• Journal of Hydrogen and New Energy
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• v.25 no.6
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• pp.609-619
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• 2014

In the present study, the frequency of the undesired accident was estimated for a quantitative risk assessment of a large-scale hydrogen liquefaction plant. As a representative example, the hydrogen liquefaction plant located in Ingolstadt, Germany was chosen. From the analysis of the liquefaction process and operating conditions, it was found that a $LH_2$ storage tank was one of the most dangerous facilities. Based on the accident scenarios, frequencies of possible accidents were quantitatively evaluated by using both fault tree analysis and event tree analysis. The overall expected frequency of the loss containment of hydrogen from the $LH_2$ storage tank was $6.83{\times}10^{-1}$times/yr (once per 1.5 years). It showed that only 0.1% of the hydrogen release from the $LH_2$ storage tank occurred instantaneously. Also, the incident outcome frequencies were calculated by multiplying the expected frequencies with the conditional probabilities resulting from the event tree diagram for hydrogen release. The results showed that most of the incident outcomes were dominated by fire, which was 71.8% of the entire accident outcome. The rest of the accident (about 27.7%) might have no effect to the population.

• Journal of the Korean Society of Safety
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• v.31 no.5
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• pp.187-194
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• 2016

A single fire event within a fire area can cause multiple initiating events considered in internal events probabilistic safety assessment (PSA). For an example, a fire event in turbine building fire area can cause a loss of the main feed-water and loss of off-site power initiating events. This fire initiating event could result in special plant responses beyond the scope of the internal events PSA model. One approach to address a fire initiating event is to develop a specific fire event tree. However, the development of a specific fire event tree is difficult since the number of fire event trees may be several hundreds or more. Thus, internal fire events PSA model has been generally constructed by modifications of the pre-developed internal events PSA model. New accident sequence logics not covered in the internal events PSA model are separately developed to incorporate them into the fire PSA model. Recently, many fire PSA models have fire induced initiating event fault trees not shown in an internal event PSA model. Up to now, there has been no analytical comparative study on the constructions of fire events PSA model using internal events PSA model with and without fault trees of initiating events. In this study, the changing process of internal events PSA model to fire events PSA model is analytically presented and discussed.

• 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.

• Journal of the Korea Computer Industry Society
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• v.6 no.5
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• pp.689-696
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• 2005

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, Goel-Okumoto and Yamada-Ohba-Osaki model was reviewed, proposes the Kappa(2) reliability model, which can capture the nomotonic decreasing nature of the failure occurrence rate per fault. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on sum of the squared errors and Kolmogorov distance, for the sake of efficient model, was employed. Analysis of failure using real data set, SYS2(Allen P.Nikora and Michael R.Lyu), for the sake of proposing two parameter of the Kappa distribution, was employed. This analysis of failure data compared with the Kappa model and the existing model using arithmetic and Laplace trend tests, bias tests is presented.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.837-848
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• 2018

Monitoring technology of machining has a long history since unmanned machining was introduced. Despite the long history, many researchers have presented new approaches continuously in this area. Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sound is corrupted by the surrounding work environment. Therefore, the most important part of the diagnosis is to find hidden elements inside the data that can represent the error pattern. This paper presents a feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by tools. The magnitude spectrum of the sound is extracted using the Fourier analysis and the band-pass filter is applied to further characterize the data. Statistical functions are also used as input to the nonlinear classifier for the final response. The results prove that the proposed feature extraction method accurately captures the hidden patterns of the sound generated by the tool, unlike the conventional features. Therefore, it is shown that the proposed method can be applied to a sound based automatic diagnosis system.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.4 s.44
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• pp.55-66
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• 2005

