• Proceedings of the KSME Conference
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• 2001.11a
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• pp.853-858
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• 2001

Generally, mixed materials of LDPE, EVA and foaming agent are manufactured by crosslinking foaming or chemical foaming process. Above materials were used in a microcellular foaming injection molding process. Influence of each factor such as injection type, temperature of barrel, rate of mixed materials and contents of foaming agent was estimated by DOE(Design of Experiments). As a result of experiments, injection type and rate of LDPE, EVA have an influence on foaming rate. This data can be used in field of application of LDPE and EVA.

• 한국전산유체공학회:학술대회논문집
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• 2009.04a
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• pp.223-226
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• 2009

This paper presents three-dimensional flow analysis for a mixed-flow pump which consists of a rotor and a stator. Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved by the commercial CFD code CFX 11.0. Structured grid system is constructed in the computational domain, which has O-type grids near the blade surfaces and H-type grids in other regions. Validation of the numerical results was performed with experimental data for head coefficients and hydraulic efficiencies at different flow coefficients. This paper shows that the pump characteristics can be predicted effectively by numerical analysis.

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.25-30
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• 2015

The enormous increase of data with the development of the information technology make internet users to be hard to find suitable information tailored to their needs. In the face of changing environment, the information filtering method, which provide sorted-out information to users, is becoming important. The data on the internet exists as various type. However, similarity calculation algorithm frequently used in existing collaborative filtering method is tend to be suitable to the numeric data. In addition, in the case of the categorical data, it shows the extreme similarity like Boolean Algebra. In this paper, We get the similarity in SNS user's information which consist of the mixed data using the Gower's similarity coefficient. And we suggest a method that is softer than radical expression such as 0 or 1 in categorical data. The clustering method using this algorithm can be utilized in SNS or various recommendation system.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• KIEAE Journal
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• v.10 no.5
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• pp.63-69
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• 2010

Recently, by the increase of demand on Super High-rise mixed-use housing and it's advanced quality, the interest on it has been raised socially. In accordance with it, the matter of resident's health and energy efficiency has been controversial in terms of living in super high-rise housing. This study started from the idea that it is necessary to have an objective data which that has many residents in narrow space with high density. The purpose of this study are as follows; Firstly, with the quantitative data analysis on energy, it will confirm the objective information on the unclear negative idea of super high-rise mixed-use housing. Secondly, it will establish the fundamental data on the energy of super high-rise mixed-use housing by examining the characteristic of energy consumption of the complex which was built more than 5 years ago. There are 4 methods of this study. Firstly, it follows the steps of theoretical view, and defines concept to study on the characteristic of super high-rise mixed-use housing. Secondly, referring to the previous study, it provides better understanding on th stream of this research and the limit as well to guide the direction in terms of energy consumption. Thirdly, it evaluates the characteristic of monthly consumption by researching the use of electricity energy and heating energy of super high-rise mixed-use housing. The major conclusions of this study are as follows; Firstly, the heating use of apartment complex is same, which is not influenced by the type of the building. Secondly, the electricity use of super high-rise mixed-use housing is from 1,2 to 1.5 as high as the normal apartment.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1024-1027
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• 2009

In this letter, we evaluate the classification performance of mixed numeric and categorical data for comparing the efficiency of feature filtering and feature wrapping. Because the mixed data is composed of numeric and categorical features, the feature selection method was applied to data set after discretizing the numeric features in the given data set. In this study, we choose the feature subset for improving the classification performance of the data set after preprocessing. The experimental result of comparing the classification performance show that the feature wrapping method is more reliable than feature filtering method in the aspect of classification accuracy.

