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Parameters (mean and covariance matrix) estimation is often a problem of interest since it provides information about the location and variation of the data and correlation between features and can be used for hypothesis testing, principle component analysis, etc. However, it is also common that values in some features of a dataset are missing. A popular way to deal with this problem is to use an Expectation-Maximization algorithm or to impute the missing values and then estimate the parameters based on imputed data. However, the first approach is a local optimization approach that may not converge under a fixed number of iterations. The second one, a two-step approach of imputation and analysis, is computationally inefficient. Therefore, we follow the recent trends of estimating the parameters directly from the data and propose PMF (Parameter estimation for Missing data in some Features) to deal with the aforementioned problems. The experiments show that our approach achieves better performance than other methods under comparison in performance and speed. Moreover, our estimates are asymptotic.

Understanding the Heat transfer performance of refrigerant for convective boiling in horizontal microfin tube and smooth tube is place an importance role on the designing of evaporator, the main equipment on refrigeration system. Reviewing the general concept especially the theory of boiling in the tube, the formation of the flow pattern map, the calculating procedure for heat transfer coefficient and pressure drop during boiling process of refrigerant in microfin tube. Besides, a typical example will be presented more detail in step by step to define the heat transfer coefficient and pressure drop for one working condition to estimate the data results without doing experiments.