The effectiveness of fuzzy supervisory control technique for the control of seismic responses of smart base isolation system is investigated in this study. To this end, first generation base isolated building benchmark problem is employed for the numerical simulation. The benchmark structure under consideration is an eight-story base isolated building having irregular plan and is equipped with low-damping elastometric bearings and magnetorheological (MR) dampers for seismic protection. Lower level fuzzy logic controllers (FLC) for far-fault or near-fault earthquakes are developed in order to effectively control base isolated building using multi-objective genetic algorithm. Four objectives, i.e. reduction of peak structural acceleration, peak base drift, RMS structural acceleration and RMS base drift, are used in multi-objective optimization process. When earthquakes are applied to benchmark building, each of low level FLCs provides different command voltage and supervisory fuzzy controller combines two command voltages io one based on fuzzy inference system in real time. Results from the numerical simulations demonstrate that base drift as well as superstructure responses can be effectively reduced using the proposed supervisory fuzzy control technique.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.1-6
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• 1998

The evaluation of reliability is very important in the development process of software. There may be lack of trustfulness on the results that come from the analysis and evaluation of reliability of softwares which do not divide the test phases. At this point, this article studies how to evaluate the reliability dividing the test phases in order to settle the these problems. In doing so, I apply the fault data to be found in Unit Test, Integration Test, Validation Test and System Test to SRGM(Software Reliability Growth Model), Exponential SRGM, Delayed S-shaped SRGM and Inflection S-shaped SRGM. The result is that Inflection S-shaped is best suitable with Unit Test Delayed S-shaped is best suitable with Integration and Validation Test, and Exponential SRGM is best suitable with System test. In this respect, I can show that the results of this study on parameter estimation, difference square summation, number of fault remained is superior to the established methods.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.8 s.350
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• pp.25-34
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• 2006

In this paper DynaMoNET is suggested as a novel IPv6-based multi-homed mobile network architecture which is composed of nested mobile ad hoc networks dynamically coming together through wireless personal area networks. Each ad hoc network has a mobile router which may work as a root mobile router instead of fixed mobile routers in a DynaMoNET. A root mobile router provides the reliable Internet connectivity for the entire mobile network. This paper includes a innovative handover protocol for multi-homed mobile networks, network switchover algorithm considering multiple decision factors, root mobile router election process based on token-based algorithm fast root mobile router discovery algorithm and fault avoidance mechanism to support reliable Internet connectivity. Finally the system architecture of a mobile router is given in detail.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.12
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• pp.13-22
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• 1998

In this paper, we propose a high-performance BICS(built-in current sensor) which is fabricated in 0.8${\mu}{\textrm}{m}$ single-poly two-metal process for IDDQ testing of CMOS VLSI circuit. The CUT(circuit under test) is 4-bit full adder with a bridging fault. Using two nMOSs that have different size, two bridging faults that have different resistance values are injected in the CUT. And controlling a gate node, we experimented various fault effects. The proposed BICS detects the faulty current at the end of the clock period, therefore it can test a CUT that has a much longer critical propagation delay time and larger area than conventional BICSs. As expected in the HSPICE simulation, experimental results of fabricated chip demonstrated that the proposed BICS can exactly detect bridging faults in the circuit.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.5
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• pp.672-678
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• 2009

Acoustic Emission (AE) technique is a non-destructive testing method and widely used for the early detection of faults in rotating machines in these days, because the sensitivity of AE transducers is higher than normal accelerometers. So it can detect low energy vibration signals. The faults in the rotating machines are generally occurred at bearings and gearboxes which are the principal parts of the machines. It was studied to detect the bearing faults by envelop analysis in several decade years. And the researches showed that AE had a possibility of the application in condition monitoring system(CMS) using the envelope analysis for the rolling bearing. And peak ratio (PR) was developed for expression of the bearing condition in condition monitoring system using AE. Noise level is needed to reduce to take exact PR value because the PR is calculated from total root mean square (RMS) and the harmonics peak levels of the defect frequencies of the bearing. Therefore, in this paper, the discrete wavelet transform (DWT) was added in the envelope analysis to reduce the noise level in the AE signals. And then, the PR was calculated and compared with general envelope analysis result and the result of envelope analysis added the DWT. In the experiment result about inner fault of bearing, defect frequency was difficult to find about only envelop analysis. But it's easy to find defect frequency after wavelet transform. Therefore, Envelop analysis added wavelet transform was useful method for early detection of default in signal process.