal or false pressurizer water levels (PWLs) is critical to the safe operation of marine pressurized water reactors (PWRs). Recently, deep-learning-based models have proved to be a powerful feature extractor to perform high-accuracy prediction. However, the effectiveness of models still suffers from two issues in PWL prediction: the correlations shifting over time between PWL and other feature parameters, and the example imbalance between fluctuation examples (minority) and stable examples (majority). To address these problems, we propose a cost-sensitive mechanism to facilitate the model to learn the feature representation of later examples and fluctuation examples. By weighting the standard mean square error loss with a cost-sensitive factor, we develop a Cost-Sensitive Long Short-Term Memory (CSLSTM) model to predict the PWL of PWRs. The overall performance of the CSLSTM is assessed by a variety of evaluation metrics with the experimental data collected from a marine PWR simulator. The comparisons with the Long Short-Term Memory (LSTM) model and the Support Vector Regression (SVR) model demonstrate the effectiveness of the CSLSTM.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.125-140
• /
• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• International Area Studies Review
• /
• v.13 no.3
• /
• pp.729-757
• /
• 2009

This paper examines if China' each province.city manages its organization well after China's WTO affiliation; and on the ground, judges how much each province city needs to improve. China's each province city data from 2002 to 2006 is used to evaluate technical efficiency by using the input-oriented CCR model and the input-oriented BCC model. Analytical results show that only Shanghai gets continuously the highest efficiency score from 2002 to 2006 and so the other provinces cities need to benchmark Shanghai to elevate their efficiency. There can be regional, cultural and emotional differences among the provinces cities but Tibet, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang get low efficiency scores even after China's WTO affiliation. The Chinese government can make use of efficiency evaluation results by DEA as basic materials in making economic development schemes in order to reduce these deviations as various minority races constitutes China and there are regional deviations of degree of economic development in China.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.11
• /
• pp.455-464
• /
• 2022

There are two unique characteristics of the datasets from a manufacturing process. They are the severe class imbalance and lots of Out-of-Distribution samples. Some good strategies such as the oversampling over the minority class, and the down-sampling over the majority class, are well known to handle the class imbalance. In addition, SMOTE has been chosen to address the issue recently. But, Out-of-Distribution samples have been studied just with neural networks. It seems to be hardly shown that Out-of-Distribution detection is applied to the predictive model using conventional machine learning algorithms such as SVM, Random Forest and KNN. It is known that conventional machine learning algorithms are much better than neural networks in prediction performance, because neural networks are vulnerable to over-fitting and requires much bigger dataset than conventional machine learning algorithms does. So, we suggests a new approach to utilize Out-of-Distribution detection based on SVM algorithm. In addition to that, bagging technique will be adopted to improve the precision of the model.

• Human Ecology Research
• /
• v.55 no.3
• /
• pp.275-287
• /
• 2017

The recent Great Recession of 2008 was a period of sharp economic decline throughout the late 2000s. All socio-demographic groups were impacted by the economic downturn, however, Hispanic households were particularly hard hit. It is not a recent phenomenon that minority groups often have greater problems related to credit and debt repayments. A better understanding of these racial/ethnic differences in credit and debt has been hindered by the propensity of many studies to pool all racial/ethnic minorities together and compare them to white households. Using a Heckman-type selection model with a combination of the 2010 and 2013 Survey of Consumer Finances datasets to study household debt repayment problems, we found that racial/ethnic groups have been differently impacted by the recent Great Recession in terms of debt repayment problems. Hispanic households were less likely to hold debt; however, those with debt were just as likely as white households and African American households to be delinquent in repayments. This finding is contrary to prior research that indicated Hispanics with debt were less likely than white and African American households to be delinquent on repayments prior to the Great Recession of 2008. We propose possible explanations for the increase in debt repayment problems, that includes increased assimilation into the U.S. culture of credit use, the circumstance of being more recent home buyers prior to the decline, and living in states that suffered the greatest decline in housing value.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2015.08a
• /
• pp.196.2-196.2
• /
• 2015

