• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.45-52
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• 2008

As the recent development of the IT services, there is a urgent need of effective diagnosis system to present appropriate solution for the complicated problems of breakdown control, a cause analysis of breakdown and others. So we propose an intelligent diagnosis system that integrates the case-based reasoning and the artificial neural network to improve the system performance and to achieve optimal diagnosis. The case-based reasoning is a reasoning method that resolves the problems presented in current time through the past cases (experience). And it enables to make efficient reasoning by means of less complicated knowledge acquisition process, especially in the domain where it is difficult to extract formal rules. However, reasoning by using the case-based reasoning alone in diagnosis problem domain causes a problem of suggesting multiple causes on a given symptom. Since the suggested multiple causes of given symptom has the same weight, the unnecessary causes are also examined as well. In order to resolve such problems, the back-propagation learning algorithm of the artificial neural network is used to train the pairs of the causes and associated symptoms and find out the cause with the highest weight for occurrence to make more clarified and reliable diagnosis.

• Journal of Gifted/Talented Education
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• v.22 no.4
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• pp.943-966
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• 2012

The purpose of this research was to compare the leadership characteristics and emotional intelligence between scientifically gifted middle school students and general students and to investigate the emotional intelligence's effects on leadership characteristics. For this study, 150 scientifically gifted middle school students and 130 general students were participated. The results obtained from this study were as follows. First, the total score of leadership characteristic and sub-domains of leadership characteristic showed significant difference. The leadership characteristic of the gifted students turned out to be significantly higher than that of general students. Investigating gender difference, it showed that the score of girls significantly higher than that of boys in some sub-domain both gifted and general students. Second, the total score of emotional intelligence and sub-domains of emotional intelligence showed significant difference. There were significant differences between the two groups in 'thinking-acceleration ability by emotion' and 'ability of utilizing emotional knowledge'. Investigating gender difference, it showed that the score of girls significantly higher than that of boys in some sub-domain both gifted and general students. Third, it proved to be significantly positive correlation between leadership characteristic and emotional intelligence of gifted middle school students. Forth, the gifted students' emotional intelligence affected leadership characteristic significantly by multiple regression analysis.

• Journal of the Korean Chemical Society
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• v.63 no.2
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• pp.111-122
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• 2019

In this study, the characteristics of the reading materials in the chemistry domain of elementary school science and middle school science textbooks and chemistry I and II textbooks developed under the 2009 Revised National Science Curriculum were investigated. The criteria for classifying the reading materials were the types of theme, purpose, types of presentation, and students' activity. The inscriptions in the reading materials were also analyzed from the viewpoint of type, role, caption and index, and proximity type. The results indicated that more reading materials were included in the elementary science textbooks compared to middle school science, chemistry I, and/or chemistry II textbooks. The percentage of application in everyday life theme was high in the reading materials of elementary science textbooks, whereas the percentage of scientific knowledge theme was high in those of middle school science, chemistry I, and/or chemistry II textbooks. It was also found that the percentage of expanding concepts purpose was high in the reading materials of elementary science textbooks, whereas the percentage of supplementing concepts purpose was high in those of middle school science, chemistry I, and/or chemistry II textbooks. Several limitations in the use of inscriptions were found to exist; most inscriptions were photograph and/or illustration; most inscriptions were supplementing or elaborating texts; many inscriptions were presented without a caption or an index; there was a problem in the proximity of inscriptions to text.

• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.276-284
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• 2020

This paper proposes a new convolutional neural network (CNN) based spectrum sensing technique for cognitive radio communications. The proposed technique determines the existence of the primary user (PU) by using energy detection without any prior knowledge of the PU's signal. In the proposed method, the received signal is high-rate sampled to sense the entire spectrum bands of interest. After that, fast Fourier transform (FFT) of the signal converts the time domain signal to frequency domain spectrum and by stacking those consecutive spectrums, a 2 dimensional signal is made. The 2 dimensional signal is cut by the sensing channel bandwidth and inputted to the CNN. The CNN determines the existence of the primary user. Since there are only two states (existence or non-existence), binary classification CNN is used. The performance of the proposed method is examined through computer simulation and indoor experiment. According to the results, the proposed method outperforms the conventional threshold-based method by over 2 dB.

