传统Pearson相关系数计算公式具有不稳健性,离群值的存在会导致计算结果与实际不符。针对此问题,文章给出了一种稳健估计方法。在模拟样本量分别为20、50、100、200,污染率分别为1%、5%、10%情形下,比较传统相关系数值与稳健相关系数值...传统Pearson相关系数计算公式具有不稳健性,离群值的存在会导致计算结果与实际不符。针对此问题,文章给出了一种稳健估计方法。在模拟样本量分别为20、50、100、200,污染率分别为1%、5%、10%情形下,比较传统相关系数值与稳健相关系数值,发现:稳健相关系数公式正确率均显著高于传统相关系数。在实例分析中进一步验证了稳健相关系数的可行性和有效性。文章研究结论可用于含离群值变量的相关系数稳健估计。The traditional Pearson correlation coefficient calculation formula is not robust, and the existence of outliers will cause the calculation results to be inconsistent with reality. To solve this problem, this paper presents a robust estimation method. When the simulated sample size is 20, 50, 100 and 200 respectively, the pollution rate is 1%, 5% and 10% respectively, it is found that the accuracy of the robust correlation coefficient formula is significantly higher than that of the traditional correlation coefficient. The feasibility and effectiveness of a robust correlation coefficient are further verified in the example analysis. The conclusions of this paper can be used for robust estimation of correlation coefficients with outlier variables.展开更多
With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in th...With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.展开更多
The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas.The radiation cost and quality factor of the antenna are influenced by the size of the antenna.Metamateri...The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas.The radiation cost and quality factor of the antenna are influenced by the size of the antenna.Metamaterial antennas allow for the circumvention of the bandwidth restriction for small antennas.Antenna parameters have recently been predicted using machine learning algorithms in existing literature.Machine learning can take the place of the manual process of experimenting to find the ideal simulated antenna parameters.The accuracy of the prediction will be primarily dependent on the model that is used.In this paper,a novel method for forecasting the bandwidth of the metamaterial antenna is proposed,based on using the Pearson Kernel as a standard kernel.Along with these new approaches,this paper suggests a unique hypersphere-based normalization to normalize the values of the dataset attributes and a dimensionality reduction method based on the Pearson kernel to reduce the dimension.A novel algorithm for optimizing the parameters of Convolutional Neural Network(CNN)based on improved Bat Algorithm-based Optimization with Pearson Mutation(BAO-PM)is also presented in this work.The prediction results of the proposed work are better when compared to the existing models in the literature.展开更多
Background: The signal-to-noise ratio (SNR) is recognized as an index of measurements reproducibility. We derive the maximum likelihood estimators of SNR and discuss confidence interval construction on the difference ...Background: The signal-to-noise ratio (SNR) is recognized as an index of measurements reproducibility. We derive the maximum likelihood estimators of SNR and discuss confidence interval construction on the difference between two correlated SNRs when the readings are from bivariate normal and bivariate lognormal distribution. We use the Pearsons system of curves to approximate the difference between the two estimates and use the bootstrap methods to validate the approximate distributions of the statistic of interest. Methods: The paper uses the delta method to find the first four central moments, and hence the skewness and kurtosis which are important in the determination of the parameters of the Pearsons distribution. Results: The approach is illustrated in two examples;one from veterinary microbiology and food safety data and the other on data from clinical medicine. We derived the four central moments of the target statistics, together with the bootstrap method to evaluate the parameters of Pearsons distribution. The fitted Pearsons curves of Types I and II were recommended based on the available data. The R-codes are also provided to be readily used by the readers.展开更多
