• The Journal of Korean Association of Computer Education
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• v.7 no.5
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• pp.23-32
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• 2004

Many web-based learning systems have been developed and used widely. Most of the researches on web-based learning systems assume learners' active participation in the learning activity. However, learners do not always actively participate in the learning. This paper presents a novel self-regulated learning strategy to create a learning environment that encourages learner's active participation. We have derived sub-strategies that can be implemented as a web-based system. The derived sub-strategies have been implemented as an advanced web-base system and are verified by an empirical study.

• Journal of the Korea Safety Management & Science
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• v.14 no.2
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• pp.193-204
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• 2012

This paper deals with an empirical analysis of the structural relationship among the factors such as quality management activities, organizational learning and firm performance of manufacturing corporations. The findings of the analysis are expected to make lots of contribution to manufacturing corporations establishing strategies for quality management activities and organizational learning. From the analysis, following conclusions and suggestions could be drawn: First, an analysis of the relationship between quality management activities and organizational learning showed that most activities of quality management turned out to exercise great influence upon the factors of organizational learning. This means that the activities of quality management will prompt the members of an organization to actively engage in learning activities individually, by team and organizationally, motivating them to spread such activities across the whole organization, leading ultimately to fundamental renovation of the very organization. Second, from an analysis of the relationship between organizational learning and firm performance, that is, financial and non-financial performances of a company, it was found that most factors of organizational learning have tremendous impact upon financial and non-financial performances of the company. Such result implies that decision and management of the things to be performed in the process of organizational performances are essential to determining firm performance because firm performance depend largely on the outcomes of organizational learning.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.909-918
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• 2020

This study, not only investigates the important role of lifelong learning in shaping young people's knowledge and in maximizing their potential, but also aims to shed light on the influencing factors of lifelong learning of young people in Vietnam. The author applied STATA and SPSS to analyze quantitative data collected from questionnaires with 332 respondents aged between 19 years old and 24 years old. Based on a holistic review of literature, this study concludes that four driver factors affect young people's lifelong learning ability, comprising: organizational culture, motivation, human resource development, and domestic private type of enterprise. The results emphasize the positivity of organizational culture, human resource development, and the nature of work, especially organizational culture and human resource development, which are dominant reasons for young people to maintain lifelong learning. The relationship between demographics and lifelong learning was tested and it indicated that male has a stronger interest in learning than female. The result of the study also shows the impact of different types of business sectors on employees' learning intentions. It points out that the domestic private type of enterprise is the most effective factor that has a positive relationship with the lifelong learning of the individual.

• Proceedings of the Korea Association of Information Systems Conference
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• 2004.11a
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• pp.431-443
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• 2004

Many e-Learning companies are incorporated for the last five years, but most of them are failed or merged by the other company. The main reasons are the absence of competitive strategies and recognition of critical success factors. There are many researches on the critical success factors of Information System (IS) and Electronic Commerce (EC) . We derived e-Learning success factors from the previous IS and EC researches. We classified the success factors into five dimensions, (1) contents management, (2) learner management, (3) business strategy, (4) organizational support and ability, (5) learning management system (LMS), and each dimension has 9 or more success factors measurement items. We surveyed the perceived importance of the success factors from the manager of South Korea e-Learning firms. The paper categorized the items into two or more factors for each dimension by the exploratory factor analysis. Finally, we conducted one-way ANOVA for each success factors by the business model. As a result, there is different importance level for each success factors by the business model. We concluded that each e-Learning company needs different strategies to their business model.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.85-94
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• 2020

The importance of organizational learning is increasing. Drawing on regulatory focus theory and upper echelon theory, this study aims to identify the relationship of the regulatory focus of managers and three aspects of organizational learning, namely breadth, depth, and speed of organizational learning. While identifying the significant influence of promotion focus on the three aspects of organizational learning, we found that the influence of promotion focus of breadth of organizational learning is statistically stronger than that of prevention focus.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.358-364
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• 2007

Intelligent walking modeling of humanoid robot using learning based neuro-fuzzy system is presented in this paper. Walking pattern, trajectory of the zero moment point (ZMP) in a humanoid robot is used as an important criterion for the balance of the walking robots but its complex dynamics makes robot control difficult. In addition, it is difficult to generate stable and natural walking motion for a robot. To handle these difficulties and explain empirical laws of the humanoid robot, we are modeling practical humanoid robot using neuro-fuzzy system based on the two types of natural motions which are walking trajectories on a t1at floor and on an ascent. Learning based neuro-fuzzy system employed has good learning capability and computational performance. The results from neuro-fuzzy system are compared with previous approach.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1231-1238
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• 1999

This paper presents an approach for the optimal heating load Identification using Diagonal Recurrent Neural Networks(DRNN). In this paper, the DRNN captures the dynamic nature of a system and since it is not fully connected, training is much faster than a fully connected recurrent neural network. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer. The hidden layer is comprised of self-recurrent neurons, each feeding its output only into itself. In this study, A dynamic backpropagation (DBP) with delta-bar-delta learning method is used to train an optimal heating load identifier. Delta-bar-delta learning method is an empirical method to adapt the learning rate gradually during the training period in order to improve accuracy in a short time. The simulation results based on experimental data show that the proposed model is superior to the other methods in most cases, in regard of not only learning speed but also identification accuracy.

• Journal of Technology Innovation
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• v.10 no.2
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• pp.131-148
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• 2002

This paper explores how international political factors influence the role of conventional external factors in the course of technological learning. The research goes on to investigate whether the role of the techno-economic factors has changed due to the involvement of international political factors in the technological learning mechanism. To this end, this paper examines how US political intervention affected Korean technological learning in the back-end of the nuclear fuel cycle. The export policy, prior consent policy and international political influence of the US are employed as international political factors. The empirical findings show that international political factors are very likely to restrain the impact of the techno-economic factors on technological learning process. Accordingly, this paper hypothesizes that the role of techno-economic factors in the technological learning mechanism is weaker when international political intervention is involved.

• ETRI Journal
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• v.41 no.6
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• pp.750-759
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• 2019

Presently, the extraction of hand-crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high-level abstract representations from the time-frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning-based architecture for multi-platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.519-527
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• 2021

Alzheimer's disease is one of the challenges to tackle in the coming aging era and is attempting to diagnose and predict through various biomarkers. While the application of various deep learning-based technologies as powerful imaging technologies has recently expanded across the medical industry, empirical design is not easy because there are various deep earning neural networks architecture and categorical hyperparameters that rely on problems and data to solve. In this paper, we show the possibility of optimizing a deep learning neural network structure and hyperparameters for Alzheimer's disease classification in amyloid brain images in a representative deep earning neural networks architecture using genetic algorithms. It was observed that the optimal deep learning neural network structure and hyperparameter were chosen as the values of the experiment were converging.