• Journal of The Korean Association For Science Education
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• v.28 no.1
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• pp.32-46
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• 2008

The purpose of this study was to analyse the qualitative and quantitative characteristics of paper-pencil tests for exploring its appropriateness as a selection tool of the gifted in science. For this purpose, we developed two (internal and external) item analysis frameworks, and applied these frameworks to analyse qualitative characteristics. Also, we analysed the relationship between two characteristics. The results of analysing qualitative characteristics revealed that the portion of items with acceleration context exceeding middle school curriculum level was relatively large, which caused low content validity. Furthermore, there was considerable deviation in content and context by subject matter and year, which caused test unstability. Items measuring knowledge domain was the most prevalent, and too much weight on data interpretation & analysis domain in inquiry process skills. In case of creativity test, the portion of items measuring convergent thinking was much larger than that of divergent or associative thinking. Most of these items were represented by using pictures and tables rather than using graphs. Item types of multiple-choice and short answers were superior to essay types. Discrimination index, on the whole, was appropriate (above 0.3), but item difficulty showed a vast deviation ($0.01{\sim}0.90$). Correlation coefficients among subject matters and test tools were very low, and test reliabilities were also low. Low item difficulty & high discrimination index item types were distinguishable. Items with acceleration context were more discriminating than enrichment context. Implications of developing quality paper-pencil test items in the selection of gifted students are discussed.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.7-17
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• 2009

The precise analysis of exercise data for designing an effective rehabilitation system is very important as a feedback for planing the next exercising step. Many subjective and reliable research outcomes that were obtained by analysis and evaluation for the human motor ability by various methods of biomechanical experiments have been introduced. Most of them include quantitative analysis based on basic statistical methods, which are not practical enough for application to real clinical problems. In this situation, data mining technology can be a promising approach for clinical decision support system by discovering meaningful hidden rules and patterns from large volume of data obtained from the problem domain. In this research, in order to find relational rules between posture training type and muscle activation pattern, we investigated an application of the WAR(Weishted Association Rule) to the biomechanical data obtained mainly for evaluation of postural control ability. The discovered rules can be used as a quantitative prior knowledge for expert's decision making for rehabilitation plan. The discovered rules can be used as a more qualitative and useful priori knowledge for the rehabilitation and clinical expert's decision-making, and as a index for planning an optimal rehabilitation exercise model for a patient.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.876-884
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• 2003

It is essential to identify both the pupil and iris boundaries for iris recognition. The circular edge detector proposed by Daugman is the most common and powerful method for the iris region extraction. The method is accurate but requires lots of computational time since it is based on the exhaustive search. Some heuristic methods have been proposed to reduce the computational time, but they are not as accurate as that of Daugman. In this paper, we propose a pupil and iris boundary finding algorithm which is faster than and as accurate as that of Daugman. The proposed algorithm searches the boundaries using the Daugman's circular edge detector, but reduces the search region using the problem domain knowledge. In order to find the pupil boundary, the search region is restricted in the maximum and minimum bounding circles in which the pupil resides. The bounding circles are obtained from the binarized pupil image. Two iris boundary points are obtained from the horizontal line passing through the center of the pupil region obtained above. These initial boundary points, together with the pupil point comprise two bounding circles. The iris boundary is searched in this bounding circles. Experiments show that the proposed algorithm is faster than that of Daugman and more accurate than the conventional heuristic methods.

• 대한공업교육학회지
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• v.36 no.2
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• pp.219-238
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• 2011

The purpose of this study was to analyze the industrial education items in the teacher recruitment examination for secondary school and make a better proposal. To achieve the purpose, all the industrial education items, which had taken an examination for ten times from the school year 2002 to the most recent year 2011, were analyzed. The results of this study were as follows; First, the number and score ratio of industrial education items had been increased quarterly from the school year 2002 to 2011, but had same sharing in all subjects and fixed since the school year 2010. Second, as the industrial education items had been increased quarterly, they were taken from more various subcategory of industrial education. Recently, the industrial education items on "complex domain" including two more subcategory of industrial education were increased. Third, the industrial education items with science education items was compared from the school year 2009 to 2011. The items on PCK(Pedagogical Content Knowledge) had been increased in the industrial education but the proportion of PCK was lower than science education.