• Journal of applied mathematics & informatics
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• 제7권3호
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• pp.801-812
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• 2000

Two-dimensional slow viscous flow on infinite half-plane past a perpendicular infinite cavity is considered on the basis of the Stokes approximation. Using complex representation of the two-dimensional Stokes flow, the problem is reduced to solving a set of Fredholm integral equations of the second kind. The streamlines and the pressure and vorticity distribution on the wall are numerically determined.

• Journal of applied mathematics & informatics
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• 제7권1호
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• pp.101-113
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• 2000

In this paper, we introduce and study a new class of variational inclusions, called the set-valued quasi variational inclusions. The resolvent operator technique is used to establish the equivalence between the set-valued variational inclusions and the fixed point problem. This equivalence is used to study the existence of a solution and to suggest a number of iterative algorithms for solving the set-valued variational inclusions. We also study the convergence criteria of these algorithms.

• Proceedings of the Korean Society of Computer Information Conference
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• 한국컴퓨터정보학회 2018년도 제58차 하계학술대회논문집 26권2호
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• pp.83-84
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• 2018

개별논문 평가를 위해 제안된 altmetric가 주목받고 있다. altmetrics에서는 개별 논문의 트윗의 건수를 평가요소 중 하나로 활용한다. 그러나 여러가지 목적으로 작성된 트윗을 단일하게 처리하는 것은 문제가 있다. 본 논문은 과학 논문에 달린 트윗들을 분석하여 기능의 범주를 정의하고 분류체계를 제시하였으며, 기존의 논문의 인용기능 분류 실험을 실시하여 그 결과와 비교 분석을 수행하였다. 향후 도출한 트윗 기능 분류에 대한 개선과 추가적인 연구를 수행할 계획이다.

• 대한공업교육학회지
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• 제40권2호
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• pp.175-195
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• 2015

This study is aimed at exploring the educational meaning of cooperative hands-on activity in the technology subject from the perspective of a student who is an education consumer. For this purpose, this study selected 12 first year student of a middle school located at G City of Gyeonggi-do Province as research participants through purposeful sampling, and conducted an in-depth interview and group discussion based on stimulated recall questionary techniques. This study utilized area analysis, classification analysis and component analysis as a data analysis method, and secured the verity of the research through the examination between research participants and triangulation. As a result of this research work, it was found that the cooperative hands-on class in the technology subject had the meaning of 'Space between a burden and excitement about the technical making', 'Clue and ignition point of technological problem solving', and 'Self-discovery through Technical capability'. To be more concrete, 'Space between a burden and excitement about the technical making' means that students, whose usual school record is excellent, felt great psychological burdens of performance assessment, but their pre-experience and interest in 'Making' induced them to feel exhilaration of hands-on activity. 'Clue and ignition point of technological problem solving' means that students get to make much of the understanding & formation of the relationship with teammates in the process of resolving an unfamiliar hands-on activity task and to have the continuous problem-solving ability. 'Self-discovery through Technical capability' means that students get to realize the importance of learning experience of one's own making through hands-on activity learning, which could be the opportunity to meet the operant demands of the inner side. This study hopes that such results could be utilized as the basic data needed for designing the hands-on activity education in the technology subject more meaningfully and systematically for the time to come.

• Journal of KIISE:Software and Applications
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• 제32권12호
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• pp.1260-1270
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• 2005

Support Vector Machines (SVM) is well known for a representative learner as one of the kernel methods. SVM which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, SVM is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with a binary SVM. One-Per-Class (OPC) and All-Pairs have been applied to solve the face recognition problem, which is one of the multi-class problems, with SVM. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. In this paper, we introduce the output coding methods as an approach for extending binary SVM to multi-class SVM and propose new output coding schemes based on the Error-Correcting Output Codes (ECOC) which is a dominant theoretical foundation of the output coding methods. From the experiment on the face recognition, we give empirical results on the properties of output coding methods including our proposed ones.

• Journal of Positioning, Navigation, and Timing
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• 제5권1호
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• pp.37-45
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• 2016

In this paper, design and implementation issues for a network-oriented RF emitter localization system with array antenna are discussed. For hardware, the problem of array mismatch and RF/IF channel mismatch are introduced and the calibration schemes for solving those problems are also provided. For software, it is explained how to overcome the drawback of conventional MUltiple Signal Identification and Classification (MUSIC) algorithm in a point of identifying the number of received signals and problems such as Data Association Problem and Ghost Node Problem in regard to multiple emitter localization are presented with some approaches for getting around those problems. Finally, for implementation, a criterion for arranging each of sensors and a requirement for alignment of array antenna' orientation are also given.

