• Journal of Educational Research in Mathematics
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• v.16 no.1
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• pp.59-78
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• 2006

As a theoretical background for this research, the literatures which focus on the rationale of teaching and learning of connecting with mathematics and science in calculus were investigated. And teaching and learning material of connecting with mathematics and science in calculus was developed. And then, based on the case study using this material, the research questions were analyzed in depth. Students could understand mean-velocity, instant-velocity, and acceleration in the experimenting process of constant acceleration movement. Also Students could understand fundamental ideas that instant-velocity means slope of the tangent line at one point on the time-displacement graph and rate of distance change means rate of area change under a time-velocity graph.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.941-946
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• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.293-297
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• 1998

This paper presents a newly developed speed sensorless drive using RLS based on Neural Network Training Algorithm. The proposed algorithm has just the time-varying learning rate, while the wellknown back-propagation algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The theoretical analysis and experimental results to verify the effectiveness of the proposed control strategy are described.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.85-90
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• 2019

This paper related with the M-CMA adaptive equalization algorithm which is possible to improve the residual isi and robustness performance compare to the current MMA algorithm that is reduce the intersymbol interference occurs in channel when transmitting the QAM signal. The current MMA algorithm depend on the cost function and error function using fixed signal dispersion constant, but the M-CMA algorithm depend on the new proposed cost function and error function using multiple dispersion constant. By this, it is possible to having robustness of the CMA and simultaneous compensation of amplitude and phase of MMA. The computer simulation was performed in the same channel and noise environment for compare the proposed M-CMA and current MMA algorithm. The equalizer output signal constellation, residual isi, MD, MSE learning courves and SER, represents the robustness were used for performance index. As a result of simulation, the M-CMA has more superior to the MMA in robustness and other performance index.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.568-570
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• 2004

The error-backpropagation (EBP) algerithm for training multilayer perceptrons (MLPs) is known to have good features of robustness and economical efficiency. However, the algorithm has difficulty in selecting an optimal constant learning rate and thus results in non-optimal learning speed and inflexible operation for working data. This paper Introduces an elastic learning rate that guarantees convergence of learning and its local realization by online upoate of MLP parameters Into the original EBP algorithm in order to complement the non-optimality. The results of experiments on a speaker verification system with Korean speech database are presented and discussed to demonstrate the performance improvement of the proposed method in terms of learning speed and flexibility fer working data of the original EBP algorithm.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.583-587
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• 2015

Scenario-based learning (SBL) has been used in a variety of training situations across different disciplines. Despite its seemly widespread use in construction management discipline, very few attempts have been made to explore its effectiveness and the respective students' learning experience. Using a survey research design, this study aims to investigate students' perceptions on SBL approach in construction management courses. The specific objectives are: (i) to identify the characteristics of a favourable SBL environment, and (ii) to explore the students' learning experience and effectiveness of the SBL approach. The results show that the four characteristics of a favourable SBL environment are: effective team formulation, constant engagement with lecturer, working in a group, and incorporation of motivational incentive for participation. The students really appreciated the opportunities to apply concepts learnt in the lectures in their SBL group work. Also, they perceived that the SBL approach is effective in developing their reflective and critical thinking skills, analytic and problem-solving skills and their ability to work as a team. These findings should facilitate more critical approaches to similar form of teaching methods.

• Knowledge Management Research
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• v.13 no.3
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• pp.105-125
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• 2012

Previous studies have focused on individual and organizational learning. Amid an increasingly complex business environment, a team system designed to improve flexibility and adaptability constitutes the most basic part of an organization. Still, team learning has rarely been discussed. In addition, team learning behavior, despite being an important part of a team process, is often mentioned as a team-level outcome variable. Given that team learning behavior involves constant changes in thinking and behavior, a shared belief among team members is needed in order to positively influence innovative performance of a team. In spite of that, there has been only limited discussion of it. Besides, few domestic studies have dealt with R&D teams that can clearly demonstrate team learning behavior and team innovative performance. This study is an empirical analysis of the impact of team efficacy on team innovative performance and the mediating role of team learning behavior based on materials collected from team leaders and their immediate subordinates in 268 R&D teams. The analysis showed that team learning behavior actually has a positive effect on team innovative performance. Team efficacy also turned out to have a positive influence on team learning behavior. Lastly, the study found that team learning behavior played a mediating role in the relationship between team efficacy and team innovative performance. Based on those results, the study has identified implications and suggested directions for future research.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.185-190
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• 2022

The study of the meaning of learning began with the question of what causes people to start learning. Learning is humanization and personification. Learning is a basic human instinct, and the essence of learning is to understand other people and my life, learn community, and learn social capital. Learning gives humans nomadic judgment and provides an opportunity for a productive life for mankind, who must live in constant harmony with the social environment. Learning provides opportunities for self-management, communication with various generations, and self-actualization.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.73-80
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• 2010

Complex-valued blind algorithms which are based on constant modulus error and Euclidian distance (ED) between two probability density functions show relatively poor performance in spite of the advantages of information theoretic learning since the inherent characteristics of the constant modulus error prevent the algorithm from coping with the symbol phase rotation caused by the complex channels. In this paper, we show that the symbol phase rotation problem can be avoided and the advantages of information theoretic learning can be preserved by introducing decision-directed mode to the blind algorithm whenever the equalizer output power lies in the neighborhood of multi-modulus levels. Simulation results through MSE convergence and constellation comparison for severely distorted complex channels show significantly enhanced performance of symbol-point concentration and no phase rotation problems caused by the complex channel models.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.