• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.1090-1097
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• 1999

the multi computers operated in harsh environments should be designed to guarantee normal operations in the presence of the component faults. One solution for this is a fault-tolerant routing. In the paper, we consider n-dimensional meshed network for the basic topology and propose a simple fault-tolerant routing algorithm that can transfer messages to their destination as desired in the presence of some component faults. the built algorithms basically adopts a WormHole(WH) routing method and uses the virtual channels sharing a physical channel for deadlock-freedom. Consequently, we show that the suggested algorithm has a higher performance than the X-Y routing algorithm through simulation results.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.4
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• pp.221-221
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• 2004

Power system has recently used Double-circuit Line and Multi-circuit Line in the industrial development. This has an advantage of system stability and reliability, but the complexity of the system has a disadvantage that makes it difficult to protect the power line. Double-circuit Line has two operation conditions in the Single-circuit operation and Double-circuit operation, so it has mutual impedance. To make it possible for the remaining single-line to operate independently while there is a fault with first line or when maintenance is needed, a trip region for the single-circuit operation should be set in order to set the relay trip region. An optimal trip region for each operation, a different operational conditions for the relay setting should be calculated. In this paper, trip regions of each operation condition have been compared by considering mutual impedance and fault resistance that led to the calculation of fault impedance. Also, as we know that one of the advantages in the distance relay is the back-up protection, we calculated the trip region(Zone-2) in consideration of the mutual impedance.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.6
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• pp.556-561
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• 2009

The UAV(Unmanned Aerial Vehicle) that is remotely operating in various and long flight environments must have a very reliable propulsion system. Precise fault diagnosis of the turbo shaft engine for the Smart UAV that has the vertical take-off, landing and forward flight behaviors can promote reliability and availability. This work proposes a new diagnostic method that can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. The proposed diagnostic method can detect not only single fault but also multiple faults.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.995-1000
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• 2017

In this paper, we present an electrical arc detection algorithm using the mash-up analysis technique which is the core technology for the autonomous electrical safety management system(AESMS) of the multi-unit dwellings. The mash-up analysis technique analyzes the voltage, load current, zero phase current data simultaneously to judge arc faults. In order to develop the arc fault detection algorithm, the characteristics of series arc and parallel arc were analyzed. Also, we propose the mash-up analysis technique that analyzes waveforms of voltage, load current, and zero phase current at the same time. The arc fault detection algorithm was developed using the mash-up analysis technique. The developed algorithm can prevent electrical disasters in an effective way through accident prediction, and it will be used as a basic technology to introduce an autonomous electrical safety management system.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2184-2186
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• 2001

The research presented in this paper focuses on a method for the detection of High Impedance Fault(HIF). The method will use the fast wavelet transform and neural network system. HIF on the multi-grounded three-phase four-wires primary distribution power system cannot be detected effectively by existing over current sensing devices. These paper describes the application of fast wavelet transform to the various HIF data. These data were measured in actual 22.9kV distribution system. Wavelet transform analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate HIF from the normal status by a gradient descent method. The proposed method performed very well by proving the right state when it was applied staged fault data and normal load mimics HIF, such as arc-welder.

• Economic and Environmental Geology
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• v.53 no.4
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• pp.479-489
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• 2020

A fault is a geological structure that can be a migration path or a cap rock of hydrocarbon such as oil and gas, formed from source rock. The fault is one of the main targets of seismic exploration to find reservoirs in which hydrocarbon have accumulated. However, conventional fault detection methods using lateral discontinuity in seismic data such as semblance, coherence, variance, gradient magnitude and fault likelihood, have problem that professional interpreters have to invest lots of time and computational costs. Therefore, many researchers are conducting various studies to save computational costs and time for fault interpretation, and machine learning technologies attracted attention recently. Among various machine learning technologies, many researchers are conducting fault interpretation studies using the support vector machine, multi-layer perceptron, deep neural networks and convolutional neural networks algorithms. Especially, researchers use not only their own convolution networks but also proven networks in image processing to predict fault locations and fault information such as strike and dip. In this paper, by investigating and analyzing these studies, we found that the convolutional neural networks based on the U-Net from image processing is the most effective one for fault detection and interpretation. Further studies can expect better results from fault detection and interpretation using the convolutional neural networks along with transfer learning and data augmentation.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.371-374
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• 2004

