• Journal of The Korean Association of Information Education
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• v.18 no.1
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• pp.57-64
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• 2014

This study aims to suggest lesson plans and evaluation methods for primary pre-service teachers by reviewing the concept of computational thinking(CT) skills and its sub components. To pursue this goal, a literature review has been conducted in regards to CT and the effectiveness of programming courses. In addition, the Scratch educational programming functions were analyzed yielding six CT elements(data representation, problem decomposition, abstraction, algorithm & procedures, parallelization, simulation). With these six elements, one semester lesson plans for 15 weeks that represent the connections with six CT elements were designed. Based on the PECT(Progression of Early Computational Thinking) model and the CT framework a rubric to evaluate learners' proficiency levels(basic, developing, proficient) revealed in their final projects was developed as well. Upon a follow-up empirical study, the lesson plans and the rubric suggested in the current study are expected to be utilized in teachers' colleges.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.83-90
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• 2020

Prototype selection offers the advantage of ensuring low learning time and storage space by selecting the minimum data representative of in-class partitions from the training data. This paper designs a new training data generation method using hyper-rectangles that can be applied to general classification algorithms. Hyper-rectangular regions do not contain different class data and divide the same class space. The median value of the data within a hyper-rectangle is selected as a prototype to form new training data, and the size of the hyper-rectangle is adjusted to reflect the data distribution in the class area. A set cover optimization algorithm is proposed to select the minimum prototype set that represents the whole training data. The proposed method reduces the time complexity that requires the polynomial time of the set cover optimization algorithm by using the greedy algorithm and the distance equation without multiplication. In experimented comparison with hyper-sphere prototype selections, the proposed method is superior in terms of prototype rate and generalization performance.

• Journal of The Korean Association of Information Education
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• v.20 no.6
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• pp.553-562
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• 2016

Although software education pursues developing learners' computational thinking skills, more studies are needed in terms of designing software education lessons to enhance CT skills and to measure the effects of the lessons. This study aims to investigate the effects of a course designed to enhance pre-service teachers' CT skills by using CT-based teaching materials. Through a literature review the study has selected the five core competencies of CT: algorithmic thinking, evaluation, problem decompositions, abstraction, and generalization. The participants of the study are 47 pre-service teachers who took the one-semester course in a national university of education. A survey was developed and conducted and qualitative analyses on the team projects were performed focusing on the core competencies of CT. The results revealed pre-service teachers' perceived degree of experiencing CT and their competencies represented in their projects. The present study provides important implications to future software education programs in terms of designing and implementing of software education.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.343-348
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• 2014

Face recognition system has advanctage to automatically recognize a person without causing repulsion at deteciton process. However, the face recognition system has a drawback to show lower perfomance according to illumination variation unlike the other biometric systems using fingerprint and iris. Therefore, this paper proposed a robust face recogntion method against illumination varition by slective fusion technique using both visible and infrared faces based on fuzzy linear disciment analysis(fuzzy-LDA). In the first step, both the visible image and infrared image are divided into four bands using wavelet transform. In the second step, Euclidean distance is calculated at each subband. In the third step, recognition rate is determined at each subband using the Euclidean distance calculated in the second step. And then, weights are determined by considering the recognition rate of each band. Finally, a fusion face recognition is performed and robust recognition results are obtained.

• Journal of Distribution Research
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• v.10 no.3
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• pp.1-14
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• 2005

It is important to control performance and a Sustainable Collaboration (SC) for the successful Supply Chain Management (SCM). This research developed a control model which analyzed SCM performances based on a Balanced Scorecard (ESC) and an SC using Support Vector Machine (SVM). 108 specialists of an SCM completed the questionnaires. We analyzed experimental data set using SVM. This research compared the forecasting accuracy of an SCMSC through four types of SVM kernels: (1) linear, (2) polynomial (3) Radial Basis Function (REF), and (4) sigmoid kernel (linear > RBF > Sigmoid > Polynomial). Then, this study compares the prediction performance of SVM linear kernel with Artificial Neural Network. (ANN). The research findings show that using SVM linear kernel to forecast an SCMSC is the most outstanding. Thus SVM linear kernel provides a promising alternative to an SC control level. A company which pursues an SCM can use the information of an SC in the SVM model.

