Critical for metering and protection in electric railway traction power supply systems(TPSSs),the measurement performance of voltage transformers(VTs)must be timely and reliably monitored.This paper outlines a three-s...Critical for metering and protection in electric railway traction power supply systems(TPSSs),the measurement performance of voltage transformers(VTs)must be timely and reliably monitored.This paper outlines a three-step,RMS data only method for evaluating VTs in TPSSs.First,a kernel principal component analysis approach is used to diagnose the VT exhibiting significant measurement deviations over time,mitigating the influence of stochastic fluctuations in traction loads.Second,a back propagation neural network is employed to continuously estimate the measurement deviations of the targeted VT.Third,a trend analysis method is developed to assess the evolution of the measurement performance of VTs.Case studies conducted on field data from an operational TPSS demonstrate the effectiveness of the proposed method in detecting VTs with measurement deviations exceeding 1%relative to their original accuracy levels.Additionally,the method accurately tracks deviation trends,enabling the identification of potential early-stage faults in VTs and helping prevent significant economic losses in TPSS operations.展开更多
Although there are many measures of variability for qualitative variables, they are little used in social research, nor are they included in statistical software. The aim of this article is to present six measures of ...Although there are many measures of variability for qualitative variables, they are little used in social research, nor are they included in statistical software. The aim of this article is to present six measures of variation for qualitative variables of simple calculation, as well as to facilitate their use by means of the R software. The measures considered are, on the one hand, Freemans variation ratio, Morals universal variation ratio, Kvalseths standard deviation from the mode, and Wilcoxs variation ratio which are most affected by proximity to a constant random variable, where the measures of variability for qualitative variables reach their minimum value of 0. On the other hand, the Gibbs-Poston index of qualitative variation and Shannons relative entropy are included, which are more affected by the proximity to a uniform distribution, where the measures of variability for qualitative variables reach their maximum value of 1. Point and interval estimation are addressed. Bootstrap by the percentile and bias-corrected and accelerated percentile methods are used to obtain confidence intervals. Two calculation situations are presented: with a sample mode and with two or more modes. The standard deviation from the mode among the six considered measures, and the universal variation ratio among the three variation ratios, are particularly recommended for use.展开更多
Accelerated life testing has been widely used in product life testing experiments because it can quickly provide information on the lifetime distributions by testing products or materials at higher than basic conditio...Accelerated life testing has been widely used in product life testing experiments because it can quickly provide information on the lifetime distributions by testing products or materials at higher than basic conditional levels of stress,such as pressure,temperature,vibration,voltage,or load to induce early failures.In this paper,a step stress partially accelerated life test(SSPALT)is regarded under the progressive type-II censored data with random removals.The removals from the test are considered to have the binomial distribution.The life times of the testing items are assumed to follow lengthbiased weighted Lomax distribution.The maximum likelihood method is used for estimating the model parameters of length-biased weighted Lomax.The asymptotic confidence interval estimates of the model parameters are evaluated using the Fisher information matrix.The Bayesian estimators cannot be obtained in the explicit form,so the Markov chain Monte Carlo method is employed to address this problem,which ensures both obtaining the Bayesian estimates as well as constructing the credible interval of the involved parameters.The precision of the Bayesian estimates and the maximum likelihood estimates are compared by simulations.In addition,to compare the performance of the considered confidence intervals for different parameter values and sample sizes.The Bootstrap confidence intervals give more accurate results than the approximate confidence intervals since the lengths of the former are less than the lengths of latter,for different sample sizes,observed failures,and censoring schemes,in most cases.Also,the percentile Bootstrap confidence intervals give more accurate results than Bootstrap-t since the lengths of the former are less than the lengths of latter for different sample sizes,observed failures,and censoring schemes,in most cases.Further performance comparison is conducted by the experiments with real data.展开更多
