• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.86-93
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• 2016

Information technology, such as IoT, Big Data, Drone, Cloud etc., is evolving every year. Information Society is changing Intelligence Society and Creative Society. A new Construction Projects Management System Roadmap is required because it is difficult to reflect the current IT environments based on the CALS(Continuous Acquisition & Life-cycle Support) master plan, which is performed to establish every five years since 1998. This study was prepared for the Roadmap with a focus on Construction Management System based on the 4th CALS master plan, which was performed to establish the 2012 year. To this end, the construction environment and several information systems were investigated and analyzed. The problems of the construction project information system were derived using SWOT analysis, the vision, goal, direction, strategy, main tasks, specific tasks, and timetable of the Construction Project Management System are presented. This roadmap is designed to be used as operational indicators of a future construction project management system.

• Korean Journal of Heritage: History & Science
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• v.51 no.4
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• pp.170-191
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• 2018

Until now, the Korean archeological community has only been able to introduce the findings of classical archaeology developed in the West, and it also suffers from a lack of concepts and academic achievements. The domestic archeological community also started to develop later than that of the West, when it began to analyze ancient history and relics through the classic works of history titled Samguk sagi (三國史記) and Samguk yusa (三國遺事). Furthermore, it is actively utilizing the Chinese classics, such as the Samgukji (三國志) and Huhanseo (後漢書), as well as certain Japanese classics such as Ilbonsegi (日本書紀). Due to the total lack of domestic classics, however, there are few details about the formation of ancient polities, national changes, and inter-country negotiations and exchanges, as well as numerous other unresolved issues. This study raises the need to revamp classical archaeology in order to solve these problems. The concept of classical means 'all records made in the past' in the shallow sense, while the meaning of the historiography means "historical records according to the taxonomy of the old book." Classical archaeology is a field in which the classics are analyzed and interpreted so as to study the culture of the past. This section has set up a wide range of classical categories, and has found that the classics can be used in a meaningful way in classical archaeology through the use of the Gongjagae (孔子家語). The use of the classics in classical archaeology could produce significant results if the relevant DB is managed by various institutions and organizations using proper techniques of analysis including big data analysis.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.2
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• pp.367-374
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• 2017

This paper discusses management problems of Hadoop distributed node, which is a platform for big data processing, and proposes a novel technique for enabling flexible node management of Hadoop Distributed File System. Hadoop cannot configure Hadoop cluster dynamically because it judges temporarily unavailable nodes as a failure. Delayed block replication scheme proposed in this paper delays the removal of unavailable node as much as possible so as to be easily rejoined. Experimental results show that the proposed scheme increases flexibility of node management with little impact on distributed processing performance when the cluster size changes.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.111-120
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• 2020

As an application of big data and artificial intelligence techniques, this study proposes an atypical language-based sentimental opinion poll methodology, unlike conventional opinion poll methodology. An alternative method for the sentimental classification model based on existing statistical analysis was to collect real-time Twitter data related to parliamentary elections and perform empirical analyses on the Polarity and Intensity of public opinion using attribute-based sensitivity analysis. In order to classify the polarity of words used on individual SNS, the polarity of the new Twitter data was estimated using the learned Lasso and Ridge regression models while extracting independent variables that greatly affect the polarity variables. A social network analysis of the relationships of people with friends on SNS suggested a way to identify peer group sensitivity. Based on what voters expressed on social media, political opinion sensitivity analysis was used to predict party approval rating and measure the accuracy of the predictive model polarity analysis, confirming the applicability of the sensitivity analysis methodology in the political field.

• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.123-141
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• 2021

As the dependence of technology on the economic structure increases, the importance of National Core Technology is increasing. However, due to the nature of the technology itself, it is difficult to determine the scope of the technology to be protected because the scope of the relation is abstract and information disclosure is limited due to the nature of the National Core Technology. To solve this problem, we propose the most appropriate literature type and method of analysis to distinguish important technologies related to National Core Technology. We conducted a pilot test to apply TF-IDF, and LDA topic modeling, two techniques of text mining analysis for big data analysis, to four types of literature (news, papers, reports, patents) collected with National Core Technology keywords in the field of Display industry. As a result, applying LDA theme modeling to patent data are highly relevant to National Core Technology. Important technologies related to the front and rear industries of displays, including OLEDs and microLEDs, were identified, and the results were visualized as networks to clarify the scope of important technologies associated with National Core Technology. Throughout this study, we have clarified the ambiguity of the scope of association of technologies and overcome the limited information disclosure characteristics of national core technologies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.41-43
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• 2022

