• Journal of Information Management
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• v.42 no.3
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• pp.1-26
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• 2011

The domain of LED is analyzed for describing the current status of Korea's R&D in the domain comparing with those of others quantitatively. Fourteen sub-domains of LED manufacturing technology are selected and the time span for analysis is ten-year: 2001-2010. Bibiliometric analysis is performed by the unit of publication, core researcher, institution and country. Strategical diagram is also produced with devised two indicators: NGI and NPI. As a result, Korea is competitive in the area of Chip Scale Package, but R&D supports in another promising areas, such as large-caliber sapphire wafer, are necessary. It is also revealed that research activities are expanded dominantly in academia, but practical technologies are developed in industrial circle. It is suggested that to support core corporate and to encourage industrial-academic collaboration is essential for systematical technology development and high achievement in prominent areas.

• Journal of the Korea Society for Simulation
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• v.20 no.1
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• pp.69-77
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• 2011

Modeling and Simulation(M&S) technology gets an attention from various parts such as industry and military. Especially, military uses the technology to cope with a different situation from the one in the Cold War and maximize the effect of training against the cost in the new environment. In order for the training based on M&S technology to be effective, the situations of a battlefield and a combat must be more realistically simulated. For this, a technique development on Computer-Generated Forces(CGF) which represents a unit's simulation logic and a human's simulated behaviors is focused. The CGF simulating a human's behaviors can be used in representing an enemy force, experimenting behaviors in a future war, and developing a new combat idea. This paper describes a methodology to accomplish Computer-Generated Forces' autonomous intelligence. It explains the process of applying a task behavior list based on the METT+T element onto CGFs. On the other hand, in the domain knowledge of military field manual, fuzzy facts such as "fast" and "sufficient" whose real values should be decided by domain experts can be easily found. In order to efficiently implement military simulation logics involved with such subjectivity, using a fuzzy inference methodology can be effective. In this study, a fuzzy inference methodology is also applied.

• Journal of the Korean Chemical Society
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• v.57 no.1
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• pp.138-146
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• 2013

This study examined the characteristics of the National Assessment of Educational Achievement (NAEA) in Science according to gender. It investigated gender achievement differences in the science section of the 2010 NAEA, the ratio of gender difference in achievement, the ratio of correct answers within each content domain and behavioral domain, and the items which showed high differences between males and females. The results indicate first, that, for 9th graders, females outperformed males in academic achievement in science. Second, with respect to the ratio of correct answers, males outperformed females in the advanced and below basic level groups, but females outperformed males in intermediate level groups. Third, females outperformed males in knowledge and inquiry in the behavior domains, and in chemistry and biology in the content domains. Fourth, an analysis of the items showing the largest gender gap indicated that males outperformed females in interpreting data, while females outperformed males in the items concerned with daily life and items related to the memorization of rules. This research on gender differences in science will allow teachers to complement the weaknesses of students when they study science, and support improved instructional methods in science.

• 대한공업교육학회지
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• v.31 no.2
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• pp.111-127
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• 2006

The purpose of this study is to analyze current recruiting examination and to categorize and construct test item that assess a variety of Mechanic Metal teachers in technical high school for appointing new Mechanic Metal teacher. The future developmental directions in this study are as follows: First, the examination for appointing secondary school new Mechanic Metal Teacher reflects the curriculum of the teacher education and technical high school, must be nomalize. Second, the rational readjustment of the basic necessary subjects for Machinery Metal recruiting examination is required. Third, the Mechanic Metal recruiting examination must prepare the criteria for the domain ratio of presenting problems and improve with level of presenting the question items which demands a knowledge, application and critical thinking. Fourth, in order to avoid the bias of the some subject tendency with committee making questions of different domain, more participation of a committee making questions is required. Fifth, the practical evaluation must be executed by the effective method to be able to make up for the limit of paper and pencil tests Sixth, as the long-term prospect to secure the professionalism of teacher, the recruiting examination of teachers must be carried out with the Machinery and Metal subject, respectively.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.55-71
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• 2013

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

• 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.