Singh, Gewali, and Khatiwada proposed a skewness measure for probability distributions called Area Skewness (AS), which has desirable properties but has not been widely applied in practice. One reason for this may be ...Singh, Gewali, and Khatiwada proposed a skewness measure for probability distributions called Area Skewness (AS), which has desirable properties but has not been widely applied in practice. One reason for this may be the lack of dissemination and further exploration of this new measure. Additionally, the authors focused mainly on specific probability distributions. One of the advantages of AS is that it considers the entire shape of the distribution, rather than focusing only on moments or the linear distance between central tendency statistics or quantiles. This holistic approach makes AS particularly robust in cases where the distribution deviates from normality or contains outliers. This paper aims to generalize its use to random samples with either known or unknown distributions. The study has three objectives: 1) to develop an R script for point and interval estimation of AS;2) to provide interpretive norms of normality by examining normality in bootstrap sampling distributions;and 3) to compare asymptotic and bootstrap standard errors. Interval estimation is approached asymptotically and through bootstrap. The script was illustrated using two examples: one with generated data and another with real-world data. Interpretive norms of normality are derived from 40 samples of various sizes, created by inverse transform sampling to follow a standard normal distribution. Bootstrap intervals at three confidence levels (0.9, 0.95, and 0.99) were obtained using the normal method, with two exceptions: the bias-corrected and accelerated percentile method for the 60-data sample and the percentile method for the 600-data sample, as these deviated from normality. Asymptotic 95% confidence intervals are also provided. The asymptotic standard error was larger than the bootstrap one, with the difference decreasing as the sample size increased. The script is concluded to have practical and educational utility for estimating AS, whose asymptotic sampling distribution is normal.展开更多
The intrinsic optimum temperature for the development of ectotherms is one of the most important factors not only for their physiological processes but also for ecolog- ical and evolutional processes. The Sharpe-Schoo...The intrinsic optimum temperature for the development of ectotherms is one of the most important factors not only for their physiological processes but also for ecolog- ical and evolutional processes. The Sharpe-Schoolfield-Ikemoto （SSI） model succeeded in defining the temperature that can thermodynamically meet the condition that at a par- ticular temperature the probability of an active enzyme reaching its maximum activity is realized. Previously, an algorithm was developed by Ikemoto （Tropical malaria does not mean hot environments. Journal of Medical Entomology, 45, 963-969） to estimate model parameters, but that program was computationally very time consuming. Now, investi- gators can use the SSI model more easily because a full automatic computer program was designed by Shi et al. （A modified program for estimating the parameters of the SSI model. Environmental Entomology, 40, 462-469）. However, the statistical significance of the point estimate of the intrinsic optimum temperature for each ectotherm has not yet been determined. Here, we provided a new method for calculating the confidence interval of the estimated intrinsic optimum temperature by modifying the approximate bootstrap confidence intervals method. For this purpose, it was necessary to develop a new program for a faster estimation of the parameters in the SSI model, which we have also done.展开更多
基金supported by the National Natural Science Foundation of China(No.52107125)Applied Basic Research Project of Sichuan Province(No.2022NSFSC0250)Chengdu Guojia Electrical Engineering Co.,Ltd.(No.KYL202312-0043).
文摘Critical for metering and protection in electric railway traction power supply systems(TPSSs),the measurement performance of voltage transformers(VTs)must be timely and reliably monitored.This paper outlines a three-step,RMS data only method for evaluating VTs in TPSSs.First,a kernel principal component analysis approach is used to diagnose the VT exhibiting significant measurement deviations over time,mitigating the influence of stochastic fluctuations in traction loads.Second,a back propagation neural network is employed to continuously estimate the measurement deviations of the targeted VT.Third,a trend analysis method is developed to assess the evolution of the measurement performance of VTs.Case studies conducted on field data from an operational TPSS demonstrate the effectiveness of the proposed method in detecting VTs with measurement deviations exceeding 1%relative to their original accuracy levels.Additionally,the method accurately tracks deviation trends,enabling the identification of potential early-stage faults in VTs and helping prevent significant economic losses in TPSS operations.
文摘Although there are many measures of variability for qualitative variables, they are little used in social research, nor are they included in statistical software. The aim of this article is to present six measures of variation for qualitative variables of simple calculation, as well as to facilitate their use by means of the R software. The measures considered are, on the one hand, Freemans variation ratio, Morals universal variation ratio, Kvalseths standard deviation from the mode, and Wilcoxs variation ratio which are most affected by proximity to a constant random variable, where the measures of variability for qualitative variables reach their minimum value of 0. On the other hand, the Gibbs-Poston index of qualitative variation and Shannons relative entropy are included, which are more affected by the proximity to a uniform distribution, where the measures of variability for qualitative variables reach their maximum value of 1. Point and interval estimation are addressed. Bootstrap by the percentile and bias-corrected and accelerated percentile methods are used to obtain confidence intervals. Two calculation situations are presented: with a sample mode and with two or more modes. The standard deviation from the mode among the six considered measures, and the universal variation ratio among the three variation ratios, are particularly recommended for use.
