基于无标度先验的有向无环图结构学习

图模型是一种分析网络结构的有效方法，其中有向无环图由于可表示因果关系而受到广泛关注。而大量真实网络中节点的度服从幂律分布，即具有无标度特征。因此，研究了在无标度先验下，节点序已知的有向无环图结构学习问题。通过引入网络中节点度的信息和边的稀疏先验，提出罚项为 Log 型与 $l_q (0相似文献   

基于节点度和社区信息的复杂网络链接预测

网络结构信息被广泛用于研究复杂网络的链接预测问题，本文在信息理论模型的基础上，通过结合不同的网络结构特征，提出了更通用的信息理论模型．对于无标度网络，通过抑制度大的邻居节点的贡献，提出基于节点度信息的邻居集信息（NSI）指标．进一步，引入社区结构信息计算节点对连接的先验概率，提出基于社区结构特征的邻居集信息指标．在真实网络上的实验结果表明，本文所提出的指标具有更好的预测效果．     

协变量随机缺失下部分线性模型的变量选择（英文）

考虑协变量随机缺失下部分线性模型,采用惩罚加权最小二乘提出了一种变量选择方法,研究了所提出方法的有限样本性质,证明了非零系数的估计具有Oracle性质.进一步,基于局部线性逼近方法给出了一步稀疏估计.通过模拟研究了所提出方法的有限样本性质.     

地下物流系统网络数学模型构建

本文基于最优化理论，研究地下物流系统的数学模型构建问题．首先做出合理假设，在此基础上利用集合覆盖模型确定一级和二级节点的数量及位置．基于鲍摩-瓦尔夫模型，以总成本最小为目标函数确定各级节点之间的管道建设，Matlab编程遗传算法求解模型，得到最优路线．考虑到建设期间应满足实际交通需求递增的可能，依据运输量对节点进行模糊聚类，确定每个建设期的建设路线，行成动态的时序演进图．最后结合求解的过程和结果，进行总结分析．     

环境变化下基于AR模型系数和协整的海洋平台结构损伤识别

结构损伤引起的损伤特征参数的变化往往被环境因素（温度、质量）变化引起的损伤特征参数的变化所掩盖,从而导致基于振动的损伤识别方法失效。该文利用时间序列中的AR模型系数和计量经济学中的协整进行环境因素（温度、质量）影响下的海洋平台结构损伤识别研究。首先利用AR模型对实测的加速度响应信号进行拟合,选择第一阶AR系数为协整变量,然后对不同节点的协整变量进行协整,将协整残差作为损伤指标,最后通过X-bar控制图进行结构的损伤识别。海洋平台结构的数值模拟和振动台模型试验结果验证了该方法的有效性。     

频域组稀疏滚动轴承特征提取方法

针对时域非平稳振动信号模式混叠、信噪比低,以及传统稀疏表示算法模型复杂、优化求解算法难以确定,导致故障特征提取难的问题,提出了频域组稀疏和群桥约束改进迭代收缩阈值优化的故障特征提取方法（Group Sparse Representation in Frequency Domain,GSRF）。将振动信号转换至频域并对变量分组,构造施加群桥约束的最小二乘回归模型,准确筛选冲击相关变量;引入迭代重加权系数简化方程,以软阈值收缩优化求解频域稀疏信号;对重构的时域稀疏信号进行包络频谱分析提取故障特征。试验结果表明,提出的频域组稀疏算法优于传统的结合L21范数约束的组稀疏索套方法,可有效提取微弱故障特征,实现稀疏域下的轴承故障诊断。     

动力学自然单元法的谐波激励下的连续体结构拓扑优化

自然单元法是一种基于Voronoi图构造形函数的无网格方法,根据自然单元法的优点,提出了动力学自然单元法频率激励载荷下连续体的结构拓扑优化计算。采用各向同性固体微结构惩罚(SIMP)模型,将节点相对密度作为设计变量,建立以动柔度最小为目标函数,频率激励载荷作用下的拓扑优化模型。采用伴随分析法进行灵敏度分析并利用优化准则法对优化模型进行求解。通过数值算例计算,不仅得到了无棋盘格现象的优化结果,而且相比其它无网格方法提高了计算效率,说明该方法具有可行性和优越性。     

