• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.21-32
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• 2021

Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly increasing. The increased transactions of electronic prepayment, however, also leads to the increased fraud attempts. It is mainly because electronic prepayment can easily be converted into cash. The objective of this paper is to develop a methodology that can effectively detect fraud transactions in electronic prepayment, by using sequential pattern mining techniques. To validate our approach, experiments on real transaction data were conducted and the applicability of the proposed method was demonstrated. As a result, the accuracy of the proposed method has been 95.6 percent, showing that the proposed method can effectively detect fraud transactions. The proposed method could be used to reduce the damage caused by the fraud attempts of electronic prepayment.

• Journal of Internet of Things and Convergence
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• v.7 no.3
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• pp.69-74
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• 2021

With the recent development of biometric authentication technology, the user authentication techniques using biometric authentication are increasing. Various problems arised in certification techniques that use various existing methods such as ID/PW. Therefore, recently, a method of improving security by introducing biometric authentication as secondary authentication has been used. In this thesis, proposal of the user authentication system that can detect user identification and anomalies using ECGs that are extremely difficult to falsify through the electrical biometric signals from the heart among various biometric authentication devices is studied. The system detects user anomalies by comparing ECG data received from a wrist-mounted wearable device-type ECG measurement tool with identification and ECG data stored in blockchain form on the database and identifying the user's location through a beacon system.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.157-164
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• 2019

This research examines how IoT makes a significant contribution to the innovation of media firms. The media firms will be able to find new reveue sources and strengthen firms' competence through innovating product, process and business model. While IoT increases the experience of interactivity and immersion for consumption, it improves the way ads are exposed and its impact is measured, leading to revenue increase. For these benefits fulfilled, innovation friendly media eco-system must be established. It is the most critical that media firms should change skeptical attitude toward IoT's potential and actively invest it to employ IoT. The government should create regulatory framework to best utilize the innovative advantages of IoT.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.75-86
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• 2020

With the advent of the 4.0 era of logistics due to the Fourth Industrial Revolution, infrastructures have been built to receive the same services online and offline. Logistics services affected by logistics 4.0 and IT technology are rapidly changing. Logistics services are developing using technologies such as big data, artificial intelligence, blockchain, Internet of things, and augmented reality. The convergence of logistics services and various IT new technologies is accelerating, and the development of data management solution technology has led to the emergence of electronic cargo waybill to replace paper cargo waybill. The electronic waybill was developed to supplement paper waybill that lack economical and safety. However, the electronic waybill that appeared to complement the paper waybill are also in need of complementation in terms of efficiency and reliability. New research is needed to ensure that electronic cargo waybill gain the trust of users and are actively utilized. To solve this problem, electronic cargo waybill that combine blockchain technology are being developed. This study aims to improve the reliability, operational efficiency and safety of blockchain electronic cargo waybill. The purpose of this study is to analyze the blockchain-based electronic cargo waybill system and to derive evaluation indicators for system supplementation.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.761-770
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• 2023

This study was conducted to identify factors affecting depression in the elderly using three-year Community Health Survey data from 2020, when COVID-19 was declared an epidemic. Differences in depression according to general characteristics, health behavior, subjective health level, and medical use among 220,921 elderly were analyzed using complex samples t-test and ANOVA, and multiple regression analysis was performed to identify factors affecting depression it was carried out. As a result of the study, the level of depression among elderly women was found to be high, with an average of 1.21±0.01 for elderly men and 1.74±0.02 for elderly women, and there was a difference in generation type, with depression being higher in the first generation for elderly men and the third generation for elderly women. Variables that had a great influence on depression were the experience of depression and perceived stress.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.625-632
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• 2024

This paper analyzed the changes in the curriculum of computer and software-related majors that educate the core ICT technologies needed for the 4th Industrial Revolution, before and after the COVID-19 pandemic. According to the standard classification of university education units, 172 majors classified into Applied Software Engineering, Computer Science·Computer Engineering, and Artificial Intelligence Engineering were targeted, and the curricula of 2023 and 2019 were compared and analyzed. As a result of the analysis, the introduction of the related curriculum for each curriculum group increased by about 2.6%p before and after the COVID-19 pandemic (2023 84.2%, 2019 81.6%). and the 4th Industrial Revolution response index increased by 9.5 points (37.0 in 2023, 27.5 in 2019)

• The Journal of Bigdata
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• v.8 no.2
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• pp.15-25
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• 2023

Extreme learning machines (ELMs) are a major analytical method in various prediction fields. ELMs can accurately predict even if the data contains noise or is nonlinear by learning the complex patterns of time series data through optimal learning. This study presents the recent trends of machine learning models that are mainly studied as tools for analyzing online time series data, along with the application characteristics using existing algorithms. In order to efficiently learn large-scale online data that is continuously and explosively generated, it is necessary to have a learning technology that can perform well even in properties that can evolve in various ways. Therefore, this study examines a comprehensive overview of the latest machine learning models applied to big data in the field of time series prediction, discusses the general characteristics of the latest models that learn online data, which is one of the major challenges of machine learning for big data, and how efficiently they can learn and use online time series data for prediction, and proposes alternatives.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.664-672
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• 2004

