• IE interfaces
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• 제13권2호
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• pp.211-216
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• 2000

A methodology that determines the optimal shutdown time of a nuclear power plant is suggested. The shutdown time is decided considering the trade off between the cost of accident and the loss of profit due to the early shutdown. We adopt the bayesian approach in manipulating the model parameter that predicts the accidents. We build decision tree models and apply dynamic programming approach to decide whether to shutdown immediately or operate one more period. The branch parameters in decision trees are updated by bayesian approach. We apply real data to this model and provide the cost of accidents that guarantees the immediate shutdown.

• Korean Journal of Organic Agriculture
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• 제21권4호
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• pp.589-600
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• 2013

The Purpose of this paper is to analysis on consumer's purchasing behavior and intention of local food. To analysis consumer's purchasing behavior, a series of homemaker surveys were conducted. The sample size of the survey is 416 respectively. As a survey result, consumer's purchasing behavior shows that purchasing ratio of local food and buying place is various type. By decision tree model analysis showed that consumer's purchasing intention is enough to establishing local food system in region. Therefore, strategies for regional consumption are needed expression of the place city and county of origin, diversification of purchasing item and buying area, and sustainable improvement for safety and trust on local food.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2004년도 춘계학술대회
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• pp.141-149
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• 2004

The various methods have been studied to develop discriminant model for Pregnancy Induced Hypertension(PIH) as high risk pregnant. In this study, we adapt the approximate entropy which is the non-linear chaotic measuring method. Then, we develop the system to discriminant PIH pregnant using QUEST with S-PLUS.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.961-968
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• 2018

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

• The Korean Journal of Applied Statistics
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• 제25권6호
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• pp.933-945
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• 2012

In this paper we used the KLIPS(Korean Labor and Income Panel tudy) data that surveyed from 2006(wave 9) to 2009(wave 12). Other previous studies are concerned with the panel attrition in the early wave, but this study classifies the response pattern and investigates some factors that influence panel attrition when the panel tends to stabilize. It was revealed that panel attrition was influenced by relocation and housing type through the logit model. Besides it was appeared that panel attrition was affected by the monthly living expenses and the overall household income through the decision tree.

• Environmental Engineering Research
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• 제23권4호
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• pp.430-441
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• 2018

The adverse health impact of air pollution is becoming more serious. The purpose of this study is twofold: One is to analyze the effect of air pollution and temperatures on human health by analyzing the number of deaths from cardiovascular disease in Seoul, Korea; the other is to determine what impact the location of a monitoring site has on the results of a health study. For this latter purpose, air pollution and temperature monitors are sited at three locations termed green, public, and residential. Then, a decision tree model is used to analyze factors linked with deaths occurring at each monitoring site. The results show that the environmental temperatures before death and the $PM_{2.5}$ concentrations on the day of death are highly linked with the number of deaths regardless of the monitoring location. However, results are most accurate with residential data. The results of this study can be used as base data for a similar analysis and ultimately, as a guide to minimize the health impact of air pollution.

• 대한상한금궤의학회지
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• 제14권1호
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• pp.41-50
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• 2022

Objective : The purpose of this study is to explore the most suitable machine learning model algorithm for Shanghanlun diagnostic system classification using natural language processing (NLP). Methods : A total of 201 data items were collected from 『Shanghanlun』 and 『Clinical Shanghanlun』, 'Taeyangbyeong-gyeolhyung' and 'Eumyangyeokchahunobokbyeong' were excluded to prevent oversampling or undersampling. Data were pretreated using a twitter Korean tokenizer and trained by logistic regression, ridge regression, lasso regression, naive bayes classifier, decision tree, and random forest algorithms. The accuracy of the models were compared. Results : As a result of machine learning, ridge regression and naive Bayes classifier showed an accuracy of 0.843, logistic regression and random forest showed an accuracy of 0.804, and decision tree showed an accuracy of 0.745, while lasso regression showed an accuracy of 0.608. Conclusions : Ridge regression and naive Bayes classifier are suitable NLP machine learning models for the Shanghanlun diagnostic system classification.

• Journal of the Korea Safety Management & Science
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• 제12권3호
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• pp.333-339
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• 2010

Data mining is the process of finding and analyzing data from a big database and summarizing it into useful information for a decision-making. A variety of data mining techniques have been being used for wide range of industries. One application of those is especially so for gathering meaningful information from process data in manufacturing factories for quality improvement. The purpose of this paper is to provide a methodology to improve manufacturing quality of fuel tanks which are auto-parts. The methodology is to analyse influential attributes and establish a model for optimal manufacturing condition of fuel tanks to improve the quality using decision tree, association rule, and feature selection.

• Journal of Korea Artificial Intelligence Association
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• 제1권2호
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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.

• Journal of the Korean Data and Information Science Society
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• 제23권5호
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• pp.949-959
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• 2012

This article is to find the right size of decision trees that performs better for boosting algorithm. First we defined the tree size D as the depth of a decision tree. Then we compared the performance of boosting algorithm with different tree sizes in the experiment. Although it is an usual practice to set the tree size in boosting algorithm to be small, we figured out that the choice of D has a significant influence on the performance of boosting algorithm. Furthermore, we found out that the tree size D need to be sufficiently large for some dataset. The experiment result shows that there exists an optimal D for each dataset and choosing the right size D is important in improving the performance of boosting. We also tried to find the model for estimating the right size D suitable for boosting algorithm, using variables that can explain the nature of a given dataset. The suggested model reveals that the optimal tree size D for a given dataset can be estimated by the error rate of stump tree, the number of classes, the depth of a single tree, and the gini impurity.