• Korean Journal of Veterinary Research
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• v.46 no.2
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• pp.159-164
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• 2006

A stochastic simulation model incorporated with Reed-Frost approach was derived for evaluating diagnostic performance of a test used for a screening program of an infectious disease. The Reed-Frost model was used to characterize the within-herd spread of the disease using a hypothetical example. Specifically, simulation model was aimed to estimate the number infected animals in an infected herd, in which imperfect serologic tests are performed on samples taken from herds and to illustrate better interpreting survey results at herd-level when uncertainty inevitably exists. From a risk analysis point of view, model output could be appropriate in developing economic impact assessment models requiring probabilistic estimates of herd-level performance in susceptible populations. The authors emphasize the importance of knowing the herd-level diagnostic performance, especially in performing emergency surveys in which immediate control measures should be taken following the survey. In this context this model could be used in evaluating efficacy of a survey program and monitoring infection status in the area concerned.

• 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.

• Journal of International Society for Simulation Surgery
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• v.2 no.1
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• pp.43-45
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• 2015

Recent progress on CT such as multi-detector row CT, oral contrast agents and multiplanar reconstruction have markedly improved the image quality as well as diagnostic performance of gastric cancer. Multiplanar reformatted images at predetermined orientations can be easily performed and embedded into routine CT protocol without increasing medical expense or labor. Currently, many institutions have adopted routine multiplanar reformatted protocols and therefore knowledge on them can improve the diagnostic accuracy of gastric cancer.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.7
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• pp.1-14
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• 1999

In the present study, the flame factor which primarily influence the simulation accuracy of the combustion process in a gasoline engine was modeled as a nonlinear function of turbulent intensity to laminar flame speed ratio. Multi-length-scale production rate model for turbulent kinetic energy equation was introduced to consider the different length scales of the swirling and tumbling motions in cylinder on the production rte of turbulent kinetic energy. By7 introducing the multi-length-scale production rate model for the turbulent kinetic energy equation, the predictions of turbulent burning velocity , cylinder pressure, mass burning rate and engine performance of a gasoline engine can much be improved.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.10
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• pp.1833-1840
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• 1992

In the diagnostic process for automobile, Neuro-Fuzzy technique was compared with the conventional diagnostic method for the verification of performance, and proto-type system was developed. For the utilities of the system, 1/3 octave filter(band-pass filter) and A/D converter were used for data acquisition and then data were analyzed using octave band processing and pattern recognition using hamming network algorithm. In order to raise the reliability of the diagnostic results by considering many operating variables and condition of automobile to be diagnosed, fuzzy inference technique was applied in combining several information. The validation of this diagnostic system was examined through computer simulation and experiment, and it showed an acceptable performance for diagnostic process.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.213-217
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). For teaming the NN, a BPN with one hidden, one input and one output layer was used. The input layer had seven neurons of variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer used 6 neurons of degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network teaming and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.15-22
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle) which is being developed by KARI (Korea Aerospace Research Institute). For teeming the NN(Neural Network), a BPN(Back Propagation Network) with one hidden, one input and one output layer was used. The input layer has seven neurons: variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer uses 6 neurons: degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine, respectively, Database for network teaming and test was constructed using a gas turbine performance simulation program. From application of the learned networks to diagnostics of the PW206C turboshaft engine, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.289-301
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• 1996

This paper suggests a new diagnostic measure for detecting influential observations in two group linear discriminant analysis(LDA). It is developed from an information theoretic point of view using the minimum discrimination information(MDI) methodology. MDI estimator of symmetric divergence by Kullback(l967) is taken as a measure of the power of discrimination in LDA. It is shown that the effect of an observation over the power of discrimination is fully explained by the diagnostic measure. Asymptotic distribution of the proposed measure is derived as a function of independent chi-squared and standard normal variables. By means of the distributions, a couple of methods are suggested for detecting the influential observations in LDA. Performance of the suggested methods are examined through a simulation study.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.1-16
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• 2011

In this paper, we proposed the Failure-Diagnostic Context-awareness Middleware (FDCM) for improving the reliability in the USN application service. The middleware diagnoses the failure occurred in sensors or facilities in the indoor USN application system. The new middleware suggested in this paper consists of DataManagement module, ContextProvider module, Contextlnterpreter module, ServiceProvider module and DataStorage module. By analysing the data obtained by the interaction between modules through the diagnostic algorithm, the FDCM determines the malfunction of sensors and equipment devices. Then we verified the performance of middleware by using simulation. As a result, the FDCM showed the high performance in the large systems that many of the sensors and devices are installed.