• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.1
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• pp.10-19
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• 1990

A fault tolerant control system, in which a digital back-up controller system is added on the existing analog control system, is developed for enhancing reliability of boiler control system in power plant. The digital back-up controller system(DBCS) has a multi-processor structure with capabilities of fault diagnosis, back-up control, self test, and graphic monitoring. Specifically, switching mechanism composed of expandable modules is designed so that back-up controller takes over any faulty control loops and the number of back-up control loops is determined as that of simultaneous faults. A process simulator that simulates the boiler analog control system is developed for safety test and performance evaluation prior to real plant application. DBCS is installed at the Ulsan thermal power plant, and fault tolerant control performance is assured under the faults that some controller modules are pulled out.

• Journal of the Semiconductor & Display Technology
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• v.9 no.4
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• pp.93-97
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• 2010

Fast incipient fault diagnosis is becoming one of the key requirements for economical and optimal process operation management in high-tech industries. Artificial neural networks have been used to detect faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for fault detection and classification for module process of TFT-LCD manufacture using support vector machines (SVMs). In order to evaluate SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.

• Journal of Power System Engineering
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• v.8 no.1
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• pp.62-68
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• 2004

This paper describes a fault tolerant control in distributed PLC(Programmable Logic Controller) system to ensure reliability of controllers which have some faults simultaneously. First, the behavior of PLC is modeled as discrete expressions using Galois field. Then, we design the control laws for additional spare controllers to generate parity code with two dimensions. Finally, the algorithm for estimating normal output instead of abnormal output from the controllers with fault is suggested. Comparing to the traditional duplication method, the suggested method can reduce the number of spare controllers significantly to ensure control reliability. This method will be applied to an automatic system in order to increase reliability. Also, it can improve cost performance of the system.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.313-315
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• 2005

Recently There has been growing interest in new renewable energy systems with high-energy efficiency due to the increasing energy consumption and environmental pollution problems. But an insertion of new distributed generation to existng power distribution systems can cause several problems such as voltage variations, harmonics, protective coordination, increasing fault current etc, because of reverse power. This paper was applied to fault location defecting a method as each Relay sensing fault current value and carried out short-circuit analysis by MATLAB and PSCAD/EMTDC programs and identity the faulted section o f22.9[kV] distribution system interconnected a large number of distributed generation. The existing protection system of 22.9[kV] power distribution system analyzed and the study on protective coordination recloser and Sectionalzer accomplished

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.960-966
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• 2011

In this paper, an $\mathfrak{H}$_/$\mathfrak{H}_{\infty}$ fault detection and isolation (FDI) observer design problem is investigated for discrete-time delayed systems. To that end, a bank consisting of the sensor's number of observers is introduced. Each residual should be sensitive to a certain partial group of faults, but robust against the disturbance as far as possible. We formulate this multiobjective FDI problem as $\mathfrak{H}$_/$\mathfrak{H}_{\infty}$ observers design problem. Sufficient design condition is expressed as iterative linear matrix inequalities. The fault is then detected and isolated by evaluating the residuals through an FDI decision logic. A computer simulation is provided for verification of the proposed technique.

• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.59-67
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• 2011

This paper proposes a model to analyze the fault factors of air-to-ground weapon delivery utilizing software fault localization methods. In the previous study, to figure out the factors to affect the accuracy of air-to-ground weapon delivery, the FBEL (Factor-based Error Localization) method had been proposed and the fault factors were analyzed based on the method. But in the study, the correlation between weapon delivery accuracy and the fault factors could not be revealed because the firing accuracy among several factors was fixed. In this paper we propose a more precise fault analysis model driven through a study of the correlation among the fault factors of weapon delivery, and a method to estimate the possibility of faults with the limited number of test cases utilizing the model. The effectiveness of proposed method is verified through the simulation utilizing real delivery data. and weapons delivery testing in the evaluation of which element affecting the accuracy of analysis that was available to be used successfully.

• Journal of Internet Computing and Services
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• v.4 no.6
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• pp.25-31
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• 2003

Multistage Interconnection networks (MIN) for the high performance computing and communications must be efficient and reliable. While a number of fault tolerance schemes have been developed, some of them are not efficient enough with respect to all evaluation measures or overheads of others are too significant. In this paper we develop a new efficient fault tolerant MIN which displays high reliability and fault tolerance capability using a simple structure. Structure and reliabilities of Enhanced Cube Network are evaluated and compared with previous designs to show the effectiveness of new design.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.211-218
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• 2012

In this paper, we propose a fault injection attack on AES-CMAC, which is defined by IETF. The fault assumption used in this attack is based on that introduced at FDTC'05. This attack can recover the 128-bit secret key of AES-CMAC by using only small number of fault injections. This result is the first known key recovery attack result on AES-CMAC.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.965-973
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• 2022

As IoT(Internet of Things) devices become common, many algorithms have been developed to protect users' personal information. The laser fault injection attack that threatens those algorithms is a side-channel analysis that intentionally injects a laser beam to the outside of a device to acquire confidential information or abnormal privileges of the system. There are many studies to determine the timing of fault injection to reduce the number of necessary fault injections, but the location to inject faults is only repeatedly searched for the entire area of the device. However, when fault injection is performed in an algorithm-independent area, the attacker cannot obtain the intended faulted statement or attempt to bypass authentication, so finding areas vulnerable to fault injection and performing an attack is an important consideration in achieving a high attack success rate. In this paper, we show that a 100% attack success rate can be achieved by determining the vulnerable areas for fault injection by using electromagnetic and thermal information generated from the device's chip. Based on this, we propose an efficient fault injection attack system.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.