• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.739-752
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• 2018

Purpose: The purpose of this study was to investigate the relationship between kiln processing parameters and NOx emissions that occur in the sintering and calcination steps of the cement manufacturing process and to derive the main factors responsible for producing emissions outside emission limit criteria, as determined by category models and classification rules, using data mining techniques. The results from this study are expected to be useful as guidelines for NOx emission control standards. Methods: Data were collected from Precalciner Kiln No.3 used in one of the domestic cement plants in Korea. Thirty-four independent variables affecting NOx generation and dependent variables that exceeded or were below the NOx emiision limit (>1 and <0, respectively) were examined during kiln processing. These data were used to construct a detection model of NOx emission, in which emissions exceeded or were below the set limits. The model was validated using SPSS MODELER 18.0, artificial neural network, decision treee (C5.0), and logistic regression analysis data mining techniques. Results: The decision tree (C5.0) algorithm best represented NOx emission behavior and was used to identify 10 processing variables that resulted in NOx emissions outside limit criteria. Conclusion: The results of this study indicate that the decision tree (C5.0) can be applied for real-time monitoring and management of NOx emissions during the cement firing process to satisfy NOx emission control standards and to provide for a more eco-friendly cement product.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.1
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• pp.23-28
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• 2002

Sets of weather variables for estimation of LWD were evaluated using CART(Classification And Regression Tree) models. Input variables were sets of hourly observations of air temperature at 0.3-m and 1.5-m height, relative humidity(RH), and wind speed that were obtained from May to September in 1997, 1998, and 1999 at 15 weather stations in iowa, Illinois, and Nebraska, USA. A model that included air temperature at 0.3-m height, RH, and wind speed showed the lowest misidentification rate for wetness. The model estimated presence or absence of wetness more accurately (85.5%) than the CART/SLD model (84.7%) proposed by Gleason et al. (1994). This slight improvement, however, was insufficient to justify the use of our model, which requires additional measurements, in preference to the CART/SLD model. This study demonstrated that the use of measurements of temperature, humidity, and wind from automated stations was sufficient to make LWD estimations of reasonable accuracy when the CART/SLD model was used. Therefore, implementation of crop disease-warning systems may be facilitated by application of the CART/SLD model that inputs readily obtainable weather observations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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• pp.676-682
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• 2014

In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.594-600
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• 2017

The new design methodology of prediction model and pattern classification, which is based on the dimension reduction algorithm called principal component analysis, is introduced in this paper. Principal component analysis is one of dimension reduction techniques which are used to reduce the dimension of the input space and extract some good features from the original input variables. The extracted input variables are applied to the prediction model and pattern classifier as the input variables. The introduced prediction model and pattern classifier are based on the very simple regression which is the key point of the paper. The structural simplicity of the prediction model and pattern classifier leads to reducing the over-fitting problem. In order to validate the proposed prediction model and pattern classifier, several machine learning data sets are used.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.641-648
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• 2022

This paper is a study to find out the factors affecting the defects that occur during the use of Electron Beam Melting (EBM), one of the 3D printer output methods, through data analysis. By referring to factors identified as major causes of defects in previous studies, log files occurring between processes were analyzed and related variables were extracted. In addition, focusing on the fact that the data is time series data, the concept of a window was introduced to compose variables including data from all three layers. The dependent variable is a binary classification problem with the presence or absence of defects, and due to the problem that the proportion of defect layers is low (about 4%), balanced training data were created through the SMOTE technique. For the analysis, I use XGBoost using Gridsearch CV, and evaluate the classification performance based on the confusion matrix. I conclude results of the stuy by analyzing the importance of variables through SHAP values.

