• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1207-1216
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• 2015

This study aims to compare the model power in developing corporate credit rating models and to suggest a good way to build models based on the characteristic of data. Among many measurement methods, AR is used to measure the model power under various conditions. SAS/MACRO is in use for similar repetitions to reduce time to build models under several combination of conditions. A corporate credit rating model is composed of two sub-models; a credit scoring model and a default prediction model. We verify that the latter performs better than the former under various conditions. From the result of size comparisons, models of large size corporate are more powerful and more meaningful in financial viewpoint than those of small size corporate. As a corporate size gets smaller, the gap between sub-models becomes huge and the effect of outliers becomes serious.

• KIEAE Journal
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• v.12 no.3
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• pp.89-94
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• 2012

The international community is paying close attention to the climatic changes caused by the meteorological anomalies. In response to such phenomena, after the adoption of the United Nations Framework Convention on Climate Change in 1992, efforts to actively respond to the meteorological changes are proliferating all over the world; even in the Republic of Korea, the issue to tackle the meteorological changes has emerged as a top-priority national agenda. In the year of 2008, after the declaration of the low-carbon, green-growth paradigm by the government, the UNFCCC COP15 has announced a 30% reduction target of the emission of the greenhouse gases by 2020 as compared to the "Business As Usual, BAU" and has also confirmed, as a commitment plan to achieve reduction in the emission of greenhouse gases, the reduction target of greenhouse gases for all sectors, industries and years. (26.9% for buildings) Since the construction of the new apartment houses in the year of 2001, the "Building Energy Efficiency Rating", has been applied to newly constructed building complexes, built in 2010; the accumulated emission reduction has been evaluated at around 450,000toe and the accumulated carbon dioxide emission reduction is at $826,000tCO_2$ And through the prediction of these values under various scenarios (New construction, new construction / expansion of existing uses, when transferred to 1stgrade), the effects on the degree of reduction of greenhouse gases by the increased certification of the Building Energy Efficiency Rating are an alyzed and it is our aim to express the importance of the certification system capable of carrying out a quantitative evaluation of the building energy in order to establish the strategy to reduce the emission of carbon dioxide.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.6
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• pp.158-169
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• 2004

A method for predicting the short-term dynamic line ratings in overhead transmission lines using real-time weather forecast data is proposed in this paper. Through some inspections for the 3-hour interval forecasting factors such as ambient temperature, wind speed grade and weather code given by KMA(Korea Meteorological Administration), correlation properties between forecast weather data and actual measured data are analyzed. To use these variable in determining the dynamic line ratings, they are changed into suitable numerical values. Furthermore adaptive neuro-fuzzy systems to improve reliabilities for wind speed and solar heat radiation ate designed It was verified that the forecast weather data can be used to predict the line rating with reliable. As a result it can be possible that the proposed predicting system can be effectively utilized by their anticipation a short-time in advance.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.125-134
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• 2021

With the rapid development of the film industry, the number of films is significantly increasing and movie recommendation system can help user to predict the preferences of users based on their past behavior or feedback. This paper proposes a movie recommendation system based on the latent factor model with the adjustment of mean and bias in rating. Singular value decomposition is used to decompose the rating matrix and stochastic gradient descent is used to optimize the parameters for least-square loss function. And root mean square error is used to evaluate the performance of the proposed system. We implement the proposed system with Surprise package. The simulation results shows that root mean square error is 0.671 and the proposed system has good performance compared to other papers.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.166-176
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• 2021

Due to urbanization and industrialization, the importance of large-scale fire prevention, management and measures is increasing day by day. However, the fire site arrival rate in Golden Time, which is a factor that can minimize large-scale fire damage, of Pohang, a large city with a population of over 500,000, is relatively low. So additional fire fighting power deployment and infrastructure investment are required. However, as budget and manpower are limited, it is necessary to selectively deploy fire fighting power and invest in infrastructure. Therefore, this study attempted to present a fire damage rating that can compare the level of fire damage, which is an index that can help selectively provide fire fighting services in Pohang and make related decisions. For the index, the OD cost matrix was used to predict fire vulnerable areas with a high probability of increasing the fire scale in the event of a fire. Also fire damage was measured by predicting the level of fire damage in the event of a fire according to population, building density, and access of fire trucks. It is expected that the fire damage rating will be able to help in various decisions related to fire fighting service deployment and services not only in Pohang city, but also in other regions.

