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

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.25-32
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• 2018

In the massive online fashion market, it is not easy for consumers to find the fashion style they want by keyword search for their preferred style. It can be resolved into consumer needs based fashion recommendation. Most of the existing online shopping sites have collected cumtomer's preference style using the online quastionnair. In this paper, we propose a simple but effective novel model that resolve the traditional method in fashion profiling for consumer's preference style and needs using implicit profiling method. In addition, we proposed a learning model that reflects the characteristics of the images itself through the deep learning-based intelligent preferred fashion model learned from the collected data. We show that the proposed model gave meaningful results through the qualitative evaluation.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1353-1361
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• 2018

With the availability of real-time educational data collection and analysis techniques, the education paradigm is shifting from educator-centric to data-driven lectures. However, most offline and online education frameworks collect students' feedback from question-answering data that can summarize their understanding but requires instructor's attention when students need additional help during lectures. This paper proposes a content restructure recommendation framework based on collected student feedback. We list the types of student feedback and implement a web-based framework that collects both implicit and explicit feedback for content restructuring. With a case study of four-week lectures with 50 students, we analyze the pattern of student feedback and quantitatively validate the effect of the proposed content restructuring measured by the level of student engagement.

• Educational Technology International
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• v.9 no.2
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• pp.79-95
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• 2008

This paper aims to investigate modeling scripts based on the mode of group interaction in a computer-supported collaborative learning environment. Based on a literature review, this paper assumes that group interaction and its mode would have strong influence on the online collaborative learning process, and furthermore lead learners to create and share significant knowledge within a group. This paper deals with two different modes of group interaction- distributed and shared interaction. Distributed interaction depends on the external representation of individual knowledge, while shared interaction is concerned with sharing knowledge in group action. In order to facilitate these group interactions, this paper emphasizes the utilization of appropriate CSCL scripts, and then proposes the conceptual framework of CSCL scripts which integrate the existing scripts such as implicit, explicit, internal and external scripts. By means of the model regarding CSCL scripts based on the mode of group interaction, the implications for research on the design of CSCL scripts are explored.

• Journal of the Korean School Mathematics Society
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• v.9 no.3
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• pp.265-286
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• 2006

Given that less attention has been paid to teachers than students in mathematics education, this study attempted to provide theoretical foundations to understand better mathematics teacher learning and teaching practice in transition. First, this paper summarized three conceptions of teacher learning on the basis of the relationships of knowledge and practice followed by several implications to mathematics teacher education. Second, this paper provided a brief overview of cognition as situated, social, and distributed. This paper then explored new implications and issues about mathematics teacher learning that the overview brought to light. It is expected for teacher educators and researchers to participate in rich discussion of many implicit issues about teacher learning that this paper begins to raise.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.792-812
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• 2022

Multi-agent systems often need to achieve the goal of learning more effectively for a task through coordination. Although the introduction of deep learning has addressed the state space problems, multi-agent learning remains infeasible because of the joint action spaces. Large-scale joint action spaces can be sparse according to implicit or explicit coordination structure, which can ensure reasonable coordination action through the coordination structure. In general, the multi-agent system is dynamic, which makes the relations among agents and the coordination structure are dynamic. Therefore, the explicit coordination structure can better represent the coordinative relationship among agents and achieve better coordination between agents. Inspired by the maximization of social group utility, we dynamically construct a factor graph as an explicit coordination structure to express the coordinative relationship according to the utility among agents and estimate the joint action values based on the local utility transfer among factor graphs. We present the application of such techniques in the scenario of multiple intelligent vehicle systems, where state space and action space are a problem and have too many interactions among agents. The results on the multiple intelligent vehicle systems demonstrate the efficiency and effectiveness of our proposed methods.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• Journal of Gifted/Talented Education
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• v.21 no.3
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• pp.611-630
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• 2011

The purpose of the study was to investigate which psychological factors influence on the gifted students' achievement. As a psychological factor, self-determination, learning goal-orientation, self-efficacy, belief of intelligence, and self-regulated learning strategy were examined. The difference in psychological factors between the gifted with high achievement and the gifted with low achievement was to explored. For the study 128 gifted students' data from second-year data of Korean Education Longitudinal Study (KELS) were selected and analyzed. The findings indicate that the predictors of gifted students' achievement are extrinsic regulation, identified regulation, mastery-approach goal, self-efficacy, elaboration, and meta-cognition factor. Especially, the factor of elaboration and identified regulation are the strongest predictors. The findings from t-test analysis indicate that the gifted with low achievement show the low level in self-determination, mastery-approach, self-efficacy, elaboration, meta-cognition, place management and seeking social assistance from teacher. Therefore the developing elaboration, one of regulation learning strategy, is essential to improve the achievement of the gifted students with low scores.

• Journal of Contemporary Eastern Asia
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• v.17 no.1
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• pp.94-111
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• 2018

This study examines Korea's international development volunteer program as a citizen diplomacy initiative. Informed by a cultural perspective of transmission and relational models of public diplomacy, I examine the ways in which volunteer training incorporates cultural-learning into its program. The study finds that volunteer training is largely based on an instrumentalist approach to culture that places emphasis on learning the "explicit" side of culture, such as Korean traditional dance, art, and food as a strategy to promote the country's national image. In contrast, much less covered in the training program is a relational approach to culture-learning that is guided by a reflexive understanding of the "implicit" side of culture, or the values and beliefs that guide the worldviews and behavior of both volunteers and host constituents. Whereas the value of the volunteer program as a citizen diplomacy initiative is in its potential to build relationships based on two-way engagement, its conception of culture is mostly guided by that of the transmission model of public diplomacy. Based on the findings, this study calls for an integrated approach to culture-learning in volunteer training program to move the citizen diplomacy initiative forward.