• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.185-207
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• 2022

Recently, with the development of deep learning technology, researches to apply a deep learning algorithm to analyze unstructured data such as text and images are being actively conducted. Text classification has been studied for a long time in academia and industry, and various attempts are being performed to utilize data characteristics to improve classification performance. In particular, a hierarchical relationship of labels has been utilized for hierarchical classification. However, the top-down approach mainly used for hierarchical classification has a limitation that misclassification at a higher level blocks the opportunity for correct classification at a lower level. Therefore, in this study, we propose a methodology for classifying hierarchical data using the autoencoder-based deeply supervised network that high-level classification does not block the low-level classification while considering the hierarchical relationship of labels. The proposed methodology adds a main classifier that predicts a low-level label to the autoencoder's latent variable and an auxiliary classifier that predicts a high-level label to the hidden layer of the autoencoder. As a result of experiments on 22,512 academic papers to evaluate the performance of the proposed methodology, it was confirmed that the proposed model showed superior classification accuracy and F1-score compared to the traditional supervised autoencoder and DNN model.

• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.4
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• pp.235-243
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• 2016

Korea is part of a region of low to moderate seismicity located inside the Eurasian plate with bedrock located at depths less than 30 m. However, the spectral acceleration obtained from site response analyses based on the geologic conditions of inland areas of the Korean peninsula are significantly different from the current Korean seismic code. Therefore, suitable site classification scheme and design response spectra based on local site conditions in the Korean peninsula are required to produce reliable estimates of earthquake ground motion. In this study, site-specific response analyses were performed at more than 300 sites with at least 100 sites at each site categories of $S_C$, $S_D$, and $S_E$ as defined in the current seismic code in Korea. The process of creating a huge database of input parameters - such as shear wave velocity profiles, normalized shear modulus reduction curves, damping curves, and input earthquake motions - for site response analyses were described. The response spectra and site coefficients obtained from site response analyses were compared with those proposed for the site categories in the current code. Problems with the current seismic design code were subsequently discussed, and the development and verifications of new site classification system and corresponding design response spectra are detailed in companion papers (II-development of new site categories and design response spectra and III-Verifications)

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.293-310
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• 2022

In this study, we analyzed the performance of the BERT-based document classification model by automatically classifying documents in the field of library and information science based on the KoBERT. For this purpose, abstract data of 5,357 papers in 7 journals in the field of library and information science were analyzed and evaluated for any difference in the performance of automatic classification according to the size of the learned data. As performance evaluation scales, precision, recall, and F scale were used. As a result of the evaluation, subject areas with large amounts of data and high quality showed a high level of performance with an F scale of 90% or more. On the other hand, if the data quality was low, the similarity with other subject areas was high, and there were few features that were clearly distinguished thematically, a meaningful high-level performance evaluation could not be derived. This study is expected to be used as basic data to suggest the possibility of using a pre-trained learning model to automatically classify the academic documents.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.3
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• pp.47-57
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• 2020

This study focuses on the proposition that the qualities and environmental conditions required for exploring entrepreneurial opportunities and realizing entrepreneurial opportunities depend on the types of entrepreneurial opportunities. In particular, this study aims to identify the type of entrepreneurial opportunities for laboratory start-ups, which have recently been gaining policy level attention. If the type of entrepreneurial opportunities for laboratory start-ups is identified as discriminative, appropriate start-up support policies and training programs can be established. For this study, eight major papers were identified among the papers of last 30 years related to the types of entrepreneurial opportunities. After, the classification attributes for each opportunity type were derived. Then, the existing theories of recognition, discovery and creative opportunities were organized, critically reviewed and reorganized. In addition, the substance of laboratory start-ups was verified according to the standardized classification attributes of the revised and reorganized opportunity types and newly classified as 'creative opportunity'. This study also presents networking capabilities and market orientation as examples of the capabilities needed for entrepreneurs of creative opportunity type. The implication of this study is that it makes it easy to discriminate ontological typology of entrepreneurial opportunity, derives important classification attributes, and that it organizes them conceptually. In addition, it critically reconstructs the problems of confusion in the existing typology, and based on this, the type of entrepreneurial opportunities for laboratory start-ups is determined as creative opportunity. These achievements can contribute to the improvement of start-up policies and start-up training programs according to the types of entrepreneurial opportunity and laboratory start-ups in the future, resulting in realization of actual results at the start-up sites.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1302-1309
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• 2014

In this paper, we propose a method to improve the performance of the direction-of-arrival (DOA) estimation of a speech source using a multiple signal classification (MUSIC)-based algorithm. Basically, the proposed algorithm utilizes a complex coefficient band pass filter to generate the narrow band signals for signal analysis. Also, reverberation component reduction and quadratic function-based response approximation in MUSIC spatial spectrum are utilized to improve the accuracy of DOA estimation. Experimental results show that the proposed method outperforms the well-known generalized cross-correlation (GCC)-based DOA estimation algorithm in the aspect of the estimation error and success rate, respectively.Abstract should be placed here. These instructions give you guidelines for preparing papers for JICCE.

• 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.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.155-174
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• 2022

From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (