• 한국수학교육학회지시리즈D:수학교육연구
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• 제19권1호
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• pp.81-88
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• 2015

This study used a posttest control group design and to find out differences between students' self-regulated learning strategies in traditional and non-traditional classroom. To this end, 131 first year university students within the experimental and control groups took part in the study. While ICT-based approach was used as the main medium of instruction in the experimental group, in the control group the paper-based traditional method was used. A survey adapted from Davaanyam [Davaanyam, T. (2013). The structural relationships among Mongolian students' attitudes toward mathematics, motivational beliefs, self-regulated learning strategies, and mathematics achievement. Ph. D. Dissertation. Jeonju, Jeonbuk, Korea: Chonbuk National Unversity.] was used to gather the data. The results of the study indicated a significant difference between the control and experimental groups in regard with their self-regulated learning. That is to say, the experimental group taught through ICT tools acquired higher levels of self-regulation as compared with the control group instructed through the traditional teaching method.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.226-238
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• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.

• KNOM Review
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• 제21권2호
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• pp.1-9
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• 2018

네트워크 가상화 (Network virtualization)는 물리 네트워크상에서 각 사용자 별로 독립된 가상의 네트워크 환경을 생성하는 기술을 지칭한다. 네트워크 가상화 기술은 물리 네트워크 자원을 공유하여 사용자 별로 네트워크를 구축하는 데 필요한 비용을 절감할 수 있으며, 네트워크 관리자가 요구사항에 따라 동적으로 네트워크를 관리할 수 있도록 돕는다. 하지만 동적으로 네트워크 관리를 수행할 수 있다는 장점에도 불구하고, 관리자가 여전히 직접 판단을 내리고 관리 기능을 실행하는 과정은 동일하다. 네트워크 관리 기능 실행 전까지 관리자에 의해 네트워크 상황을 파악하고 결정을 내리는 과정에는 많은 시간이 소요될 수 있기 때문에 네트워크 가상화로 얻을 수 있는 동적 네트워크 관리라는 장점을 최대화 하지 못하고 있다. 본 논문에서는 기계학습 (Machine Learning) 기술을 도입하여 사람의 도움 없이 네트워크가 스스로 학습하여 동적으로 네트워크 관리를 수행하는 방법을 제안한다. 제안하는 방법은 가상 네트워크 관리에서 핵심적이고 필수적인 문제인 자원관리 최적화 문제를 서비스 펑션 체인(Service Function Chaining) 문제로 정의하고, VNF의 자원 수요를 예측하여 적절한 자원을 동적으로 할당해 서비스 중단이 일어나는 것을 방지하면서 네트워크 운용비용을 절감하는 것을 목표로 한다.

• 한국도서관정보학회지
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• 제34권1호
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• pp.379-397
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• 2003

이 연구는 인터넷 환경 하에서 대학도서관 참고사서가 어떠한 역할을 수행해야 할 것인가에 대해 해답하는데 목적을 두고 있다. 먼저, 이 연구에서는 오늘날 참고사서의 역할이 바뀌어야 될 여러 요인들을 살펴봄으로써 새로운 역할모델 설정의 당위성을 살펴보았다. 그리고 역할모델 설정의 전제로서 미래 대학도서관의 모습을 가상도서관체제와 학습자원센터의 두 가지 형태로 가정하였다. 이를 토대로 설정한 참고사서의 역할모델은 다음과 같이 일곱 가지이다. 1) 내용분석가, 2) 컨텐츠관리자, 3) 학습자원제공자. 4) 정보중재자, 5) 원격교육운영자, 6) 교육전문가, 7) 참고정보원 개발자

• 지식경영연구
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• 제11권4호
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• pp.41-66
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• 2010

This research examines the effect of the relatedness and the gap between Resources and mechanisms on effectiveness of inter-organizational knowledge transfer. According to the literature, there has been a competing theory between two claims; one is that inter-organizational knowledge transfer will be more effective due to the reduction of the transaction cost as the relatedness increases. And the other is that the mutual complementarity of different organizational characteristics will increase synergy. In total, the relatedness and the gap of the Resource and mechanism makes the inverted U-shaped relationship with the inter-organizational knowledge transfer. As the result of empirical analysis about 109 Korean-based Joint Ventures entered country, it shows that the relatedness of parent company's production Resources, learning mechanisms, and coordination mechanisms made the inverted U-shaped relations with the inter-organizational knowledge transfer and the gap of production Resources and adjustment mechanism formed the same relationship. However, the U-shaped relationship has been established in the relatedness of market Resources, but the gap of market Resources and the learning mechanism was not statistically significant. Through this study, I can draw a best conclusion that the inter-organizational knowledge transfer will be more effective when the relatedness and the gap of management resources and mechanisms is in optimal level. However, when it comes to market Resources, it can be inferred that the result could be the opposite because the partner country's market environment would be different.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권10호
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• pp.2376-2394
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• 2013

