• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1507-1515
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• 2008

It is essential for electric power companies to have a quick restoration system of the faulted pole transformers which occupy most of transformers to supply stable electricity. However, it takes too much time to restore it when a transformer is out of order suddenly because we now count on operator in investigating causes of failure and making decision of recovery methods. This paper presents the concept of 'Fault pattern analysis and Restoration prediction model using Data mining techniques’, which is based on accumulated fault record of pole transformers in the past. For this, it also suggests external and internal causes of fault which influence the fault pattern of pole transformers. It is expected that we can reduce not only defects in manufacturing procedure by upgrading quality but also the time of predicting fault patterns and recovering when faults occur by using the result.

• Journal of Life Science
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• v.32 no.12
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• pp.947-955
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• 2022

Silkworms, which have recently shown promise as functional health foods, show functional differences between varieties; therefore, the need for variety identification is emerging. In this study, we analyzed the whole silkworm genome to identify 10 unique silkworm varieties (Baekhwang, Baekok, Daebaek, Daebak, Daehwang, Goldensilk, Hansaeng, Joohwang, Kumkang, and Kumok) using single nucleotide polymorphisms (SNP) present in the genome as biomarkers. In addition, nine SNPs were selected to discriminate between varieties by selecting SNPs specific to each variety. We subsequently created a decision tree capable of cross-verifying each variety and classifying the varieties through sequential analysis. Restriction fragment length polymorphism (RFLP) was used for SNP867 and SNP9183 to differentiate between the varieties of Daehwang and Goldensilk and between Kumkang and Daebak, respectively. A tetra-primer amplification refractory (T-ARMS) mutation was used to analyze the remaining SNPs. As a result, we could isolate the same group or select an individual variety using the nine unique SNPs from SNP780 to SNP9183. Furthermore, nucleotide sequence analysis for the region confirmed that the alleles were identical. In conclusion, our results show that combining SNP analysis of the whole silkworm genome with the decision tree is of high value as a discriminative marker for classifying silkworm varieties.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.53-59
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• 2018

In this paper, we propose a sequence mining based manufacturing process using a decision model in cognitive factory. The proposed model is a method to increase the production efficiency by applying the sequence mining decision model in a small scale production process. The data appearing in the production process is composed of the input variables. And the output variable is composed the production rate and the defect rate per hour. We use the GSP algorithm and the REPTree algorithm to generate rules and models using the variables with high significance level through t-test. As a result, the defect rate are improved by 0.38% and the average hourly production rate was increased by 1.89. This has a meaning results for improving the production efficiency through data mining analysis in the small scale production of the cognitive factory.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.859-867
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• 2019

Severe weather conditions, such as heavy snowfall, rain, heatwave, etc., may affect travel behaviors of people and finally change traffic patterns in transportation networks. To deal with those changes and prevent any negative impacts on the transportation system, understanding those impacts of severe weather conditions on the travel patterns is one of the critical issues in the transportation fields. Hence, this study has focused on the impacts of a weather condition on travel patterns of public transportations, especially when a heavy snowfall which is one of the most critical weather conditions. First, this study has figured out the most significant weather condition affecting changes of public transport ridership using weather information, card data for public transportation, mobile phone data; and then, developed a decision-tree model to determine complex inter-relations between various factors such as socio-economic indicators, transportation-related information, etc. As a result, the trip generation of public transportations in Seoul during a heavy snowfall is mostly related to average access times to subway stations by walk and the number of available parking lots and spaces. Meanwhile, the trip attraction is more related to business and employment densities in that destination.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.5
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• pp.1100-1108
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• 2009

The Objectives: The purpose of this study is to identify the factors related to management of DM in Korea. Methods: The subjects selected by using data of National Health and Nutrition Survey(NHANS) in 2005 were 415 adults, aged 20 and older, and diagnosed with DM. This study used data mining algorithms. This study validated the predictive power of data mining algorithms by comparing the performance of logistic regression, decision tree, and Neural Network on the basic of validation, it was found that the model performance of decision tree was the best among the above three techniques. Result: First, awareness of DM was positively associated with age, residential area, and job. The most important factor of DM awareness is age. Awareness rate of DM with 52 age over is 76.1%. Among the ${\geq}52$ age group, an important factor is family history. Among patients who are 52 years or over with family history of DM, an important factor is job. The awareness rate of patients who are 52 age over, family, history of DM, and professionals is 95.0%. Second, treatment of DM was also positively associated with awareness, region, and job. The most important factor of DM treatment is DM awareness. Treatment rate of patients who are aware of DM is 84.8%. Among patients who have awareness of DM, an important factor is region. The awareness rate of patients who are aware of DM in rural area is 10.4%. Conclusion: Finally, the result of analysis suggest that DM management programs should consider group characteristic of DM patients.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1229-1237
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• 2009

In the long-term care insurance(LTCI) system, the question of how to provide the most appropriate care has become a major issue for the elderly, their family, and for policy makers. To help beneficiaries use LTC services appropriately to their needs of care, National Health Insurance Corporation(NHIC) provide them with the individualized care plan, named the Long-term Care User Guide. It includes recommendations for beneficiaries' most appropriate type of care. The purpose of this study is to develop a recommending system for care plan(Res-CP) in LTCI system. We used data set for Long-term Care User Guide in the 3rd long-term care insurance pilot programs. To develop the model, we tested four models, including a decision-tree model in data-mining, a logistic regression model, and a boosting and boosting techniques in an ensemble model. A decision-tree model was selected to describe the Res-CP, because it may be easy to explain the algorithm of Res-CP to the working groups. Res-CP might be useful in an evidence-based care planning in LTCI system and may contribute to support use of LTC services efficiently.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.4
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• pp.23-30
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• 2013

Reliability Centered Maintenance(RCM) is one of most widely used methods in the modern power system to schedule a maintenance cycle and determine the priority of inspection. A precedence study for the new structure of rearranged system should be performed due to introduction of additional installation. This paper proposes a new method to evaluate the priority of maintenance and inspection of the power system facilities. In order to calculate that risk index, it is required that the reliability block diagram should be analyzed for the power system. Additionally, a fault cause analysis is also performed through the event-tree analysis.

• Proceedings of the KIEE Conference
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• summer
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• pp.722-724
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• 2004

Demand estimates in electric power systems have traditionally consisted of time-series analyses over long time periods. The resulting database consisted of huge amounts of data that were then analyzed to create the various coefficients used to forecast power demand. In this research, we take advantage of universally used analysis techniques analysis, but we also use easily available data-mining techniques to analyze patterns of days and special days(holidays, etc.). We then present a new method for estimating and forecasting power flow using decision tree analysis. And because analyzing the relationship between the estimate and power system ceiling Trices currently set by the Korea Power Exchange. We included power system ceiling prices in our estimate coefficients and estimate method.

• IE interfaces
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• v.16 no.spc
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• pp.123-126
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• 2003

In this paper, we use three cluster algorithms (K-means, Self-Organizing Map, and Fuzzy K-means) to find proper graded stock market brokerage commission rates based on the cumulative transactions on both stock exchange market and HTS (Home Trading System). Stock trading investors for both modes are classified in terms of the total transaction as well as the corresponding mode of investment, respectively. Empirical analysis results indicated that fuzzy K-means cluster analysis is the best fit for the segmentation of customers of both transaction modes in terms of robustness. We then propose the rules for three grouping of customers based on decision tree and apply different brokerage commission to be 0.4%, 0.45%, and 0.5% for exchange market while 0.06%, 0.1%, 0.18% for HTS.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.630-644
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• 2021

Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important; education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.