• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1111-1120
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• 2005

In collaborative filtering systems most users do not rate preferences; so User-Item matrix shows great sparsity because it has missing values for items not rated by users. Generally, the systems predict the preferences of an active user based on the preferences of a group of users. However, default voting methods predict all missing values for all users in User-Item matrix. One of the most common methods predicting default voting values tried two different approaches using the average rating for a user or using the average rating for an item. However, there is a problem that they did not consider the characteristics of items, users, and the distribution of data set. We replace the missing values in the User-Item matrix by the default noting method using user coefficient of variance. We select the threshold of user coefficient of variance by using equations automatically and determine when to shift between the user averages and item averages according to the threshold. However, there are not always regular relations between the averages and the thresholds of user coefficient of variances in datasets. It is caused that the distribution information of user coefficient of variances in datasets affects the threshold of user coefficient of variance as well as their average. We decide the threshold of user coefficient of valiance by combining them. We evaluate our method on MovieLens dataset of user ratings for movies and show that it outperforms previously default voting methods.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.115-117
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• 2001

기존의 인터넷 웹사이트에서는 사용자의 만족을 극대화시키기 위하여 사용자별로 개인화 된 서비스를 제공하는 협력적 필터링 방식을 적용하고 있다. 협력적 필터링 기술은 사용자의 취향에 맞는 아이템을 예측하여 추천하며, 비슷한 선호도를 가진 다른 사용자들과의 상관관계를 구하기 위하여 일반적으로 피어슨 상관계수를 많이 이용한다. 그러나, 피어슨 상관계수를 이용한 방법은 사용자가 평가를 한 아이템이 있을 때에만 상관관계를 구할 수 있다는 단점과 예측의 정확성이 떨어진다는 단점을 가지고 있다. 따라서, 본 논문에서는 피어슨 상관관계 기반 예측 기법을 보완하여 보다 정확한 사용자 유사도를 구하는 방법을 제안한다. 제안된 방법에서는 사용자들을 대상으로 사용자가 평가를 한 아이템의 선호도를 사용해서 엔트로피를 적용하였고, 사용자가 선호도를 표시하지 않은 상품에 대해서는 Default Voting 방법을 이용하여 보다 정확한 헙력적 필터링 방식을 구현하였다.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.55-80
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• 2009

Technology innovation activity plays a pivotal role in constructing the entrance barrier for other firms and making process improvement and new product. and these activities give a profit increase and growth to firms. Thus, technology innovation activity can reduce the default risk of firms. However, technology innovation activity can also increase the firm's default risk because technology innovation activity requires too much investment of the firm's resources and has the uncertainty on success. The purpose of this study is to examine the effect of technology innovation activity on the default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Securities Market and The Kosdaq Market from January 1,2000 to December 31, 2008. This study makes use of R&D intensity as an proxy variable of technology innovation activity. The default probability which proxies the default risk of firms is measured by the Merton's(l974) debt pricing model. The main empirical results are as follows. First, from the empirical results, it is found that technology innovation activity has a negative and significant effect on the default risk of firms independent of the Korea Securities Market and Kosdaq Market. In other words, technology innovation activity reduces the default risk of firms. Second, technology innovation activity reduces the default risk of firms independent of firm size, firm age, and credit score. Third, the results of robust analysis also show that technology innovation activity is the important factor which decreases the default risk of firms. These results imply that a manager must show continuous interest and investment in technology innovation activity of one's firm. And a policymaker also need design an economic policy to promote the technology innovation activity of firms.

• Journal of Climate Change Research
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• v.5 no.4
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• pp.349-357
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• 2014

Greenhouse gases (GHGs) from agricultural sector were categorized in a guideline book from Intergovernmental Panel on Climate Change (IPCC) as methane from rice paddy fields and nitrous oxide from agricultural soils. In general, GHG emissions were calculated by multiplying the activity data by emission factor. Tier 1 methodology uses IPCC default factors and Tier 2 uses country specific emission factors (CS). The CS and Scaling factors (SF) had been developed by NAAS (National Academy of Agricultural Science) projects from 2009 to 2012 to estimate how the advanced emissions. The purpose of this study was to compare GHG emissions calculated from IPCC default factors and NAAS CS and SF of agricultural sector in Korea. Methane emissions using CS and SF in rice paddy field was about 79% higher than those using IPCC default factors. In the agricultural soils, nitrous oxide emissions using CS from the 5 crops were about 40% lower than those using IPCC default. Except those 5 crops, approximately up to 52% lower emissions were calculated using CS compared to those using IPCC default factors. The total GHG emissions using CS and SF were about 33% higher than those using Tier 1 method by IPCC default factors.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.309-319
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• 2016

For binary classification models, we consider a risk score that is a function of linear scores and estimate the coefficients of the linear scores. There are two estimation methods: one is to obtain MLEs using logistic models and the other is to estimate by maximizing AUC. AUC approach estimates are better than MLEs when using logistic models under a general situation which does not support logistic assumptions. This paper considers imbalanced data that contains a smaller number of observations in the default class than those in the non-default for credit assessment models; consequently, the AUC approach is applied to imbalanced data. Various logit link functions are used as a link function to generate imbalanced data. It is found that predicted coefficients obtained by the AUC approach are equivalent to (or better) than those from logistic models for low default probability - imbalanced data.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1289-1312
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• 2015

This paper studies the comovement of industry defaults among listed companies. Rank correlation coefficients of Spearman's ${\rho}$ and Kendall's ${\tau}$ measure the concordance of default. These non-parametric coefficients do not require distributional assumptions and are easily used even with less data and extreme values. This study predicts a future financial crisis by looking at the comovement of industry defaults. We expect our analyses will aid market participants (including company executives) in making investment or risk management decisions.

