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Data-driven computing in elasticity via kernel regression

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Theoretical & Applied Mechanics Letters 2018年第6期8卷 361-365,I0003页

作者： Yoshihiro Kanno Mathematics and Informatics Center The University of Tokyo

This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set.

关键词： Data-driven computational mechanics Model-free method Nonparametric method kernel regression Nadaraya–Watson estimator

Feature Extraction of kernel Regress Reconstruction for Fault Diagnosis Based on Self-organizing Manifold Learning

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Chinese Journal of Mechanical Engineering 2013年第5期26卷 1041-1049页

作者： CHEN Xiaoguang LIANG Lin XU Guanghua LIU Dan School of Mechanical Engineering Xi’an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University

The feature space extracted from vibration signals with various faults is often nonlinear and of high ***,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings,such as manifold ***,these methods are all based on manual intervention,which have some shortages in stability,and suppressing the disturbance *** extract features automatically,a manifold learning method with self-organization mapping is introduced for the first *** the non-uniform sample distribution reconstructed by the phase space,the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual *** that,the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional ***,the signal is reconstructed by the kernel *** typical states include the Lorenz system,engine fault with piston pin defect,and bearing fault with outer-race defect are *** with the LTSA and continuous wavelet transform,the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified.A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed.

关键词： feature extraction manifold learning self-organize mapping kernel regression local tangent space alignment

Background knowledge based privacy metric model for online social networks

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The Journal of China Universities of Posts and Telecommunications 2014年第2期21卷 75-82页

作者： CHENG Cheng ZHANG Chun-hong JI Yang School of Information and Communication Engineering Beijing University of Posts and Telecommunications

The data of online social network （OSN） is collected currently by the third party for various purposes. One of the problems in such practices is how to measure the privacy breach to assure users. The recent work on OSN privacy is mainly focus on privacy-preserving data publishing. However, the work on privacy metric is not systematic but mainly focus on the traditional datasets. Compared with the traditional datasets, the attribute types in OSN are more diverse and the tuple is relevant to each other. The retweet and comment make the graph character of OSN notably. Furthermore, the open application programming interfaces （APIs） and lower register barrier make OSN open environment, in which the background knowledge is more easily achieved by adversaries. This paper analyzes the background knowledge in OSN and discusses its characteristics in detail. Then a privacy metric model faces OSN background knowledge based on kernel regression is proposed. In particular, this model takes the joint attributes and link knowledge into consideration. The effect of different data distributions is discussed. The real world data set from *** has been adopted. It is demonstrated that the privacy metric algorithm in this article is effective in OSN privacy evaluation. The prediction error is 30% lower than that of the work mentioned above

关键词： privacy metric social network background knowledge kernel regression

Semiparametric Model Averaging for Ultrahigh-Dimensional Conditional Quantile Prediction

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Acta Mathematica Sinica,English Series 2023年第6期39卷 1171-1202页

作者： Chao Hui GUO Jing LV Hu YANG Jing Wen TU Chen Xiao TIAN School of Mathematical Sciences Chongqing Normal UniversityChongqing 401331P.R.China School of Mathematics and Statistics Southwest UniversityChongqing 400715P.R.China College of Mathematics and Statistics Chongqing UniversityChongqing 401331P.R.China College of Finance University of International Business and EconomicsBeijing 100020P.R.China

In this paper,we develop a flexible semiparametric model averaging marginal regression procedure to forecast the joint conditional quantile function of the response variable for ultrahighdimensional ***,we approximate the joint conditional quantile function by a weighted average of one-dimensional marginal conditional quantile functions that have varying coefficient ***,a local linear regression technique is employed to derive the consistent estimates of marginal conditional quantile ***,based on estimated marginal conditional quantile functions,we estimate and select the significant model weights involved in the approximation by a nonconvex penalized quantile *** some relaxed conditions,we establish the asymptotic properties for the nonparametric kernel estimators and oracle estimators of the model averaging *** further derive the oracle property for the proposed nonconvex penalized model averaging ***,simulation studies and a real data analysis are conducted to illustrate the merits of our proposed model averaging method.

关键词： kernel regression model averaging oracle property penalized quantile regression ultrahigh-dimension data

Empirical likelihood inference for estimating equation with missing data

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Science China Mathematics 2013年第6期56卷 1233-1245页

作者： WANG XiuLi CHEN Fang LIN Lu School of Mathematical Sciences Shandong Normal University Department of Electronics Communication & Software Engineering Nanfang College of Sun Yat-Sen University School of Mathematics Shandong University

In this article, empirical likelihood inference for estimating equation with missing data is considered. Based on the weighted-corrected estimating function, an empirical log-likelihood ratio is proved to be a standard chiqsquare distribution asymptotically under some suitable conditions. This result is different from those derived before. So it is convenient to construct confidence regions for the parameters of interest. We also prove that our proposed maximum empirical likelihood estimator θ is asymptotically normal and attains the semiparametric efficiency bound of missing data. Some simulations indicate that the proposed method performs the best.

关键词： empirical likelihood estimating equation kernel regression missing at random

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非参数回归在物流配送中心库存量中的应用