• Journal of the Korean Society for information Management
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• v.24 no.3
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• pp.43-65
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• 2007

Automatic recommendation system of panel pool should be designed to support universal, expertness, fairness, and reasonableness in the process of review of proposals. In this research, we apply the theory of probabilistic ontology to measure relatedness between terms in the classification of academic domain, enlarge the number of review candidates, and rank recommendable reviewers according to their expertness. In addition, we construct a researcher network connecting among researchers according to their various relationships like mentor, coauthor, and cooperative research. We use the researcher network to exclude inappropriate reviewers and support fairness of reviewer recommendation process. Our methodology recommending proper reviewers is verified from experts in the field of proposal examination. It propose the proper method for developing a resonable reviewer recommendation system.

• Journal of KIISE:Software and Applications
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• v.35 no.7
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• pp.431-441
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• 2008

The academic information analysis service is including automatic recommendation of reviewers and analysis of researchers' accomplishments. The service of recommendation of reviewers should be processed in a transparent, fair and accountable way. When selecting reviewers, the following information must be considered: subject of project, reviewer's maj or, expertness of reviewer, relationship between applicant and reviewer. The analysis service of researchers' accomplishments is providing statistic information of researcher, institution and location based on accomplishments including book, article, patent, report and work of art. In order to support these services, we designed ontology for academic information, converted legacy data to RDF triples, expanded knowledge appropriate to services using OntoFrame. OntoFrame is service framework which includes ontology, reasoning engine, triple store. In our study, we propose the design methodology of ontology and service system for academic information based on OntoFrame. And then we explain the components of service system, processing steps of automatic recommendation of reviewers and analysis of researchers' accomplishments.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.17-22
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• 2021

In this paper, a machine learning-based automatic infographic recommendation system is proposed to improve the existing infographic production method. This system consists of a part that machine learning multiple infographic images and a part that automatically recommends infographics with artificial intelligence only by inputting basic data from the user. The recommended infographics are provided in the form of a library, and additional data can be input by drag & drop method. In addition, the infographic image is designed to be dynamically adjusted according to the size of the input data. As a result of analyzing the machine learning-based automatic infographic recommendation process, the matching success rate for layout and keyword was very high, and the matching success rate for type was rather low. In the future, a study to improve the matching success rate for the image type for each part of the infographic will be needed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.658-663
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• 1994

The classification and coding of part for group technology applications continus to be labour intensive and time-consuming process, and therefore much effort is dedicated to the structure and creation of automatic coding systems. IN this paper, Neural networks is used to generate processes-related digit as well as part geometry-related digit of the TS code where part name is provided as input.since part name, which is appropriately designated, provides much information about part geometry and manufacturing processes. THe developed GT recommendation system is integrated with interactive TS coding system and database in order to handle the changes of production environment, such as the change of production part of plant. It is found to recommend codes accurately and promises to be a useful tool for consistent, reliable and convenient coding processes.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.1
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• pp.31-40
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• 2020

In this study, we developed an algorithm for automatic travel itinerary planning based on expert recommendation. The proposed algorithm generates an itinerary by patterning a number of travel routes based on the automatic itinerary generation method based on the routes recommended by travel experts. To evaluate the proposed algorithm, we generated 30 itinerary for Singapore, Bankok, and Da Nang using both algorithms and analyzed the mean difference of trip distances with t-test and interater reliability of those itineraries. The result shows that the itineraries based on the proposed algorithm is not different from that of VRP(Vehicle routing problem) algorithm and interater reliability is high enough to show that the proposed algorithm is effective enough for real-world usage.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1346-1357
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• 2021

The drone technology, which is receiving a lot of attention due to the 4th industrial revolution, requires an Unmanned Aerial Vehicles'(UAVs) flight path search algorithm for automatic operation and driver assistance. Various studies related to flight path prediction and recommendation algorithms are being actively conducted, and many studies using the A-Star algorithm are typically performed. In this paper, we propose an Optimal 3D Flight Path Recommendation System for unmanned aerial vehicles. The proposed system was implemented and simulated in Unity 3D, and by indicating the meaning of the route using three different colors, such as planned route, the recommended route, and the current route were compared each other. And obstacle response experiments were conducted to cope with bad weather. It is expected that the proposed system will provide an improved user experience compared to the existing system through accurate and real-time adaptive path prediction in a 3D mixed reality environment.

