• Journal of Korean Institute of Industrial Engineers
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• v.18 no.1
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• pp.37-45
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• 1992

Common assumption we frequently encounter in early models of software reliability is that no new faults are introduced during the fault removal process. In real life, however, there are situations in which new faults are introducted as a result of imperfect debugging. This study alleviating this assumption by introducting the probability of perfect error-correction is an extension of Littlewood's work. In this model, the system reliability, failure rates, mean time to failure and average failure frequency are obtained. Here, when the probability of perfect error-correction is one, the results appear identical with those of the previous studies. In the respect that the results of previous studies are special cases of this model, the model developed can be considered as a generalized one.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.125-127
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• 1995

A superconducting parallel bifilar winding with one strand in each branch was tested and analyzed. When the superconducting parallel bifilar winding was quenched by fault current, it showed the so-called 'fast quench' and the results of experiment well explained the resistance curve. Also the experiments confirm that the current redistribution of magnetically coupled strands develops new normal zones after the initial quench, and that the size of normal zones at an early stage increases almost linearly as dI/dt increases. By these results, we could explain the resistance rise of superconducting parallel bifilar winding when it is quenched by the current above the critical value.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.749-752
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• 2014

본 논문에서는 저속으로 동작하는 구름 베어링의 다중 결함 조기 검출을 위해 결함 특징 추출, 효과적인 특징 선택, 선택된 특징을 이용한 결함 분류의 세 단계로 구성된 결함 진단 기법을 제안한다. 1단계에서 이산 웨이블릿 변환을 이용하여 미세성분으로부터 통계적 결함 특징을 추출하고, DET(distance evaluation technique)를 이용하여 추출한 결함 특징 가운데 베어링 다중 결함 검출에 효과적인 특징을 선택한다. 마지막으로 선택된 특징을 k-NN(k-Nearest Neighbors) 분류기 입력으로 사용함으로써 결함을 진단한다. 본 논문에서는 제안한 결함 진단 기법의 성능을 분류 정확도 측면에서 평가한 결과 95.14%의 높은 분류 정확도를 보였다.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.219-228
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• 2009

The growing application and the numerous qualities of induction motors (1M) in industrial processes that require high security and reliability levels has led to the development of multiple methods for early fault detection. However, various faults can occur, such as stator short-circuits and rotor failures. Traditionally the diagnosis machine is done through a sinusoidal power supply, in the present paper we study experimentally the effects of the rotor failures, such as broken rotor bars in function of the ac supplying, the load and show the impact of the converter from diagnosis of the machine. The technique diagnosis used is based on the spectral analysis of stator currents or stator voltages respectively according to the types of induction motor ac supplying. So, four different ac supplying are considered: ${\odot}$ the IM is directly by the balanced three-phase network voltage source, ${\odot}$ the IM is fed by a sinusoidal current source given the controlled by hysteresis, ${\odot}$ the IM is fed (in open loop) by a scalar control imposing through ratio V/f=constant, ${\odot}$ the IM is controlled through a vector control using space vector pulse width modulation (SVPWM) technique inverter with an outer speed loop.

• The Journal of the Korea Contents Association
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• v.8 no.7
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• pp.66-74
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• 2008

Criticality prediction models to identify most fault-prone modules in the system early in the software development process help in allocation of resources and foster software quality improvement. Many models for identifying fault-prone modules using design complexity metrics have been suggested, but most of them are training models that need training data set. Most organizations cannot use these models because very few organizations have their own training data. This paper builds a prediction model based on a well-known supervised learning model, error backpropagation neural net, using design metrics quantifying SDL system specifications. To solve the problem of other models, this model is trained by generated virtual training data set. Some simulation studies have been performed to investigate feasibility of this model, and the results show that suggested model can be an alternative for the organizations without real training data to predict their software qualities.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.33-40
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• 2003

Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development costs because the problems in early phases largely affect the quality of the late products. Real-time systems such as telecommunication systems are so large that criticality prediction is mere important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing causes of the prediction results and low extendability. This paper builds a new prediction model, GAM, based on Genetic Algorithm. GAM is different from other models because it produces a criticality function. So GAM can be used for comparison between entities by criticality. GAM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering Internal characteristics and accuracy of prediction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.1
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• pp.7-12
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• 2003

The purpose of testability analysis is to estimate the difficulty of testing a stuck-at fault in logic circuits. A good testability measurement can give an early warning about the testing problem so as to provide guidance in improving the testability of a circuit. There have been researches attempting to efficiently compute the testability analysis. Conventional testability measurements, such as COP and SCOAP, can calculate the testability value of a stuck-at fault efficiently in a tree-structured circuit but may be very inaccurate for a general circuit. The inaccuracy is due to the ignorance of signal correlations for making the testability analysis linear to a circuit size. This paper proposes an efficient method for computing testability analysis, which takes into account signal correlation to obtain more accurate testability. The proposed method includes the algorithm for identifying all reconvergent fanouts in a given n circuit and the gates reachable from them, by which information related to signal correlation is gathered.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1B
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• pp.38-47
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• 2008

The system test was accomplished in delivery time for a suitable of various requirements at the software market. Especially, critical faults must be detected and removed for a close main functions and users against target system. In generally, proposed test methods are executed with a calendar time, not a competitive and effectiveness method as selective software testing. These methods are inapplicable to short term test or early system development stage. Moreover, it's accompanied by heavy cost. Overcoming of these problems, must attempted to new software test method role of core function in the system test. Selective software testing method is decided to mixing with the three-information such as a frequency, complexity of use scenario and fault impact. Using this information, searching a fatal error and usefully system test for an executed test scenario. In this paper, we have proposed new test method and verified testing results for the detection of critical faults or search a fatal errors with a system main function.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.447-454
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• 2020

Since SF₆ gas was discovered in the early 1900s, it has been widely used as an insulation material for electrical equipment. While various indicators have been developed to diagnose oil-immersed transformers, there are still insufficient indicators for the diagnosis of gas-insulated transformers. When necessary, chemical diagnostic methods can be used for gas-insulated transformers. However, the field suitability and accuracy of those methods for transformer diagnosis have not been verified. In addition, since various types of decomposition gases are generated therein, it is also necessary to establish appropriate analysis methods to cover the variety of gases. In this study, a gas-insulated transformer was diagnosed through the analysis of decomposition gases. Reliability assessments of both simple analysis methods suitable for on-site tests and precise analysis methods for laboratory level tests were performed. Using these methods, a gas analysis was performed for the internal decomposition gases of a 154 kV transformer in operation. In addition, simulated discharge and thermal fault experiments were demonstrated. Each major decomposition gas generation characteristics was identified. The results showed that an approximate diagnosis of the inside of a gas-insulated transformer is possible by analyzing SO₂, SOF₂, and CO using simple analysis methods on-site. In addition, since there are differences in the types of decomposition gas generation patterns with various solid materials of the internal transformer, a detailed examination should be performed by using precise analysis methods in the laboratory.

• The Korean Journal of Petroleum Geology
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• v.14 no.1
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• pp.63-73
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• 2008

The seismic interpretation was carried out to understand the evolution of the Sora and North Sora Sub-basins, South Sea of Korea. Both sub-basins belong to the Domi Basin, which is located in the northeastern margin of East China Sea Basin with Fukue Basin of Japan. Age assignment of each strata in this study was based on the data of boreholes and seismic interpretation in NW Japan. Four regional horizons were identified, and five geological units; Y(basement), Q(Eocene$\sim$Middle Oligocene), M(Middle Oligocene$\sim$Early Miocene), L(Early Miocene$\sim$Late Miocene) and D(Late Miocene$\sim$Present) groups in ascending order. Structural trends of the main boundary faults and the basin-fill sediment are different between the Sora and North Sora Sub-basins; i.e., trend of the main boundary-faults, dip of horizons, distribution of basin and development of growth fault. These results imply that the Sora Sub-basin would have opened earlier than the North Sora Sub-basin.