位移、应力、尺寸约束下二维连续体的形状优化

以二级控制理论为切入点,通过构造设计变量与关键点坐标之间的关系和关键点坐标与节点坐标之间的关系,得到了设计变量和节点坐标之间的关系。根据这一关系建立了目标函数跟设计变量之间的关系,采用有限差分法得到应力和位移的约束函数,建立了满足应力、位移和尺寸约束的形状优化模型,并采用序列二次规划方法求解二维连续体的形状优化问题。以MSC/Nastran软件为结构分析求解器,借助PCL语言开发了二维连续体的形状优化程序。为了有效地避免约束近似造成的迭代振荡乃至发散,在程序实现中,采用了区间因子来调整设计变量的上下限。数值算例表明程序算法的可靠性、精确性、高效性。     

最小二乘网格的模型修补

最小二乘网格是在给定连接图和离散控制点集的基础上,通过求解线性系统对网格中的顶点重新定位而形成的网格.本文提出了一种最小二乘网格的模型修补算法,首先根据模型孔洞构造合适的连接图,然后根据网格连接图以及边界几何信息构造一个线性稀疏系统,最后求解连接网格中所有顶点的三维几何坐标.该算法计算速度快,能取得理想的效果.     

基于节点度和社区信息的复杂网络链接预测（英文）

网络结构信息被广泛用于研究复杂网络的链接预测问题,本文在信息理论模型的基础上,通过结合不同的网络结构特征,提出了更通用的信息理论模型.对于无标度网络,通过抑制度大的邻居节点的贡献,提出基于节点度信息的邻居集信息(NSI)指标.进一步,引入社区结构信息计算节点对连接的先验概率,提出基于社区结构特征的邻居集信息指标.在真实网络上的实验结果表明,本文所提出的指标具有更好的预测效果.     

Model Selection for Good Estimation and Prediction over a User‐Specified Covariate Distribution for Linear Models under the Frequentist Paradigm

Model selection is an important part of estimation and prediction for linear models with multiple explanatory variables (covariates). A variety of approaches exist that focus on the estimation of model parameters or the fit of the model where data have been observed. This article proposes an alternative strategy that selects models based on the mean squared error of the estimated expected response for a user‐specified distribution of interest on the covariate space. We discuss numerical and graphical tools for detailed comparisons among different models. These tools help select a best model based on its ability to estimate the mean response over covariate locations likely to arise from a distribution of interest and can be combined with cost for deciding whether to include specific covariates. The proposed method is illustrated with three examples. We also present simulation results demonstrating situations where the proposed method shows improvement over some standard alternatives. Copyright © 2011 John Wiley & Sons, Ltd.     

Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data

Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online.     

Material Degradation Modeling and Failure Prediction Using Microstructure Images

Degradation data, frequently along with low-dimensional covariate information such as scalar-type covariates, are widely used for asset reliability analysis. Recently, many high-dimensional covariates such as functional and image covariates have emerged with advances in sensor technology, containing richer information that can be used for degradation assessment. In this article, motivated by a physical effect that microstructures of dual-phase advanced high strength steel strongly influence steel degradation, we propose a two-stage material degradation model using the material microstructure image as a covariate. In Stage 1, we show that the microstructure image covariate can be reduced to a functional covariate while statistical properties of the image are preserved up to the second order. In Stage 2, a novel functional covariate degradation model is proposed, based on which the time-to-failure distribution in terms of degradation level passages is derived. A penalized least squares estimation method is developed to obtain the closed-form point estimator of model parameters. Analytical inferences on interval estimation of the model parameters, the mean degradation levels, and the distribution of the time-to-failure are also developed. Simulation studies are implemented to validate the developed methods. Physical experiments on dual-phase advanced high strength steel are designed and conducted to demonstrate the proposed model. The results show that a significant improvement is achieved for material failure prediction by using material microstructure images compared with multiple benchmark models.     