In order to prevent and block infectious diseases caused by the recent COVID-19 pandemic, non-contact biometric information acquisition and analysis technology is attracting attention. The invasive and attached biometric information acquisition method accurately has the advantage of measuring biometric information, but has a risk of increasing contagious diseases due to the close contact. To solve these problems, the non-contact method of extracting biometric information such as human fingerprints, faces, iris, veins, voice, and signatures with automated devices is increasing in various industries as data processing speed increases and recognition accuracy increases. However, although the accuracy of the non-contact biometric data acquisition technology is improved, the non-contact method is greatly influenced by the surrounding environment of the object to be measured, which is resulting in distortion of measurement information and poor accuracy. In this paper, we propose a context-based bio-signal modeling technique for the interpretation of personalized information (image, signal, etc.) for bio-information analysis. Context-based biometric information modeling techniques present a model that considers contextual and user information in biometric information measurement in order to improve performance. The proposed model analyzes signal information based on the feature probability distribution through context-based signal analysis that can maximize the predicted value probability.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.131-143
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• 2022

Focusing on FinTech keywords, this study is analyzing newspaper articles and Twitter data by using text mining methodology in order to understand trends in the industry of domestic digital financial service. In the growth of FinTech lifecycle, the frequency analysis has been performed by four important points: Mobile Payment Service, Internet Primary Bank, Data 3 Act, MyData Businesses. Utilizing frequency analysis, which combines the keywords 'China', 'USA', and 'Future' with the 'FinTech', has been predicting the FinTech industry regarding of the current and future position. Next, sentiment analysis was conducted on Twitter to quantify consumers' expectations and concerns about FinTech services. Therefore, this study is able to share meaningful perspective in that it presented strategic directions that the government and companies can use to understanding future FinTech market by combining frequency analysis and sentiment analysis.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.4
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• pp.73-89
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• 2023

This study aims to analyze the frequency of keywords used in Korean abstracts, which are unstructured text data in the domestic record management research field, using text mining techniques to identify domestic record management research trends through distance analysis between keywords. To this end, 1,157 keywords of 77,578 journals were visualized by extracting 1,157 articles from 7 journal types (28 types) searched by major category (complex study) and middle category (literature informatics) from the institutional statistics (registered site, candidate site) of the Korean Citation Index (KCI). Analysis of t-Distributed Stochastic Neighbor Embedding (t-SNE) and Scattertext using Word2vec was performed. As a result of the analysis, first, it was confirmed that keywords such as "record management" (889 times), "analysis" (888 times), "archive" (742 times), "record" (562 times), and "utilization" (449 times) were treated as significant topics by researchers. Second, Word2vec analysis generated vector representations between keywords, and similarity distances were investigated and visualized using t-SNE and Scattertext. In the visualization results, the research area for record management was divided into two groups, with keywords such as "archiving," "national record management," "standardization," "official documents," and "record management systems" occurring frequently in the first group (past). On the other hand, keywords such as "community," "data," "record information service," "online," and "digital archives" in the second group (current) were garnering substantial focus.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.378-378
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• 2022

상수도시스템의 과정 중 최종 단계인 급수단계에서 지역전반에 수질문제가 발생할 경우, 직간접적인 피해의 해결은 장기간 지속될 수 있다. 본 연구에서는 실시간 비정형정보의 빅데이터 분석을 통해 상수도시스템에서 수질사고 문제의 파급력과 2차 피해 등의 연결 관계 변화 추적을 위한 기초적 분석을 수행하였다. 과거 대규모 수질사고가 발생된 바 있는 인천광역시 유충발생 사고를 대상으로 뉴스 기사 웹크롤링 절차를 정립하고, 그 결과를 분석하였다. '인천 유충'이 최초 보도되었던 2020년 7월 13일 부터 이후 1년을 대상으로 네이버 통합검색에 의해 표출되는 뉴스기사를 웹크롤링하였으며, 프로그래밍을 통한 불용어 제거 및 관련성 검토를 통해 총 920건의 기사를 분석하였다. 수질사고의 전개양상에 따라 사고발생, 확산, 수습, 그리고 보상의 4단계로 임의 구분하여 분석하였다. 의미분석을 위한 토픽모델링 기법은 잠재 디리클레 할당(Latent Dirichlet Allocation, LDA) 방법을 적용하였으며, 긍부정 감정분석은 KNU 한국어 감성사전(KNU sentiment lexicon)을 활용하여 수행하였다. 토픽 모델링 결과, 사고 발생에서부터 확산, 수습, 보상의 단계에 맞춰 적절한 주제어의 조합에 따른 기사들이 도출되었으며, 단계별 긍부정 기사 비율역시 사고의 전개단계에 따라 적절히 나타남을 확인하였다. 제시된 수질사고 관련 비정형정보 분석 방법론과 결과는 과거 사고 사례 분석을 통한 검색 및 긍부정 키워드 확정, 키워드 발생 비율 변동(사고전과 후)에 따른 상황판단 기준설정 등에 활용이 가능하다.