• Journal of Korea Technology Innovation Society
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• v.17 no.4
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• pp.733-762
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• 2014

The government mandates that national research facilities & equipments through R&D business budget should be registered on the National Science and Technology Information Service (NTIS) for the purpose of the efficient use of the research facilities & equipments. This study is to contribute to the national policies on the efficient management of the research facilities & equipments by recognition methodology with the university's members and analysis of the impact factors of the universities' registration process improvement through the Define level and Measure level of the Six Sigma DAMIC. The survey and interview were conducted on research directors, professors joining university administration, graduate students, researchers, and staffs of A University. The findings are the lack of understanding specific steps and life-cycle management of research facilities & equipments. It is necessary to collect suggestions from universities and pursue policies considered the unique characteristics of the university for advanced operating and maximizing use of university's national research facilities & equipments. Research facilities & equipments enrollment compliance rate and registration accuracy were selected as CTQ-Y through the Six Sigma. 72 potential cause variables were derived through Process Map and C & E Diagram. 13 variables were determined as core potential factors through the X-Y Matrix and Pareto Chart. Research institutions should maximize utilization of research facilities & equipments through deriving a potential variables of the process improvements and designing a detail improvements based on the characteristics of each institutions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.503-523
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• 2022

Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 - 45 degrees) and left (30 - 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.