• 대한원격탐사학회지
• /
• 제39권6_3호
• /
• pp.1771-1777
• /
• 2023

최근 딥러닝의 발전으로 의미론적 분할을 통한 토지피복 분류 방법들이 제안되고 있다. 그러나 기존의 딥러닝 기반 모델들은 영상 정보만을 이용하기 때문에 시공간적 일관성을 담보할 수 없는 한계점이 있다. 이에 본 연구에서는 좌표 정보를 활용한 토지피복 분류 모델을 제안한다. 먼저 암시적 신경 표현 기법인 다중해상도 해시 인코더를 위경도 좌표계로 확장한 좌표 해시 인코더를 통해 좌표의 특징을 추출하였다. 다음으로 추출된 좌표 특징을 다양한 단계의 U-net 디코더와 결합하는 아키텍처를 제안하였다. 실험 결과, 제안 방법이 약 32% 향상된 분류 정확도를 보였고, 시공간적 일관성이 향상됨을 확인하였다.

• 한국융합학회논문지
• /
• 제9권12호
• /
• pp.25-32
• /
• 2018

방대해지고 있는 온라인 패션 시장에서는 소비자도 자신이 원하는 스타일에 대해 키워드 검색으로 원하는 패션 스타일을 일일이 찾기란 쉽지 않은 일이다. 이를 해소해줄 수 있는 것은 소비자의 니즈를 반영한 패션 추천이다. 기존 온라인 쇼핑 사이트는 소비자의 니즈를 파악하고 추천하기 위하여 설문조사 형식으로 소비자의 선호 스타일을 파악하는 것이 대부분이었다. 본 논문에서는 기존 방법의 한계점을 해소하고자 암묵적 프로파일링 방법을 통하여 소비자들의 니즈와 선호하는 스타일에 대해 간편하고 효과적으로 파악할 수 있는 모델을 제안하였다. 또한 이렇게 수집된 데이터로 학습한 딥러닝기반의 지능형 선호 패션 모델을 통하여 이미지 자체에 대한 특성을 반영하도록 학습하는 방법을 제안하였다. 제안한 모델의 정성적 평가를 통하여 의미있는 결과를 얻을 수 있었다.

• 한국멀티미디어학회논문지
• /
• 제21권11호
• /
• pp.1353-1361
• /
• 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
• /
• 제9권2호
• /
• pp.79-95
• /
• 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.

• 한국학교수학회논문집
• /
• 제9권3호
• /
• pp.265-286
• /
• 2006

본 논문은 수학 교사 학습 및 교수법 변화에 관한 이론적 고찰을 추구하기 위한 노력으로 먼저 교사교육 프로그램의 저변에 암묵적으로 반영되어 있는 지식과 교수 관행간의 관계를 분석하여 교사 학습을 세 가지 개념으로 정리한 후, 각각의 모델이 교사교육에 제공하는 시사점을 고려해보았다. 또한 최근에 새롭게 부각되는 인지의 상황적 사회적 분배적 본질의 핵심적인 아이디어를 바탕으로 각각의 관점이 수학교사 교육에 제공하는 구체적인 시사점을 도출하였다.

• 한국지능시스템학회논문지
• /
• 제14권6호
• /
• pp.764-770
• /
• 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)
• /
• 제16권3호
• /
• pp.792-812
• /
• 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
• /
• 제29권3호
• /
• pp.43-55
• /
• 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.

• 영재교육연구
• /
• 제21권3호
• /
• pp.611-630
• /
• 2011

본 연구는 영재들의 학업성취에 영향을 주는 심리적 요인인 자기결정성, 학습목표지향성, 자기효능감, 지능관 및 자기조절학습전략의 영향력을 조사하는데 목적이 있다. 또한 영재들 중 학업성취가 높은 학생과 낮은 학생들의 심리적 특성에 차이가 있는지도 살펴보았다. 한국교육종단연구 자료 중 중학교 2학년 영재학생 128명의 자료가 최종 분석되었다. 분석 결과 외적 조절동기는 학업성취에 부적영향을 주는 것으로 나타났고 확인된 조절동기, 숙달접근, 자기효능감, 정교화, 초인지 전략은 정적영향을 주는 것으로 나타났다. 단계적 중다 회귀분석을 통해 학업성취에 영향을 준 6개 하위요인들의 상대적 영향력을 비교하였는데 분석결과 영재들의 학업성취는 정교화에 의해 15.5% 가량이 설명되었고 확인된 조절동기에 의해 추가로 5% 정도 더 설명되었다. 독립표본 t 검정 분석결과 학업성취가 낮은 영재학생들은 자기결정성, 숙달접근성, 자기효능감, 정교화, 초인지, 공간관리, 교사도움에서 낮은 수준을 보였다. 따라서 성적이 낮은 중학교 영재들의 학업문제를 해결하기 위해서는 우선적으로 정교화 인지전략을 발달시킬 필요가 있다.

• Journal of Contemporary Eastern Asia
• /
• 제17권1호
• /
• pp.94-111
• /
• 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.