• International Journal of Advanced Culture Technology
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• 제8권1호
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• pp.199-206
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• 2020

I suggested the design learning environment based on affordance concept approach for supporting and improving learners' behavior and outcome for convergent design education in this study. The design learning space should be applied teaching and learning activity, especially learners' behavior, physical space condition to support the design thinking process. The design learning space needs openness, individuality and connectivity to support the learners' behavioral to immerse, participate, cooperate, understand, think and fulfill the design thinking process. The composition principles of the learning environment for convergent design education supports communication and collaboration among members for independence and interaction. The spaces for design research and teaching needs high privacy while facilitating visual communications through special materials and wall structure design. Also, for connectivity to improve the learners' physical and visual contact, the environment of the classrooms requires flexibility and mobility by providing an open space integrating unit cells for realizing learning purpose. These are provided by formed of an open structure for inducing visual communication and physical contact to involve the design activities and the mutual interchange.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• 한국음향학회지
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• 제40권2호
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• pp.121-129
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• 2021

본 논문에서는 학습 기반 압축 센싱을 이용하여 측면 주사 소나 영상의 해상도를 향상하는 초해상도 기법을 다룬다. 딥러닝과 압축 센싱이 접목된 학습 기반 압축 센싱은 구조적인 측면에서 피드-포워드(feed forward) 네트워크 형태이며 학습을 통하여 파라미터들을 자동으로 설정하게 된다. 본 논문에서는 초해상도 과정에서 필요한 추가 정보들을 다양한 초기화 방법을 통해 효과적으로 추출할 수 있는 방법을 제안한다. 다양한 모의 실험에서 제안하는 방법은 기존 방식보다 Peak Signal-to-Noise Ratio(PSNR) 및 Structure Similarity Index Measure(SSIM) 지표상 향상된 성능 결과를 나타내었다.

• International Journal of Advanced Culture Technology
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• 제6권4호
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• pp.262-265
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• 2018

Deep learning, a sub-field of machine learning changing the prospects of artificial intelligence (AI) because of its recent advancements and application in various field. Deep learning deals with algorithms inspired by the structure and function of the brain called artificial neural networks. This works reviews basic architecture and recent advancement of deep structured learning. It also describes contemporary applications of deep structured learning and its advantages over the treditional learning in artificial interlligence. This study is useful for the general readers and students who are in the early stage of deep learning studies.

• 컴퓨터교육학회논문지
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• 제6권4호
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• pp.181-193
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• 2003

본 연구의 목적은 하이퍼텍스트 유형과 자기주도성이 학업성취에 미치는 효과를 규명하려는 것이다. 이를 위해 경기 H시 소재 H고등학교 1학년 2학급 69명을 대상으로 자기주도성 검사를 실시하여 평균점수 이상을 얻은 학생을 자기주도성이 높은 집단으로, 그 미만을 얻은 학생을 자기주도성이 낮은 집단으로 분류하여 실험처치를 하였다. 연구 결과, 하이퍼텍스트 유형은 학업성취에 영향을 미치지 않았으나 학습자의 자기주도성은 학업성취도에 영향을 미치는 것으로 나타났다. 또한 하이퍼텍스트 유형과 학습자의 자기주도성이 학업성취도에 미치는 상호작용 효과가 있는 것으로 밝혀졌다. 본 연구는 비교적 짧은 기간에 이루어져 지속적인 학습효과를 검증하는데 한계가 있기 때문에 장기간에 걸친 연구가 필요하다. 또한 자기주도성 이외의 학습자 특성을 고려한 연구와 컴퓨터일반 교과 외 다양한 학습내용으로 하는 후속 연구가 필요하다.

