• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1137-1144
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• 2020

This paper aims to provide strategic suggestions by analyzing technology trends related to big data, IoT, and artificial intelligence. To this end, analysis was performed using the 2018 national R&D information, and major basic analysis and language network analysis were performed. As a result of the analysis, research and development related to big data, IoT, and artificial intelligence are being conducted by focusing on the basic and development stages, and it was found that universities and SMEs have a high proportion. In addition, as a result of the language network analysis, it is judged that the related fields are mainly research for use in the smart farm and healthcare fields. Based on these research results, first, big data is essential to use artificial intelligence, and personal identification research should be conducted more actively. Second, they argued that full-cycle support is needed for technology commercialization, not simple R&D activities, and the need to expand application fields.

• Information Systems Review
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• v.21 no.4
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• pp.175-195
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• 2019

This study conducted both big data and netnography analysis to analyze consumer needs and behaviors of online consumer community. Big data analysis is easy to identify correlations, but causality is difficult to identify. To overcome this limitation, we used netnography analysis together. The netnography methodology is excellent for context grasping. However, there is a limit in that it is time and costly to analyze a large amount of data accumulated for a long time. Therefore, in this study, we searched for patterns of overall data through big data analysis and discovered outliers that require netnography analysis, and then performed netnography analysis only before and after outliers. As a result of analysis, the cause of the phenomenon shown through big data analysis could be explained through netnography analysis. In addition, it was able to identify the internal structural changes of the community, which are not easily revealed by big data analysis. Therefore, this study was able to effectively explain much of online consumer behavior that was difficult to understand as well as contextual semantics from the unstructured data missed by big data. The big data-netnography integrated model proposed in this study can be used as a good tool to discover new consumer needs in the online environment.

• Industry Promotion Research
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• v.8 no.4
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• pp.257-266
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• 2023

The research focuses on the provincial universities in the western region of China and investigates the research level of 12 provincial universities from 2017 to 2021, considering both static efficiency and dynamic efficiency. The static efficiency is examined using Data Envelopment Analysis (DEA), while the dynamic efficiency is analyzed using the Malmquist model. The analysis results are as follows: the scientific research efficiency of universities in the 12 western provinces is generally not high. Against the background of the "Double First-Class" construction, the overall efficiency of scientific research in universities is showing an increasing trend. The main reason for the increase in scientific research efficiency is the increase in scale efficiency in recent years. The total factor productivity (TFP) of research activities is influenced by the technology progress index and exhibits a pattern of initial increase, followed by a decline, and then an increase again. Research conclusion: Western colleges and universities should reasonably allocate resources for scientific research activities, perfect scientific research mechanisms, improve management standards, promote scientific innovation and corresponding achievements, and ultimately raise the scientific and technological level in western China.

• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.128-133
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• 2017

In recent years, the number of systems for the analysis of large volumes of data is increasing. Hadoop, a representative big data system, stores and processes the large data in the distributed environment of multiple servers, where system-resource management is very important. The authors attempted to detect anomalies from the rapid changing of the log data that are collected from the multiple servers using simple but efficient anomaly-detection techniques. Accordingly, an Apache Hive storage architecture was designed to store the log data that were collected from the multiple servers in the Hadoop ecosystem. Also, three anomaly-detection techniques were designed based on the moving-average and 3-sigma concepts. It was finally confirmed that all three of the techniques detected the abnormal intervals correctly, while the weighted anomaly-detection technique is more precise than the basic techniques. These results show an excellent approach for the detection of log-data anomalies with the use of simple techniques in the Hadoop ecosystem.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.10
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• pp.796-800
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• 2009

Currently, there are many software tools that can generate 3D human figure models and animations based on the advancement of computer graphics technology. However, we still have problems in interoperability of human data models in different applications because common data models do not exist. To address this issue, the Web3D Consortium and the ISO/IEC JTC1 SC24 WG6 have developed the H-Anim standard. However, H-Anim does not include human motion data formats although it defines the structure of a human figure. This research is intended to obtain interoperable human animation by defining the data for human motions in H- Anim figures. In this paper, we describe a syntactic method to define motion data for the H-Anim figure and its implementation. In addition, we describe a method of specifying motion parameters necessary for generating animations by using an arbitrary character model data set created by a general graphics tool.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.324-325
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• 2016

The sensor network-based service collects data by using the sensor, the data is aware of the situation via the analysis, and the service provider provides a service suitable for the user via the context-awareness. However, this data is generated, it is difficult to match the metadata and standard units. The data integration is required to use the data generated by the different specifications of the sensor efficiently. Accordingly, in this paper we propose a method using an ontology as a method to integrate the data generated by the existing sensors and the new sensor. The ontology is mapping to the standard item and sensors, also include a type and structural difference. The mapping is comprised of two:data mapping, and metadata mapping. There are standard items that are created in this way, type of data exchange between services. This can solve the heterogeneous problem generated by sensors.

