• Proceedings of the Korea Multimedia Society Conference
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• 2001.06a
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• pp.265-269
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• 2001

멀티캐스트 라우팅 데몬은, 패킷 포워딩이 가능한 호스트 상에서 작동하여 호스트를 멀티캐스트 라우팅이 가능한 라우터로 만들어주는 역할을 하는 응용 프로그램이다. 현재의 리눅스 IP$_{v}$ 6 멀티캐스트 환경을 살펴보면 로컬 네트워트 상에서의 멀티캐스트 통신만을 지원하도록 되어있다. 즉 서로 다른 서브넷 상에 존재하는 호스트들 사이에서는 멀티캐스트 통신을 할 수 없다. 따라서, 본 논문에서는 리눅스 IP$_{v}$ 6 환경에서 멀티캐스트 라우팅이 가능하도록 IP$_{v}$ 6용 멀티캐스트 라우팅 데몬을 구현하였다. 멀티캐스트 라우팅이 가능하기 위해서는 두가지 문제가 해결되어야 하는데 첫째는 멀티캐스트 라우팅 정보를 주고받는 데몬 프로그램이고, 둘째는 데몬이 주고받은 라우팅 정보를 이용하여 멀티캐스트 패킷을 포워딩하는 커널 포워딩 루틴이다. 이 두가지가 본 논문에서 중심으로 다룰 내용이다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.12C
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• pp.1245-1250
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• 2002

For recent years, the environment of use on internet has been changed from the wired internet access to the wireless internet access increasingly, and the use of real time application data has been increased. Therefore the research and evolution of wireless network for providing of user's mobility and QoS at the same time must be essential. IETF already proposed Mobile IPv6 for providing mobility and RSVP for QoS through resource reservation. These future are expected to be used not only wired network but also wireless network. But there are not yet proposed the integration and efficient interworking of two mechanism. For the solution of problem on efficient interworking issue, for instance signaling overhead and reservation delay increasing due to often handoff, this paper first proposed to use of IETF Cellular IP based on Mobile IPv6 for wireless network model with fast mobility and a improved RSVP mechanism using the flow label field in IPv6 header.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.267-273
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• 2024

Using a cloud-based vertex AI platform that can develop an artificial intelligence learning model without coding, this study easily developed an artificial intelligence learning model by the non-professional general public and confirmed its clinical applicability. Nine dental diseases and 2,999 root disease X-ray images released on the Kaggle site were used for the learning data, and learning, verification, and test data images were randomly classified. Image classification and multi-label learning were performed through hyper-parameter tuning work using a learning pipeline in vertex AI's basic learning model workflow. As a result of performing AutoML(Automated Machine Learning), AUC(Area Under Curve) was found to be 0.967, precision was 95.6%, and reproduction rate was 95.2%. It was confirmed that the learned artificial intelligence model was sufficient for clinical diagnosis.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.30 no.4
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• pp.29-50
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• 2019

This study was to suggest the considerations for application profiles of 2019 revised RDA using literature reviews and case studies according to new RDA that revised in order to reflect the LRM and linked data. First, the additional elements were recommended as the contents of application profiles such as inverse element, broader element, narrow element, domain, range, alternate label name, mapping to MARC, mapping to BIBFRAME, and RDA description examples as new elements as well as element name, element ID, element URL, description method, vocabulary encoding scheme, data provenance element, data provenance value, and notes as the elements that were already suggested by previous researches. Second, RDA rules' representations in forms of flow chart and application profiles through analyzing RDA rules were suggested in order to apply the rules to RDA application profiles to structure the rules in which every element has 4 types of description method, many conditions, and options. Third, the RDA mapping to BIBFRAME was suggested in RDA application profiles because RDA and BIBFRAME are co-related in context of content standard and encoding format, and mapping BIBFRAME and RDA is necessitated for programming BIBFRAME editors with RDA as content standard. This study will contribute to find the methods for constructing RDA application profiles and BIBFRAME application profiles with RDA as content standard.