Abstract: Accurate determination of wind turbine performance is necessary for economic operation of a wind farm. At present, the procedure to carry out the power performance verification of wind turbines is based on a standard of the International Electrotechnical Commission (IEC). In this paper, nonparametric statistical inference is applied to designing a simple, inexpensive method of verifying the power performance of a wind turbine. A statistical test is explained, examined, and the adequacy is tested over real data. The methods use the information that is collected by the SCADA system (Supervisory Control and Data Acquisition) from the sensors embedded in the wind turbines in order to carry out the power performance verification of a wind farm. The study has used data on the monthly output of wind farm in the Republic of Macedonia, and the time measuring interval was from January 1, 2016, to December 31, 2016. At the end, it is concluded whether the power performance of a wind turbine differed significantly from what would be expected. The results of the implementation of the proposed methods showed that the power performance of the specific wind farm under assessment was acceptable.
Abstract: Electroencephalogram (EEG) recordings are often
contaminated with ocular and muscle artifacts. In this paper, the
canonical correlation analysis (CCA) is used as blind source
separation (BSS) technique (BSS-CCA) to decompose the artifact
contaminated EEG into component signals. We combine the BSSCCA
technique with wavelet filtering approach for minimizing both
ocular and muscle artifacts simultaneously, and refer the proposed
method as wavelet enhanced BSS-CCA. In this approach, after
careful visual inspection, the muscle artifact components are
discarded and ocular artifact components are subjected to wavelet
filtering to retain high frequency cerebral information, and then clean
EEG is reconstructed. The performance of the proposed wavelet
enhanced BSS-CCA method is tested on real EEG recordings
contaminated with ocular and muscle artifacts, for which power
spectral density is used as a quantitative measure. Our results suggest
that the proposed hybrid approach minimizes ocular and muscle
artifacts effectively, minimally affecting underlying cerebral activity
in EEG recordings.
Abstract: The purpose of this study was to explore the correlation
between leisure participation and perceived wellness, with the students
of a nursing college in southern Taiwan as the subjects. One thousand
six hundred and ninety-six (1,696) surveys were sent, and 1,408
surveys were received for an 83.02% valid response rate. Using
canonical correlation analysis to analyze the data, the results showed
that the linear combination of the two sets of variable produces five
significant canonical variates. Out of the five canonical variates, only
the first has sufficient explanatory power. The canonical correlation
coefficient of first canonical variate is 0.660. This indicated that
leisure participation and perceived wellness are significantly
correlated.