• 한국시뮬레이션학회논문지
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• 제20권4호
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• pp.13-20
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• 2011

현재 일상생활에서 얼굴 인식은 신원확인, 보안 등의 목적으로 사용되고 있다. 얼굴인식의 과정은 첫 번째로 얼굴이미지의 특징을 추출해야 한다. 다음으로 추출된 특징을 학습하고 그 중 학습이 잘된 식별력 있는 특징을 선택하게 된다. 그 이후 식별력 있는 특징을 이용하여 얼굴이미지를 인식하게 된다. 얼굴인식을 위해 사용하는 얼굴이미지의 특징의 수는 매우 많다. 이 많은 특징을 학습 및 인식에 다 사용할 경우 학습 시간과 컴퓨팅 자원의 효율성이 떨어지는 문제점을 가지고 있다. 이러한 문제를 해결하기 위해서 최근 여러가지의 Boosting 기법이 소개되어왔다. Boosting 기법은 특징을 효율적으로 선택하여 학습 알고리즘의 성능을 좋게해주는 기법이다. 그 중 MutualBoost라는 기법이 있는데 이 기법은 특징간의 상호정보를 이용하여 특징을 효율적으로 선택하게 하는 기법이다. 본 논문에서는 MutualBoost의 효과를 더 증대시키기 위해서 개별적인 특징학습이 아니라 특징들을 Group화하여 특징학습을 하는 GroupMutual-Boost기법을 제안한다. 특징들을 Group화 함으로써 특징의 학습 및 선택 시간이 줄어들게 되고 컴퓨팅 자원을 보다 효율적으로 사용할 수 있다.

• 지식경영연구
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• 제13권3호
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• pp.37-54
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• 2012

The purpose of this study is to investigate determinants of partner opportunism in Korean discount store distribution channels. In addition, this study also try to examine moderating role of relational learning in the relationship. This study deals with transaction specific investment asymmetry, mutual hostages, payoff inequity, cultural diversity, and goal incompatibilities as determinants of partner opportunism. For empirical testing, 293 respondents of suppliers of discount store in Korea were surveyed and the analysis utilizing partial least square model indicated that TSI asymmetry, payoff inequity, and goal incompatibilities had positive effects on partner opportunism. On the other hand, mutual hostages had negative effect on partner opportunism. In addition, relational learning had moderating effect on the relationship between TSI asymmetry, mutual hostages, and payoff inequity and partner opportunism.

• 예술인문사회 융합 멀티미디어 논문지
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• 제5권6호
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• pp.317-325
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• 2015

이 연구는 팀 학습행동, 개인 창의성, 팀 공유정신모형, 상호 수행 모니터링이 대학 수업에서 팀 창의성에 미치는 영향을 회귀분석을 통해 검증해냄으로써 이와 관련된 이론적·실천적 기초자료를 제공해내는데 목적이 있다. 이를 위해 팀 창의성에 대한 연구 참가자들의 예측 변인과 결과 변인의 관계를 검토하였다. 2014년 1학기에 총 257명의 대학생들이 참여하였으며, 팀 학습은 6주간 이루어졌다. 자료분석방법은 상관분석과 중다회귀분석을 실시하였다. 주요 연구결과로는 첫째, 팀 학습행동, 개인 창의성, 팀 공유정신모형, 상호 수행 모니터링은 팀 창의성을 통계적으로 유의미하게 예측해내지 못했다. 이러한 연구결과는 창의성이 우수한 개인이 팀에 참가한다거나 일반적인 팀 활동에서 나타나는 팀 학습행동이나 팀 공유정신모형, 상호 수행 모니터링이 팀 창의성을 설명할 수 없다는 점을 시사하고 있다. 따라서 팀 창의성을 제대로 발휘되기 위한 집단 내 과정에 대한 후속 연구가 요청된다.

• International Journal of Computer Science & Network Security
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• 제24권6호
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• pp.1-7
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• 2024

Comparing text features involves evaluating the "similarity" between texts. It is crucial to use appropriate similarity measures when comparing similarities. This study utilized various techniques to assess the similarities between newspaper articles, including deep learning and a previously proposed method: a combination of Pointwise Mutual Information (PMI) and Word Pair Matching (WPM), denoted as PMI+WPM. For performance comparison, law data from medical research in Japan were utilized as validation data in evaluating the PMI+WPM method. The distribution of similarities in text data varies depending on the evaluation technique and genre, as revealed by the comparative analysis. For newspaper data, non-deep learning methods demonstrated better similarity evaluation accuracy than deep learning methods. Additionally, evaluating similarities in law data is more challenging than in newspaper articles. Despite deep learning being the prevalent method for evaluating textual similarities, this study demonstrates that non-deep learning methods can be effective regarding Japanese-based texts.

