• 가족자원경영과 정책
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• 제20권2호
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• pp.43-56
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• 2016

Time poverty is a kind of objective and subjective state which a person does not have a enough time to do his/her work or is in the mood to do something in a hurry. The major of family resources management has studied time as a resource to manage for long years. How to manage time has been a major part in education of family resources management. The education itself in nature has focused to inform knowledge and the disciplines of time management, to the students, on the other way, has a rare interest with a each student how to apply them or whether do in practical. Coaching is characterized as a practical learning and mutual communication skills with open questions, which help for a individual student to find his/her own goal related with time poverty or furthermore, whatever he/she wants to achieve in life. If the benefits of the education of family resources management as well as the benefits of practical learning of coaching could be merged in education on time management, the effectiveness of education to resolve time poverty is able to be increased. For the purpose, this study suggests a coaching approach in education of family resources management to resolve time poverty, by some comparisons of family resources management and coaching about time and time management.

• Journal of Information Technology Applications and Management
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• 제14권2호
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• pp.27-47
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• 2007

In an effort to apply IT to practices of learning, universities are trying to implement e-Learning systems and expanding the extent of their usage. Nowadays, e-Learning systems are not only used for conducting web-based distance learning, but also used for supporting traditional classes education by encouraging communication and information sharing between instructors and learners or among the learners. There is relatively lack of studies on the exploitation of e-Learning systems in the traditional classes, in comparison with a distance education. Specifically, howe-Learning systems could support the traditional class and/or which benefits e-Learning systems could offer in the classes are among the important issues. In this study, we suggest that e-Learning systems would be the rich media to communicate and exchange information among people who participate in a class. We derive key variables like media richness and media experience from Media Richness Theory and from Channel Expansion Theory. Moreover, Media usefulness and Satisfaction of a learner with e-Learning system is drawn from the literature on IS success. We examine the effects of perceived media richness, media usefulness, and media experience on leaner's satisfaction with e-Learning systems. In addition, we also investigate learner's media usefulness perception which is positively related to media richness and media experience. Finally, learner's experience with e-Learning systems affects perceived media richness. Based on the results of an empirical test. we first suggest that perceived media richness with e-Learning systems contributes to increase media usefulness and satisfaction of a learner. Second, media experience is an important predictor of media richness and media usefulness perception. Consequently, the result can support Channel Expansion Theory. Finally, media usefulness perception affects learner's satisfaction with e-Learning systems.

• 지식경영연구
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• 제16권2호
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• pp.249-278
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• 2015

Employees' knowledge integration capabilities and innovative behavior are still of crucial importance in the effective knowledge management. Recently researchers and practitioners are interested in both the potential benefits of unlearning and the negative aspects of learning inertia. The purpose of this study is to examine the relationships among learning inertia, unlearning, knowledge integration capabilities(knowledge exploitation and knowledge exploration) and innovative behavior. The results of analysis show that learning inertia is employees' psychological obstacle factor affecting knowledge integration capabilities and unlearning, that unlearning of employees is a key factor affecting knowledge integration capabilities, and that knowledge integration capabilities are driving forces leading to innovative behaviors of employees. For theoretical and practical implications, the research presents the grounds for arguments that knowledge integration capabilities are employees' dynamic capabilities from the knowledge management perspective, that unlearning is a driving force of employees' positive behaviors, and that organizations trying to perform the dynamic knowledge management need to identify the causes of employees' psychological resistance to learning. Limitations arisen in the course of the research and suggestions for future research directions are also discussed.

• Nuclear Engineering and Technology
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• 제52권7호
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• pp.1436-1442
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• 2020

Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this paper, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction. These machine learning techniques have been used to explore and compare rare events that could occur in nuclear infrastructure. As per our literature review, support vector machines provide better performance metrics. In this paper, we have performed parameter optimization for both algorithms mentioned. Existing research has been done in conditions with a great volume of data, but this paper presents a novel approach to correlate nuclear infrastructure data samples where the density of probability is very low. This paper also identifies the respective motivations and distinguishes between benefits and drawbacks of the selected machine learning algorithms.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.90-104
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• 2020

It is difficult for non-experts to build machine learning (ML) models at the level that satisfies their needs. Deep learning models are even more challenging because it is unclear how to improve the model, and a trial-and-error approach is not feasible since training these models are time-consuming. To assist these novice users, we examined how interactive and explainable feedback while training a deep learning network can contribute to model performance and users' satisfaction, focusing on the data preparation process. We conducted a user study with 31 participants without expertise, where they were asked to improve the accuracy of a deep learning model, varying feedback conditions. While no significant performance gain was observed, we identified potential barriers during the process and found that interactive and explainable feedback provide complementary benefits for improving users' understanding of ML. We conclude with implications for designing an interface for building ML models for novice users.

