• Air Cleaning Technology
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• v.1 no.3 s.3
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• pp.40-48
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• 1988

• Air Cleaning Technology
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• v.21 no.4
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• pp.29-36
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• 2008

• Air Cleaning Technology
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• v.18 no.3 s.70
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• pp.15-33
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• 2005

• Air Cleaning Technology
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• v.12 no.4 s.47
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• pp.32-57
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• 1999

• Air Cleaning Technology
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• v.16 no.4 s.63
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• pp.7-20
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• 2003

• Air Cleaning Technology
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• v.16 no.4 s.63
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• pp.21-33
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• 2003

• Air Cleaning Technology
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• v.13 no.2 s.49
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• pp.70-82
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• 2000

• 건축구조
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• v.21 no.5
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• pp.77-82
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• 2014

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.7-13
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• 2020

In this paper, we propose a system that monitors and controls the environment of a server room in real-time based on IoT. Recently, as the information society has been maximized, the damage has been significant when the computer system is down. In particular, damage such as a fire in the server room leads to loss of data and data recovery becomes impossible. In order to reduce such damage, a node capable of real-time monitoring using an IoT system was proposed. In addition, we proposed a coordinator node that can collect and monitor node information. In order to verify that the proposed system works, we have developed software that can control node monitoring and air conditioning. As a result of the experiment, we confirmed that the proposed system detects fire and controls the air conditioner.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.319-330
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• 2017

Recently, various universities in Korea have started to work on strengthening their liberal arts and character education through the residential college (RC) system, carrying out various community programs for this purpose. However, because most programs are based on student-to-student relationships, problems can often arise within the community living environments. This paper proposes the roommate assignment algorithm in the context of a residential college, as to effectively achieve character education goals. The clustering algorithm we propose is based on the similarity hypothesis. As a result of the assignment, the degree of similarity (euclidean distance) between roommates was significantly higher than that assigned randomly. The algorithm developed in this study was applied to the data of the students living in the international campus of H University.