• Educational Technology International
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• 제11권2호
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• pp.123-148
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• 2010

Relationships between students' perception of psychological distance with online professors and their academic learning achievement and their intention to continue online learning were examined. The courses selected for this study are two online courses: 1) 'English Grammar' and 2) 'TOEIC (Test of English for International Communication) Preparation' offered by a campus-based, medium-sized university. This study employed a mixed-methods approach by conducting a survey as well as one-on-one interviews with students. Students who feel psychologically distant with the online professors show significantly lower degree of perceived learning achievement, and higher tendency not to take online courses any more. All the three scales measuring the psychological distance -mutual awareness, connectedness, and availability- with professors turned out to be significantly related with students' perceived learning achievement. According to the result of the interview data analysis, the student interviewees unanimously said that the university should limit the number of online courses that students can register in a semester to one or two courses. Most students regard low interactivity of online learning as inevitable phenomenon. There is a statistically significant difference in perceived learning achievement between the online preferred group and the offline preferred group. Also, there is a significant difference in connectedness and availability and no significant difference in the degree of mutual awareness between the online and the offline preferred group.

• Communications for Statistical Applications and Methods
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• 제12권3호
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• pp.659-672
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• 2005

This paper evaluates discretization of continuous variables to select relevant variables for supervised learning using mutual information. Three discretization methods, MDL, Histogram and 4-Intervals are considered. The process of discretization and variable subset selection is evaluated according to the classification accuracies with the 6 real data sets of UCI databases. Results show that 4-Interval discretization method based on quantiles, is robust and efficient for variable selection process. We also visually evaluate the appropriateness of the selected subset of variables.

• 대한건축학회논문집:구조계
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• 제34권6호
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• pp.63-69
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• 2018

The machine learning model can capture the dynamics of building systems with less inputs than the first principle based simulation model. The training data for developing a machine learning model are usually selected in a heuristic manner. In this study, the authors developed a machine learning model which can describe supply air temperature from an AHU in a real office building. For rational reduction of the training data, the progressive sampling method was used. It is found that even though the progressive sampling requires far less training data (n=60) than the offline regular sampling (n=1,799), the MBEs of both models are similar (2.6% vs. 5.4%). In addition, for the update of the machine learning model, the normalized mutual information (NMI) was applied. If the NMI between the simulation output and the measured data is less than 0.2, the model has to be updated. By the use of the NMI, the model can perform better prediction ($5.4%{\rightarrow}1.3%$).

• International Journal of Advanced Culture Technology
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• 제8권1호
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• pp.44-55
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• 2020

The degree of mutual understanding between nursing students and instructors regarding simulation-based education remains unknown. The purpose of this study was to identify the subjectivity of nursing students and instructors about simulation-based learning, and was intended to expand the mutual understand by employing the co-orientation model. Q-methodology was used to identify the perspectives of 46 nursing students and 38 instructors. Perception types found among students in relation to simulation-based learning were developmental training seekers, instructor-dependent seekers, and learning achievement seekers. The instructors estimated the student perception types as passive and dependent, positive commitment, demanding role as facilitators, and psychological burden. Perception types found among instructors included nursing capacity enhancement seekers, self-reflection seekers, and reality seekers. The students classified the instructors' perception types as nursing competency seekers, learning reinforcement seekers, and debriefing-oriented seekers. As a result of the analysis of these relations in the co-orientation model, instructors identified psychological burden and passive and dependent cognitive frameworks among students; however, these were not reported in the students' perspectives. Likewise, the reality seekers type found among the perception types of instructors was not identified by the students. These findings can help develop and implement simulation-based curricula aimed at maximizing the learning effect of nursing students.

• 융합신호처리학회논문지
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• 제23권3호
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• pp.160-165
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• 2022

상호증류는 교사 네트워크 도움 없이 다수의 네트워크 사이에 지식을 전달함으로써 협력적으로 학습하도록 유도하는 지식증류 방법이다. 본 논문은 상호증류가 초해상화 네트워크에도 적용 가능한지 확인하는 것을 목표로 한다. 이를 위해 상호증류를 SRGAN의 판별자에 적용하는 실험을 수행하고, 상호증류가 SRGAN의 성능 향상에 미치는 영향을 분석한다. 실험 결과, 상호증류를 통해 판별자의 지식을 공유한 SRGAN은 정량적, 정성적 화질이 개선된 초해상화 영상을 생성하였다.

• Journal of Information Processing Systems
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• 제18권1호
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• pp.75-88
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• 2022

With the development of the sharing economy, existing recommender services are changing from user-item recommendations to user-user recommendations. The most important consideration is that all users should have the best possible satisfaction. To achieve this outcome, the matching service adds information between users and items necessary for the existing recommender service and information between users, so higher-level data mining is required. To this end, this paper proposes a user-to-user matching service (UTU-MS) employing the prediction of mutual satisfaction based on learning. Users were divided into consumers and suppliers, and the properties considered for recommendations were set by filtering and weighting. Based on this process, we implemented a convolutional neural network (CNN)-deep neural network (DNN)-based model that can predict each supplier's satisfaction from the consumer perspective and each consumer's satisfaction from the supplier perspective. After deriving the final mutual satisfaction using the predicted satisfaction, a top recommendation list is recommended to all users. The proposed model was applied to match guests with hosts using Airbnb data, which is a representative sharing economy platform. The proposed model is meaningful in that it has been optimized for the sharing economy and recommendations that reflect user-specific priorities.