• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.65-78
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• 2007

This paper proposes a new method of weighted template matching for machine-printed numeral recognition. The proposed weighted template matching, which emphasizes the feature of a pattern using adaptive Hamming distance on local feature areas, improves the recognition rate while template matching processes an input image as one global feature. Template matching is vulnerable to random noises that generate ragged outlines of a pattern when it is binarized. This paper offers a method of chain code trimming in order to remove ragged outlines. The method corrects specific chain codes within the chain codes of the inner and the outer contour of a pattern. The experiment compares confusion matrices of both the template matching and the proposed weighted template matching with chain code trimming. The result shows that the proposed method improves fairly the recognition rate of the machine-printed numerals.

• Journal of the Korean Society for information Management
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• v.40 no.1
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• pp.121-148
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• 2023

The purpose of this study is to propose a scholarly paper recommendation system based on metadata attribute similarity with excellent performance. This study suggests a scholarly paper recommendation method that combines techniques from two sub-fields of Library and Information Science, namely metadata use in Information Organization and co-citation analysis, author bibliographic coupling, co-occurrence frequency, and cosine similarity in Bibliometrics. To conduct experiments, a total of 9,643 paper metadata related to "inequality" and "divide" were collected and refined to derive relative coordinate values between author, keyword, and title attributes using cosine similarity. The study then conducted experiments to select weight conditions and dimension numbers that resulted in a good performance. The results were presented and evaluated by users, and based on this, the study conducted discussions centered on the research questions through reference node and recommendation combination characteristic analysis, conjoint analysis, and results from comparative analysis. Overall, the study showed that the performance was excellent when author-related attributes were used alone or in combination with title-related attributes. If the technique proposed in this study is utilized and a wide range of samples are secured, it could help improve the performance of recommendation techniques not only in the field of literature recommendation in information services but also in various other fields in society.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.13-22
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• 2020

This study aims to find out success factors of crowdfunding on movie projects. For empirical analysis, we collected 583 data of the movie projects from the crowdfunding platform 'Tumblbug'. To figure out the success factors, we examined effects of 10 independent variables on 1 dependent variable. The independent variable includes target amount, project information, reward options, creator funding power, editor recommendation, creator contents power, movie type, number of comments, number of replies, and number of SNS information. The final achievement rate of crowdfunding was set as dependent variable. This study found that the target amount, number of text information, number of video information, editor recommendation, number of backers' reply, and number of SNS information had a significant impact on the achievement rate of the movie crowdfunding project. This study has implications in that it has discovered a variable of editor recommendation and the number of SNS information, and both of them have a positive effect on crowdfunding achievement.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2001.11a
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• pp.89-90
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• 2001

최근의 대기확산 모델링 분야는 모델링 수행에 큰 장애요인이었던 계산속도에 의한 제한요소가 Computer H/W 성능의 향상으로 상당히 제거되면서, 학계의 연구를 통해서 보다 진보된 확산이론을 사용하는 새로운 개념의 모델들이 속속 개발되고 있다. 이 중, 일부 모델들은 2000년도부터 미국 EPA(환경보호청)으로부터 새롭게 추천받고 있는데, 근래 미국 EPA에서 새로이 추천하고 있는 모델로서 ISC3-PRIME, AERMOD, 및 CALPUFF이 있다. (중략)

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• Defense and Technology
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• no.5 s.207
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• pp.48-61
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• 1996

한국방위산업진흥회는 매년 자주국방의 핵심분야인 방위산업의 중요성을 통보하고, 방위산업에 관한 조사연구활동을 활성화하기 위해 국방관련 학교기관의 피교육생들이 재학중에 연구한 방산 관련 논문을 학교장의 추천을 받아 심사해 우수논문을 시상하고 있다. 이에 교육기관에서 연구된 방산관련 아이디어를 정책개발 및 방산업체 운영에 활용되기를 기대하며 '96 우수 논문으로 선발된 논문을 요약 발표한다.

• Defense and Technology
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• no.4 s.206
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• pp.16-25
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• 1996

한국방위산업진흥회는 매년 자주국방의 핵심분야인 방위산업의 중요성을 통보하고, 방위산업에 관한 조사연구활동을 활성화하기 위해 국방관련 학교기관의 피교육생들이 재학중에 연구한 방산 관련 논문을 학교장의 추천을 받아 심사해 우수논문을 시상하고 있다. 이에 교육기관에서 연구된 방산관련 아이디어를 정책개발 및 방산업체 운영에 활용되기를 기대하며 '96 우수 논문으로 선발된 논문을 3회에 걸쳐 요약 발표한다

• The Journal of Society for e-Business Studies
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• v.17 no.2
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• pp.129-147
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• 2012

Collaborative filtering (CF), one of the most successful recommendation techniques, has been used in a number of different applications such as recommending web pages, movies, music, articles and products. One of the critical issues in CF is why recommendation performances are different depending on application domains. However, prior literatures have focused on only data characteristics to explain the origin of the difference. Scant attentions have been paid to provide systematic explanation on the issue. To fill this research gap, this study attempts to systematically explain why recommendation performances are different using structural indexes of social network. For this purpose, we developed hypotheses regarding the relationships between structural indexes of social network and recommendation performance of collaboration filtering, and empirically tested them. Results of this study showed that density and inconclusiveness positively affected recommendation performance while clustering coefficient negatively affected it. This study can be used as stepping stone for understanding collaborative filtering recommendation performance. Furthermore, it might be helpful for managers to decide whether they adopt recommendation systems.

• Journal of Korean Library and Information Science Society
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• v.53 no.1
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• pp.231-263
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• 2022

The understanding and capability to utilize artificial intelligence (AI) incorporated technology has become a required basic skillset for the people living in today's information age, and various members of the university have also increasingly become aware of the need for AI education. Amidst such shifting societal demands, both domestic and international university libraries have recognized the users' need for educational content centered on AI, but a user-centered service that aims to provide personalized recommendations of digital AI educational content is yet to become available. It is critical while the demand for AI education amongst university students is progressively growing that university libraries acquire a clear understanding of user intention towards an AI educational content recommender system and the potential factors contributing to its success. This study intended to ascertain the factors affecting acceptance of such system, using the Extended Technology Acceptance Model with added variables - innovativeness, self-efficacy, social influence, system quality and task-technology fit - in addition to perceived usefulness, perceived ease of use, and intention to use. Quantitative research was conducted via online research surveys for university students, and quantitative research was conducted through written interviews of university librarians. Results show that all groups, regardless of gender, year, or major, have the intention to use the AI-related Educational Content Recommendation System, with the task suitability factor being the most dominant variant to affect use intention. University librarians have also expressed agreement about the necessity of the recommendation system, and presented budget and content quality issues as realistic restrictions of the aforementioned system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1628-1633
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• 2014

In this paper, we propose a news article recommendation system that reflects users' areas of interest and reading habits. Users can select interesting subject then our proposed system displays interesting articles above the other articles. Also the proposed system reflects users' dynamic interests using analyse of user's reading habits. The method of dynamic interest applies the different weight values from users simply clicking and reading entire articles. When users read articles from specific areas, the prosed system increases the weight of these specific areas using XML structure information. Hence users can read their articles of interest with ease.