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motor applications. Preventive maintenance of induction motors has been a topic great interest to industry because of their wide range application of industry. Since the use of mechanical sensors, such as vibration probes, strain gauges, and accelerometers is often impractical, the motor current signature analysis (MACA) techniques have gained murk popularity as diagnostic tool. Fault tolerant control (FTC) strives to make the system stable and retain acceptable performance under the system faults. All present FTC method can be classified into two groups. The first group is based on fault detection and diagnostics (FDD). The second group is independent of FDD and includes methods such as integrity control, reliable stabilization and simultaneous stabilization. This paper presents the fundamental FDD-based FTC methods, which are capable of on-line detection and diagnose of the induction motors. Therefore, our group has developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. This paper presents its architecture. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module is checking stator current, voltage, temperatures, vibration and speed of the motor. The DSPs share information from each sensor or DSP through DPRAM with hardware implemented semaphore. And it communicates the motor status through field bus (CAN, RS485). From the designed system, we get primitive sensors data for the case of normal condition and two abnormal conditions of 3 phase induction motor control system is implemented. This paper is the first step to drive multi-motors with serial communication which can satisfy the real time operation using CAN protocol.

• Economic and Environmental Geology
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• v.37 no.5
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• pp.543-554
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• 2004

Analysis of multi-channel seismic reflection and well data serves to detail the structural evolution of the northern Okinawa Trough, southern offshore Korea. The overall structural style of the area is characterized by a series of half grabens and tilted fault blocks bounded by basement-involved listric normal faults. Most half grabens and tilted fault blocks developed in the direction of NNE-SSW, parallel to the axis of the Okinawa Trough. Orientation and distribution of the listric faults also suggest the development of transfer faults in NW-SE direction. The rifting phase of the northern Okinawa Trough have been established on the basis of structural and stratigraphic analyses of depositional sequences and their seismic expressions. Major phase of rifting probably started in the Late Miocene and the most active rifting occurred during the Early Pliocene. The rifting produced a series of half grabens and tilted fault blocks bounded by listric normal faults. It appears that the rifting activity has become weaker since the Late Pliocene, but the Pleistocene sediments faulted by listric faults bounding tilted fault blocks suggest that the rifting activity is probably still in progress.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.187-194
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• 2008

Because the types and severities of most engine faults are various and complex, it is not easy that the conventional model based fault detection approach like the GPA(Gas Path Analysis) method can monitor all engine fault conditions. Therefore this study proposed newly a diagnostic algorithm for isolating and diagnosing effectively the faulted components of the smart UAV propulsion system, which has been developed by KARI(Korea Aerospace Research Institute), using the fuzzy logic and the neural network algorithms. A precise performance model should be needed to perform the model-based diagnostics. The based engine performance model was developed using SIMULINK. For the work and mass flow matching between components of the steady-state simulation, the state-flow library was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with manufacturer's performance deck data. According to comparison results, it was confirm that the steady-state model well agreed with the deck data within 3% in all flight envelop. The diagnosis procedure of the proposed diagnostic system has the following steps. Firstly after obtaining database of fault patterns through performance simulation, then secondly the diagnostic system was trained by the FFBP networks. Thirdly after analyzing the trend of the measuring parameters due to fault patterns, then fourthly faulted components were isolated using the fuzzy logic. Finally magnitudes of the detected faults were obtained by the trained neural networks. Because the detected faults have almost same as degradation values of the implanted fault pattern, it was confirmed that the proposed diagnostic system can detect well the engine faults.

• 한국가스학회:학술대회논문집
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• 2004.11a
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• pp.43-48
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• 2004