• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.410-418
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• 2007

For the purpose of fault detection of the primary control surface, real-time estimation of the longitudinal stability and control derivatives of the DURUMI-II using the flight data is considered in this paper. The DURUM-II, a research UAV developed by KARI, is designed to have split control surfaces for the redundancy and to guarantee safety during the fault mode flight test. For fault mode analysis, the right elevator was deliberately fixed to the specified deflection condition. This study also mentions how to implement the multi-step control input efficiently, and how to switch between the normal mode and the fault mode during the flight test. As a realtime parameter estimation technique, Fourier transform regression method was used and the estimated data was compared with the results of the analytical method and the other available method. The aerodynamic derivatives estimated from the normal mode flight data and the fault mode data are compared and the possibility to detect the elevator fault by monitoring the control derivative estimated in real time by the computer onboard was discussed.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.361-366
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• 2019

In developing the avionics system, a system integration laboratory (SIL) is established to verify the function and interworking of individual components. In case of individual verification of SIL's components and system integration, a SIL model that simulates the function and interworking of each equipment is developed and used. A SIL model shall be pre-verified against all data defined in the interface control document (ICD) before interworking with the actual equipment and reverified even when the ICD changes or functions change. However, if the verification of the SIL model is performed manually, the verification of the individual SIL model takes considerable time. For this reason, selective regression tests are often performed to determine a impact of SIL models on ICD changes and some functional changes. In this paper, we designed SIL model verification automation method to perform regession test by reducing verification time of SIL model and verify the usefulness of verification automation design by developing SIL model verification automation tool.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1002-1005
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• 1991

It is important to express the specific growth rate of a fermentation reaction as a function of substrate and product concentration in developing bioprocess automation techniques such as modeling of the reactor and controlling it via an advanced control scheme. Typical methods of identification utilize graphical representation of the rate constant data or nonlinear regression with an appropriate noise filter. But the former method fails when the data are erroneous and the latter are mathematically complicated to apply in the field. Neural network is another candidate for the identification from time series data since it is insensitive to the random data error and easy to implement. In this study, we will develop a neural network method of specific growth rate estimation from the time series state variable data and test the performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8753-8759
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• 2015

Establishment of analysis procedures and validated performance measurements for generator output is required to maintain stable management of generator output in turbine power generation cycle. We developed turbine expansion model and measurement validation model for the performance calculation of generator using turbine output based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). We also developed verification model for uncertain measurement data related to the turbine and generator output. Although the model in previous researches was developed using artificial neural network and kernel regression, the verification model in this paper was based on algorithms through Support Vector Machine (SVM) model to overcome the problems of unmeasured data. The selection procedures of related variables and data window for verification learning was also developed. The model reveals suitability in the estimation procss as the learning error was in the range of about 1%. The learning model can provide validated estimations for corrective performance analysis of turbine cycle output using the predictions of measurement data loss.

• Journal of Energy Engineering
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• v.23 no.4
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• pp.263-271
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• 2014

Corrective thermal performance analysis is required for thermal power plants to determine performance status of turbine cycle. We developed classification method for main feed water flow to make precise correction for performance analysis based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). The classification is based on feature identification of status of main water flow. Also we developed predictive algorithms for corrected main feed-water through Support Vector Machine (SVM) Model for each classified feature area. The results was compared to estimations using Neural Network(NN) and Kernel Regression(KR). The feature classification and predictive model of main feed-water flow provides more practical methods for corrective thermal performance analysis of turbine cycle.

• Journal of Veterinary Science
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• v.25 no.3
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• pp.38.1-38.16
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• 2024

Importance: Deaths due to neonatal calf diarrhea are still one of the most critical problems of cattle breeding worldwide. Determining the parameters that can predict diarrhea-related deaths in calves is especially important in terms of prognosis and treatment strategies for the disease. Objective: The primary purpose of this study was to determine mortality rates and durations, survival status, and predictive prognosis parameters based on vital signs, hematology, and blood gas analyses in neonatal diarrheic calves. Methods: The hospital automation system retrospectively obtained data from 89 neonatal diarrheic calves. Results: It was found that 42.7% (38/89) of the calves brought with the complaint of diarrhea died during hospitalization or after discharge. Short-term and long-term fatalities were a median of 9.25 hours and a median of 51.50 hours, respectively. When the data obtained from this study is evaluated, body temperature (℃), pH, base excess (mmol/L), and sodium bicarbonate (mmol/L) parameters were found to be lower, and hemoglobin (g/dL), hematocrit (%), lactate (mmol/L), chloride (mmol/L), sodium (mmol/L) and anion gap (mmol/L) parameters were found to be higher in dead calves compared to survivors. Accordingly, hypothermia, metabolic acidosis, and dehydration findings were seen as clinical conditions that should be considered. Logistic regression analysis showed that lactate (odds ratio, 1.429) and CI^- (odds ratio, 1.232) concentration were significant risk factors associated with death in calves with diarrhea. Conclusions and Relevance: According to the findings obtained from this study, the determination of lactate and Cl^- levels can be used as an adjunctive supplementary test in distinguishing calves with diarrhea with a good prognosis.