• Journal of The Korean Association For Science Education
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• v.20 no.2
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• pp.297-306
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• 2000

The purpose of this study was to examine the relationship among an achievement, a meaningful learning orientation and a reasoning ability. 149 third grade middle school students were tested. The achievement test was designed to measure students' interrelated understanding of genetics. A modified LPQ(Learning Process Questionnaires) was used to measure students' meaningful learning orientation. Students' reasoning ability were identified by the short version GALT(Group Assessment of Logical Thinking). Correlations between different variables were examined. Regression analyses were conducted to examine the predictive influence of meaningful learning orientation and reasoning ability on the achievement of students. And ANCOVA was used to identify the interaction of these variables on students' achievement. Students did not understand well enough the concepts of genetics. Meaningful learning orientation indicated a significant gender difference. Girls tend to do more meaningful learning than boys(p<.05). Many students(48.76%) were at the transitional cognitive level. Results of correlations indicated that students' attainment of meaningful understanding was significantly and positively related with a meaningful learning orientation and a reasoning ability. But there was no significant correlation between students' meaningful learning orientation and reasoning ability. Regression analyses indicated that learning orientation and reasoning ability were able to predict the achievement of students. They predicted better on solving genetics problem than understanding genetics problem. Results of ANCOVA showed that the test scores of genetics were significantly different according to not only learning orientation levels but also cognitive levels. But, there was no interaction between learning orientation and cognitive levels. Within the transitional and formal cognitive level, the meaningful learners performed significantly better than the rote learners on the test of genetics.

• Korean Journal of Childcare and Education
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• v.6 no.1
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• pp.67-93
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• 2010

The purpose of the present study was to examine how 25~36-month-old toddlers' temperament, mother's rearing attitude, teacher interaction relates to their language abilities and to analyze the predictive power of these variables in predicting language ability. The subjects were 206 toddlers, 206 mothers and 38 class teachers at child care center in G metropolitan city. The collected data were analyzed using Pearson's correlation analysis and stepwise multiple regression analysis on SPSS program. The results of this study were as follow. First, there was a significant relationship between mother's rearing attitude, teacher interaction and toddlers language abilities. However, toddlers' temperament showed no significant correlations with toddlers language abilities. Second, teacher's positive attitude was the best predictor of toddlers language abilities. Toddlers' reactivity and mother's rational guidance were also predictors of toddlers' language abilities.

• Journal of Korean Medicine Rehabilitation
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• v.26 no.3
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• pp.109-117
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• 2016

Objectives The purpose of this study was to investigate the correlation between walking ability of lumbar spinal stenosis patients and the cross-sectional area (CSA) of lumbar paraspinal muscles. Methods This study was carried out on 62 lumbar spinal stenosis patients who had limited walking abilities because of neurogenic claudication (NC). All patients received more than 2 weeks of complex treatment at Mokhuri Neck&Back Hospital. CSA of lumbar paraspinal muscles was measured from axial T2-weighted MRI and divided by CSA of adjacent vertebral body to avoid influence of body statues (RCSA-Relative CSA). Pain Free Walking Distance and Numerical Rating Scale (NRS) was measured before and after treatment. Results The Pain Free Walking Distance had significantly increased in patients who had bigger RCSA of psoas muscle (r=0.313, p<0.05). Conclusions The psoas muscle can be a predictive factor for restoring walking ability of lumbar spinal stenosis patients who have limitations walking.

• English Language & Literature Teaching
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• v.13 no.3
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• pp.1-22
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• 2007

The study examined the effects of explicit derivational morphology instruction (henceforth DMI) on the incidental vocabulary acquisition and reading comprehension of 132 Korean 1st-year high school students who responded to a battery of tests (two vocabulary tests and a reading comprehension test). Multiple statistical tools were used to analyze the data: Analysis of Covariance (ANCOVA), Analysis of Variance (ANOVA), Simple Regression Analysis, Tests of Simple Main Effects, and effect size computation using Cohen's d. The results indicated that (a) DMI enhanced students' ability to infer word meanings in context, (b) DMI promoted high proficiency students' reading comprehension, whereas it impeded intermediate proficiency students' reading comprehension, (c) vocabulary knowledge has a strong positive predictive value for reading comprehension, and (d) the gaps of vocabulary knowledge across proficiency levels were still substantial, despite the observation that DMI promoted students' vocabulary acquisition. These results have a bearing on English as Foreign Language (EFL) reading pedagogy.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.159-167
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• 2000

In optimization problems, computationally intensive or expensive simulations hinder the use of standard optimization techniques because the computational expense is too heavy to implement them at each iteration of the optimization algorithm. Therefore, those expensive simulations are often replaced with approximation models which can be evaluated nearly free. However, because of the limited accuracy of the approximation models, it is practically impossible to find an exact optimal point of the original problem. Significant efforts have been made to overcome this problem. The approximation models are sequentially updated during the iterative optimization process such that interesting design points are included. The interesting points have a strong influence on making the approximation model capture an overall trend of the original function or improving the accuracy of the approximation in the vicinity of a minimizer. They are successively determined at each iteration by utilizing the predictive ability of the approximation model. This paper will focuses on those approaches and introduces various approximation methods.