In order to ensure both of the whole system capacity and users QoS requirements in heterogeneous wireless networks, admission control mechanism should be well designed. In this paper, Multi-agent Q-learning based Admission Control Mechanism (MQACM) is proposed to handle new and handoff call access problems appropriately. MQACM obtains the optimal decision policy by using an improved form of single-agent Q-learning method, Multi-agent Q-learning (MQ) method. MQ method is creatively introduced to solve the admission control problem in heterogeneous wireless networks in this paper. In addition, different priorities are allocated to multiple services aiming to make MQACM perform even well in congested network scenarios. It can be observed from both analysis and simulation results that our proposed method not only outperforms existing schemes with enhanced call blocking probability and handoff dropping probability performance, but also has better network universality and stability than other schemes.

• Journal of Information Technology Applications and Management
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• 제13권1호
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• pp.77-94
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• 2006

Case-based Reasoning (CBR) is widely used in business and industry prediction. It is suitable to solve complex and unstructured business problems. Recently, the prediction accuracy of CBR has been enhanced by not only various machine learning algorithms such as genetic algorithms, relative weighting of Artificial Neural Network (ANN) input variable but also data mining technique such as feature selection, feature weighting, feature transformation, and instance selection As a result, CBR is even more widely used today in business area. In this study, we investigated the usefulness of the CBR method in forecasting success in implementing ERP systems. We used a CBR method based on the feature weighting technique to compare the performance of three different models : MDA (Multiple Discriminant Analysis), GECBR (GEneral CBR), FWCBR (CBR with Feature Weighting supported by Analytic Hierarchy Process). The study suggests that the FWCBR approach is a promising method for forecasting of successful ERP implementation in Small and Medium sized Enterprises.

• International Journal of Contents
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• 제14권2호
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• pp.1-6
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• 2018

In a wireless mobile communication system, a pilot signal has been considered to be a necessary signal for estimating a changing channel between a base station and a terminal. All mobile communication systems developed so far have a specification for transmitting pilot signals. However, although the pilot signal transmission is easy to estimate the channel,(Ed: unclear wording: it is easy to use the pilot signal transmission to estimate the channel?) it should be minimized because it uses radio resources for data transmission. In this paper, we propose a pilotless channel estimation scheme (PCE) by introducing the clustering method of unsupervised learning used in our deep learning into channel estimation.(Ed: highlight- unclear) The PCE estimates the channel using only the data symbols without using the pilot signal at all. Also, to apply PCE to a real system, we evaluated the performance of PCE based on the resource block (RB), which is a resource allocation unit used in LTE. According to the results of this study, the PCE always provides a better mean square error (MSE) performance than the least square estimator using pilots, although it does not use the pilot signal at all. The MSE performance of the PCE is affected by the number of data symbols used and the frequency selectivity of the channel. In this paper, we provide simulation results considering various effects(Ed: unclear, clarify).

• Journal of Information Processing Systems
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• 제18권4호
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• 한국초등과학교육학회지:초등과학교육
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• 제33권4호
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• pp.683-699
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• 2014

The purpose of this study was to explore the characteristics of teacher learning and changes in teachers' epistemic beliefs within a learning community of elementary science teachers. Three in-service elementary teachers who majored in elementary science education in a doctoral course of a graduate school of education participated in the study, and learning activities in the teachers' beginning learning community provided a context for the study. Data sources included field notes produced by the researcher who engaged jointly in the teacher learning community as a coach, audio-recordings of the teachers' narratives, and artifacts generated by the teachers during the process of teacher learning. Complementary analyses of these multiple sources of data revealed that epistemic beliefs of the three elementary teachers were different and that each teacher made a different plan of science instruction based on his own epistemic belief even after the learning experiences within the teacher community. It was therefore suggested that science teacher education programs should be organized in consideration of the nature of teachers as constructivist learners and their practical resources.