• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.255-261
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• 2013

In this paper, we developed and tested a program for prediction flash fire in a combat system. Purposes of the program are flash fire prediction of combat system for analysis vulnerability and survivability, and visualization for fire-related information. To do this, we defined critical components of the combat system which has probabilities of flash fire occurrence, and proposed Flash Fire Probability Tree which is based on Fault Tree Analysis(FTA). The program visualizes positions of critical components in combat system, positions of penetrated components, selected Flash Fire Probability Tree, temperature profile, and tables for properties of matters.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.785-796
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• 2023

In this study, a methodology for real-time classification and prediction of defects that may appear in PVDF(Polyvinylidene fluoride) sensors, which are widely used for structural integrity monitoring, is proposed. The types of sensor defects appearing according to the sensor attachment environment were classified, and an impact test using an impact hammer was performed to obtain an output signal according to the defect type. In order to cleary identify the difference between the output signal according to the defect types, the time domain statistical features were extracted and a data set was constructed. Among the machine learning based classification algorithms, the learning of the acquired data set and the result were analyzed to select the most suitable algorithm for detecting sensor defect types, and among them, it was confirmed that the highest optimization was performed to show SVM(Support Vector Machine). As a result, sensor defect types were classified with an accuracy of 92.5%, which was up to 13.95% higher than other classification algorithms. It is believed that the sensor defect prediction technique proposed in this study can be used as a base technology to secure the reliability of not only PVDF sensors but also various sensors for real time structural health monitoring.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.8
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• pp.1492-1501
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• 2011

A wide-area protection intelligent technique has been used to improve a reliability in power systems and to prevent a blackout. Nowadays, voltage and current phasor estimation has been executed by GPS-based synchronized PMU, which has become an important way of wide-area blackout protection for the prevention of expending faults in power systems. As this technique has the difficulties in collecting and sharing of information, there have been used a FNET method for the wide-area intelligent protection. This technique is very useful for the prediction of the inception fault and for the prevention of fault propagation with accurate monitoring frequency and frequency deviation. It consists of FDRs and IMS. It is well known that FNET can detect the dynamic behavior of system and obtain the real-time frequency information. Therefore, FDRs must adopt a optimal frequency estimation method that is robust to noise and fault. In this paper, we present comparative studies for the frequency estimation method using IRDWT(improved recursive discrete wavelet transform), for the frequency estimation method using FRDWT(fast recursive discrete wavelet transform). we used the Republic of Korea 345kV power system modeling data by EMTP-RV. The user-defined arbitrary waveforms were used in order to evaluate the performance of the proposed two kinds of RDWT. Also, the frequency variation data in various range, both large range and small range, were used for simulation. The simulation results showed that the proposed frequency estimation technique using FRDWT can be the optimal frequency measurement method applied to FDRs.

• Structural Engineering and Mechanics
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• v.23 no.4
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• pp.409-430
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• 2006

The aim of this study is to investigate the reliability of strong motion records processed by causal and acausal Butterworth filters in comparison to the results obtained from a synthetic accelerogram. For this purpose, the fault parallel component of the Bolu record of the Duzce earthquake is modeled with a sum of exponentially damped sinusoidal components. Noise-free velocities and displacements are then obtained by analytically integrating the synthetic acceleration model. The analytical velocity and displacement signals are used as a standard with which to judge the validity of the signals obtained by filtering with causal and acausal filters and numerically integrating the acceleration model. The results show that the acausal filters are clearly preferable to the causal filters due to the fact that the response spectra obtained from the acausal filters match the spectra obtained from the simulated accelerogram better than that obtained by causal filters. The response spectra are independent from the order of the filters and from the method of integration (whether analytical integration after a spline fit to the synthetic accelerogram or the trapezoidal rule). The response spectra are sensitive to the chosen corner frequency of both the causal and the acausal filters and also to the inclusion of the pads. Accurate prediction of the static residual displacement (SRD) is very important for structures traversing faults in the near-fault regions. The greatest adverse effect of the high pass filters is their removal of the SRD. However, the noise-free displacements obtained by double integrating the synthetic accelerogram analytically preserve the SRD. It is thus apparent that conventional high pass filters should not be used for processing near-fault strong-motion records although they can be reliably used for far-fault records if applied acausally. The ground motion parameters such as ARIAS intensity, HUSID plots, Housner spectral intensity and the duration of strong-motion are found to be insensitive to the causality of filters.

