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For wind farms, the relationship between wind speed and output can be described by power curve of wind turbines, and it is an important embodiment of power performance of wind turbines. Based on the mathematical model of the power curve of wind turbine, monitoring performance of the wind turbine can be designed. Power curve model of wind turbines can be established by using Gaussian process. Within...
For wind farms, wind turbine condition monitoring is important to reduce maintenance costs and improve the competitiveness in the electricity market, particularly for offshore wind farms. This paper seeks to establish wind turbine gearbox temperature model under the normal working state using Gaussian process, the forecast and evaluation of temperature is also described. Within the Bayesian context,...
In this paper, we present an approach for evolutionary learning of motif in biopolymer sequences. The focuses in this paper is evolutionary inference of Gaussian model, Differential Evolution for optimization and Markov chain Monte Carlo(MCMC) for sampling are applied in the probability learning of Gaussian model. The framework involves calculations of corresponding weight, mean and covariance. To...
Mixture model comprises a finite or infinite number of different distributional types of components and offers a much wider range of modeling possibilities than its components. In his paper, we present an approach for Bayesian inference of mixture model with Differential Evolution and Markov chain Monte Carlo(MCMC). Bayesian inference on Gaussian mixture model via Gibbs sampling and optimization with...
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