A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

Change-Point Detection for General Nonparametric Regression Models

A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underlying processes which assume remove the stringent condition of bounded total variation of the regression function and need only second moments. Since many quantities, such as the regression function, the distribution of the covariates and the distribution of the errors, are unspecified, the results are not distribution-free. A weighted bootstrap approach is proposed to approximate the limiting distributions. Results of a simulation study for this paper show good performance for moderate samples sizes.

Composite Quantile Regression for Nonparametric Model with Random Censored Data

The composite quantile regression should provide estimation efficiency gain over a single quantile regression. In this paper, we extend composite quantile regression to nonparametric model with random censored data. The asymptotic normality of the proposed estimator is established. The proposed methods are applied to the lung cancer data. Extensive simulations are reported, showing that the proposed method works well in practical settings.

Asymptotic Property for the Estimator of Nonparametric Regression Models Under Negatively Orthant Dependent Errors

In this paper, by using some inequalities of negatively orthant dependent(NOD,in short) random variables and the truncated method of random variables, we investigate the nonparametric regression model. The complete consistency result for the estimator of g(x) is presented.

Posterior propriety in nonparametric mixed efects model

It is well known that spline smoothing estimator relates to the Bayesian estimate under partially informative normal prior. In this paper, we derive the conditions for the pro- priety of the posterior in the nonparametric mixed effects model under this class of partially informative normal prior for fixed effect with inverse gamma priors on the variance compo- nents and hierarchical priors for covariance matrix of random effect, then we explore the Gibbs sampling procedure.

Heteroscedasticity check in nonlinear semiparametric models based on nonparametric variance function

The assumption of homoscedasticity has received much attention in classical analysis of regression. Heteroscedasticity tests have been well studied in parametric and nonparametric regressions. The aim of this paper is to present a test of heteroscedasticity for nonlinear semiparametric regression models with nonparametric variance function. The validity of the proposed test is illustrated by two simulated examples and a real data example.

Classification and Appraisement for Nonparametric Software Reliability Models

A taxonomy of software reliability models is developed that the models are classified as parametric and nonparametric models, and the nonparametric models are classified according to the mathematical methods they used. Then, a practical appraising index system for nonparametric software reliability models are put forward. The nonparametric software reliability models are classified into 5 classes, that is time series analysis models, grey theo- ry forecasting models, artificial neural network models, wavelet analysis models and kernel estimation models, and they are evaluated by the practical index system.

Convergence Rate of Estimator forNonparametric Regression Model under ρ-mixing Errors

In this paper, we investigate the nonparametric regression model based on ρ-mixing errors, which are stochastically dominated by a nonnegative random variable. Weobtain the convergence rate for the weighted estimator of unknown function g（x） in pth-mean, which yields the convergence rate in probability. Moreover, an example of the nearestneighbor estimator is also illustrated and the convergence rates of estimator are presented.

Nonparametric estimations of the sea state bias for a radar altimeter

To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 altimeter data. Selecting from different combinations of the Gaussian kernel function, spherical Epanechnikov kernel function, a fixed bandwidth and a local adjustable bandwidth, it is observed that the LLR method with the spherical Epanechnikov kernel function and the local adjustable bandwidth is the optimal nonparametric model for the SSB estimation. The comparisons between the nonparametric and parametric models are conducted and the results show that the nonparametric model performs relatively better at high-latitudes of the Northern Hemisphere. This method has been applied to the HY-2A altimeter as well and the same conclusion can be obtained.

Using Evolutionary Computation to Solve Problems in Nonparametric Regression

This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building.

Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis

The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.

Inference for accelerated bivariate dependent competing risks model based on Archimedean copulas under progressive censoring

Dependent competing risks model is a practical model in the analysis of lifetime and failure modes.The dependence can be captured using a statistical tool to explore the re-lationship among failure causes.In this paper,an Archimedean copula is chosen to describe the dependence in a constant-stress accelerated life test.We study the Archimedean copula based dependent competing risks model using parametric and nonparametric methods.The parametric likelihood inference is presented by deriving the general expression of likelihood function based on assumed survival Archimedean copula associated with the model parameter estimation.Combining the nonparametric estimation with progressive censoring and the non-parametric copula estimation,we introduce a nonparametric reliability estimation method given competing risks data.A simulation study and a real data analysis are conducted to show the performance of the estimation methods.

脱贫户视角下的返贫风险预测模型

根据凯里市乡村振兴局提供的2021年脱贫户帮扶台账数据,建立风险度量的统计模型.首先,使用混料多项式模型构建年收入预测模型;其次,使用logistics回归模型建立返贫风险预测模型,并结合SVM等机器学习算法得到“三类户”的线性分类模型;然后,通过评价得分数据构建与年人均年收入的非参数回归模型.通过多重模型的分析,为基层开展返贫风险排查工作提供技术辅助参考.