• 한국초등수학교육학회지
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• 제11권2호
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• pp.177-197
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• 2007

본 연구에서는 Saito Noboru(재등 승(齋藤 昇)) 교수에 의해 실천 연구되었던 등산학습법 - 학습 요소 사이의 관계 및 전체 학습 내용의 체계적, 구조적 관계를 파악할 수 있도록 학습구조차트를 활용한 학습 방법 - 을 초등학교 저학년과 고학년 학생들에게 적용하는 실험 수업을 통해, 등산학습법이 학습자의 수학 학습에 미치는 영향을 살펴보고자 하였다.

• 건축역사연구
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• 제13권4호
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• pp.7-18
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• 2004

From the results of an examination of the transition process of the site plan divided into 5 stages based on literature and materials relating to the Sangju Confucian School as well as the construction history, we can see the general transition flow as follows. The arrangement form of Sangju Confucian School shows the structures with both the sacrificial rites function and the learning function in the early period. This shows a large general flow where the form with the learning function structure at the front and sacrificial rites function structure at the back changed to a form where the learning function structure was positioned behind the boarding facilities, after which there was a transformation which left only the learning function (the form where the learning function structure was positioned in front of the boarding facilities). The type where the learning function structure is positioned in front of the boarding facilities is hard to find in the Yeongnam area, also, there are not many examples of the 2 story Myeonglyundang (hall of confucianism teachings) throughout the country Sangju Confucian School which possess the value of rarity is appraised as being a precious material showing another area characteristic in Sangju of the Yeongnam area. Also, during the late Chosun period the scale of the Dongseojae (boarding facility) was reduced and the appearance of Yangsajae can be said to be a typical example of confucian school constructions of late Chosun era.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.993-1001
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• 2021

This study aims to determine the factors influencing student performance using the teaching and learning process through e-learning based on the unified theory of acceptance and use technology (UTAUT). This study also sets out to propose additional variables to expand the UTAUT model to be more suitable to use in higher education. This research conducted a literature review, expert interviews, and a self-administered survey involving 200 students at tertiary institutions in Riau province, Indonesia. The questionnaire data were analyzed using SmartPLS 2. This study shows that UTAUT constructs, namely, social influence, facility conditions, and effort expectancy have a significant influence on student behavior and performance, while the performance expectancy variable shows no significant effect. The additional variables, including lecturer characteristics, external motivation, and organizational structure, directly affect student performance. However, concerning student behavior, motivation and environment are the only variables with a significant effect. The results of this study suggest the behavior deteminant such as lecturer characteristics, motivation and environment, and organizational structure improve student performance. This study investigates factors affecting the performance of university students through the learning employing e-learning by developing the UTAUT constructs to include the lecturer characteristics, motivation and environment, and organizational structure in improving student performance.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.395-398
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• 1998

In this paper, a method for the improvement of learning speed and convergence rate was proposed applied it to physiological neural structure with the advantages of artificial neural networks and fuzzy theory to physiological neuron structure, To compare the proposed method with conventional the single layer perception algorithm, we applied these algorithms bit parity problem and pattern recognition containing noise. The simulation result indicated that our learning algorithm reduces the possibility of local minima more than the conventional single layer perception does. Furthermore we show that our learning algorithm guarantees the convergence.

• 로봇학회논문지
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• 제16권2호
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• pp.122-129
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• 2021

For the Simultaneous Localization and Mapping (SLAM) problem, a different path results in different SLAM results. Usually, SLAM follows a trail of input data. Active SLAM, which determines where to sense for the next step, can suggest a better path for a better SLAM result during the data acquisition step. In this paper, we will use reinforcement learning to find where to perceive. By assigning entire target area coverage to a goal and uncertainty as a negative reward, the reinforcement learning network finds an optimal path to minimize trajectory uncertainty and maximize map coverage. However, most active SLAM researches are performed in indoor or aerial environments where robots can move in every direction. In the urban environment, vehicles only can move following road structure and traffic rules. Graph structure can efficiently express road environment, considering crossroads and streets as nodes and edges, respectively. In this paper, we propose a novel method to find optimal SLAM path using graph structure and reinforcement learning technique.