• Journal of the Korean Geophysical Society
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• v.2 no.1
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• pp.65-74
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• 1999

From refraction data along four seismic profiles near Eonyang which the Yangsan fault passes through, the Slope Variation Indicators (SVI) are computed and interpreted in terms of fault distribution. The average velocities of 2,250-2,870 m/s are estimated using velocity-analysis functions for the target boundary along those profiles. The average velocity for Line 1 is approximately 600 m/s lower than ones for the other lines. The SVI's with amplitude greater than or equal to 0.5 ms/m are turned out to be located near faults shown on the high-resolution reflection section, as closely as one station spacing (3 m). Large amplitude SVI's are densely distributed near National Road 35, and the fault having the largest vertical slip is indicated to be located approximately 930 m west of the inferred fault on the published geologic map.

• KNOM Review
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• v.24 no.1
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• pp.13-19
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• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.271-277
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• 2022

PBFT (Practical Byzantine Fault Tolerant) is a consensus algorithm that can achieve consensus by resolving unintentional and intentional faults in a distributed network environment and can guarantee high performance and absolute finality. However, as the size of the network increases, the network load also increases due to message broadcasting that repeatedly occurs during the consensus process. Due to the characteristics of the PBFT algorithm, it is suitable for small/private blockchain, but there is a limit to its application to large/public blockchain. Because PBFT affects the performance of blockchain networks, the industry should test whether PBFT is suitable for products and services, and academia needs a unified evaluation metric and technology for PBFT performance improvement research. In this paper, quantitative evaluation metrics and evaluation frameworks that can evaluate PBFT family consensus algorithms are studied. In addition, the throughput, latency, and fault tolerance of PBFT are evaluated using the proposed PBFT evaluation framework.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2233-2244
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• 1999

It is essential to guarantee high reliability of embedded real-time systems since the failure of such systems may result in large financial damage or threaten human life. Though many researches have devoted to fault tolerant mechanisms, most of them are object-level fault tolerant mechanisms that can detect errors occurred in a single object and treat the errors in object-level. As embedded real-time systems become more complex and larger, there exist faults that cannot be detected by or tolerated with object-level fault tolerance. Hence, system-level fault tolerance is needed. System-level fault tolerance examines the status of a system whether the system is normal or not by analyzing the status of objects. When an error is detected it should be capable of locating the fault and performing an appropriate recovery and reconfiguration action. In this paper, we propose RobustRTO(Robust Real-Time Object) that provides object-level fault tolerance capability and RMO(Region Monitor real-time Object) that offers system-level fault tolerance capability. Then we show how highly dependable fault tolerant systems can be modeled by RobustRTO and RMO. The model is presented based on real-time objects.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.293-300
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• 2023

The Shape Preferred Orientation (SPO) method has been used to analyze the orientation of fault motion, which is utilized as basic data for fault kinematics studies. The rigid grains, which as quartz, feldspar, and rock fragments, in the fault gouge are arranged in the P-shear direction through rigid body rotation by a given shear stress. Using this characteristic, the fault motion can be estimated from the SPO inversely. Recently, a method for securing precision and reliability by measuring 3D-SPO using X-ray CT images and examining the shape of a large number of particles in a short time has been developed. As a result, the SPO method analyzes the orientation of thousands to tens of thousands of particles at high speed, suggests the direction of fault motion, and provides easy accessibility and reliable data. In addition, the shape information and orientation distribution data of particles, which are by-products obtained in the SPO analysis process, are expected to be used as basic data for conducting various studies such as the local deformation of fault rocks and the fault generation mechanism.

• Geophysics and Geophysical Exploration
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• v.9 no.1
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• pp.50-59
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• 2006

The risk of fault reactivation in the Gippsland Basin was calculated using the FAST (Fault Analysis Seal Technology) technique, which determines fault reactivation risk by estimating the increase in pore pressure required to cause reactivation within the present-day stress field. The stress regime in the Gippsland Basin is on the boundary between strike-slip and reverse faulting: maximum horizontal stress $({\sim}\;40.5\;Mpa/km)$ > vertical stress (21 Mpa/km) ${\sim}$ minimum horizontal stress (20 MPa/km). Pore pressure is hydrostatic above the Campanian Volcanics of the Golden Beach Subgroup. The NW-SE maximum horizontal stress orientation $(139^{\circ}N)$ determined herein is broadly consistent with previous estimates, and verifies a NW-SE maximum horizontal stress orientation in the Gippsland Basin. Fault reactivation risk in the Gippsland Basin was calculated using two fault strength scenarios; cohesionless faults $(C=0;{\mu}=0.65)$ and healed faults $(C=5.4;\;{\mu}=0.78)$. The orientations of faults with relatively high and relatively low reactivation potential are almost identical for healed and cohesionless fault strength scenarios. High-angle faults striking NE-SW are unlikely to reactivate in the current stress regime. High-angle faults oriented SSE-NNW and ENE-WSW have the highest fault reactivation risk. Additionally, low-angle faults (thrust faults) striking NE-SW have a relatively high risk of reactivation. The highest reactivation risk for optimally oriented faults corresponds to an estimated pore pressure increase (Delta-P) of 3.8 MPa $({\sim}548\;psi)$ for cohesionless faults and 15.6 MPa $({\sim}2262\;psi)$ for healed faults. The absolute values of pore pressure increase obtained from fault reactivation analysis presented in this paper are subject to large errors because of uncertainties in the geomechanical model (in situ stress and rock strength data). In particular, the maximum horizontal stress magnitude and fault strength data are poorly constrained. Therefore, fault reactivation analysis cannot be used to directly measure the maximum allowable pore pressure increase within a reservoir. We argue that fault reactivation analysis of this type can only be used for assessing the relative risk of fault reactivation and not to determine the maximum allowable pore pressure increase a fault can withstand prior to reactivation.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.67-69
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• 2002

This paper presents the statistical analysis of the circuit breaker's making current in large sacle industrical loads using EMTDC. Typically, the making current includes DC components as the phase angel of making instances varies and represents as a instantaneous valus not rms. Consequently, in this paper, the statistical analysis results of making current and steady state fault current for typical industrial loads presents.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.134-135
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• 2003

Abstract in case of the small scale power system separated from large scale power system N-2 contingency, the Over excitation, Under Frequency and Power swing Tripping Relay operate depending on unbalance Of Generation and Load. and then we studied and measured the angle and frequency deviation between generators. moreover, we compared real fault example with simulation results.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.241-241
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• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.102-108
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• 2010

The photovoltaic system changes solar energy into DC by solar cell and this DC is inverted into AC which is used in general houses by inverter. Recently, the use of power of the photovoltaic system is increased. Therefore, the study of 3 phase solar system to transmit large power is very important. This paper proposes a method that finds simply faults and diagnoses the switch open faults of 3-phase pulse width modulation (PWM) inverter of grid-connected photovoltaic system. The proposed method in $\alpha\beta$ plane uses the patterns of trajectory image as the characteristic parameters and differenciates a normal state and open states of switches. Then, the result is made into tree. The tree is composed of 21 fault patterns and the parameters to classify faults are a shape, a trajectory area, a distributed angle, and a typical vector angle. The result shows that the proposed method diagnosed fault diagnoses, classified correctly them, and made a pattern tree by fault patterns.