• Journal of Korean Library and Information Science Society
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• v.41 no.1
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• pp.149-165
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• 2010

In this research, we analyze about the general service provided by Society of Korean journal articles of science and technology. Personalized retrieval services which are suitable to the articles service were developed based on this. That is, there are personalized retrieval system based on user's keyword, authors navigation system, automatic topic recommendation system based on author's keyword, and similar user automatic recommendation system. In this research, personalized service methods being suitable to the articles service of Society tries to be considered through the user survey.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.62-65
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• 2011

최근 화두가 되고 있는 스마트 폰 앱의 관심으로 스마트 TV의 앱에 대한 관심도 함께 증가하고 있다. TV시청 이용자들의 편의를 위해 증가하고 있는 수많은 채널과 콘텐츠 중, 개인 사용자의 이용 습관 및 대중의 선호 프로그램을 고려하여, 편리하게 원하는 TV프로그램에 접근하도록 해 주는 TV 앱이 있다면 이는 매우 중요한 기능으로 자리 잡을 가능성이 높을 것으로 예상된다. 이에 본 논문은 사용자의 시청 이용행태를 기반으로 주제모델링 기술의 고전적 모델인 LDA을 기반으로 협업필터링을 결합한 TV 선호 프로그램 추천 알고리듬을 제안한다. 개인의 관심 선호도는 일반적으로 특정 개수로 한정지어지는 특성을 고려하여, 개인 선호도 특성이 구별 되도록 두 가지 방법을 적용하였다. 하나는 개인 선호도 프로파일의 특정 상위 주제만을 고려하는 것이고, 또 다른 하나는 개인별 주제에 대한 선호도의 다양성이 드러나도록 비대칭 하이퍼-파라미터를 갖는 LDA를 사용 하였다. 실험 결과, 두 가지 방식에 대해 사용자의 실제 TV시청 이용내역 데이터를 기반으로 추천 성능의 향상을 평균 Precision 값을 측정하여 확인하였다. 또한, 본 논문에서는 주제 모델링을 통해 학습된 각 주제의 상위 확률의 TV 프로그램들을 분석한 결과, 하나의 주제가 개인별 시청의 특성 보다는 가족단위의 시청 특성을 드러냄을 확인할 수 있었다.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.288-294
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• 2022

The media content market has been growing, as various types of content are being mass-produced owing to the recent proliferation of the Internet and digital media. In addition, platforms that provide personalized services for content consumption are emerging and competing with each other to recommend personalized content. Existing platforms use a method in which a user directly inputs video metadata. Consequently, significant amounts of time and cost are consumed in processing large amounts of data. In this study, keyframes and audio spectra based on the YCbCr color model of a movie trailer were extracted for the automatic generation of metadata. The extracted audio spectra and image keyframes were used as learning data for genre recognition in deep learning. Deep learning was implemented to determine genres among the video metadata, and suggestions for utilization were proposed. A system that can automatically generate metadata established through the results of this study will be helpful for studying recommendation systems for media super-personalization.

• Journal of the Korean Society for Library and Information Science
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• v.54 no.3
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• pp.41-69
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• 2020

The purpose of this study is to design and develop a alternative material recommendation system using automatic classification, based on user preference. Details of usage data by users from DREAM was analysed in order to develop the way of a method on selecting proper alternative material, and then the data by user preference were allocated under each category of 10 KDC categories. The keyword, selected from the title of users' usage data from a certain period of time, were divided into 10 subject categories and ranked by the order of frequency of appearance. Books including high frequency of the keyword in title can be selected as a preferred target for producing alternative materials. Lastly, a dynamic linkage for sharing usage data among National Library for the Disabled and other libraries is proposed to produce more proper alternative materials, based on user preference.