There are various issues fabricating the successful and efficient solar cell structures. One of the most important issues is band alignment technique. The solar cells make the carrier in their active region over the p-n junction. Then, electrons and holes diffuse by minority carrier diffusion length. After they reach the edge of solar cells, there exist large energy barrier unless the good electrode are chosen. Many various conductor with different work functions can be selected to solve this energy barrier problem to efficiently extract carriers. Tungsten oxide has large band gap known as approximately 3.4 eV, and usually this material shows n-type property with reported work function of 6.65 eV. They are extremely high work function and trap level by oxygen vacancy cause them to become the hole extraction layer for optical devices like solar cells. In this study, we deposited tungsten oxide thin films by sputtering technique with various sputtering conditions. Their electrical contact properties were characterized with transmission line model pattern. The structure of tungsten oxide thin films were measured by x-ray diffraction. With x-ray photoelectron spectroscopy, the content of oxygen was investigated, and their defect states were examined by spectroscopic ellipsometry, UV-Vis spectrophotometer, and photoluminescence measurements.

• Journal of Distribution Science
• /
• v.14 no.8
• /
• pp.51-57
• /
• 2016

Purpose - If control-ownership disparity is large, managers will not actively reduce costs; rather, they will maintain unutilized resources or possess surplus resources even when sales decrease with the purpose of increasing personal utility from status, power, compensation, and prestige. These managers' utility maximizing tendencies cause cost stickiness. We examine whether asymmetric behavior related to costs becomes stronger when there is a large disparity between ownership and control rights. Research design, data, and methodology - We construct a regression model to examine the relationship between control-ownership disparity and cost stickiness. STICKY, a dependent variable representing cost stickiness is a value found using the method of Weiss (2010), and Disparity is an interest variable that shows control-ownership disparity. Results - This study is based from the unique situations in Korea, in which high control-ownership disparity is common in firms. Large control-ownership disparity was found to increase cost stickiness of corporations. Conclusions - The results of this study imply that controlling shareholders may be regarded as a threat to the interests of minority shareholders and corporate values especially when controlling shareholders have significant influence over managers or the power to make managerial decisions as owners of a corporation.

• Korean Journal of Health Education and Promotion
• /
• v.20 no.3
• /
• pp.239-254
• /
• 2003

Purposes： The purposes of this study were to investigate the smoking cessation counselling activities among community health practitioners(CHP) and to identify the predictors of their smoking cessation counselling activities. Method： A descriptive-correlation study using self-administered questionnaires was conducted. Questionnaires were mailed to all the CHP in a community. A total of 330 CHPs participated in this survey. Results： Of the CHPs surveyed, 245(74.2％) returned completed questionnaires. Most CHPs(90.7％) believed that if a health professional advises their patient to quit, the patient's chances of quitting smoking are increased. While the majority of CHPs “asked, advised, and assessed” their clients, a minority of CHPs “assisted, arranged, and recorded”. In the final stepwise multiple regression model, attitude about smoking cessation policies and counselling activities, self-efficacy of smoking cessation counselling knowledge and skills, and perceived barriers of smoking cessation counselling activities were identified as significant predictors of smoking cessation counselling activities among CHPs. Conclusion： Smoking cessation counselling activities are not a routine part of CHP practice. Efforts should be made to increase the self-efficacy of smoking cessation counselling knowledge and skills among CHPs. Helping CHPs to overcome their barriers to smoking counselling may open up new channels for smoking intervention.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.9 s.180
• /
• pp.2319-2325
• /
• 2000

This thesis studied that seven kinds of residual elements(inclusions) had influenced on fracture toughness($K_{IC}$) obtained by Begley-Logsdon and Rolfe-Novak model equation using tensile an d impact test data of I%CrMoV HP(high pressure) rotor steel. $K_{IC}$ design curve of ASME and fracture surface by SEM were also considered, obtained results are summarized as follows $K_{IC}$ was linearly increased with increase of temperature, effect of the inclusions was significantly over FATT. $K_{IC}$ at lower shelf temperature was quantitatively related to yield strength and was agreed well with Begley's equation. It was difficult to determine $K_{IC}$ because of specimen size and tester capacity at upper shelf temperature, but for this view point Rolfe-Novak's equation was useful. The degree of brittle fracture was dependent on FATT fundamentally, adding S, Sb to matrix decreased impact energy and adding Cu, As increased yield(tensile) strength, and the influence of the others minority inclusion was comparatively insignificant.