• International Journal of Concrete Structures and Materials
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• v.5 no.1
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• pp.29-33
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• 2011

This article employs Least Square Support Vector Machine (LSSVM) for determination of fracture parameters of concrete: critical stress intensity factor ($K_{Ic}^s$) and the critical crack tip opening displacement ($CTOD_c$). LSSVM that is firmly based on the theory of statistical learning theory uses regression technique. The results are compared with a widely used Artificial Neural Network (ANN) Models of LSSVM have been developed for prediction of $K_{Ic}^s$ and $CTOD_c$, and then a sensitivity analysis has been performed to investigate the importance of the input parameters. Equations have been also developed for determination of $K_{Ic}^s$ and $CTOD_c$. The developed LSSVM also gives error bar. The results show that the developed model of LSSVM is very predictable in order to determine fracture parameters of concrete.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6B
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• pp.888-894
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• 2010

For the indoor positioning, wireless fingerprinting is most favorable because fingerprinting is most accurate among the technique for wireless network based indoor positioning which does not require any special equipments dedicated for positioning. The deployment of a fingerprinting method consists of off-line phase and on-line phase and more efficient and accurate methods have been studied. This paper proposes a bayesian algorithm for wireless fingerprinting and indoor location determination using fuzzy clustering with bayesian learning as a statistical learning theory.

• Journal of The Korean Association of Information Education
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• v.18 no.4
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• pp.559-566
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• 2014

This is to understand the neurological dynamic cognitive processes of math learning based on the abstract mappings( level A2), abstract systems(level A3), and single principles(level A4), which are principles of Fischer's cognitive development theory. Math learning requires flexibility to adapt existing brain function in selecting new neurophysiological activities to learn desired knowledge. This study suggests a general statistical framework for the identification of neurological patterns in different abstract learning change with optimal support. We expected that functional brain networks derived from a simple math learning would change dynamically during the supportive learning associated with different abstract levels. Task based patterns of the brain structure and function on representations of underlying connectivity suggests the possible prediction for the success of the supportive learning.

• Journal of the Korean Society for information Management
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• v.17 no.4
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• pp.27-45
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• 2000

National statistical data such as Korean Census is fundamental data for national administration. In this paper, we present an automatic coding system utilizing morphological analyser and knowledge dictionaries. Knowledge bases are constructed based on an authority dictionaries which were developed by authors utilizing a newly learning theory. Test data indicates 99.5% of productivity and 83.3% of accuracy. The presented methods can be effectively applied to analyze statistical information.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.

• Journal of Elementary Mathematics Education in Korea
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• v.7 no.1
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• pp.65-86
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• 2003

Mathematics textbooks are substitutive showing real characters of didactic transposition in pseudo-contextualization and pseudo-personalization. This study analyzed statistical graphs in elementary school mathematics textbooks according to the first to the 7th curriculum in Korea. It focused on the didactic principles used in those methods through those view of Didactic Transposition Theory. The features of the elementary school mathematics textbooks in Korea are investigated and described ethnomethodologically according to each curriculum periods in dividing bar graph, line graph, pictograph, graph of ratio, histogram. The teaching sequences and methods of the statistical graphs, order and methods of sub-learning activities, teaming data, matter of the learning activity indicator were summarized. Usually, the teaching sequences, excepting the graphs of ratio, statistical graphs are introduced in the second semester of each grade. The graph of ratio is introduced in the first semester of 6th grade. As a result of analysing sub-Loaming activities, using them increased from the first to the 7th curriculum and its form was fixed constructive and stable at the 4th curriculum textbooks. As a result of analysing the teaming data, the data of the social aspects are used more frequently and the data of the individual preferences trended more gradually. As a result of analysing the matter of the teaming activity indicators, concept-explanation question style were used more frequently. Statement-practice style and consideration style trended gradually. Concluding remarks are: First, the didactic transposition of the elementary school mathematics textbooks developed systematically according to the first to the 7th curriculum; Second, mathematics textbooks gradually introduced the positive learning style of activity and the learners' spontaneousness; Third, more concrete practice activities and reflective activities were variously introduced considering the level and interest of each elementary student.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.257-264
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• 2014

This paper presents the core competencies diagnosis system which targeted our collegiate students in an attempt to induce the core competencies for reinforcing the learning and employment capabilities. Because these days data give rise to a high level of redundancy and dimensionality with time complexity, they are more likely to have spurious relationships, and even the weakest relationships will be highly significant by any statistical test. So as to address the measurement of uncertainties from the classification of categorical data and the implementation of its analytic system, an uncertainty measure of rough entropy and information entropy is defined so that similar behaviors analysis is carried out and the clustering ability is demonstrated in the comparison with the statistical approach. Because the acquired and necessary competencies of the collegiate is deduced by way of the results of the diagnosis, i.e. common core competencies and major core competencies, they facilitate not only the collegiate life and the employment capability reinforcement but also the revitalization of employment and the adjustment to college life.

• School Mathematics
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• v.17 no.2
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• pp.203-222
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• 2015

This study analyzed the classroom discourse between teacher and students based on the Mehan(1979a)'s theory to examine the characteristics of the classroom discourse between teacher and students in high school statistics class. The results of this study on the structure of class showed that the statistics class in this study adopted knowledge transmission-oriented teacher-led class in which the framework of introductiondevelopment- arrangement, which is Mehan's basic 3 stages, is clearly represented. The results of examining I-R-E sequence showed that $I_T-R_T$ structure, in which the teacher asks questions and the teacher talks about the answer, frequently appeared. And the statistics class in this study was monological class in which students hardly participated. Through these results of this study, it was found that teacher should form the statistical context, in which students can participate in discourse, and build discourse learning community and induce argumentational discourse through metaprocess elicitation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.229-233
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• 2005

모집단의 최적군집 수를 자동으로 결정하고 군집내의 분산은 최소로 하고 군집 간의 분산은 최대로 하는 최적 군집화에 대한 연구는 대부분의 지능형 시스템에서 필요로 하는 모형전략이다. 하지만 아직도 대부분의 군집화 과정에서 분석가의 주관적인 경험에 의존하여 군집수가 결정되어 군집화가 이루어지고 있다. 예를 들어 K-평균 군집화 알고리즘에서도 초기에 K 값을 결정해 주어야 한다. 모집단을 제대로 대표하지 못한 K 값에 의한 군집화 결과는 심각한 오류를 범하게 된다. 본 논문에서는 통계적 학습이론을 이용하여 이러한 문제점을 해결하려고 하였다. VC-차원에 의한 Support Vector를 이용하여 최적의 군집화 기법을 제안하였다. 제안 방법의 성능 평가를 위하여 UCI 기계학습 데이터를 이용하여 객관적인 실험을 수행하였다.