• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.7
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• pp.630-642
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• 2013

To transmit vital signal stream of mobile patients remotely, it requires mobility of patient and watcher, sensing function of patient's abnormal symptom and self-organizing service binding of related computing resources. In the existing relative researches, the vital signal stream is transmitted as a centralized approach which exposure the single point of failure itself and incur data traffic to central server although it is localized service. Self-organizing middleware platform based on heterogenous overlay network is a middleware platform which can transmit real-time data from sensor device(including vital signal measure devices) to Smartphone, TV, PC and external system through overlay network applied self-organizing mechanism. It can transmit and save vital signal stream from sensor device autonomically without arbitration of management server and several receiving devices can simultaneously receive and display through interaction of nodes in real-time.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.429-438
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• 1997

Mechanical valve is one of the most widely used implantable artificial organs of which the reliability is so important that its failure means the death of patient. Therefore early noninvasive detection is essentially required, though mechanical valve failure with thrombosis is the most common. The objective of this paper is to detect the thrombosis formation by spectral analysis and neural network. Using microphone and amplifier, we measured the sound from the mechanical valve which is attached to the pneumatic ventricular assist device. The sound was sampled by A/D converter(DaqBook 100) and the periodogram is the main algorithm for obtaining spectrum. We made the thrombosis models using pellethane and silicon and they are thrombosis model on the valvular disk, around the sewing ring and fibrous tissue growth across the orifice of valve. The performance of the measurment system was tested firstly using 1 KHz sinusoidal wave. The measurement system detected well 1KHz spectrum as expected. The spectrum of normal and 5 kinds of thrombotic valve were obtained and primary and secondary peak appeared in each spectrum waveform. We find that the secondary peak changes according to the thrombosis model. So to distinguish the secondary peak of normal and thrombotic valve quantatively, 3 layer back propagation neural network, which contains 7, 000 input node, 20 hidden layer and 1 output was employed The trained neural network can distinguish normal and valve with more than 90% probability. As a conclusion, the noninvasive monitoring of implanted mechanical valve is possible by analysing the acoustical spectrum using neural network algorithm and this method will be applied to the performance evaluation of other implantable artificial organs.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.2
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• pp.295-310
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• 2014

DCS (Distributed Control System), the main control system of power plants, is an automated system for enhancing operational efficiency by monitoring, tuning and real-time operation. DCS is becoming more intelligent and open systems as Information technology are evolving. In addition, there are a large amount of investment to enable proactive facility management, maintenance and risk management through the predictive diagnostics. However, new upcoming weaponized malware, such as Stuxnet designed for disrupting industrial control system(ICS), become new threat to the main control system of the power plant. Even though these systems are not connected with any other outside network. The main control systems used in the power plant usually have been used for more than 10 years. Also, this system requires the extremely high availability (rapid recovery and low failure frequency). Therefore, installing updates including security patches is not easy. Even more, in some cases, installing security updates can break the warranty by the vendor's policy. If DCS is exposed a potential vulnerability, serious concerns are to be expected. In this paper, we conduct the penetration test by using NESSUS, a general-purpose vulnerability scanner under the simulated environment configured with the Ovation version 1.5. From this result, we suggest a log analysis method to detect the security infringement and react the incident effectively.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.3
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• pp.181-187
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• 2016

A thermal power plant has a heat exchanger tube to collect and convert the heat generated from the high temperature and pressure steam to energy, but the tubes are arranged in a complex manner. In the event that a leakage occurs in any of these tubes, the high-pressure steam leaks out and may cause the neighboring tubes to rupture. This leakage can finally stop power generation, and hence there is a dire need to establish a suitable technology capable of detecting tube leaks at an early stage even before it occurs. As shown in this paper, by applying acoustic emission (AE) technology in existing boiler tube leak detection equipment (BTLD), we developed a system that detects these leakages early enough and generates an alarm at an early stage to necessitate action; the developed system works better that the existing system used to detect fine leakages. We verified the usability of the system in a 560MW-class thermal power plant boiler by conducting leak tests by simulating leakages from a variety of hole sizes (ⵁ2, ⵁ5, ⵁ10 mm). Results show that while the existing fine leakage detection system does not detect fine leakages of ⵁ2 mm and ⵁ5 mm, the newly developed system could detect leakages early enough and generate an alarm at an early stage, and it is possible to increase the signal to more than 18 dB.