Abstract: We introduce a new model called the Marshall-Olkin Rayleigh distribution which extends the Rayleigh distribution using Marshall-Olkin transformation and has increasing and decreasing shapes for the hazard rate function. Various structural properties of the new distribution are derived including explicit expressions for the moments, generating and quantile function, some entropy measures, and order statistics are presented. The model parameters are estimated by the method of maximum likelihood and the observed information matrix is determined. The potentiality of the new model is illustrated by means of a simulation study.
Abstract: The investigation on wind turbine degradation was
carried out using the nacelle wind data. The three Vestas V80-2MW
wind turbines of Sungsan wind farm in Jeju Island, South Korea were
selected for this work. The SCADA data of the wind farm for five
years were analyzed to draw power curve of the turbines. It is assumed
that the wind distribution is the Rayleigh distribution to calculate the
normalized capacity factor based on the drawn power curve of the
three wind turbines for each year. The result showed that the reduction
of power output from the three wind turbines occurred every year and
the normalized capacity factor decreased to 0.12%/year on average.
Abstract: A new approach is adopted in this paper based
on Turk and Pentland-s eigenface method. It was found that the
probability density function of the distance between the projection
vector of the input face image and the average projection vector of
the subject in the face database, follows Rayleigh distribution. In
order to decrease the false acceptance rate and increase the
recognition rate, the input face image has been recognized using two
thresholds including the acceptance threshold and the rejection
threshold. We also find out that the value of two thresholds will be
close to each other as number of trials increases. During the training,
in order to reduce the number of trials, the projection vectors for each
subject has been averaged. The recognition experiments using the
proposed algorithm show that the recognition rate achieves to
92.875% whilst the average number of judgment is only 2.56 times.