• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.61-77
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• 2017

Recently, the number of academic conference information on the Internet has rapidly increased, the automatic classification of academic conference information according to research subjects enables researchers to find the related academic conference efficiently. Information provided by most conference listing services is limited to title, date, location, and website URL. However, among these features, the only feature containing topical words is title, which causes information insufficiency problem. Therefore, we propose methods that aim to resolve information insufficiency problem by utilizing web contents. Specifically, the proposed methods the extract main contents from a HTML document collected by using a website URL. Based on the similarity between the title of a conference and its main contents, the topical keywords are selected to enforce the important keywords among the main contents. The experiment results conducted by using a real-world dataset showed that the use of additional information extracted from the conference websites is successful in improving the conference classification performances. We plan to further improve the accuracy of conference classification by considering the structure of websites.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.21-22
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• 2021

Early academic warning is considered as an inherent problem in education data mining. Early and timely concern and guidance can save a student's university career. It is widely assumed as a multi-class classification system in view of machine learning. Therefore, An accurate and precise methodical solution is a complicated task to accomplish. For this issue, we present a hybrid model employing rough set theory with a back-propagation neural network to ameliorate the predictive capability of the system with an illustrative example. The experimental results show that it is an effective early academic warning model with an escalating improvement in predictive accuracy.

• Proceedings of the Korean Society for Information Management Conference
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• 2002.08a
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• pp.37-41
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• 2002

최근 정보처리 기술의 발달과 인터넷의 확산으로 웹 학술 정보원의 양은 방대히 증가하였지만, 이용자들이 원하는 정보를 정확하게 찾는 것이 매우 어려워졌다. 이를 해결하기 위해서는 웹 상에서 생산되는 정보를 효과적으로 조직화하고 체계화하는 작업이 필요하다. 이에 본 논문에서는 문헌정보학 분야의 웹 학술정보자원을 선별하여 제공하는 IFL을 통해 웹 학술정보원의 주제분류체계를 제시해보았다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.169-172
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• 2022

재난안전 분야의 기술개발 동향을 파악하고 지적 관계를 분석하기 위한 연구에서 신뢰성과 최신성을 겸비한 학술정보를 활용하는 것은 매우 유용하다. 기존의 논문 기반 계량정보분석 연구에서는 관련 분야의 학술지와 키워드를 중심으로 분석 대상 논문을 선별하여 연구재료로 사용하였다. 본 논문에서는 재난안전 분야의 보다 세부적인 연구 특성 파악을 위해 국내 대학기관의 방재 및 안전공학 학과에 소속된 저자들의 논문 정보를 대상으로 기관식별, 학과유형 분류, 재난안전유형 분류. 표준산업분류를 매핑하고 주요 측면별로 분석 연구를 수행하였다. 분석 결과, 재난안전 분야 연구에서 저자소속 기관의 유형 및 지역적 분포, 공저 학과 유형의 구성, 재난안전유형 및 표준산업분류의 현황과 핵심 키워드가 자세히 파악되었다. 연구 결과는 향후 지능형 위기경보 체계 구축을 위한 재난유형별 주요 기관 및 전문가 식별과 추천에 활용이 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1211-1212
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• 2023

현재 대부분의 국내 학술 데이터 베이스는 개별 학술지 논문의 주제를 파악하는 표준화된 정보를 거의 제공하지 않고 있다. 본 연구에서는 논문의 제목만을 활용하여 학술 논문의 분야를 자동으로 분류하는 방법을 제안한다. 이를 위해 한국어로 사전 훈련된 KLUE-RoBERTa 모델을 사용하며, Back Translation 과 Chat-GPT 를 활용한 데이터 증강을 통해 모델의 성능을 향상한다. 연구 결과, Back Translation 과 Chat-GPT 를 사용하여 증강한 모델이 원본 데이터를 학습한 모델보다 약 11%의 성능 향상을 보였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.04a
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• pp.357-362
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• 2004

Due to the rapid growth of Chinese ports, currently, research for the improvement of competitiveness of Busan port have been intensively studied. These research have mostly evaluated or analyzed competitiveness of ports, and then, based on results of analysis, have suggested some strategies for enhancing competitiveness of Busan port. However, although implications of these previous studies are practically available to build policies for Busan port, basic studies such as identification of port competitiveness( or port selection) related factors for reasonable evaluation and analysis which have contributions in academic area have been very rare. The primary objective of this study is to investigate port selection criteria, based on empirical data. Especially, in this study, we classify internal／external factors, and present importances of external factors that have yet to be empirically identified in previous studies.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1397-1399
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• 2003