Modeling the effects of AADT on predicting multiple-vehicle crashes at urban and suburban signalized intersections

Annual Average Daily Traffic (AADT) is often considered as a main covariate for predicting crash frequencies at urban and suburban intersections. A linear functional form is typically assumed for the Safety Performance Function (SPF) to describe the relationship between the natural logarithm of expected crash frequency and covariates derived from AADTs. Such a linearity assumption has been questioned by many researchers. This study applies Generalized Additive Models (GAMs) and Piecewise Linear Negative Binomial (PLNB) regression models to fit intersection crash data. Various covariates derived from minor-and major-approach AADTs are considered. Three different dependent variables are modeled, which are total multiple-vehicle crashes, rear-end crashes, and angle crashes. The modeling results suggest that a nonlinear functional form may be more appropriate. Also, the results show that it is important to take into consideration the joint safety effects of multiple covariates. Additionally, it is found that the ratio of minor to major-approach AADT has a varying impact on intersection safety and deserves further investigations.     

Analysis of Large Heterogeneous Repairable System Reliability Data With Static System Attributes and Dynamic Sensor Measurement in Big Data Environment

ABSTRACT

In the age of Big Data, one pressing challenge facing engineers is to perform reliability analysis for a large fleet of heterogeneous repairable systems with covariates. In addition to static covariates, which include time-invariant system attributes such as nominal operating conditions, geo-locations, etc., the recent advances of sensing technologies have also made it possible to obtain dynamic sensor measurement of system operating and environmental conditions. As a common practice in the Big Data environment, the massive reliability data are typically stored in some distributed storage systems. Leveraging the power of modern statistical learning, this article investigates a statistical approach which integrates the random forests algorithm and the classical data analysis methodologies for repairable system reliability, such as the nonparametric estimator for the mean cumulative function and the parametric models based on the nonhomogeneous Poisson process. We show that the proposed approach effectively addresses some common challenges arising from practice, including system heterogeneity, covariate selection, model specification and data locality due to the distributed data storage. The large sample properties as well as the uniform consistency of the proposed estimator are established. Two numerical examples and a case study are presented to illustrate the application of the proposed approach. The strengths of the proposed approach are demonstrated by comparison studies. Datasets and computer code have been made available on GitHub.     

Finite mixture modeling approach for developing crash modification factors in highway safety analysis

This study aimed to investigate the relative performance of two models (negative binomial (NB) model and two-component finite mixture of negative binomial models (FMNB-2)) in terms of developing crash modification factors (CMFs). Crash data on rural multilane divided highways in California and Texas were modeled with the two models, and crash modification functions (CMFunctions) were derived. The resultant CMFunction estimated from the FMNB-2 model showed several good properties over that from the NB model. First, the safety effect of a covariate was better reflected by the CMFunction developed using the FMNB-2 model, since the model takes into account the differential responsiveness of crash frequency to the covariate. Second, the CMFunction derived from the FMNB-2 model is able to capture nonlinear relationships between covariate and safety. Finally, following the same concept as those for NB models, the combined CMFs of multiple treatments were estimated using the FMNB-2 model. The results indicated that they are not the simple multiplicative of single ones (i.e., their safety effects are not independent under FMNB-2 models). Adjustment Factors (AFs) were then developed. It is revealed that current Highway Safety Manual’s method could over- or under-estimate the combined CMFs under particular combination of covariates. Safety analysts are encouraged to consider using the FMNB-2 models for developing CMFs and AFs.     

