• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.856-859
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• 1997

In this paper we treat the application of fault detection method in DC motor having both model mismatch and noise problems. A fault detection method presented by Kwon et al. (1994) for SISO systems has been here experimented. The model mismatch includes here linearization error as well as undermodelling. Comparisons are made with the real plant, DC motor. The experimental result of robust fault detection method is shown to have good performance via with the alternative fault detection method which do not account noise.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.38-45
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• 2017

Attacking of computer and network is increasing as information processing technology heavily depends on computer and network. To prevent the attack of system and network, host and network based intrusion detection system has developed. But previous rule based system has a lot of difficulties. For this reason demand for developing a intrusion detection system which detects and cope with the attack of system and network resource in real time. In this paper we develop a real time intrusion detection system which is combination of APEX and LS-SVM classifier. Proposed system is for nonlinear data and guarantees convergence. While real time processing system has its advantages, such as memory efficiency and allowing a new training data, it also has its disadvantages of inaccuracy compared to batch way. Therefore proposed real time intrusion detection system shows similar performance in accuracy compared to batch way intrusion detection system, it can be deployed on a commercial scale.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1106-1116
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• 2011

LINS (Laser Inertial Navigation System) consists of RLG (Ring Laser Gyroscopes)/accelerometers and provides real-time navigation information to the target system. Therefore it is very important to make a decision in the real time whether LINS is in the normal operation or not. That is called a fault detection method. In this paper, we propose the fault detection method of LINS based on the overlapping model. We also show that the fault detection probability is increased through overlapping the hardware model and the software model. It is verified through the long-term operation and RAM (Reliability Availability Maintainability) analysis of LINS that the fault detection method proposed in this paper is able to detect about 97% of probable system failures.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.7-11
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• 2004

This paper proposes a new immunotronic approach for the fault detection in hardware. The suggested method is, inspired by biology and its implementation is based on genetic algorithm. Tolerance conditions in the immunotronic system for fault detection correspond to the antibodies in the biological immune system. A novel algorithm of generating tolerance conditions is suggested based on the principle of the antibody diversity and GA optimization is employed to select mature tolerance conditions in immunotronic fault detection system. The suggested method is applied to the fault detection for MCNC benchmark FSMs (finite state machines) and its effectiveness is demonstrated by the computer simulation.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.3
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• pp.144-149
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• 2017

There is a little research on DC arc detection when compared to a large number of literature and patents on AC arc detection. However, as DC energy sources such as photovoltaic power generation facilities and fuel cells are introduced, research on DC arc has become as important as AC arc detection in terms of circuit protection and system reliability enhancement. In this paper, we have developed an arc detection method for photovoltaic system using frequency analysis. Through various experiments, it was confirmed that the proposed method effectively detects the arc.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5079-5099
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• 2018

Regarding to the huge number of connections and the large flow of data on the Internet, Intrusion Detection System (IDS) has a difficulty to detect attacks. Moreover, irrelevant and redundant features influence on the quality of IDS precisely on the detection rate and processing cost. Feature Selection (FS) is the important technique, which gives the issue for enhancing the performance of detection. There are different works have been proposed, but a map for understanding and constructing a state of the FS in IDS is still need more investigation. In this paper, we introduce a survey of feature selection algorithms for intrusion detection system. We describe the well-known approaches that have been proposed in FS for IDS. Furthermore, we provide a classification with a comparative study between different contribution according to their techniques and results. We identify a new taxonomy for future trends and existing challenges.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.426-429
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• 2007

When a fire happens in builds or apartments, peoples are tend to be caught in dangerous situations owing to the failure of searching escape route to the outside. In this work, an efficient fire detection and alarm system which makes it possible for the escapers to take adequate actions is proposed. The proposed system consists of two parts. One is fire detection modules which are located at each compartments in a building. The other is fire warning modules equipped with portable flashes having ability of visual/voice warning. Fire detection information is transmitted between each modules wirelessly. In this work, an efficient communication protocol for sensor network-based fire detection system is proposed and its feasibility is verified by practical experiments using MANGO ZDK Development tools

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.136-144
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• 1999

This paper deals with the fault detection problem in uncertain linear multivariable systems and its application. A robust fault detection method presented by Kim et a. (1998) for MIMO (Multi Input/Multi Output) systems has been adopted and applied to the twin rotor MIMO experimental setup using industrial DSP. The system identification problem is formulated for the twin rotor MIMO system and its parameters are estimated using experimental data. Based on the estimated parameters, some fault detection simulations are performed using the robust fault detection method, which shows that the preformance is satisfied.

• Journal of Radiation Industry
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• v.6 no.1
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• pp.67-73
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• 2012

The development of radiation detection systems mainly consist of two parts-radiation detector fabrication including material development, and its appropriate electronics development. For the core-technology incubation of a radiation detection system, radiation fabrication and an evaluation facility are scheduled to be founded at the RFT (Radiation Fusion Technology) Center at KAERI (Korea Atomic Energy Research Institute) by 2015. This facility is utilized for the development and incubation of bottleneck-technologies to accelerate the industrialization of a radiation detection system in the industrial, medical, and radiation security fields. This facility is also utilized for researchers to develop next-generation radiation detection instruments. In this paper, the establishment of core-technology development is introduced and its technological mission is addressed.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.19-24
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• 2024

It is not an exaggeration to say that a single fire causes a lot of damage, so fires are one of the disaster situations that must be alerted as soon as possible. Various technologies have been utilized so far because preventing and detecting fires can never be completely accomplished with individual human efforts. Recently, deep learning technology has been developed, and fire detection systems using object detection neural networks are being actively studied. In this paper, we propose a new fire detection system that improves the previously studied fire detection system. We train the YOLOv8 model using refined datasets through improved labeling methods, derive results, and demonstrate the superiority of the proposed system by comparing it with the results of previous studies.