• Journal of the Korean Society of Urban Environment
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• v.18 no.4
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• pp.401-408
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• 2018

EQPS (Effluent Quality Prediction System, Dynamita, France) was applied to analyze the appropriateness of the design of a bioreactor in A sewage treatment plant. A sewage treatment plant was designed by setting the design concentration of the secondary clarifier effluent to total nitrogen and total phosphorus, 10 mg/L and 1.8 mg/L, respectively, in order to comply with the target water quality at the level of the hydrophilic water. The retention time of the 4-stage BNR reactor was 9.6 hours, which was 0.5 for the pre-anoxic tank, 1.0 for the anaerobic tank, 2.9 for the anoxic tank, and 5.2 hours for the aerobic tank. As a result of the modeling of the winter season, the retention time of the anaerobic tank was increased by 0.2 hours in order to satisfy the target water quality of the hydrophilic water level. The default coefficients of the one step nitrification denitrification model proposed by the software manufacturer were used to exclude distortion of the modeling results. Since the process modeling generally presents optimal conditions, the retention time of the 4-stage BNR should be increased to 9.8 hours considering the bioreactor margin. The accurate use of process modeling in the design stage of the sewage treatment plant is a way to ensure the stability of the treatment performance and efficiency after construction of the sewage treatment plant.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.1
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• pp.41-49
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• 2011

The power plants are one of the GHG major source among the sectors of fossil fuel combustion, therefore information of its emission factors is very essential to the establishing control strategies for the greenhouse gas emissions. The $CH_4$ and $N_2O$ concentration from power plants were measured using GC-FID and GC-ECD. The results showed that $CH_4$ emission factor was 0.33 kg/TJ and $N_2O$ emission factor was 0.88 kg/TJ. The $CH_4$ and $N_2O$ emission factors developed in this study were compared with those for IPCC default value and other countries emission factors. The results showed that $CH_4$ emission factor was lower than IPCC default value and Finnish emission factor, but higher than Japanese emission factor. $N_2O$ emission factor was higher Japanese emission factor and IPCC default emission factor however lower than Finnish emission factor. More research is needed on our own emission factors of various energy-consuming facilities in order to stand on a higher position in international negotiations regarding the treaties on climate changes.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.149-157
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• 2011

Undecided inference could be regarded as a missing data problem such as MARand MNAR. Under the assumption of MAR, undecided inference make use of logistic regression model. The probability of default for the undecided group is obtained with regression coefficient vectors for the decided group and compare with the probability of default for the decided group. And under the assumption of MNAR, undecide dinference make use of logistic regression model with additional feature random vector. Simulation results based on two kinds of real data are obtained and compared. It is found that the misclassification rates are not much different from the rate of rawdata under the assumption of MAR. However the misclassification rates under the assumption of MNAR are less than those under the assumption of MAR, and as the ratio of the undecided group is increasing, the misclassification rates is decreasing.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.6
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• pp.1232-1238
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• 2011

This experiment was conducted to measure concentration of dissolved $N_2O$ in ground-water of 59 wells and to make emission factor for assessment of indirect $N_2O$ emission at agricultural sector in agricultural areas of Gyeongnam province from 2007 to 2010. Concentrations of dissolved $N_2O$ in ground-water of 59 wells were ranged trace to $196.6{\mu}g-N\;L^{-1}$. $N_2O$ concentrations were positively related with $NO_3$-N suggesting that denitrification was the principal reason of $N_2O$ production and $NO_3$-N concentration was the best predictor of indirect $N_2O$ emission. The ratio of dissolved $N_2O$-N to $NO_3$-N in ground-water was very important to make emission factor for assessment of indirect $N_2O$ emission at agricultural sector. The mean ratio of $N_2O$-N to $NO_3$-N was 0.0035. It was greatly lower than 0.015, the default value of currently using in the Intergovernmental Panel on Climate Change (IPCC) methodology for assessing indirect $N_2O$ emission in agro-ecosystems (IPCC, 1996). It means that the IPCC's present nitrogen indirect emission factor ($EF_{5-g}$, 0.015) and indirect $N_2O$ emission estimated with IPCC's emission factor are too high to use adopt in Korea. So we recommend 0.0034 as national specific emission factor ($EF_{5-g}$) for assessment of indirect $N_2O$ emission at agricultural sector. Using the estimated value of 0.0034 as the emission factor ($EF_{5-g}$) revised the indirect $N_2O$ emission from agricultural sector in Korea decreased from 1,801,576 ton ($CO_2$-eq) to 964,645 ton ($CO_2$-eq) in 2008. The results of this study suggest that the indirect Emission of nitrous oxide from upland recommend 0.0034 as national specific emission factor ($EF_{5-g}$) for assessment of indirect $N_2O$ emission at agricultural sector.