• Journal of Information Technology Applications and Management
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• 제21권3호
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• pp.79-96
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• 2014

In Kenya during the last one decade, public and private sectors have invested a lot of resources in computer based Information and Communication Technologies (ICT) to improve the quality of education in schools. The main objective has been to integrate ICT in the delivery of curriculum in order to improve the quality of teaching-learning and to produce ICT literate workforce. The computer based technologies are used in management, pedagogy, and communication. This paper presents results from a study that was conducted in Nairobi County in Kenya to determine the key factors in the integration of computer based ICT in teaching-learning in pre-primary and lower primary schools. Results had revealed that the use of computer based ICT in teaching-learning by both pre-primary and lower primary schools was influenced by accessibility of resources, capacity to use the technology, availability of time, and provision of technical support.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• International journal of advanced smart convergence
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• 제13권3호
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• pp.143-149
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• 2024

As the size of data and models in machine learning training continues to grow, training on a single server is becoming increasingly challenging. Consequently, the importance of distributed machine learning, which distributes computational loads across multiple machines, is becoming more prominent. However, several unresolved issues remain regarding the performance enhancement of distributed machine learning, including communication overhead, inter-node synchronization challenges, data imbalance and bias, as well as resource management and scheduling. In this paper, we propose ParamHub, which utilizes orchestration to accelerate training speed. This system monitors the performance of each node after the first iteration and reallocates resources to slow nodes, thereby speeding up the training process. This approach ensures that resources are appropriately allocated to nodes in need, maximizing the overall efficiency of resource utilization and enabling all nodes to perform tasks uniformly, resulting in a faster training speed overall. Furthermore, this method enhances the system's scalability and flexibility, allowing for effective application in clusters of various sizes.

• 한국콘텐츠학회논문지
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• 제7권12호
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• pp.283-291
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• 2007

유비쿼터스 컴퓨팅의 시대로 접어들면서, 컴퓨터 시스템은 언제 어디서나 우리의 일상생활에서 필요로 하는 적절한 서비스와 정보를 제공할 수 있게 되었다. 이러한 유비쿼터스 컴퓨팅은 언제 어디서나 학습을 할 수 있는 유비쿼터스 학습의 개념으로 발전시켰다. 본 논문에서는 기존의 ERSS(Korea's Educational Resources Sharing System)를 발전시켜, 유비쿼터스 컴퓨팅 기술이 적용된 U-스쿨을 위한 프레임워크를 제안한다. 제안된 프레임워크는 기존의 ERSS를 기반으로 하여 모바일 기술, 센서 단말 기술과 상황 인식 기술을 적용하였고 사용자 중심의 시나리오를 사용하여 사용자 중심의 러닝 환경을 제공한다. 특히 유비쿼터스 교육 환경에서의 상황인식 서비스는 학생, 교사, 객체 및 환경의 동적인 상황 정보를 기반으로 즉시 학습 및 개인별 맞춤 학습에 적용될 수 있다.

• 대한전자공학회논문지TC
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• 제49권6호
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• pp.39-47
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• 2012

차세대 무선통신 시스템에서 다중 경로 페이딩의 영향을 효율적으로 감소시키기 위한 방법으로 최근 협력통신 시스템이 각광을 받고 있다. 협력통신 시스템은 정보를 전송하기 위해서 다양한 페이딩 계수를 가지고 있는 협력 릴레이를 사용하기 때문에, 모든 릴레이를 협력통신에 참여 시키는 것은 자원의 낭비를 초래한다. 그러므로 무선자원을 효율적으로 사용하기 위해서는 최적의 릴레이를 선택적으로 사용할 필요가 있다. 본 논문에서는 무선 협력통신 네트워크에서 발생하는 이러한 문제를 해결하기 위하여 Q-Learning 알고리즘을 이용한 협력 릴레이 선택 기법을 제안한다. Q-Learning에서는 자가 학습을 위해서 상태, 행동, 그리고 보상에 대한 파라미터를 정의한다. 이러한 파라미터가 잘 정의 될 때 Q-Learning을 이용하여 우수한 통신 성능을 얻을 수 있다. Q-Learning 알고리즘의 우수성을 보이기 위해서, 수학적인 분석을 통해서 최적의 협력 릴레이를 얻는 기법과 통신 성능을 비교하였다. 모의실험 결과, 제안된 기법에서 Q-Learning 알고리즘 내의 보상을 주는 방식에 따라, 비교 기법과 유사한 심벌오율 성능을 얻으면서 보다 더 적은 협력 릴레이를 선택하는 것을 보였다. 따라서 본 논문에서 제안된 기법은 다수의 릴레이를 사용하는 차세대 무선통신 시스템의 성능 향상을 위한 좋은 접근 방식의 하나로 판단된다.