相依部件平均剩余强度的非参数贝叶斯估计

应力-强度系统是一种普遍的系统结构,估计系统平均剩余强度时通常假设部件间相互独立。然而,基于部件独立假设会造成平均剩余强度估计的不准确。利用copula理论建立部件强度间的相依关系,得到了系统平均剩余强度的表达式。采用非参数贝叶斯方法估计了系统的平均剩余强度,对串联系统和并联系统数据进行蒙特卡罗模拟,验证了方法的有效性。结果表明,在部件强度相依的情况下,基于独立假定的串联系统平均剩余强度被低估,并联系统的平均剩余强度则被高估。因此,系统部件间的相依影响不能忽略。

不确定转子系统动力学降阶模型构建与模型散度参数辨识

在航空、航天、船舶等领域的实际工程转子系统中,广泛存在高维复杂的非线性系统。在航空发动机转子系统、燃气轮机转子系统等重点研究领域,通常还难以对高维复杂非线性系统进行直接的数据处理和分析统计。针对不确定性转子系统的模型维度较高等问题,提出了一种模型不确定性动力学降阶计算模型构建和模型散度参数辨识方法。首先,根据确定性动力学模型和静态矩阵降阶方法,完善了确定性动力学降阶模型;然后,基于随机矩阵理论和非参数动力学建模方法,提出了不确定性动力学降阶模型;最后,利用系统确定性模型的一阶临界转速、振型和实验数据,对不确定性动力学模型的散度参数进行了辨识;为了验证散度参数辨识方法的有效性,笔者又在转子实验平台上进行了实验验证。研究结果表明:实验结果与降阶之后振动响应均值的差异性较小,且与不确定性动力学模型相差不超过10%,表明所采用的理论模型在描述转子系统行为方面具备了较高的准确性和可靠性,该模型可以为深入研究模型不确定性转子系统提供参考。

NPDM及改进模型在淮河流域的月径流模拟研究

【目的】参数模型大多数基于模型驱动,并且对于水文序列的概率分布和相关关系做了一定程度的假设。然而,水文序列的真实分布复杂多样,相关关系也无法仅用线性相关描述,因此基于模型驱动的传统随机模型难以模拟实际月径流概率分布中的强不对称或多模态性,与变量真实概率分布存在差异。【方法】非参数解集模型(Nonparametric Disaggregation Model, NPDM)从数据驱动出发能有效模拟径流间的随机性和相关性变动规律。改进模型(Improved Nonparametric Disaggregation Model, INPDM)考虑年径流和前期月径流综合影响建立条件概率分布函数,使用基于黄金分割搜索和抛物线插值方法的优化算法以最小二乘交叉验证指标(Least Squares cross Validation, LSCV)为目标函数寻求最优带宽,并结合可变核方法修正边界。为深入探讨两模型在淮河流域的适用性,建立淮河流域1950—2007年吴家渡站、1951—2007年鲁台子站年、月径流随机模拟的NPDM和INPDM模型,利用一系列统计特征值和相关特性对比分析模拟效果。【结果】结果表明:在均值方面,NPDM和INPDM模拟效果均较为理想,实现100%控制在一个均方差标准下;吴家渡站和鲁台子站模拟序列的标准差和变异系数均控制在两个月的均方偏差标准下;INPDM模型对于原序列的最大值、月径流间互相关性和非线性状态相关统计特性的再现能力要优于传统模型,NPDM模型在吴家渡站和鲁台子站的2阶自相关系数无法控制在一个均方差标准下的月份占比分别比INPDM模型高50%和16.6%,1阶自相关系数也呈现出类似结果,表明INPDM模拟序列的自相关系数统计特征值与原序列统计特性更接近;但NPDM模型描述月径流序列偏态系数的能力更强。【结论】综上表明基于优化算法改进后的非参数解集模型能充分再现径流序列的统计特性,可为淮河流域开展年、月径流的随机模拟研究提供参考。

收入不平等对居民幸福感的影响研究

本文基于2012-2021年共6期的中国综合社会调查(CGSS)数据,利用事前非参数法分解得到努力不平等和机会不平等,从结构分解的研究视角揭示了收入不平等对居民幸福感的内在机理。研究发现,努力不平等对居民幸福感存在正向作用,机会不平等产生负向影响,这一研究结论经相关稳健性检验后仍稳健可靠。本研究利用分组回归和双边随机前沿模型探析了这一影响效应在不同收入水平、不同区域以及不同年份的异质性特点。进一步地,本文构建起中介效应检验模型,机制检验结果表明在机会不平等影响居民幸福感的作用路径中,社会公平感发挥着不容忽视的部分中介效应。本文研究结论为提高我国居民幸福感水平、寻求“伊斯特林悖论”的破解之道提供了政策启示与建议。

带有删失函数型协变量的非参数模型的估计研究

该文在删失函数型协变量背景下,研究非参数模型的估计问题,通过使用曲线扩展算法把删失函数型数据扩展为完整函数型数据.该算法具有很好的准确性和灵活性,避免了删失函数型数据难以建模的问题.使用函数型核估计方法得到模型中非线性算子的估计值.通过数值模拟验证该算法的有效性和删失函数型数据对非参数模型的影响,并应用于肝硬化数据集的数据分析中.

基于组合模型的球员贡献度评价

介绍了两种评价球员贡献度的统计模型,基于组合模型的优势,提出了1个组合评价模型,以2021—2022年NBA季后赛为例,利用非参数统计中Friedman检验进行了实证分析。通过基于熵权法改进的Topsis、主成分综合评价与组合模型3种评价模型来探究2021—2022年NBA总冠军金州勇士队球员的贡献,并对其贡献进行排名,采用Friedman检验得到3种模型的结论基本一致。