• Applied Chemistry for Engineering
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• v.35 no.3
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• pp.200-208
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• 2024

Chung's equations-IX and Chung's equation-XII were utilized to predict the fire risk and evaluate fire risk ratings for four types of wood: camphor, cherry, rubber, and elm trees. The combustion tests were conducted using a cone calorimeter test method by ISO 5660-1 standards. The fire risk and fire risk rating (FRR) were compared for Fire Risk Index-IX (FRI-IX) and Fire Risk Index-XII (FRI-XII). The results yielded Fire Performance Index-XI (FPI-XI) ranging from 0.08 to 11.48 and Fire Growth Index-XI (FGI-XI) ranging from 0.67 to 111.89. The Fire Risk Index-XII (FRI-XII), indicating fire risk rating, exhibited an increasing order of cherry (0.45): Grade A (Ranking 5) < PMMA (1): Grade A (Ranking 4) < elm (1.23): Grade A (Ranking 3) < rubber (1.56): Grade A (Ranking 2) << camphor (148.23): Grade G (Ranking 1). Additionally, the fire risk index-IX (FRI-IX) was cherry (0): Grade A (Ranking 3) ≈ rubber (0): Grade A (Ranking 3) ≈ elm tree (0): Grade A (Ranking 3) < PMMA (1): Grade A (Ranking 2) << camphor tree (66.67): Grade G (Ranking 1). In general, camphor was found to have the highest fire risk. In conclusion, although the expression of the index is different as shown based on the standards of FRI-IX and FRI-XII, predictions based on fire risk assessment of combustible materials showed similar trends.

• Journal of East-West Nursing Research
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• v.11 no.1
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• pp.42-50
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• 2005

Purpose: The purpose this research was to provide with basic data in the control of the fatiguer found in the patients with fibromyalgia by analysing the factors that predict that. Method: At three university medical center, appointed 245 out-patients diagnosed of fibromyalgia according to the conditions by American College of Rheumatology (1990). The research instruments used in this study were graphic rating scale(Anxiety, sleep disturbance, pain, joint stiffness and depression), physical activity, the number of tender points, life satisfaction and Self-efficacy scale. In data analysis, SPSS 12.0 program was utilized and data were analyzed using descriptive statistics, Pearson's correlation coefficient and multiple regression. Result: The factors that predict the fatigue of patients with fibromyalgia were sleep disturbance, life satisfaction, pain, joint stiffness, illness duration, and anxiety which explained 50.1% of the fatigue. Conclusion: It has been confirmed that the regression equation model of this research may serve as a fatigue prediction factors in patients with fibromyalgia.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.7
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• pp.1654-1663
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• 1994

Even if the ball bearing was conservatively designed considering the dynamic capacity and the rating life, sometimes the bearing was early failed on account of the misalignment and the lubricant contaminations etc. Misalignment was generated when bearing-shaft system transmitted large power and when the bearing was inadequately mounted. It was possible to predict the fatigue life of ball bearing under the misalignment considering the motions of ball, cage and raceway, and the factors of the effect on fatigue life. Misalignment affected on ball bearing as radial and moment load and the relationships between misalignment and moment were obtained. In this paper, the analysis of the load distributions between ball and raceway, and the prediction of fatigue life of deep groove ball bearing under radial and moment loads were carried out. And, the new formulation of equivalent dynamic load considering the effects of moment load was proposed.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.659-668
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• 2007

Collaborative filtering approach for recommender systems are now widely applied in e-commerce to assist customers to find their needs from many that are frequently available. this approach makes recommendations for users based on the opinions to similar users in the system. But this approach is opened to users who present their preference to items or acquire the preference information form other users, noise in the system makes significant problem for accurate recommendation. In this paper, we analyze the relationship between the standard deviation of preference ratings for each user and the estimated ratings of them. The result shows that the possibility of the pre-filtering condition which detecting the factor of bad effect on the prediction of user's preference. It is expected that using this result will reduce the possibility of bad effect on recommender systems.

• Information Systems Review
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• v.9 no.1
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• pp.85-103
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• 2007

The purpose of this study is to evaluate the performance of collaborative filtering recommender algorithms for better prediction accuracy of the customer's preference. The accuracy of customer's preference prediction is compared through the MAE of neighborhood based collaborative filtering algorithm and correspondence mean algorithm. It is analyzed by using MovieLens 1 Million dataset in order to experiment with the prediction accuracy of the algorithms. For similarity, weight used in both algorithms, commonly, Pearson's correlation coefficient and vector similarity which are used generally were utilized, and as a result of analysis, we show that the accuracy of the customer's preference prediction of correspondence mean algorithm is superior. Pearson's correlation coefficient and vector similarity used in two algorithms are calculated using the preference rating of two customers' co-rated movies, and it shows that similarity weight is overestimated, where the number of co-rated movies is small. Therefore, it is intended to increase the accuracy of customer's preference prediction through expanding the number of the existing co-rated movies.