• Journal of the Korea Society of Computer and Information
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• 제15권7호
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• pp.11-17
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• 2010

This paper proposes an improved SMO solving a quadratic optmization problem for class imbalanced learning. The SMO algorithm is aproporiate for solving the optimization problem of a support vector machine that assigns the different regularization values to the two classes, and the prosoposed SMO learning algorithm iterates the learning steps to find the current optimal solutions of only two Lagrange variables selected per class. The proposed algorithm is tested with the UCI benchmarking problems and compared to the experimental results of the SMO algorithm with the g-mean measure that considers class imbalanced distribution for gerneralization performance. In comparison to the SMO algorithm, the proposed algorithm is effective to improve the prediction rate of the minority class data and could shorthen the training time.

• Proceedings of the Korean Society of Computer Information Conference
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• 한국컴퓨터정보학회 2022년도 제66차 하계학술대회논문집 30권2호
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• pp.541-544
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• 2022

재난안전분야 연구 및 기술개발을 위한 현황분석 및 동향파악을 위해 연구개발활동의 주요 성과물인 특허정보의 활용은 매우 중요하다. 본 논문에서는 재난안전분야 국내 특허문헌을 대상으로 산업분야별 현황 및 특성을 분석하였다. 분석연구를 위해 재난안전분야 키워드를 포함하고 표준산업분류 매핑이 가능한 국내 특허정보를 식별하여 데이터셋으로 사용하였다. 분석 결과, 표준산업분류 체계의 산업분야 레벨별 특허 분포 현황 및 출원기관 분포 현황과 산업분야별 핵심 키워드가 자세히 파악되었다. 연구결과는 국가 재난대응을 위한 지능형 위기경보 체계 등을 개발하기 위한 정보 자원으로 활용이 기대되며, 향후 논문, 보고서를 통합한 포괄적인 재난안전분야 문헌 분석 연구가 필요하다.

• The Mathematical Education
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• 제50권3호
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• pp.285-308
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• 2011

The objective of this study is to review how researches on mathematics education are being conducted currently in Korea and overseas and to examine the current state of domestic researches on mathematics education from a broader view. Although many efforts have been made to understand trends in researches on mathematics education, there have been few in depth studies on research trends in overseas or for comparison between domestic and overseas trends. Thus, this study classified and analyzed 181 domestic articles between 2005 and 2009 in the journals and and 201 overseas articles in the journals and according to year, research area, research contents, school level, research method, and key words using the PME classification system with some modification. Through these analysis, we examined research trends on secondary mathematics education in Korea and overseas. The research findings are as follows. First, 'teaching learning process' was a spotlight area both at home and overseas, and 'realistic mathematics' and 'social cultural subjects' were not covered much either at home or overseas. 'Mathematical communication' occupied a very small portion in Korea but was a highly interesting area in overseas research. Second, research contents of interest were different between Korea and overseas. Research on general area was the mainstream. But geometry and statistics were mainly studied in Korea and algebra and analysis in overseas. Third, research related to middle school was twice more than that related to high school in Korea, But, research related to middle school was the same as high school in overseas. Fourth, qualitative research was the absolute majority both at home and overseas, and philosophical didactical analysis was used only in Korea. Fifth, the order of key words were problem solving - teacher - curriculum - creativity - textbook in Korea, but teacher - teaching - semiotic - affective factor - proo f- problem solving - technology in overseas.

• Investigative Magnetic Resonance Imaging
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• 제6권2호
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• pp.137-146
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• 2002

Purpose : This paper introduces an improved classification approach which adopts a deterministic relaxation method and an agglomerative clustering technique for the classification of MRI using neural network. The proposed approach can solve the problems of convergency to local optima and computational burden caused by a large number of input patterns when a neural network is used for image classification. Materials and methods : Application of Hopfield neural network has been solving various optimization problems. However, major problem of mapping an image classification problem into a neural network is that network is opt to converge to local optima and its convergency toward the global solution with a standard stochastic relaxation spends much time. Therefore, to avoid local solutions and to achieve fast convergency toward a global optimization, we adopt MFA to a Hopfield network during the classification. MFA replaces the stochastic nature of simulated annealing method with a set of deterministic update rules that act on the average value of the variable. By minimizing averages, it is possible to converge to an equilibrium state considerably faster than standard simulated annealing method. Moreover, the proposed agglomerative clustering algorithm which determines the underlying clusters of the image provides initial input values of Hopfield neural network. Results : The proposed approach which uses agglomerative clustering and deterministic relaxation approach resolves the problem of local optimization and achieves fast convergency toward a global optimization when a neural network is used for MRI classification. Conclusion : In this paper, we introduce a new paradigm to classify MRI using clustering analysis and deterministic relaxation for neural network to improve the classification results.