A kind of cartridge servo proportional valve is discussed, which can be used for controlling large flow rate with high performance. By analyzing the structure principle of the valve, the transfer fimction of the valve...A kind of cartridge servo proportional valve is discussed, which can be used for controlling large flow rate with high performance. By analyzing the structure principle of the valve, the transfer fimction of the valve is derived. With the transfer function, some structure elements that may affect its performance are investigated. Through the numerical simulation and test study, some principles of optimality and effective methods for improving the dynamic performance of the valve are proposed. The test results conform to the results of the theoretical analysis and simulation, which proves the correctness of the study and simulation works. The paper provides theoretical basis for engineering applications and series expanding design works展开更多
Currently, a growing number of programs become available in statistical software for multiple imputation of missing values. Among others, two algorithms are mainly implemented: Expectation Maximization (EM) and Multip...Currently, a growing number of programs become available in statistical software for multiple imputation of missing values. Among others, two algorithms are mainly implemented: Expectation Maximization (EM) and Multiple Imputation by Chained Equations (MICE). They have been shown to work well in large samples or when only small proportions of missing data are to be imputed. However, some researchers have begun to impute large proportions of missing data or to apply the method to small samples. A simulation was performed using MICE on datasets with 50, 100 or 200 cases and four or eleven variables. A varying proportion of data (3% - 63%) was set as missing completely at random and subsequently substituted using multiple imputation by chained equations. In a logistic regression model, four coefficients, i.e. non-zero and zero main effects as well as non-zero and zero interaction effects were examined. Estimations of all main and interaction effects were unbiased. There was a considerable variance in the estimates, increasing with the proportion of missing data and decreasing with sample size. The imputation of missing data by chained equations is a useful tool for imputing small to moderate proportions of missing data. The method has its limits, however. In small samples, there are considerable random errors for all effects.展开更多
基金supported by Program for New Century Excellent Talents in University of China (No.NCET-05-0528).
文摘A kind of cartridge servo proportional valve is discussed, which can be used for controlling large flow rate with high performance. By analyzing the structure principle of the valve, the transfer fimction of the valve is derived. With the transfer function, some structure elements that may affect its performance are investigated. Through the numerical simulation and test study, some principles of optimality and effective methods for improving the dynamic performance of the valve are proposed. The test results conform to the results of the theoretical analysis and simulation, which proves the correctness of the study and simulation works. The paper provides theoretical basis for engineering applications and series expanding design works
基金supported by the Stiftung Rheinland-Pfalz fur Innovation(959).
文摘Currently, a growing number of programs become available in statistical software for multiple imputation of missing values. Among others, two algorithms are mainly implemented: Expectation Maximization (EM) and Multiple Imputation by Chained Equations (MICE). They have been shown to work well in large samples or when only small proportions of missing data are to be imputed. However, some researchers have begun to impute large proportions of missing data or to apply the method to small samples. A simulation was performed using MICE on datasets with 50, 100 or 200 cases and four or eleven variables. A varying proportion of data (3% - 63%) was set as missing completely at random and subsequently substituted using multiple imputation by chained equations. In a logistic regression model, four coefficients, i.e. non-zero and zero main effects as well as non-zero and zero interaction effects were examined. Estimations of all main and interaction effects were unbiased. There was a considerable variance in the estimates, increasing with the proportion of missing data and decreasing with sample size. The imputation of missing data by chained equations is a useful tool for imputing small to moderate proportions of missing data. The method has its limits, however. In small samples, there are considerable random errors for all effects.
