• Journal of the Korean School Mathematics Society
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• v.13 no.1
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• pp.125-141
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• 2010

The aim of this paper is to develop a web-based Virtual Learning System for discrete mathematics learning using CAS (Computer Algebra System), The system contains a series of contents that are common between secondary und university curriculum in discrete mathematics such as sets, relations, matrices, graphs etc. We designed and developed web-based virtual learning contents contained in the proposed system based on Mathematia, webMathematica and phpMath taking advantages of rapid computation and visualization. The virtual learning system for discrete math provides movie lectures and 'practice mode' authored with phpMath in order to enhance conceptual understanding of each movie lesson. In particular, matrix learning is facilitated with conceptual diagram that provides interactive quizzes. Once the quiz results are submitted, Bayesian inference network diagnoses strong and weak parts of learning nodes for generating diagnostic reports to facilitate personalized learning. As part of formative evaluation, the overall responses were collected for future revision of the system with 10 university students.

• Educational Technology International
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• v.9 no.1
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• pp.1-23
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• 2008

Despite considerable commitment to the application of action learning as an organization development intervention, no identified systematic investigation of action learning practices has been reported. Based on a systematic literature review, the purpose of this paper is to identify whether researchers strike a balance between action and learning in their studies of action learning. Research findings in this study included: (1) only 32 empirical studies were found from the electronic database search; (2) based on the hypothesized continuum of Revans' original proposition of balancing action and learning, the author categorized 32 studies into three groups: action-oriented, learning-oriented, and balanced action learning; (3) there were only nine studies on balanced action learning among 32 empirical studies, whose insights included an effective use of project teams, applications of action learning for organization development, and key success factors such as time, reflection, and management support; (4) case study was among the most frequently used research method and only six quality studies met key methodological traits; and (5) therefore, more rigorous empirical research employing quantitative methods as well as case studies is needed to determine whether researchers strike a balance between action and learning in studies on action learning.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.731-739
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• 1998

The probabilistic relaxation(PR) scheme based on the conditional probability and probability space partition has the important property that when its compatibility coefficient matrix (CCM) has uniform components it can classify m-dimensional probabilistic distribution vectors into different classes. When consistency or inconsistency measures have been defined, the properties of PRs are completely determined by the compatibility coefficients among labels of labeled objects and influence weight among labeled objects. In this paper we study the properties of PR in which both compatibility coefficients and influence weights are uniform, and then a learning rule for such PR system is derived. Experiments have been performed to verify the effectiveness of the learning rule.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.8 s.173
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• pp.34-41
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• 2005

Generally, it is not easy to get a suitable controller for multi variable systems. If the modeling equation of the system can be found, it is possible to get LQR control as an optimal solution. This paper suggests an LQR learning method to design LQR controller without the modeling equation. The proposed algorithm uses the same cost function with error and input energy as LQR is used, and the LQR controller is trained to reduce the function. In this training process, the Jacobian matrix that informs the converging direction of the controller Is used. Jacobian means the relationship of output variations for input variations and can be approximately found by the simple experiments. In the simulations of a hydrofoil catamaran with multi variables, it can be confirmed that the training of LQR controller is possible by using the approximate Jacobian matrix instead of the modeling equation and this controller is not worse than the traditional LQR controller.

• The Journal of Korean Academic Society of Nursing Education
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• v.21 no.4
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• pp.528-539
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• 2015

Purpose: The purpose of this study was to develop an integrated nursing practicum education matrix based on the learning outcomes of each stage. Methods: In this Delphi technique study, 10 experts, consisting of six professors and four nurses, participated in the development of the matrix. The first step was an in-depth review for the composition of the questionnaire and the second step was the Delphi technique. The Delphi survey was conducted two times in order to complete the components of the matrix. The survey data was analyzed for statistical averages and standard deviations to decide the order of priority. Results: According to each stage (i.e. fundamental stage, competent stage, and proficient stage), the matrix was composed of education contents, methods, evaluation methods, and curriculum outcomes. Conclusion: The integrated nursing practicum education matrix of Y University was completed. The developed matrix will result in a reduction in the gap between nursing education and clinical practice and an improvement in nursing competency.

