• 재활복지
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• v.18 no.4
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• pp.221-235
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• 2014

Recently, the frequency of stroke disease is increased due to the rapid aging population, and is contributed to the major occurrence factors of the posteriori acquired disability. This study is about an postural change device for the control of supine posture which is an assisted equipment using in daily rehabilitation process for overcoming the disability by the aftereffects of the stoke disease. In this paper, the existing domestic and Japan postural appliances is examined and its comparison and categorization is performed according to its functions and purposes. Here, in order to control the supine posture state, the design method for advanced multi functional system is proposed, which is devised to have an unified mattress control operations of combining the bedsore prevention tube with the supine posture tilting tube. And also, in addition of an smart function, it is designed to enable to perform an RF functions such as the monitoring of the present device state, the alteration of the basic position and the control of alternative floating and supine posture. This system control hardware consists of three main parts : the sensor detection part, the motor driving /control part, and the system control part for bluetooth communication. In results, we confirmed that the system designed by this research is possible to make it practical as an advanced smart postural change device combined by IoT technology in the application field of the recent IT technology.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.