• Journal of the Korean Applied Science and Technology
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• v.38 no.1
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• pp.196-207
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• 2021

The purpose of this study is to investigate the relationship between trust, satisfaction and cognitive performance of golf participants in golf device, focusing on the swing analyzer. A total of 328 questionnaires were collected. Collected data were analyzed by SPSSWIN and AMOS program and frequency analysis, confirmatory factor analysis, validity test, correlation analysis and structural equation model analysis were performed. The result of the study were as follows. First, the trust of golf participants in golf device data has a positive effect on satisfaction. Second, the trust of golf participants in golf device data does not affect Perceived performance. Third, the satisfaction of golf participants in golf device data does not affect Perceived performance. In conclusion, golf participants' trust and satisfaction of the golf swing analyzer are irrelevant to the perceived performance. In conclusion, it was found that golf participants trusted the data presented through the golf device and obtained satisfactory results. However, in that it did not affect the perceived performance, golf participants can think that golf devices should be used to play golf rather than thinking that golf devices enhance their performance.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.259-270
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• 2021

Recently, interests in the use of Sharing Bike is increasing in consideration of eco-friendly transportation and safety from viruses. As the technology for collecting and storing data is improved with the development of ICTs, research on mobility using the Sharing Bike Data is also actively progressing. Therefore, this paper analyzes the properties of Sharing Bike Data and cases of researches on it through literature review, and based on the results of the review, data of Eoulling, the Sharing Bike System of Sejong City is analyzed as a way to utilize Sharing Bike Data. Most of the selected literature used structured data, and analyzed it through statistical methods or data mining. Through data analysis, it identified the current status, found out problems of the Sharing Bike System, proposed a solution to solve them, developed plans to activate the use of Sharing Bike. This provides basic data for efficient management and operation plans for Sharing Bike System. Ultimately, it will be possible to explore ways to improve mobility in urban spaces by utilizing Sharing Bike Data.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.76-76
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• 2022

과거 2015년 파리협정 채택을 기점으로 전 세계는 산업화 이전 대비 지구 평균온도 상승폭을 1.5℃ 이하로 억제하기 위한 노력을 지속적으로 강조하였다. 기후변화 완화를 위한 가장 적극적인 해결책으로 탄소중립 사회 전환이 제시되고 있으며, 이를 실행하기 위해서는 각 부문별 구체화된 탄소중립 추진 계획 수립이 요구된다. 특히 국내에서는 기후기술 분야에 특화된 기술수준 정보가 부족하여 국가 정책 수립에 어려움이 있다. 기술개발을 위한 정책 수립 시에는 기후기술의 정량적인 수준을 고려한 정책 방향을 결정해야 하지만, 국내에는 기술에 대한 분석에 대한 사례가 미흡한 실정이다. 본 연구에서는 수자원 분야의 국가경쟁력을 분석하고 미래기술전략을 도출하기 위해 논문·특허정보를 기반한 정량평가(활동력, 기술력, 포트폴리오)와 미래기술 예측을 수행하였다. 수자원 분야 기술은 2017년 과학기술정보통신부가 승인한 45대 기후기술 분류체계를 기본으로 하며, 적응 부문에서 '물관리 기술'과 '기후변화 예측 및 모니터링 기술'을 대상으로 하였다. 분석을 위해 수자원 분야 기술을 주요 5개국(한국, 중국, 일본, 미국, EU) 대상으로 수행하였으며, 데이터 기간은 2009년부터 2020년까지 총 12년간이다. 기술의 미래예측하기 위해 Bass 모형, Logistic 모형, Gompertz 모형 등을 활용하였으며, 향후 기술을 전망하고자 한다. 본 분석에서 수행하는 수자원 분야 기술예측은 탄소중립 실현을 위한 미래사회에 대비하고, 기술개발에 대한 불확실성을 감소시킬 수 있을 것으로 기대된다.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.35-44
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• 2021

Recent advances in the 4th Industrial Revolution have accelerated the change of the working environment, such that the paradigm of education has been shifted in accordance with career education including the free semester system and the high school credit system. While the purpose of those systems is students' self-motivated career exploration, educational limitations for teachers and students exist due to the rapid change of the information on education. Also, education technology research to tackle these limitations is relatively insufficient. To this end, this study first defines three requirements that education technologies for the career education system should consider. Then, through data-driven artificial intelligence technology, this study proposes a data system and an artificial intelligence recommendation model that incorporates the topics for career exploration, courses, and majors in one scheme. Finally, this study demonstrates that the set-based artificial intelligence model shows satisfactory performances on recommending career education contents such as courses and majors, and further confirms that the actual application of this system in the educational field is acceptable.