• Proceedings of the Korean Geotechical Society Conference
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• 1988.06c
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• pp.3-67
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• 1988

Model studies on the response of homgeneous earth embankment dams subjected to strike-slip fault movement have been penomed via centrifuge and finite element analysis. The centrifuge model tests have shown that crack development in earth embankment experiences two major patters: shear failure deep inside the embankment and tension failure near the surface. The shear rupture zone develops from the base level and propagates upward continuously in the transverse direction but allows no open leakage chnnel. The open tensile cracks develop near the surface of the embankment, but they disappear deep in the embankment. The functional relationship has been developed based on the results of the centrifuge model tests incorporating tile variables of amount of fault movement, embankment geometry, and crack propagation extent in earth des. This set of information can be used as a guide line to evaluate a "transient" safety of the duaged embankment subjected to strike-slip fault movement. The finite element analysis has supplemented the additional expluations on crack development behavior identified from the results of the centrifuge model tests. The bounding surface time-independent plasticity soil model was employed in the numerical analysis. Due to the assumption of continuum in the current version of the 3-D FEM code, the prediction of the soil structure response beyond the failure condition was not quantitatively accurate. However, the fundamental mechanism of crack development was qualitatively evaluated based on the stress analysis for the deformed soil elements of the damaged earth embankment. The tensile failure zone is identified when the minor principal stress of the deformed soil elements less than zero. The shear failure zone is identified when the stress state of the deformed soil elements is at the point where the critical state line intersects the bounding surface.g surface.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.456-458
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• 1997

본 논문에서는 플랜트의 계측신호들에 나타나는 현상적인 특징파형들을 감지하여 플랜트의 고장을 진단하는 기법을 제시하고, 플랜트의 고장시 나타나는 여러 특징 파형들의 전형인 oscillatory 파형을 감지하는 알고리즘을 prediction filtering 기법을 적용하여 얻는 과정을 기술하였다. 또한 이의 구체적 적용 예로 500MW급 화력발전소의 보일러 제어 시스템을 대상으로 여러 고장상황에 대한 고장감지 알고리즘을 설계하였고 제시한 기법의 유용성을 보이기 위한 시뮬레이션 적용 결과를 보였다.

• ETRI Journal
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• v.21 no.3
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• pp.6-15
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• 1999

Many studies in the software reliability have attempted to develop a model for predicting the faults of a software module because the application of good prediction models provides the optimal resource allocation during the development period. In this paper, we consider the change request data collected from the field test of the software module that incorporate a functional relation between the faults and some software metrics. To this end, we discuss the general aspect if regression method, the problem of multicollinearity and the measures of model evaluation. We consider four possible regression models including two stepwise regression models and two nonlinear models. Four developed models are evaluated with respect to the predictive quality.

• Structural Engineering and Mechanics
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• v.15 no.4
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• pp.437-449
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• 2003

The aim of this study is to develop an analytical model of a beam with open cracks and external strengthening which is able to predict its modal scheme components (natural frequencies and mode shapes). The model is valid as far as the excitation level is low enough not to activate non linear effects. The application field of the model are either the prediction of the efficiency of the reinforcement or the non destructive assessment of the structural properties. The degrees of freedom associated to the fault lips must be taken into account in order to introduce the effect of the external strengthening. In a first step, an analytical formulation of a beam with thin notches is proposed according to the references. The model is then extended to incorporate the strengthening consisting in a longitudinal stiffness applied in the vicinity of the cracks. In a second step, the analytical results are compared with these obtained from a finite element simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1021-1024
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• 1993

System identification is a major component for a control system. In biosystems, which is nonlinear and dynamic, precise identification would be very helpful for implementing a control system. It is difficult to precisely identify such non-linear systems. The measurable data on products from 2,3-butanediol fermentation could not be included in a process model based on kinetic approach. Meanwhile, a predictive capability is required in developing a control system. A neural network (NN) dynamic identifier with a by/(1＋ t ) transfer function was therefore designed being able to predict this fermentation. This modified inverse NN identifier differs from traditional models in which it is not only able to see but also able to predict the system. A moving window, with a dimension of 11 and a fixed data size of seven, was properly designed. One-step ahead identification/prediction by an 11-3-1 BPNN is demonstrated. Even under process fault, this neural network is still able to perform several-step ahead prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4087-4107
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• 2016

The current cloud computing paradigm is still vulnerable to a significant number of system failures. The increasing demand for fault tolerance and resilience in a cost-effective and device-independent manner is a primary reason for creating an effective means to address system dependability and availability concerns. This paper focuses on online failure prediction for cloud computing systems using system runtime data, which is different from traditional tolerance techniques that require an in-depth knowledge of underlying mechanisms. A 'failure prediction' approach, based on Cloud Theory (CT) and the Hidden Markov Model (HMM), is proposed that extends the HMM by training with CT. In the approach, the parameter ω is defined as the correlations between various indices and failures, taking into account multiple runtime indices in cloud computing systems. Furthermore, the approach uses multiple dimensions to describe failure prediction in detail by extending parameters of the HMM. The likelihood and membership degree computing algorithms in the CT are used, instead of traditional algorithms in HMM, to reduce computing overhead in the model training phase. Finally, the results from simulations show that the proposed approach provides very accurate results at low computational cost. It can obtain an optimal tradeoff between 'failure prediction' performance and computing overhead.