• 영어어문교육
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• 제13권2호
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• pp.25-45
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• 2007

In this article, I discuss an International Collaborative Writing Course on the Internet (ICWCI) that focused on the learning effectiveness Korean EFL students (KEFLSs) perceived to be necessary to exchange with international EFL students (IEFLSs). The course development was based on an internet-based instructional module, applying widely accepted EFL theories for modern foreign language instruction: collaborative learning, process writing, project-based learning, and integrated approaches. Data from online discussion forum, mid-of-semester and end-of-semester surveys, and final oral interviews are conducted and discussed. KEFLSs and IEFLSs were questioned about (a) changes in attitude towards computers assisted language learning (CALL); (b) effect of computer background on motivation; (c) perception of their acquired writing skills; and (d) attitude towards collaborative learning. The result of this study demonstrated that the majority of ICWCI participants said they enjoyed the course, gained fruitful confidence in English communication and computer skills, and felt that they made significant progress in writing skills. In spite of positive benefits created by the ICWCI, it was found that there were some issues that are crucial to run appropriate networked collaborative courses. This study demonstrates that participants' computer skills, basic language proficiency, and local time differences are important factors to be considered when incorporating the ICWCI as these may affect the quality of online instructional courses and students' motivation toward network based collaboration interaction.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• 한국빅데이터학회지
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• 제5권1호
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• pp.89-96
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• 2020

인공신경망(artficial neural networks)를 활용한 딥러닝은 최근 이미지인식, 빅데이터 및 데이터분석 등 다양한 분야에서 연구되고 개발이 진행되고 있다. 하지만 데이터 프라이버시 침해 이슈와 학습을 많이 할수록 소모 비용과 시간이 증가하는 문제점이 있어서 이를 해결하기 위해 연합학습(Federated Learning)이 연구되었다. 연합학습에서는 프라이버시 문제를 완화하면서, 분산 처리 시스템의 이점을 가져오는 학습기법을 제시하였다. 하지만 여전히 연합학습에서도 프라이버시 및 보안 문제가 존재한다. 그래서 우리는 연합학습의 서버에 해당하는 부분을 블록체인으로 대체하여 연합학습의 문제점인 프라이버시 문제와 보안 문제를 해결하였다. 또한 사용자가 제출하는 데이터에 대한 보상을 지급하여서 동기를 부여하고, 기존 성능은 유지하면서도 더 적은 비용의 유지비를 필요로 하는 시스템을 연구하였다. 본 논문에서는 우리가 개발한 시스템의의 타당성을 보이기 위해 실험결과를 제시하면서 기존 연합학습과 연구한 블록체인 기반의 연합학습 결과를 비교한다. 또한 향후 연구로 보안문제에 대한 해법과 와 적용 가능한 비즈니스 분야를 제시를 보여주면서 논문을 마무리 하였다.

• 제어로봇시스템학회논문지
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• 제12권7호
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• pp.666-670
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• 2006

This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권3호
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• pp.162-170
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• 2014

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension of PCA (SPCA) has recently received significant attention in the research community. SPCA functions by imposing sparseness constraints on the eigenvectors, a technique that has been shown to yield more robust solutions in many applications. However, when SPCA is applied to facial images, the time and space complexity of PCA learning becomes a critical issue (e.g., real-time tracking). In this paper, we propose a very fast and scalable greedy forward selection algorithm for SPCA. Unlike a recent semidefinite program-relaxation method that suffers from complex optimization, our approach can process several thousands of data dimensions in reasonable time with little accuracy loss. The effectiveness of our proposed method was demonstrated on real-world face recognition and tracking datasets.