• Journal of the Korean Home Economics Association
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• v.44 no.3 s.217
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• pp.13-25
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• 2006

In the information age, the spread of internet has a lot of positive aspects. However, it also has very serious problems with adverse effects on immature teenagers both physically and mentally. Among them, internet addiction has recently grown into a social problem. The purpose of this study is to examine the relationship between the degree of internet addiction of teenagers and the related variables, such as individual variables, using internet variable, parent-child relationship variable, and psychological variable. The survey subjects were 452 high school students in the Gimhae area. The results were as follows: First, the average score of internet addiction among the teenagers of this study was 48.24 out of 100. Which according to Young's classification, corresponds to an early stage addiction. Second, there was a significant difference in the degree of internet addiction by students' school record. The low-graded group was highly addicted to the internet. Third, the longer the teenagers were exposed to the internet, the higher they were addicted. Fourth, the degree of internet addiction was influenced by parent-child relationship. There was low addiction in a group with their parents' high support. Fifth, the degree of internet addiction was differed by psychological variables, such as self-control, self-esteem and depression. Low self-control, low self-esteem and highly depressed teenagers were related to a higher degree of internet addiction.

• Journal of the Korean Society for information Management
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• v.33 no.2
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• pp.103-133
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• 2016

The valuation of patented technology has been recently emphasized, and the patent citation is known as an important factor. This study performed a generalized linear model to find variables that effect the patent citation. We classified 13 variables as morphological, technological and conceptual factors and used them to find out effective variables in 14 medical devices classification. Through the empirical study, we found seven effective variables (assignee nationality, assignee character, the number of inventors, the number of application countries, the number of IPC, the number of references, the strength of bibliographic coupling). In order to apply to Korean industry, this study has significance that provides basic research to citation analysis model.

• Journal of Families and Better Life
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• v.6 no.1
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• pp.51-69
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• 1988

This study was mainly concerned with providing a basis of the development of low-income consumer education program. The purpose of this study was to investigate the level of their consumer skill and the degree of consumer problems' perception. For the purpose of this study, 480 questionnaire were distributed to the housewives in Seoul. The 320 data were analyzed by Frequency, Percentage, ANOVA, Duncan's Multiple Rang Test, Multiple Classification Analysis, and Pearson's Correlation . The major findings of this study were as follows; 1) The socio-demographic variables indicating significant relation to the level of consumer skill are income level, housewife's age , and housewife's education level. The independent influence of all the variables affecting consumer skill was analyzed. It result is in confirming the income as the most influential one. 20 The socio-demographic variables indicating significant relation to the degree of consumer problem's perception are income level, and housewife's education level. The independent influence of all the variables affecting consumer problem's perception was analyzed. It results in confirming the income as the most influential one. 3) Consumer skill level differs significantly at the 0.001 level, according toe the degree of consumer problems' perception. The higher level of consumer skill, the lower degree of consumer problems' perception. The result of this study implies that a consumer education should be given to the low-income consumers, on the basis of their consumer skill level. Further research regarding the consumer skill and problems ' perception of the low-income consumers should be conducted.

• Membrane and Water Treatment
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• v.2 no.2
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• pp.105-119
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• 2011

This study presents the tests of an Immersed Membrane BioReactor (IMBR) equipped with a draft tube and focuses on the influence of hydrodynamic conditions on membrane fouling in a pilot-scale using a hollow fiber membrane module of ZW-10 under ambient conditions. In this system, the cross-flow velocities across the membrane surface were induced by a cylindrical draft-tube. The relationship between cross-flow velocity and aeration strength and the influence of the cross-flow on fouling rate (under various hydrodynamic conditions) were investigated using Multi-Dimension Scaling (MDS) analysis. MDS technique is especially suitable for samples with many variables and has relatively few observations, as the data about Membrane Bio-Reactor (MBR) often is. Observations and variables are analyzed simultaneously. According to the results, a specialized form of MDS, CoPlot enables presentation of the results in a two dimensional space and when plotting variables ratio (output/input) rather than original data the efficient units can be visualized clearly. The results indicate that: (i) aeration plays an important role in IMBR performance; (ii) implementing the MDS approach with reference to the variables ratio is consequently useful to characterize performance changes for data classification.