• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.33-57
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• 2017

This study aims to introduce an emerging prescriptive analytics method and suggest its efficient application to a category-based service system. Prescriptive analytics method provides the whole process of analysis and available alternatives as well as the results of analysis. To simulate the process of optimization, large scale journal articles have been collected and categorized by classification scheme. In the process of applying the concept of prescriptive analytics to a real system, we have fused a dynamic automatic-categorization method for large scale documents and intellectual structure analysis method for scholarly subject fields. The test result shows that some optimized scenarios can be generated efficiently and utilized effectively for reorganizing the classification-based service system.

• Journal of the Korea Convergence Society
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• v.6 no.5
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• pp.219-225
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• 2015

Nowadays, technologies are changing through industrial fusion and government & corporates need to predict the flow & direction of technologies. These flow & direction can be grasped through the analysis of patent information. The patent information uses the common classification codes in the world, and it is possible for the quantitative analysis based on objective data with the time information of technical area. The methods of patent analysis analyzed the technology fusion by using citation analysis & simultaneous classification analysis. This research analyzed patent information which used as an index to measure the technical innovation in the society based on knowledge, and would like to analyze technical trends and to describe the way of improvement in the future based on the aviation industry which is the representative fusion/complex industry.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.79-83
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• 2018

본 논문은 한국어 문장의 형태소, 음절, 자소를 동시에 각자 다른 합성곱층을 통과시켜 문장의 감성을 분류하는 Multi-channel CNN을 제안한다. 오타를 포함하는 구어체 문장들의 경우에 형태소 기반 CNN으로 추출 할 수 없는 특징들을 음절이나 자소에서 추출 할 수 있다. 한국어 감성분석에 형태소 기반 CNN이 많이 쓰이지만, 본 논문의 Multi-channel CNN 모델은 형태소, 음절, 자소를 동시에 고려하여 더 정확하게 문장의 감성을 분류한다. 본 논문이 제안하는 모델이 형태소 기반 CNN보다 야구 댓글 데이터에서는 약 4.8%, 영화 리뷰 데이터에서는 약 1.3% 더 정확하게 문장의 감성을 분류하였다.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.185-192
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• 2019

Polymer materials are used in a wide variety of fields such as automobiles, aerospace, energy, IT, and as well as simple household products. Despite high interest in the technological convergence of polymer materials for the sustaining progress, there have been only limited analyzes on the topic. This research attempted to analyze the types of convergence and the level of technology in the polymer materials field. For this purpose, we collected patent information from the PCT database and implemented a co-classification analysis. The research shows that Japan and Korea have more section-level convergence whilst US and Europe focus on field-level convergence. In terms of the quality measured by patent activity, patent competitiveness, and patent effect, Korean convergence technologies seem to be inferior to those of other countries.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2001.03a
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• pp.231-245
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• 2001

경험적 암반 분류법과 운동학적 해석을 동시에 통합하여 사용할 수 있다면, 터널의 암반 상태를 분류하고 예측하는데 매우 유용할 것이다. TMR-net 분석 기법은 RMR 시스템의 평가 기준에 기초한 절리 방향 평가 기준을 설정하고, 이를 극 투영망 상의 평점 기준을 가진 활동 범위로 표현한 평사투영 중첩기법이다. 터널의 설계 및 시공 단계에 적용된 TMR-net 분석은 절리 방향의 영향과 관련된 효과적인 결과를 제공할 수 있었다.

• Proceedings of the Korean Society for Information Management Conference
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• 2003.08a
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• pp.95-103
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• 2003

목차 및 권말색인과 같은 인쇄형태의 정보내용에 대한 구조화된 접근방식에서 착안하여 전자 문서의 내용에 대한 새로운 형태의 접근방식을 개발할 수 있는데, 이를 위한 방안으로 용어 자동분류 기법이 있다. 본 연구에서는 용어의 의미모호성 문제를 해결하는 동시에 용어간 계층관계 표현이 가능한 자동분류 기법으로 퍼지 클러스터링 기법을 제안하고, 대표적인 퍼지 클러스터링 알고리즘인 퍼지 c-means 기법에 대해 분석하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.8
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• pp.1049-1054
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• 2018

Keyword searching used in the past as a method of finding similar patents, and automated classification by machine learning is using in recently. Keyword searching is a method of analyzing data that is formalized through data refinement. While the accuracy for short text is high, long one consisted of several words like as document that is not able to analyze the meaning contained in sentences. In semantic analysis level, the method of automatic classification is used to classify sentences composed of several words by unstructured data analysis. There was an attempt to find similar documents by combining the two methods. However, it have a problem in the algorithm w the methods of analysis are different ways to use simultaneous unstructured data and regular data. In this paper, we study the method of extracting keywords implied in the document and using the LDA(Latent Semantic Analysis) method to classify documents efficiently without human intervention and finding similar patents.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.9-21
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• 2021

The study aims to identify convergence and trends in technology-based patent data for the biohealth sector in IP5 countries (KR, EP, JP, US, CN) and present the direction of development in that industry. We used patent co-classification analysis-based network analysis and TF-IDF-based text mining as the principal methodology to understand the current state of technology convergence. As a result, the technology convergence cluster in the biohealth industry was derived in three forms: (A) Medical device for treatment, (B) Medical data processing, and (C) Medical device for biometrics. Besides, as a result of trend analysis based on technology convergence results, it is analyzed that Korea is likely to dominate the market with patents with high commercial value in the future as it is derived as a market leader in (B) medical data processing. In particular, the field is expected to require technology convergence activation policies and R&D support strategies for the technology as the possibility of medical data utilization by domestic bio-health companies expands, along with the policy conversion of the "Data 3 Act" passed by the National Assembly in January 2019.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.382-390
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• 2019

As there is severe competition in the global foldable display market, strategic technology planning is required. Patent analysis as a tool for technology planning has frequently been used due to data characteristics such as openness, formality, and detailed information. However, traditional patent analysis has various limitations such as quantitative approaches are limited in evaluating contents of patents and identifying core technologies of firms as they rely on number of patents, and qualitative approaches have time and cost problems as researchers have to investigate each patent on a case-by-case basis. In this research, we analyze core technologies of firms in the foldable display sector analyzing patent co-classification Network. Results show that the number of patent applications has rapidly increased since 2014, and 92% of these patents are held by two panel manufacturers, SDC and LGD, and two device manufacturers, SEC and LGE. Network analysis shows that the two panel manufacturers' core technologies are similar and two device manufacturers are notably different. This research provides implications to the sector. Moreover, this study provides unique results drawn from co-classification network analysis, and therefore, our research suggests patent co-classification analysis as an effective tool for technology planning.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.11-19
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• 2013

This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover, the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.