• The Journal of Society for e-Business Studies
• /
• v.6 no.3
• /
• pp.63-80
• /
• 2001

The construction of the students knowledge in e-Learning systems, namely the student modeling, is a core component used to develop e-Learning systems. However, existing e-Learning systems have many problems to share the knowledge in a heterogeneous student model and a distributed knowledge base. Because the methods of the knowledge representation are different in each e-Learning systems, the accumulated knowledge cannot be used or shared without a great deal of difficulty. In order to share this knowledge, existing systems must reconstruct the knowledge bases. Consequently, we propose a new a Case Markup Language based on XML in order to overcome these problems. A distributed e-Learning systems fan have the advantage of easily sharing and managing the heterogeneous knowledge base proposed by CaseML. Moreover students can generate and share a case knowledge to use the communication protocol of agents. In this paper, we have designed and developed a CaseML by using a knowledge markup language. Furthermore, in order to construct an intelligent e-Learning systems, we have done our research based on the design and development of the intelligent agent system by using CaseML.

• Journal of Korean Elementary Science Education
• /
• v.37 no.3
• /
• pp.309-322
• /
• 2018

There is no doubt that science -and, therefore, science education- is central to the lives of all (NGSS, 2013). This manuscript focuses on ideas in astronomy that are at the foundation of elementary students' understanding of the discipline: the apparent motion of the sun explaining the day / night cycle on Earth. According to prior research demonstrating that neither children nor adults hold a scientific understanding of the big ideas of astronomy (NRC, 1996), understanding of concepts may base students' progress towards more advanced understanding in the domain of astronomy. We have analyzed the logic of the domain and synthesized prior research assessing children's spatial representation from an earth- and a space based perspective to develop a set of learning trajectories that describe how students' initial ideas about apparent celestial motion as they take school science can be build upon. In this study elementary students' representations were compared by their resident context including urban and rural. This study may present a first look at the use of a learning progression framework in analyzing the structure of astronomy education. We discuss how this work may eventually lead towards the development and empirical testing of how children learn to describe and explain apparent patterns of celestial motion.

• Journal of Korean Library and Information Science Society
• /
• v.39 no.4
• /
• pp.283-304
• /
• 2008

This is to develop Intelligent Retrieval System which can automatically present early query's category terms(association terms connected with knowledge structure of relevant terminology) through learning function and it changes searching form automatically and runs it with association terms. For the reason, this theoretical study of Intelligent Automatic Indexing System abstracts expert's index term through learning and clustering algorism about automatic classification, text mining(categorization), and document category representation. It also demonstrates a good capacity in the aspects of expense, time, recall ratio, and precision ratio.

• Korean Institute of Interior Design Journal
• /
• v.16 no.4
• /
• pp.119-126
• /
• 2007

A prototype design process visualization and guidance system, is being developed. Its purpose is to visualize the design process in more intuitive manner so that one can get an insight to the complicated aspects of the design process. By providing a tangible utility to the design process performed by the expert designers or guided by the system, novice designers will be greatly helped to learn how to approach a certain class of design. Not only as an analysis tool to represent the characteristics of the design process, the system will be useful also for learning design process. A design ontology is being developed to provide the system with a knowledge-base, representing designer's activities associated with various design information during the conceptual design process, and then to be utilized for a computer environment for design analysis and guidance. To develop the design ontology, a conceptual framework of design activity model is proposed, and then the model has been tested and elaborated through investigating the nature of the early conceptual design. A design process representation model is conceptualized based on the ontology, and reflected into the development of the system. This paper presents the development process of the visualization system, modeling of design process ontology, and how the system could be utilized for the analysis and learning of conceptual design methods using computer mediated design support environment.

• Education of Primary School Mathematics
• /
• v.18 no.3
• /
• pp.203-216
• /
• 2015

This study analyzed changes of representations which had come up in the problem-solving process of math-gifted 6th grade students that ACODESA had been applied. The class was designed on a ACODESA procedure that enhancing the use of varied representations, and conducted for 40minutes, 4 times over the period. The recorded videos and interviews with the students were transcribed for analysing data. According to the result of the analysis, which adopted Despina's using type of representation, there appeared types of 'adding', 'elaborating', and 'reducing'. This study found that there is need for a class design that can make personal representations into that of public through small group discussions and confirmation in the problem-solving process.

• Journal of Science Education
• /
• v.43 no.1
• /
• pp.173-193
• /
• 2019

An expert teachers' instruction can be helpful to other teachers because good teaching effectively guides students to develop meaningful learning. Feynman is an excellent physics lecturer as well as one of the greatest physicists of the 20th century who presented and explained physics with his unique teaching style based on his great store of knowledge. However, it is not easy to capture and visualize teaching because it is not only the complex phenomena interrelated to various factors with the content to be taught but also the tacit representation. In this study, the framework of knowledge & belief based on the integrated mental model theory was used as a tool to capture and visualize complex and tacit representation of Feynman's teaching of 'The theory of gravitation,' a chapter in The Feynman Lectures on Physics. Feynman's teaching was found to go beyond the transmission of physics concepts by showing that components of the framework of knowledge & belief were effectively intertwined and integrated in his teaching and the storyline was well-organized. On the basis of these discussions, the implications of Feynman's teaching analyzed within the framework of knowledge & belief for physics teacher education are derived. Finally, the characteristics of the framework of knowledge & belief as tools for the analysis of teaching are presented.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_3
• /
• pp.1771-1777
• /
• 2023

With the advancements of deep learning, many semantic segmentation-based methods for land cover classification have been proposed. However, existing deep learning-based models only use image information and cannot guarantee spatiotemporal consistency. In this study, we propose a land cover classification model using geographical coordinates. First, the coordinate features are extracted through the Coordinate Hash Encoder, which is an extension of the Multi-resolution Hash Encoder, an implicit neural representation technique, to the longitude-latitude coordinate system. Next, we propose an architecture that combines the extracted coordinate features with different levels of U-net decoder. Experimental results show that the proposed method improves the mean intersection over union by about 32% and improves the spatiotemporal consistency.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2024.01a
• /
• pp.419-422
• /
• 2024

본 논문에서는 다항식 보간법의 일종인 이동최소자승법(Moving least squares, MLS)을 네트워크로 학습하여, Divergence-constrained MLS 벡터장을 효율적으로 표현하는 방법을 제안한다. 벡터장을 구성하기 위해 MLS는 스칼라가 아닌 벡터 보간을 해야 하므로 행렬과 벡터의 크기가 더 커지며, 이는 계산량이 커짐을 나타낸다. 고차 보간(High-order interpolation)이 가능한 특징은 장점이 되지만, 계산량이 매우 크기 때문에 시뮬레이션에는 활용이 어렵다. Divergence-constrained MLS를 유체 시뮬레이션에 적용한 경우가 있지만, 실제로 슈퍼컴퓨터(Supercomputer)를 해야 장면 제작이 가능하므로 효용성이 떨어진다. 본 논문에서는 이러한 문제를 해결하기 위해 네트워크 학습을 통한 Divergence-constrained MLS 벡터장을 표현할 수 있는 결과를 보여준다.

• The Journal of the Acoustical Society of Korea
• /
• v.34 no.3
• /
• pp.234-239
• /
• 2015

Non-Negative Matrix Factorization (NMF) is a method for updating dictionary and gain in alternating manner. Due to ease of implementation and intuitive interpretation, NMF is widely used to detect and separate overlapping sound events. However, NMF that utilizes non-negativity constraints generates parts-based representation and this distinct property leads to a dictionary containing fragmented acoustic events. As a result, the presence of shared basis results in performance degradation in both separation and detection tasks of overlapping sound events. In this paper, we propose a new method that utilizes K-Singular Value Decomposition (K-SVD) based dictionary to address and mitigate the part-based representation issue during the dictionary learning step. Subsequently, we calculate the gain using NMF in sound event detection step. We evaluate and confirm that overlapping sound event detection performance of the proposed method is better than the conventional method that utilizes NMF based dictionary.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.9
• /
• pp.41-47
• /
• 2022

In this paper, we propose a new and simple self-supervised learning method that predicts the middle image of a face image sequence for automatic expression recognition. Automatic facial expression recognition can achieve high performance through deep learning methods, however, generally requires a expensive large data set. The size of the data set and the performance of the algorithm are tend to be proportional. The proposed method learns latent deep representation of a face through self-supervised learning using an existing dataset without constructing an additional dataset. Then it transfers the learned parameter to new facial expression reorganization model for improving the performance of automatic expression recognition. The proposed method showed high performance improvement for two datasets, CK+ and AFEW 8.0, and showed that the proposed method can achieve a great effect.