• New & Renewable Energy
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• v.9 no.2
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• pp.40-50
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• 2013

Government spending on research and development increased continuously is much more important to decision-making methodology for rational investment. Rely on a group of minority experts in the application of a general methodology, a tipping effect occur in specific technology field or difficult balanced procedure and objective control to maintain. This paper presents a qualitative-quantitative methodology to avoid such risks by utilizing Technology-Tree pertaining to energy R&D planning of the government Energy Technology Development program. Especially Energy Technology Development program "energy storage system" is applied to the analysis of Technology-Tree, mapping and analysis of existing government-supported projects during the recent 5 years, is derived essential missing elements of the technology value chain. This study suggests that significant evidence is utilized for improving efficiency of government R&D budget considering the importance of technology, domestic research-based and so forth, could be used to implement the R&D project planning.

• Child Health Nursing Research
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• v.25 no.2
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• pp.95-102
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• 2019

Purpose: The purpose of this study was to identify factors associated with career maturity among Korean high school students. Methods: A descriptive cross-sectional design was adopted using secondary data from the 2012 Korean Welfare Panel Study (KoWePS). The participants were 496 high school students who completed the supplemental survey for children, which included items on career maturity, self-esteem, study stress, teacher attachment, relationship with parents, peer attachment, depression and anxiety. Descriptive statistics, the chi-square-test, the t-test, and a decision tree were used for data analysis. Results: The decision tree identified five final nodes predicting career maturity after forcing self-esteem as the first variable. The highest predicted rate of high career maturity was associated with high self-esteem, experience of career counseling, and high teacher attachment. The lowest predicted rate of high career maturity was associated with low self-esteem and low attachment to friends. Conclusion: Factors influencing career maturity were varied by levels of self-esteem in Korean high school students. Thus, it is necessary to develop different approaches to enhance career maturity according to levels of self-esteem.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.429-436
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• 2013

Context aware environment u-health service is to provide health service with recognition of a computer. The computer recognizes that a patient can contact real life in many context. Context aware environment service for recommend have to definition of context data and service recommendations related to factors shall be identified. In this paper, Context aware environment of u-health service will be provide context data related to identifies recommendations factors using multivariate analysis method and recommendations factors creation to decision tree, association rule based decision model. health service recommend for significantly context data can be distinguish through recommendation factors of identify. Also, context data of patient can know preference factors through preference decision model.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.11 no.1
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• pp.130-145
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• 2007

Background and purpose: The purpose of this study is to search recognition patients in Eight constitution Oriental Medical Clinic. And we compare Eight constitution acupuncture methods with the another acupuncture methods. Methods: The subjects were comprised of 200 volunteers. In 3 Eight constitution Oriental Medical Clinic participants were chosen through questionnaire. Finishing answer participants put in their lacked name questionnaire to gathering box. DecisionTree (AnswerTree 3.0 Ver.) statistical software was used for statistical analysis. Results and Conclusion: As a result of the analysis of cognition to Eight constitution acupuncture methods was influenced to patients health, dietetic therapy is best influenced. Next influenced acupuncture reflex degree, age, job, constitution, cure periods, sex distinction, cure degree, diagnosed participant's Constitution by pulse diagnosis in 8 Constitution Medicine.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1155-1166
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• 2021

In this study, statistical analysis was performed to suggest the optimal analysis techniques for the main measuring instruments of the fill dam, such as seepage, crest settlement, and porewater pressure gauge. In addition, correlation analysis with water level and rainfall data was performed. Based on the results of descriptive statistical analysis for each instrument, porewater pressure gauges could be classified into 3 groups or 2 groups through principal component analysis, In the case of the group having a high correlation with the water level instrument, the correlation between the gauges was also large. In the case of seepage instrument, the distribution showed an extremely asymmetric distribution, so for quantitative analysis, the average seepage during non-precipitation and precipitation could be estimated through decision tree analysis. In the case of the crest settlement instrument, the correlation analysis showed that the correlation between the gauges was large, but the relationship with the water level instrument did not show a significant linear relationship, so EMD analysis was performed to analyze it in more detail. It is judged that principal component analysis, decision tree analysis, and data filtering method can be applied to analyze the behavior of pore water pressure meters, seepage, and crest settlement instrument as major measurement items of fill dam.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• 한국ITS학회:학술대회논문집
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• 2010.05a
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• pp.196-203
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• 2010

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.545-558
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• 2022

The purpose of this study is to explore a prediction model for accurately predicting Financial Difficulties of Chinese Cultural Industry Enterprises through Traditional Statistics and Machine Learning. To construct the prediction model, the data of 128 listed Cultural Industry Enterprises in China are used. On the basis of data groups composed of 25 explanatory variables, prediction models using Traditional Statistical such as Discriminant Analysis and logistic as well as Machine Learning such as SVM, Decision Tree and Random Forest were constructed, and Python software was used to evaluate the performance of each model. The results show that the Random Forest model has the best prediction performance, with an accuracy of 95%. The SVM model was followed with 93% accuracy. The Decision Tree model was followed with 92% accuracy.The Discriminant Analysis model was followed with 89% accuracy. The model with the lowest prediction effect was the Logistic model with an accuracy of 88%. This shows that Machine Learning model can achieve better prediction effect than Traditional Statistical model when predicting financial distress of Chinese cultural industry enterprises.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.311-319
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• 2015

In this paper, in order to increase the production efficiency of the industrial plant, and predicts the resources of the manufacturing process, we have proposed a decision-making system for resource implementing the risk management effectively forecasting and risk management. A variety of information that occurs at each step efficiently difficult the creation of detailed process steps in the scenario you want to manage, is a frequent condition change of manufacturing facilities for the production of various products even within the same process. The data that is not contiguous products production cycle also not constant occurs, there is a problem that needs to check the variation in the small amount of data. In order to solve these problems, data centralized manufacturing processes, process resource prediction, risk prediction, through a process current status monitoring, must allow action immediately when a problem occurs. In this paper, the range of change in the design drawing, resource prediction, a process completion date using a regression algorithm to derive the formula, classification tree technique was proposed decision system in three stages through the boundary value analysis.

• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.59-67
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• 2005

The main objective of the statistical analysis about industrial accidents is to find out what is the dangerous factor in its own industrial field so that it is possible to prevent or decrease the number of the possible accidents by educating those who work in the fields for safety tools. However, so far, there is no technique of quantitative evaluation on danger. Almost all previous researches as to industrial accidents have only relied on the frequency analysis such as the analysis of the constituent ratio on accidents. As an application of data mining technique, this paper presents analysis on the efficiency of the CHAID algorithm to classify types of industrial accidents data and thereby identifies potential weak points in accident risk grouping.