• Korean Journal of Community Nutrition
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• v.4 no.1
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• pp.20-29
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• 1999

This study was done to investigate the nutrient intakes and plasma biochemcial indices in 68 female college students according to their skin types. Nutrient intakes were investigated by quick estimation. The plasma TG and total cholesterol levels were measured by the Spotchem sp-4410. The plasma levels of retinol and $\alpha$-tocopherol were measured by HPLC. In addition, the activities of antioxidant defense enzymes such as plasma glutathione peroxidase(GSH-Px) and glutathione reductase(GHS-Rd) were determined. All data were statistically analyzed by SAS PC package program. The results of this study were as follows : The average age, height, weight, BMI, systolic blood pressure and diastolic blood pressure ofthe subjects were $20.9{\pm}1.9yr, 160.7{\pm}4.3cm, 53.0{\pm}7.1kg, 20.5{\pm}2.4kg/m^2, 105.3{\pm}11.5mmHg and 70.6{\pm}7.7mmHg$, respectively. Ten students(14.7%) had normal skin type, 19 students(27.9%) had dry skin type, 11 students(16.2%) had oily skin type, 17 students(25.0%) had acne and 11 students(16.2%) had mixed skin type. The intakes of energy and fats in oily skin group were significantly higher(p<0.05) than those of the dry skin group, but vitamin C intake in the mixed skin group was significantly higher(p<0.05) than those of the dry skin group, but vitamin C intake in the mixed skin group was significantly lower(p<0.05) than that in other skin types. The intakes of other nutrients were not significantly different among skin types. The analysis of lipids showed that the plasma total-cholesterol level of mixed skin group was significantly lower(p<0.05) than that of the oily skin group, whereas other lipid levels were not significantly different. The other parameters such as retinol, $\alpha$-tocopherol, GSH-Px and GSH-Rd of plasma were not significantly different among skin types. Overall results indicate that dietary intake pattern may influence skin type and thereby some blood biochemical indices can be different by skin types.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.173-184
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• 2018

In medical or public health research, it is common to encounter clustered or longitudinal count data that exhibit excess zeros. For example, health care utilization data often have a multi-modal distribution with excess zeroes as well as a multilevel structure where patients are nested within physicians and hospitals. To analyze this type of data, zero-inflated count models with mixed effects have been developed where a count response variable is assumed to be distributed as a mixture of a Poisson or negative binomial and a distribution with a point mass of zeros that include random effects. However, no study has considered a situation where data are also censored due to the finite nature of the observation period or follow-up. In this paper, we present a weighted version of zero-inflated Poisson model with random effects accounting for variable individual follow-up times. We suggested two different types of weight function. The performance of the proposed model is evaluated and compared to a standard zero-inflated mixed model through simulation studies. This approach is then applied to Medicaid data analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.12
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• pp.3265-3276
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• 1996

The studies of document recongnition have been focused mainly on Korean documents. But most of documents composed of Korean and other characters. So, in this paper, we propose the document recognition system that can recognize the multi-size, multi font and mixed type characters. We have utilized a large scale network model, "CombNET" which consists of a 4 layered network with combstructure. And we propose recognition method that can recognize characters without discrimination of character type. The first layer constitutes a Kohonen's SOFM network which quantizes an input feature vector space into several sub-spaces and the following 2-4 layers constitutes BP network modules which classify input data in each sub-space into specified catagories. An experimental result demonstrated the usefulness of this approach with the recognition rates of 95.6% for the training data. For the mixed type character documents we obtained the recognition rates of 92.6% and recognition speed of 10.3 characters per second.

• Structural Engineering and Mechanics
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• v.75 no.5
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• pp.633-642
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• 2020

Artificial neural networks (ANNs) are known as intelligent methods for modeling the behavior of physical phenomena because of it is a soft computing technique and takes data samples rather than entire data sets to arrive at solutions, which saves both time and money. ANN is successfully used in the civil engineering applications which are suitable examining the complicated relations between variables. Functionally graded materials (FGMs) are advanced composites that successfully used in various engineering design. The FGMs are nonhomogeneous materials and made of two different type of materials. In the present study, the bending analysis of functionally graded material (FGM) beams presents on theoretical based on combination of mixed-finite element method, Gâteaux differential and Timoshenko beam theory. The main idea in this study is to build a model using ANN with four parameters that are: Young's modulus ratio (E_t/E_b), a shear correction factor (k_s), power-law exponent (n) and length to thickness ratio (L/h). The output data is the maximum displacement (w). In the experiments: 252 different data are used. The proposed ANN model is evaluated by the correlation of the coefficient (R), MAE and MSE statistical methods. The ANN model is very good and the maximum displacement can be predicted in ANN without attempting any experiments.