When and Where, What kind of agricultural products will be produced and provided for the market? It is a commercial requirement, and also an academic questions to remote sensing technology. Crop physiology analysis and growth monitoring are important elements for precision agriculture management. Remote sensing technology supplies us more selections and available spaces in this dynamic change study by producing images of different spatial, spectral and temporal resolutions. Especially, the hyperspectral remote sensing should do play a key role in crop growth investigation at national, regional and global scales. In the past five years, Chinese academy of sciences and Japan NTT-DATA have made great efforts to establish a prototype information service system to dynamically survey the vegetable planting situation in Nagano area of Japan mainly based on remote sensing data. For such concern, a flexible and light-duty flight system and some practical data processing system and some necessary background information should be rationally made together. In addition, some studies are also important, such as quick pre-processing for hyperspectral data, Multi-temporal vegetation index analysis, hyperspectral image classification in support of GIS data, etc. In this paper, several spectral data analysis models and a designed airborne platform are provided and discussed here.

• Proceedings of the ESK Conference
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• 1997.04a
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• pp.56-60
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• 1997

The study of human erroneous actions has traditionally taken place along two different lines of approach. One has been concerned with finding and explaining the causes of erroneous actions, such as studies in the psychology of "error". The other has been concerned with the qualitative and quantitative prediction of possible erroneous actions, exemplified by the field of human reliability analysis (HRA). Another distinction is also that the former approach has been dominated by an academic point of view, hence emphasising theories, models, and experiments, while the latter has been of a more pragmatic nature, hence putting greater emphasis on data and methods. We have been developing a method to make predictions about error modes. The input to the method is a detailed task description of a set of scenarios for an experiment. This description is then analysed to characterise thd nature of the individual task steps, as well as the conditions under which they must be carried out. The task steps are expressed in terms of a predefined set of cognitive activity types. Following that each task step is examined in terms of a systematic classification of possible error modes and the likely error modes are identified. This effectively constitutes a qualitative analysis of the possibilities for erroneous action in a given task. In order to evaluate the accuracy of the predictions, the data from a large scale experiment were analysed. The experiment used the full-scale nuclear power plant simulator in the Halden Man-Machine Systems Laboratory (HAMMLAB) and used six crews of systematic performance observations by experts using a pre-defined task description, as well as audio and video recordings. The purpose of the analysis was to determine how well the predictions matiched the actually observed performance failures. The results indicated a very acceptable rate of accuracy. The emphasis in this experiment has been to develop a practical method for qualitative performance prediction, i.e., a method that did not require too many resources or specialised human factors knowledge. If such methods are to become practical tools, it is important that they are valid, reliable, and robust.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.6-7
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• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.251-257
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• 2008

These days Taekkeon of Shin Han-Seong is newly rased as traditional martial which made up in the academic world. The reason why is that create new shaped Traditional Taekkeon which stipulate good points of systematize Martial Arts to register Song Duck-Ki Taekkyeon eleven figures's a piece of skills to cultural assets. This research show how Shin Han-Seong make up Taekkyeon to register to intangible cultural assets, and found next conclusion and meaning. First, skill organization of Song Duk-Ki Taekkeon that is only one modern Taekkeon's initiator have been transmitted for a piece of skills of playing figure which don't have regular system. But Taekkyeon which have regular system have been developed by Shin Han-Seong, finally Taekkyeon is approved to intangible cultural assets. Second, Shin Han-Seong modernized Taekkyeon by borrowing training system of Judo, Fencing, Hapgido, Taekwondo, Karate. But people criticize that Song Duk-Ki Taekkeon is not initiate with original thing. Third, Shin Han-Seong made the grade classification and practical technique screening, Bon-Dae Buigi Twelve part like Pomse of Taekwondo (there is no for traditional Taekkyeon). And he contributed to popularization and modernized learning system by pursuiting sportization. Through this research geared up Taekkyeon's original shape and generational outline about skill transition or composition. And it gave important data for understand about Taekkyeon controversy which scattered.