• New & Renewable Energy
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• v.16 no.1
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• pp.15-24
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• 2020

For mechanical load testing of wind turbines, capture matrix is constructed for various range of wind speeds according to the international standard IEC 61400-13. The conventional method wastes considerable amount of data by its invalid data policy -segment data into 10 minutes then remove invalid ones. Previously, we have suggested an alternative way to save the total amount of data to build a capture matrix, but the efficient selection of data has been still under question. The paper introduces optimization algorithms to construct capture matrix with less data. Heuristic algorithm (simple stacking and lowest frequency first), population method (particle swarm optimization) and Q-Learning accompanied with epsilon-greedy exploration are compared. All algorithms show better performance than the conventional way, where the distribution of enhancement was quite diverse. Among the algorithms, the best performance was achieved by heuristic method (lowest frequency first), and similarly by particle swarm optimization: Approximately 28% of data reduction in average and more than 40% in maximum. On the other hand, unexpectedly, the worst performance was achieved by Q-Learning, which was a promising candidate at the beginning. This study is helpful for not only wind turbine evaluation particularly the viewpoint of cost, but also understanding nature of wind speed data.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.21-30
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• 2004

Often the accuracy is not adequate as a performance measure of classifiers when costs are different for different prediction errors. ROC and cost graphs can be used in such case to compare and identify cost-sensitive classifiers. We extend ROC and cost graphs so that they can be used when more general cost matrix is given, where not only misclassifications but correct classifications also incur penalties.

• Journal of the Korean Institute of Gas
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• v.25 no.2
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• pp.64-71
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• 2021

Research on polymer matrix composites with excellent molding processability and mechanical properties in the automotive field including hydrogen fuel cell electric vehicles is expanding to Computer-Aided Engineering (CAE) to support the design of materials with specific mechanical properties. CAE automation requires the prediction of the mechanical properties and behavior of materials. Unlike single materials, the mechanical properties prediction of polymer matrix composites is difficult to explain with formulas because the mechanical behavior is complicated to be explained only by the relationship between the matrix and the filler. In this study, the stress-strain curve according to the composition of polymer matrix composites, which was difficult to predict due to its sensitivity to large plastic deformation and composition, was predicted based on machine learning of the test data. The developed model finds a complex correlation between matrix and filler types and compositions, and predicts the total stress-strain curve meaningfully even in the absence of learned test data. It is expected that the material design AI system can be completed in the future based on the developed model that predicts the mechanical properties of polymer matrix composites even for the combination and composition that have not been learned.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.117-134
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• 2004

This study suggests a data driven optimization approach, which simulates the models of human learning processes from cognitive sciences. It shows how the human learning processes can be simulated and applied to solving combinatorial optimization problems. The main advantage of using this method is in applying it into problems, which are very difficult to simulate. 'Undecidable' problems are considered as best possible application areas for this suggested approach. The concept of an 'undecidable' problem is redefined. The learning models in human learning and decision-making related to combinatorial optimization in cognitive and neural sciences are designed, simulated, and implemented to solve an optimization problem. We call this approach 'SLO : simulated learning for optimization.' Two different versions of SLO have been designed: SLO with position & link matrix, and SLO with decomposition algorithm. The methods are tested for traveling salespersons problems to show how these approaches derive new solution empirically. The tests show that simulated learning for optimization produces new solutions with better performance empirically. Its performance, compared to other hill-climbing type methods, is relatively good.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.168-172
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• 1996

In master-slave teleoperating system, it is important that the system has good maneuverability. In this paper, it is addressed an adaptive learning control method applicable to the master-slave system. This control scheme has the ability to estimate uncertain dynamic parameters included intrinsically in the system and to achieve the desired performance without the nasty matrix operation. The proposed method is applied to a master-slave teleoperating system composed of two SCARA robots and verified experimentally.