• Steel and Composite Structures
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• 제48권5호
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• pp.531-545
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• 2023

Composite dowels are implemented as a powerful alternative to headed studs for the efficient combination of Ultra High-Performance Concrete (UHPC) with high-strength steel in novel composite structures. They are required to provide sufficient shear resistance and ensure the transmission of tensile forces in the composite connection in order to prevent lifting of the concrete slab. In this paper, the load bearing capacity of puzzle-shaped and clothoidal-shaped dowels encased in UHPC specimen were investigated based on validated experimental test data. Considering the influence of the embedment depth and the spacing width of shear dowels, the characteristics of UHPC square plate on the load bearing capacity of composite structure, 240 numeric models have been constructed and analyzed. Three artificial intelligence approaches have been implemented to learn the discipline from collected experimental data and then make prediction, which includes Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Extreme Learning Machine (ELM). Among the factors, the embedment depth of composite dowel is proved to be the most influential parameter on the load bearing capacity. Furthermore, the results of the prediction models reveal that ELM is capable to achieve more accurate prediction.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.149-149
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• 2022

Accurate quantitative evaluation of baseflow contribution to streamflow is imperative to address seasonal drought vulnerability, flood occurrence and groundwater management concerns for efficient and sustainable water resources management in watersheds. Several baseflow separation algorithms using recursive filters, graphical method and tracer or chemical balance have been developed but resulting baseflow outputs always show wide variations, thereby making it hard to determine best separation technique. Therefore, the current global shift towards implementation of artificial intelligence (AI) in water resources is employed to compare the performance of deep learning models with conventional hydrograph separation techniques to quantify baseflow contribution to streamflow of Piney River watershed, Tennessee from 2001-2021. Streamflow values are obtained from the USGS station 03602500 and modeled to generate values of Baseflow Index (BI) using Web-based Hydrograph Analysis (WHAT) model. Annual and seasonal baseflow outputs from the traditional separation techniques are compared with results of Long Short Term Memory (LSTM) and simple Gated Recurrent Unit (GRU) models. The GRU model gave optimal BFI values during the four seasons with average NSE = 0.98, KGE = 0.97, r = 0.89 and future baseflow volumes are predicted. AI offers easier and more accurate approach to groundwater management and surface runoff modeling to create effective water policy frameworks for disaster management.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.140-146
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• 2020

This study aimed to provide basic data for enhancing the structural relationship among learning motivation, learning confidence, critical thinking skill and problem-solving ability in junior high school students and factors influencing problem-solving ability, by closely examining them. To this end, it investigated the causality among variables, for 390 junior high school students in Gangwondo, based on the outcomes of a questionnaire survey conducted to verify the effectiveness of digital textbooks. Although learning motivation did not have a significant effect on critical thinking skill, learning confidence had a direct effect on it. In addition, learning motivation, learning confidence and critical thinking skill had direct effects on problem-solving ability. In order to enhance problem-solving ability, therefore, We may be necessary to make efforts to support learning capabilities and provide opportunities for them to experience rich learning and resources.

• 정보교육학회논문지
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• 제9권1호
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• pp.79-86
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• 2005

컴퓨터와 통신 기술이 발전함에 따라 네트워크를 통한 일반 사용자들의 컴퓨터 활용 빈도와 요구하는 데이터의 양이 급격히 증가되었다. 이에 따라 최근의 교육 시스템들은 정보의 활용성을 향상시키기 위하여 이질적인 시스템들을 의미상으로 연결하고 있다. 따라서 최근의 웹 기반 교수-학습은 학습자 스스로 학습 내용, 학습 시간 및 학습 순서를 선택하고 조직하는 유비쿼터스 학습방향으로 나아가고 있다. 즉, 학습자 개개인의 특성(선수 지식, 학습 양식, 흥미, 관심)에 맞는 적응적인 교수-학습 환경을 제공하는 방향으로 변화되고 있다. 본 논문은 유비쿼터스 학습 환경에서 다양한 분산정보의 통합을 위하여 사용자들이 요구하는 학습내용을 각 지역서버의 자치성을 유지하면서 효과적으로 학습하기 위한 미디에이터내의 처리방법에 대해 제안한다. 또한 과거와 최근의 학습내용의 활용형태가 다양하게 변할 수 있으므로 시간에 따른 감쇄율을 활용빈도에 적용하여 최근의 활용빈도의 변화에 민감하게 반응하고 활용형태의 변화에 따라 적응적으로 학습내용을 사용할 수 있는 방법을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2277-2298
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• 2019

Adaptive learning in e-learning has garnered researchers' interest. In it, learning resources could be recommended automatically to achieve a personalized learning experience. There are various ways to realize it. One of the realistic ways is adaptive learning path recommendation, in which learning resources are provided according to learners' requirements. This paper summarizes existing works and proposes an innovative approach. Firstly, a learner-centred concept map is created using graph theory based on the features of the learners and concepts. Then, the approach generates a linear concept sequence from the concept map using the proposed traversal algorithm. Finally, Learning Objects (LOs), which are the smallest concrete units that make up a learning path, are organized based on the concept sequences. In order to realize this step, we model it as a multi-objective combinatorial optimization problem, and an improved immune algorithm (IIA) is proposed to solve it. In the experimental stage, a series of simulated experiments are conducted on nine datasets with different levels of complexity. The results show that the proposed algorithm increases the computational efficiency and effectiveness. Moreover, an empirical study is carried out to validate the proposed approach from a pedagogical view. Compared with a self-selection based approach and the other evolutionary algorithm based approaches, the proposed approach produces better outcomes in terms of learners' homework, final exam grades and satisfaction.