Phase II monitoring of survival times with categorical covariates

Monitoring surgical outcomes is of paramount importance especially by accounting for health conditions of the patients prior to surgery. However, the problem arises as the effect of some covariates is pronounced but cannot be measured. In this paper, in order to deal with the effect of measured and unmeasured (categorical) covariates simultaneously, a class of survival analysis regression models called accelerated failure time (AFT) model and discrete frailty models is integrated and some Phase II risk-adjusted control schemes are devised to monitor the patients' lifetime. Three monitoring procedures including the cumulative sum (CUSUM), exponentially weighted moving average (EWMA), and probability limits-based control charts are developed in the presence and absence of censored observations. The performance analysis reveals that the proposed AFT frailty-based CUSUM control chart outweighs the competing counterparts in detecting shifts under various scenarios. Subsequently, two CUSUM control charts have been constructed corresponding to the cases of neglecting both the unmeasured and measured covariates and ignoring just the unmeasured covariate. The results clearly indicate that the detection ability for both of the mentioned CUSUM control charts declines, and including the unmeasured and measured covariates is critical while monitoring surgical outcomes. Finally, a real case study in a cardiac surgical center in the United Kingdom has been provided to investigate the application of the proposed AFT frailty-based CUSUM control scheme.     

Survival Analysis for Personal Loan Data Using Generalized Additive Models

This article presents flexible methods for modeling censored survival data using penalized smoothing splines when the covariate values change for the duration of the study. The Cox proportional hazards model has been widely used for the analysis of censored survival data. However, a number of theoretical problems with respect to the baseline survival function and the baseline cumulative hazard function remain unsolved. The basic concept considered in the present article is to use generalized additive models (GAM) with B-splines to estimate the survival function without the baseline hazard assumption. The proposed methods are discussed according to the way in which they deal with censored observations, competing risk, and time-dependent covariates. We evaluate the performance of the proposed method for predicting loan default with early payment as competing risk using data from a U.K. financial institution.

     

Analysis of driver and passenger crash injury severity using partial proportional odds models

The question of whether crash injury severity should be modeled using an ordinal response model or a non-ordered (multinomial) response model is persistent in traffic safety engineering. This paper proposes the use of the partial proportional odds (PPO) model as a statistical modeling technique that both bridges the gap between ordered and non-ordered response modeling, and avoids violating the key assumptions in the behavior of crash severity inherent in these two alternatives. The partial proportional odds model is a type of logistic regression that allows certain individual predictor variables to ignore the proportional odds assumption which normally forces predictor variables to affect each level of the response variable with the same magnitude, while other predictor variables retain this proportional odds assumption. This research looks at the effectiveness of this PPO technique in predicting vehicular crash severities on Connecticut state roads using data from 1995 to 2009. The PPO model is compared to ordinal and multinomial response models on the basis of adequacy of model fit, significance of covariates, and out-of-sample prediction accuracy. The results of this study show that the PPO model has adequate fit and performs best overall in terms of covariate significance and holdout prediction accuracy. Combined with the ability to accurately represent the theoretical process of crash injury severity prediction, this makes the PPO technique a favorable approach for crash injury severity modeling by adequately modeling and predicting the ordinal nature of the crash severity process and addressing the non-proportional contributions of some covariates.     

Multi-task Joint Sparse Representation Classification Based on Fisher Discrimination Dictionary Learning

Recently, sparse representation classification (SRC) and fisher discrimination dictionary learning (FDDL) methods have emerged as important methods for vehicle classification. In this paper, inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection, we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors. To improve the classification accuracy in complex scenes, we develop a new method, called multi-task joint sparse representation classification based on fisher discrimination dictionary learning, for vehicle classification. In our proposed method, the acoustic and seismic sensor data sets are captured to measure the same physical event simultaneously by multiple heterogeneous sensors and the multi-dimensional frequency spectrum features of sensors data are extracted using Mel frequency cepstral coefficients (MFCC). Moreover, we extend our model to handle sparse environmental noise. We experimentally demonstrate the benefits of joint information fusion based on fisher discrimination dictionary learning from different sensors in vehicle classification tasks.