• Journal of KSNVE
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• v.6 no.3
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• pp.325-332
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• 1996

One of the greatest shortcoming in today's predictive maintenance program is the ability to diagnose the mechanical and electrical problems within the machine when the vibration exceeds preset overall and spectral alarm levels. In this study, auto-diagnosis system is constructed by using A/D converter to convert analog to digital singal. With this device the system analyses input signal to diagonosis machine condition. Many plots, which display machine condition, and input values of every channel are calculated in this system. If the falut is found, the system diagnoses automatically using fuzzy algorithm and trend monitoring. Prediction is also performed by the grey system theory. Operator finds out eh machine operating condition intuitively based on with personal computer CRT in using this system.

• Molecular & Cellular Toxicology
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• v.4 no.2
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• pp.170-176
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• 2008

The quantitative structure-activity relationship (QSAR) of a set of 35 flavonoid compounds presenting antioxidant activity was established by means of Genetic Algorithm-Multiple Linear Regression (GA-MLR) technique. Four-parametric models for two sets of data, the 1,1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging activity $(R^2=0.788,\;Q^2_{cv}=0.699\;and\;Q^2_{ext}=0.577)$ and scavenging activity of reactive oxgen species (ROS) induced by $H_2O_2 (R^=0.829,\;Q^2_{cv}=0.754\;and\;Q^2_{ext}=0.573)$ were obtained with low external predictive ability on a mass basis, respectively. Each model gave some different mechanistic aspects of the flavonoid compounds tested in terms of the radical scavenging activity. Topological charge, H-bonding complex and deprotonation processes were likely to be involved in the radical scavenging activity.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.2000-2013
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• 2016

The purpose of this study is to validate the validity of the methodology for analyzing the space with complex characteristics and to evaluate the existing spatial structure analysis theory. Seven example models are designed and analyzed data of spatial syntax analysis and visibility graph analysis. And analyzed the agent-based model using two analytical methods: the adjacent space and the whole spatial connection. The results of this study are as follows. Based on the analysis of the agent - based model for perfectly freewalking, the validity of the method is verified in terms of predictive ability and effectiveness. Agent-based models can be simulated considering various variables, so realistic predictions will be possible and a new biography of complex systems can be met.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1962-1963
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• 2011

본 연구에서는 패턴분류를 위해 최적화된 방사형 기저 함수 신경회로망(Radial Basis Function Neural Networks) 분류기를 제안한다. RBFNN은 입력층, 은닉층, 출력층의 3층 구조로 되어 있으며 Multi Dimension, Predictive ability, Robustness한 특징이 있다. RBFNN의 은닉층에는 기존의 활성함수가 아닌 Fuzzy C-means 클러스터링 알고리즘을 사용하여 입력 데이터의 특성을 고려한 적합도를 사용하였다. RBFNN은 은닉층의 노드수와 FCM 클러스터링의 퍼지화 계수, 연결가중치의 다항식 타입이 모델의 성능의 향상에 영향을 미치기 때문에 최적화가 필요하며 본 논문에서는 Differential Evolution(DE) 알고리즘을 사용하여 모델의 구조 및 파라미터를 최적화시켜 모델의 성능을 향상시켰다. 제안된 모델을 평가하기 위해 패턴분류에 많이 사용되는 Iris 데이터와 Wine 데이터를 이용하였다.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.393-402
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• 2013

The Mahalanobis-Taguchi System (MTS) is a diagnostic and predictive method for multivariate data. In the MTS, the Mahalanobis space (MS) of reference group is obtained using the standardized variables of normal data. The Mahalanobis space can be used for multi-class classification. Once this MS is established, the useful set of variables is identified to assist in the model analysis or diagnosis using orthogonal arrays and signal-to-noise ratios. And other several techniques have already been used for classification, such as linear discriminant analysis and logistic regression, decision trees, neural networks, etc. The goal of this case study is to compare the ability of the Mahalanobis-Taguchi System and logistic regression using a data set.