Widespread popularity of wireless data communication devices, coupled with the availability of higher bandwidths, has led to an increased user demand for content-rich media such as images and videos. Since such content often tends to be private, sensitive, or paid for, there exists a requirement for securing such communication. However, solutions that rely only on traditional compute-intensive security mechanisms are unsuitable for resource-constrained wireless and embedded devices. In this paper, we propose a selective partial image encryption scheme for image data hiding , which enables highly efficient secure communication of image data to and from resource constrained wireless devices. The encryption scheme is invoked during the image compression process, with the encryption being performed between the quantizer and the entropy coder stages. Three data selection schemes are proposed： subband selection, data bit selection and random selection. We show that these schemes make secure communication of images feasible for constrained embed-ded devices. In addition we demonstrate how these schemes can be dynamically configured to trade-off the amount of ded devices. In addition we demonstrate how these schemes can be dynamically configured to trade-off the amount of data hiding achieved with the computation requirements imposed on the wireless devices. Experiments conducted on over 500 test images reveal that, by using our techniques, the fraction of data to be encrypted with our scheme varies between 0.0244％ and 0.39％ of the original image size. The peak signal to noise ratios (PSNR) of the encrypted image were observed to vary between about 9.5㏈ to 7.5㏈. In addition, visual test indicate that our schemes are capable of providing a high degree of data hiding with much lower computational costs.

• Information Systems Review
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• v.17 no.1
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• pp.117-140
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• 2015

Cloud Computing has drawn attention as one of 10 IT strategic technology trends and has various advantages such as cost reduction and enhancing business flexibility. However, corporations hesitate to adopt the service because of unexpected risks. Especially compared to large firm, medium and small ones use public cloud that security risk is high. Meanwhile, real option strategy has drawn attention as the method to hedge uncertainty in IT projects. Therefore, in this study causal relationships among technical, security, relational, and economic risks of cloud service will be investigated. Eventually, this study investigates how those risks influence the intention to choose the real option about the cloud service. For this study, five hypotheses is drawn, and a survey is conducted about the medium and small firms which are currently using cloud service to examine hypotheses. Since the study is at organizational level, 287 questionnaire replies are recalculated to 120 firms. For statistical analysis, Smart PLS and SPSS Statistics18 are used. As a result, technical risk of cloud service has significantly positive influence on security risk. Second, security risk and relational risk of cloud service has significantly positive influence on economic risk. Third, economic risk of cloud service has significantly positive influence on the intention to purchase the delay option or abandon option. Based on this result, this research discussed practical and academic implications and the limitations.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2003.11a
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• pp.23-40
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• 2003

초고속인터넷 접속서비스 시장이 성숙 단계에 접어듬으로써 성장세는 급격히 둔화되고 경쟁환경은 심화되는 과정에서 통신사업자들이 기존의 신규가입자 유치 위주의 정책에서 벗어나 초고속인터넷 가입자 인프라를 기반으로 새로운 부가 서비스 및 차별화 된 서비스의 개발을 통해 가입자 확대 및 ARPU(Average Revenue Per Unit)를 중대 시킬 수 있는 수익원 개발이 절실하다. 이러한 상황하에서 통신방송 융합 서비스는 통신사업자에게 기회와 위협이라는 상반된 현상으로 다가오고 있다. TV-based VOD 서비스는 보다 양질의 멀티미디어 서비스를 원하는 고객의 수요와 신규 고객을 유치하고 가입자를 Lock-in 시키며, 타 통신 사업자의 고객을 자사 고객으로 전환시키고자 하는 차별화 전략의 일환으로 도입되기 시작하였고, 또한 ARPU를 증대 시키고 다양한 수익원 개발의 일환으로 인터넷 접속서비스 외에 부가가치를 창출 할 수 있는 Killer Service로서 주목을 받고 있다. TV VOD 사업이 성공적으로 국내 시장에 정착될 경우, 통신사업자에게는 여러 형태의 수익원 발굴이 가능할 것으로 예상된다. 즉 가입비, 월 이용료, 부가 사용료 등과 같은 포괄적인 형태의 가입비가 주된 수익원이 될 것이이며, pay-per-view(PPV) 형태의 이용료 역시 주된 수익원이 될 것이다. 이 외에도 셋톱박스, 홈게이트웨이/서버 등과 같은 단말기의 임대와 판매를 통한 수익 창출방안과 다른 서비스들과 마찬가지로 광고도 중요한 수익원천으로 활용 할 수 있을 것이며 또한 미디어업체 또는 CP(contents provider)를 대상으로, 보안, 인증, 과금, 결제, 회원 관리, 이용정보 제공 서비스 등도 사업자의 수익원으로 활용할 이 있을 것이다. TV-based VOD 서비스는 망 진화에 따른 차세대 초고속인터넷 서비스 기술인 VDSL이나 Advanced 케이블모뎀을 이용 DVD 급 고화질의 TV-based VOD 서비스가 시작되면서 ARPU를 증대 시키고 다양한 수익원 개발의 일환으로 인터넷 접속서비스 외에 부가가치를 창출 할 수 있는 Killer Service로서 VOD서비스가 주목을 받고 있다. TV-based VOD 서비스는 가입자망의 진화 와 밀접하게 연관되어 전개되는 서비스로 Post-ADSL에 대한 예측과 VOD에 서비스에 대한 설문조사 결과를 토대로 하여 예측 한 바에 의하면 Post-ADSL 가입자는 2003년 153만명에서 2004년 444만으로 증가하고 2006년 987만 명에 이를 것으로 전망되고, VOD서비스 가입의향도 16%에서 2006년 34% 까지 증가하는 것으로 나타났다. 따라서 VOD서비스 가입자는 2003년 24만 명에서 2004년 98만으로 증가하고 2006년 335만 명으로 증가하는 것으로 예측되었다.