基金This work was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under Grant No.FP-190-42.
文摘Accelerated life testing has been widely used in product life testing experiments because it can quickly provide information on the lifetime distributions by testing products or materials at higher than basic conditional levels of stress,such as pressure,temperature,vibration,voltage,or load to induce early failures.In this paper,a step stress partially accelerated life test(SSPALT)is regarded under the progressive type-II censored data with random removals.The removals from the test are considered to have the binomial distribution.The life times of the testing items are assumed to follow lengthbiased weighted Lomax distribution.The maximum likelihood method is used for estimating the model parameters of length-biased weighted Lomax.The asymptotic confidence interval estimates of the model parameters are evaluated using the Fisher information matrix.The Bayesian estimators cannot be obtained in the explicit form,so the Markov chain Monte Carlo method is employed to address this problem,which ensures both obtaining the Bayesian estimates as well as constructing the credible interval of the involved parameters.The precision of the Bayesian estimates and the maximum likelihood estimates are compared by simulations.In addition,to compare the performance of the considered confidence intervals for different parameter values and sample sizes.The Bootstrap confidence intervals give more accurate results than the approximate confidence intervals since the lengths of the former are less than the lengths of latter,for different sample sizes,observed failures,and censoring schemes,in most cases.Also,the percentile Bootstrap confidence intervals give more accurate results than Bootstrap-t since the lengths of the former are less than the lengths of latter for different sample sizes,observed failures,and censoring schemes,in most cases.Further performance comparison is conducted by the experiments with real data.
文摘Singh, Gewali, and Khatiwada proposed a skewness measure for probability distributions called Area Skewness (AS), which has desirable properties but has not been widely applied in practice. One reason for this may be the lack of dissemination and further exploration of this new measure. Additionally, the authors focused mainly on specific probability distributions. One of the advantages of AS is that it considers the entire shape of the distribution, rather than focusing only on moments or the linear distance between central tendency statistics or quantiles. This holistic approach makes AS particularly robust in cases where the distribution deviates from normality or contains outliers. This paper aims to generalize its use to random samples with either known or unknown distributions. The study has three objectives: 1) to develop an R script for point and interval estimation of AS;2) to provide interpretive norms of normality by examining normality in bootstrap sampling distributions;and 3) to compare asymptotic and bootstrap standard errors. Interval estimation is approached asymptotically and through bootstrap. The script was illustrated using two examples: one with generated data and another with real-world data. Interpretive norms of normality are derived from 40 samples of various sizes, created by inverse transform sampling to follow a standard normal distribution. Bootstrap intervals at three confidence levels (0.9, 0.95, and 0.99) were obtained using the normal method, with two exceptions: the bias-corrected and accelerated percentile method for the 60-data sample and the percentile method for the 600-data sample, as these deviated from normality. Asymptotic 95% confidence intervals are also provided. The asymptotic standard error was larger than the bootstrap one, with the difference decreasing as the sample size increased. The script is concluded to have practical and educational utility for estimating AS, whose asymptotic sampling distribution is normal.
文摘The intrinsic optimum temperature for the development of ectotherms is one of the most important factors not only for their physiological processes but also for ecolog- ical and evolutional processes. The Sharpe-Schoolfield-Ikemoto （SSI） model succeeded in defining the temperature that can thermodynamically meet the condition that at a par- ticular temperature the probability of an active enzyme reaching its maximum activity is realized. Previously, an algorithm was developed by Ikemoto （Tropical malaria does not mean hot environments. Journal of Medical Entomology, 45, 963-969） to estimate model parameters, but that program was computationally very time consuming. Now, investi- gators can use the SSI model more easily because a full automatic computer program was designed by Shi et al. （A modified program for estimating the parameters of the SSI model. Environmental Entomology, 40, 462-469）. However, the statistical significance of the point estimate of the intrinsic optimum temperature for each ectotherm has not yet been determined. Here, we provided a new method for calculating the confidence interval of the estimated intrinsic optimum temperature by modifying the approximate bootstrap confidence intervals method. For this purpose, it was necessary to develop a new program for a faster estimation of the parameters in the SSI model, which we have also done.
