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Full-Text Articles in Engineering
Using Neural Networks To Estimate Wind Turbine Power Generation, Shuhui Li, Donald C. Wunsch, Edgar O'Hair, Michael G. Giesselmann
Using Neural Networks To Estimate Wind Turbine Power Generation, Shuhui Li, Donald C. Wunsch, Edgar O'Hair, Michael G. Giesselmann
Electrical and Computer Engineering Faculty Research & Creative Works
This paper uses data collected at Central and South West Services Fort Davis wind farm to develop a neural network based prediction of power produced by each turbine. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes—lower-than-expected wind power may be an early indicator of a need for maintenance. In this paper, characteristics of wind power generation are first evaluated in order to establish the relative importance for the neural network. A …
Abnormal Cell Detection Using The Choquet Integral, R. Joe Stanley, James M. Keller, Charles William Caldwell, Paul D. Gader
Abnormal Cell Detection Using The Choquet Integral, R. Joe Stanley, James M. Keller, Charles William Caldwell, Paul D. Gader
Electrical and Computer Engineering Faculty Research & Creative Works
Automated Giemsa-banded chromosome image research has been largely restricted to classification schemes associated with isolated chromosomes within metaphase spreads. In normal human metaphase spreads, there are 46 chromosomes occurring in homologous pairs for the autosomal classes 1-22 and the X chromosome for females. Many genetic abnormalities are directly linked to structural and/or numerical aberrations of chromosomes within metaphase spreads. Cells with the Philadelphia chromosome contain an abnormal chromosome for class 9 and for class 22, leaving a single normal chromosome for each class. A data-driven homologue matching technique is applied to recognizing normal chromosomes from classes 9 and 22. Homologue …
A Committee Of Neural Networks For Automatic Speaker Recognition (Asr) Systems, Viresh Moonasar, Ganesh K. Venayagamoorthy
A Committee Of Neural Networks For Automatic Speaker Recognition (Asr) Systems, Viresh Moonasar, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper describes how the results of speaker verification systems can be improved and made robust with the use of a committee of neural networks for pattern recognition rather than the conventional single-network decision system. It illustrates the use of a supervised learning vector quantization neural network as the pattern classifier. Linear predictive coding and cepstral signal processing techniques are utilized to form hybrid feature parameter vectors to combat the effect of decreased recognition success with increased group size (number of speakers to be recognized)