针对传统的属性约简算法无法有效分析条件属性之间耦合关系的问题,提出了一种新的算法,基于皮尔逊与邻域粗糙集的属性约简算法(pearson and neighborhood rough set,PNRS)。根据耦合关系的结果进一步确定条件属性对决策属性的重要度,提...针对传统的属性约简算法无法有效分析条件属性之间耦合关系的问题,提出了一种新的算法,基于皮尔逊与邻域粗糙集的属性约简算法(pearson and neighborhood rough set,PNRS)。根据耦合关系的结果进一步确定条件属性对决策属性的重要度,提高约简算法分类的精度。UCI数据集结果表明,经过耦合关系校正后,属性约简的能力进一步提升,平均约简率提升了1%,平均准确率提升了0.36%。与传统的属性约简算法相比,计算耦合关系的方法提高了约简算法的分类性能,为进一步优化邻域粗糙集的约简结果奠定了理论基础。展开更多
文摘传统Pearson相关系数计算公式具有不稳健性,离群值的存在会导致计算结果与实际不符。针对此问题,文章给出了一种稳健估计方法。在模拟样本量分别为20、50、100、200,污染率分别为1%、5%、10%情形下,比较传统相关系数值与稳健相关系数值,发现:稳健相关系数公式正确率均显著高于传统相关系数。在实例分析中进一步验证了稳健相关系数的可行性和有效性。文章研究结论可用于含离群值变量的相关系数稳健估计。The traditional Pearson correlation coefficient calculation formula is not robust, and the existence of outliers will cause the calculation results to be inconsistent with reality. To solve this problem, this paper presents a robust estimation method. When the simulated sample size is 20, 50, 100 and 200 respectively, the pollution rate is 1%, 5% and 10% respectively, it is found that the accuracy of the robust correlation coefficient formula is significantly higher than that of the traditional correlation coefficient. The feasibility and effectiveness of a robust correlation coefficient are further verified in the example analysis. The conclusions of this paper can be used for robust estimation of correlation coefficients with outlier variables.
基金Shanghai Rising-Star Program(Grant No.21QA1403400)Shanghai Sailing Program(Grant No.20YF1414800)Shanghai Key Laboratory of Power Station Automation Technology(Grant No.13DZ2273800).
文摘With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.
文摘The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas.The radiation cost and quality factor of the antenna are influenced by the size of the antenna.Metamaterial antennas allow for the circumvention of the bandwidth restriction for small antennas.Antenna parameters have recently been predicted using machine learning algorithms in existing literature.Machine learning can take the place of the manual process of experimenting to find the ideal simulated antenna parameters.The accuracy of the prediction will be primarily dependent on the model that is used.In this paper,a novel method for forecasting the bandwidth of the metamaterial antenna is proposed,based on using the Pearson Kernel as a standard kernel.Along with these new approaches,this paper suggests a unique hypersphere-based normalization to normalize the values of the dataset attributes and a dimensionality reduction method based on the Pearson kernel to reduce the dimension.A novel algorithm for optimizing the parameters of Convolutional Neural Network(CNN)based on improved Bat Algorithm-based Optimization with Pearson Mutation(BAO-PM)is also presented in this work.The prediction results of the proposed work are better when compared to the existing models in the literature.
文摘Background: The signal-to-noise ratio (SNR) is recognized as an index of measurements reproducibility. We derive the maximum likelihood estimators of SNR and discuss confidence interval construction on the difference between two correlated SNRs when the readings are from bivariate normal and bivariate lognormal distribution. We use the Pearsons system of curves to approximate the difference between the two estimates and use the bootstrap methods to validate the approximate distributions of the statistic of interest. Methods: The paper uses the delta method to find the first four central moments, and hence the skewness and kurtosis which are important in the determination of the parameters of the Pearsons distribution. Results: The approach is illustrated in two examples;one from veterinary microbiology and food safety data and the other on data from clinical medicine. We derived the four central moments of the target statistics, together with the bootstrap method to evaluate the parameters of Pearsons distribution. The fitted Pearsons curves of Types I and II were recommended based on the available data. The R-codes are also provided to be readily used by the readers.
文摘针对传统的属性约简算法无法有效分析条件属性之间耦合关系的问题,提出了一种新的算法,基于皮尔逊与邻域粗糙集的属性约简算法(pearson and neighborhood rough set,PNRS)。根据耦合关系的结果进一步确定条件属性对决策属性的重要度,提高约简算法分类的精度。UCI数据集结果表明,经过耦合关系校正后,属性约简的能力进一步提升,平均约简率提升了1%,平均准确率提升了0.36%。与传统的属性约简算法相比,计算耦合关系的方法提高了约简算法的分类性能,为进一步优化邻域粗糙集的约简结果奠定了理论基础。