• Journal of Educational Research in Mathematics
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• v.15 no.1
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• pp.21-38
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• 2005

The purpose of this research is to develop a probability program based on the actual condition of understanding of probability by the elementary school students from 3rd to 6th grade and search for ways to apply it to the 3rd grade of elementary school students. Based on the results from the research, the author reached a conclusion as following. After applying the learning program to five students of 3rd grade, all of the five students made progress understanding the concept of experimental and theoretical probability. However, for understanding the concept of example space, only two leading students were improved, which shows that students are having much difficulty in understanding the concept. As for under-standing the concept of experimental probability, many students gained the conceptual difference between the experimental and theoretical probability after using the program and enhanced their understanding of experimental probability.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.474-481
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• 2013

The purpose of this study is that pre-service teacher are able to design creative instruction based on NFTM-TRIZ for building up their continuous creative thinking and promoting their creative instruction activities. NFTM-TRIZ is a educational technology system to form and develop creative thinking from child to adult continuously based on TRIZ theory. TRIZ is the thinking technique of creative problem solving that can be the tool of inventory solutions by finding and get over the key of contradiction that is necessary to obtain ideal final results of suggested problems. The subjects for this study were 90 pre-service teachers who are attending third and fourth graders of Teachers' College in G university and are taking 'Curriculum and Educational Evaluation'. The creativity program for this study was carried out for ten minutes at the end of lectures. The verification for this study results were performed two faces. First, pre-service teachers presented teaching and learning plan for one time used 8 Steps' Teaching and Learning Model based on NFTM-TRIZ. Second, researcher got feedback from them about this creative program.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.4
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• pp.964-969
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• 2008

This paper proposes a HMM-based recognition method using DMSVQ(Dynamic Multi-Section Vector Quantization) codebook by DMS(Dynamic Multi-Section) model and fuzzy concept, as a study for speaker- independent speech recognition. In this proposed recognition method, training data are divided into several dynamic section and multi-observation sequences which are given proper probabilities by fuzzy rule according to order of short distance from DMSVQ codebook per each section are obtained. Thereafter, the HMM using this multi-observation sequences is generated, and in case of recognition, a word that has the most highest probability is selected as a recognized word. Other experiments to compare with the results of recognition experiments using proposed method are implemented as a data by the various conventional recognition methods under the equivalent environment. Through the experiment results, it is proved that the proposed method in this study is superior to the conventional recognition methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.73-81
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• 2015

The paper proposes a prototype selection method and evaluates the generalization performance of standard algorithms and prototype based classification learning. The proposed prototype classifier defines multidimensional spheres with variable radii within class areas and generates a small set of training data. The nearest-neighbor classifier uses the new training set for predicting the class of test data. By decomposing bias and variance of the mean expected error value, we compare the generalization errors of k-nearest neighbor, Bayesian classifier, prototype selection using fixed radius and the proposed prototype selection method. In experiments, the bias-variance changing trends of the proposed prototype classifier are similar to those of nearest neighbor classifiers with all training data and the prototype selection rates are under 27.0% on average.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.256-261
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• 2018

There are three types of noise generated inside the vehicle: BSR (Buzz, Squeak, Rattle). In this paper, we propose a classifier that automatically classifies automotive BSR noise by using features extracted from deep convolutional neural networks. In the preprocessing process, the features of above three noises are represented as noise-map using STFT (Short-time Fourier Transform) algorithm. In order to cope with the problem that the position of the actual noise is unknown in the part of the generated noise map, the noise map is divided using the sliding window method. In this paper, internal parameter of the deep convolutional neural networks is visualized using the t-SNE (t-Stochastic Neighbor Embedding) algorithm, and the misclassified data is analyzed in a qualitative way. In order to analyze the classified data, the similarity of the noise type was quantified by SSIM (Structural Similarity Index) value, and it was found that the retractor tremble sound is most similar to the normal travel sound. The classifier of the proposed method compared with other classifiers of machine learning method recorded the highest classification accuracy (99.15 %).