• International Journal of Aeronautical and Space Sciences
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• v.10 no.2
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• pp.34-42
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• 2009

Gas turbine performance diagnostics is a method for detecting, isolating and quantifying faults in gas turbine gas path components. On-line precise fault diagnosis can promote greatly reliability and availability of gas turbine in real time operation. This work proposes a GUI-type on-line diagnostic program using SIMULINK and Fuzzy-Neuro algorithms for a helicopter turboshaft engine. During development of the diagnostic program, a look-up table type base performance module are used for reducing computer calculating time and a signal generation module for simulating real time performance data. This program is composed of the on-line condition monitoring program to monitor on-line measuring performance condition, the fuzzy inference system to isolate the faults from measuring data and the neural network to quantify the isolated faults. Evaluation of the proposed on-line diagnostic program is performed through application to the helicopter engine health monitoring.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.12
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• pp.1238-1244
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• 2009

This work proposes a GUI-type on-line diagnostic program using SIMULINK and Fuzzy-Neuro algorithms for a helicopter turboshaft engine. During development of the diagnostic program, a look-up table type base performance module for reducing computer calculating time and a signal generation module for simulating real time performance data are used. This program is composed of the on-line condition monitoring program to monitor on-line measuring performance condition, the fuzzy inference system to isolate the faults from measuring data and the neural network to quantify the isolated faults. The reliability and capability of the proposed on-line diagnostic program were confirmed through application to the helicopter engine health monitoring.

• 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.

• Parasites, Hosts and Diseases
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• v.52 no.4
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• pp.377-381
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• 2014

Microscopy is considered as the gold standard for malaria diagnosis although its wide application is limited by the requirement of highly experienced microscopists. PCR and serological tests provide efficient diagnostic performance and have been applied for malaria diagnosis and research. The aim of this study was to investigate the diagnostic performance of nested PCR and a recently developed an ELISA-based new rapid diagnosis test (RDT), NovaLisa test kit, for diagnosis of malaria infection, using microscopic method as the gold standard. The performance of nested-PCR as a malaria diagnostic tool is excellent with respect to its high accuracy, sensitivity, specificity, and ability to discriminate Plasmodium species. The sensitivity and specificity of nested-PCR compared with the microscopic method for detection of Plasmodium falciparum, Plasmodium vivax, and P. falciparum/P. vivax mixed infection were 71.4 vs 100%, 100 vs 98.7%, and 100 vs 95.0%, respectively. The sensitivity and specificity of the ELISA-based NovaLisa test kit compared with the microscopic method for detection of Plasmodium genus were 89.0 vs 91.6%, respectively. NovaLisa test kit provided comparable diagnostic performance. Its relatively low cost, simplicity, and rapidity enables large scale field application.

• Korean Journal of Veterinary Research
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• v.44 no.4
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• pp.669-676
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• 2004

To evaluate the diagnostic accuracy of two commercial ELISA tests (Allied- and CSL-ELISA) for the diagnosis of Mycobacterium paratuberculosis in cattle, Meta-analysis using English language papers published during 1990-2001 was performed. Diagnostic odds ratios (DOR) were analyzed using regression analysis together with summary receiver operating characteristic (ROC) curves. The difference in diagnostic performance between the two ELISA systems was evaluated by using linear regression. Publication bias was assessed by funnel plot and linear regression. The pooled sensitivity and specificity were 44% (95% CI, 38 to 51) and 98% (95% CI, 96 to 99) for the random-effect model. The DOR between studies was heterogeneous. The area under the fitted ROC curve (AUC) was 0.72 for the unweighted and 0.77 for the weighted model. Maximum joint sensitivity and specificity for the unweighted and weighted model from their summary ROC curve were 70% and 75%, respectively. Based on the fitted model, at a specificity of 95%, sensitivity was estimated to be 52% for the unweighted and 57% for the weighted model. From the final multivariable model study characteristic, the country was the only significant variable with an explained component variance of 13.3%. There were no significant differences in discriminatory power, sensitivity, and specificity between the two ELISA tests. The overall diagnostic accuracy of two commercial ELISA tests was moderate, as judged by the AUC, maximum joint sensitivity and specificity, and estimates from the fitted model and clinical usefulness of the tests for screening program is limited because of low sensitivity and heterogeneous of DOR. It is, therefore, recommended to use ELISA tests as a parallel testing with other diagnostic tests together to increase test sensitivity in the screening program.

• Journal of Appropriate Technology
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• v.6 no.1
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• pp.45-50
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• 2020

We developed the world's first wireless ultrasound diagnostic device. For the smooth delivery of pregnant women, the WHO requires four prior examinations, and the use of ultrasound diagnostic devices is essential for this purpose. However, income levels and medical facilities in developing countries are falling short. We conducted KOICA's CTS program in Vietnam using a self-developed radio ultrasound diagnostic device. We supplied Sonon300C, a portable wireless ultrasound diagnostic device, to health centers and hospitals in Huong-Hoa district, Vietnam, and conducted an outreach program in an effort to further enhance business performance. As a result, the rate of ultrasound diagnostics in the region reached 100% and the percentage of trained graduates increased to 59%.

• The KSFM Journal of Fluid Machinery
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• v.20 no.2
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• pp.32-40
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• 2017

In this study, an in-house program to analyze the performance degradation for gas turbines is developed using MATLAB and is validated using commercial software. This program consists of design and off-design calculations. The results of design calculation is used for reference values of off-design calculation. The off-design calculation is composed of measured and expected performance analyses, and turbine inlet temperature correction. In general, performance degradation is analyzed by comparing the results of measured and expected performance analysis. However, if gas turbine performance degrades, turbine inlet temperature might increase due to the general control logic to comply with the power demand. Therefore, it is required to consider the deviation of turbine inlet temperature from the normal value in the performance diagnosis to analyze the performance degradation exactly. In this study, a special effort is given to the correction of turbine inlet temperature. The accuracy of the developed program is confirmed by comparison with commercial software, and its capability of performance diagnosis using the turbine inlet temperature correction is demonstrated.

• Proceedings of the KIEE Conference
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• 2001.07c
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• pp.1623-1625
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• 2001

This paper describes a monitoring program for a substation preventive diagnostic system. The monitoring program announces the condition of the transformer and GIS like normal, warning, abnormal and danger from the absolute value of data or change trend to user. The developed system is operating in 345kV U-Ryung substation for performance and environment tests.

• 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.