• Journal of the Korea Society of Computer and Information
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• v.19 no.5
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• pp.27-35
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• 2014

The characteristics of today's health care industry, based on the state of the art IT can be represented as a paradigm of human-oriented ubiquitous and accessible as possible by U-Health care. In addition, the healthcare industry is information and communication technologies (ICT) developments regarding the many advances and applications based on the research being carried out actively. Medical information system has been developed toward combining information systems of medical IT and it sets its sights on the fusion of developed IT and u-healthcare system. So changing distributed medical information systems into a safe PHR integrated system based on IaaS cloud computing is suggested in order to forge u-healthcare system with the times in this paper. Our experimental results show that our proposed system increased the data access time by about 24% and reduces the waiting time for processing service by about 4.3% over the web-based PHR.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.101-109
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• 2014

Medical technology development and increase the income level of a "Long and healthy Life=Wellness," with the growing interest in actively promoting and maintaining health and wellness has become enlarged. In addition, the demand for personalized health care services is growing and extensive medical moves of big data, disease prevention, too. In this paper, the main interest in the market, highlighting wellness in order to support big data-driven healthcare quality through patient-centered medical services purposes. Patients with drug dependence treatment is not to diet but to improve disease prevention and treatment based on analysis of big data. Analysing your Tweets-daily information and wellness disease prevention and treatment, based on the purpose of the dictionary. Efficient big data analysis for node while increasing processing time experiment. Test result case of total access time efficient 26% of one node to three nodes and case of data storage is 63%, case of data aggregate is 18% efficient of one node to three nodes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.38-40
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• 2021

Medical domain has a massive number of data records without the response value. Self-supervised learning is a suitable method for medical data since it learns pretext-task and supervision, which the model can understand the semantic representation of data without response values. However, since self-supervised learning performance depends on the expression learned by the pretext-task, it is necessary to define an appropriate Pretext-task with data feature consideration. In this paper, to actively exploit the unlabeled medical data into artificial intelligence research, experimentally find pretext-tasks that suitable for the medical data and analyze the result. We use the x-ray image dataset which is effectively utilizable for the medical domain.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.9-10
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• 2015

최근 임베디드 소프트웨어는 전자기기뿐만 아니라 의료기기, 자동차, 항공 등 광범위하게 적용되고 있다. 임베디드 소프트웨어의 규모가 커지는 만큼 비중도 높아지고 있어 임베디드 소프트웨어 테스팅의 비중도 높아지고 있다. 따라서 본 논문에서는 디버깅 도구인 GDB를 테스팅에 과정에 연계하여 현재 실행중인 프로세스에서 겸사하고자 하는 단위 소프트웨어의 스택 프레임으로부터 정보를 얻어 단위 테스팅 수행이 가능한 구조를 제안한다. 이를 통해 별도의 코드를 수정하지 않고도 단위 테스팅이 가능해지며 런타임 테스팅과 자동 단위 테스팅이 가능해진다.

• Journal of Service Research and Studies
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• v.5 no.2
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• pp.25-33
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• 2015

As Data mining is a method of extracting the information based on the large data, the technique has been used in many application areas to deal with data in particular. However, the status of the algorithm that can deal with the healthcare data are not fully developed. In this paper, One of clustering algorithm, the EM and DBSCAN are used for performance comparison. It could be analyzed using by the same data. To do this, EM and DBSACN algorithm are changing performance according to the variables in Health expenditure database. Based on the results of the experimental data, We analyze more precise and accurate results using by Kernel Filtering. In this study, we tried comparison of the performance for the algorithm as well as attempt to improve the performance. Through this work, we were analyzed the comparison result of the application of the experimental data and of performance change according to expansion algorithm. Especially, Collects data from the various cluster using the medical record, it could be recommended the effective spending on medical services.