Regression analysis of clustered interval-censored failure time data with informative cluster size

Correlated or clustered failure time data often occur in medical studies, among other fields ( [Cai and Prentice, 1995] and [Kalbfleisch and Prentice, 2002]), and sometimes such data arise together with interval censoring (Wang et al., 2006). Furthermore, the failure time of interest may be related to the cluster size. For example, Williamson et al. (2008) discussed such an example arising from a lymphatic filariasis study. A simple and common approach to the analysis of these data is to simplify or convert interval-censored data to right-censored data due to the lack of proper inference procedures for direct analysis of these data. In this paper, two procedures are presented for regression analysis of clustered failure time data that allow both interval censoring and informative cluster size. Simulation studies are conducted to evaluate the presented approaches and they are applied to a motivating example.     

Multi-armed bandit heterogeneous ensemble learning for imbalanced data

One of the most widely used approaches to the class-imbalanced issue is ensemble learning. The base classifier is trained using an unbalanced training set in the conventional ensemble learning approach. We are unable to select the best suitable resampling method or base classifier for the training set, despite the fact that researchers have examined employing resampling strategies to balance the training set. A multi-armed bandit heterogeneous ensemble framework was developed as a solution to these issues. This framework employs the multi-armed bandit technique to pick the best base classifier and resampling techniques to build a heterogeneous ensemble model. To obtain training sets, we first employ the bagging technique. Then, we use the instances from the out-of-bag set as the validation set. In general, we consider the basic classifier combination with the highest validation set score to be the best model on the bagging subset and add it to the pool of model. The classification performance of the multi-armed bandit heterogeneous ensemble model is then assessed using 30 real-world imbalanced data sets that were gathered from UCI, KEEL, and HDDT. The experimental results demonstrate that, under the two assessment metrics of AUC and Kappa, the proposed heterogeneous ensemble model performs competitively with other nine state-of-the-art ensemble learning methods. At the same time, the findings of the experiment are confirmed by the statistical findings of the Friedman test and Holm's post-hoc test.     

一种改进的粒子滤波算法及其在说话人跟踪中的应用

针对非线性、非高斯系统状态的在线估计问题,提出一种改进的粒子滤波算法,该算法综合考虑"优选建议分布函数"和"重采样"两种并行改进滤波性能的方法.首先通过Unscented卡尔曼滤波器产生系统的状态估计,并在协方差预测阶段引入衰减记忆因子,消弱滤波器对历史信息的依赖,增强当前量测信息对滤波器的修正作用,从而产生一个优选的建议分布函数,有效抑制了粒子退化现象;接着在重采样阶段引入MCMC(Markov Chain Monte Carlo)方法来构造马尔科夫链产生服从目标分布的粒子,使样本更加多样化,有效避免了粒子枯竭问题.最后,通过系统仿真及说话人跟踪实验,证明了该算法的有效性.     

FIRST: a flexible and interactive resampling tool for CFD simulation data

We introduce a flexible, variable resolution tool for interactive resampling of computational fluid dynamics (CFD) simulation data on versatile grids. The tool and coupled algorithm afford users precise control of glyph placement during vector field visualization via six interactive degrees of freedom. Other important characteristics of this method include: (1) an algorithm that resamples any unstructured grid onto any structured grid, (2) handles changes to underlying topology and geometry, (3) handles unstructured grids with holes and discontinuities, (4) does not rely on any pre-processing of the data, and (5) processes large numbers of unstructured grid cells efficiently. We believe this tool to be a valuable asset in the engineer's pursuit of understanding and visualizing the underlying flow field in CFD simulation results.     

移动机器人改进激光SLAM算法研究

针对传统RBPF(Rao-Blackwellised particle filter)算法存在定位精度低、粒子退化、粒子多样性丧失的问题,提出了一种基于激光雷达的改进SLAM(simultaneous localization and mapping)算法.首先基于主成分分析法对相邻帧的点云进行粗配准,再采用改进点到线...     

数据重采样技术在振动信号分析中的应用与试验

通过阐述重采样的基本原理以及实现重采样的数学算法,为重采样的实现提供了理论的基础。通过实验仿真,对比重采样前后的功率谱震动图像以及FFT功率谱图像和阶次功率谱图像,验证了阶次分析的可行性和优越性,对非平稳振动信号的处理分析有着极其重要的意义。     

基于头脑风暴算法的FastSLAM2.0算法

针对FastSLAM2.0算法粒子权值退化与粒子多样性丧失导致机器人定位建图精度下降的问题,提出了基于头脑风暴算法改进FastSLAM2.0算法.通过头脑风暴算法替换FastSLAM2.0算法重采样过程,首先将重要性采样后的粒子权值作为头脑风暴算法中个体评判的适度值,根据适度值大小差异完成K-means聚类操作;其次对聚类后的集合进行变异操作,并取消头脑风暴算法中个体选择操作,从而实现改进头脑风暴算法替代FastSLAM2.0算法重采样过程,缓解粒子的贫化现象,增加粒子多样性,最终实现对机器人定位建图精度的提升.在机器人定位建图实验中,对比经典FastSLAM2.0算法和基于遗传算法改进FastSLAM2.0算法,提出的算法定位精度最高,相较于经典FastSLAM2.0算法,提出算法定位精度提升了63％,稳定性提升了55％.     

Field Failure Data: an Alternative Proposal for Reliability Estimation

Product reliability is a very important issue for the competitive strategy of industries. In order to estimate a product's reliability, parametric inferential methods are required to evaluate survival test data, which happens to be a fairly expensive data source. Such costly information usually imposes additional compromises in the product development and new challenges to be overcome throughout the product's life cycle. However, manufacturers also keep field failure data for warranty and maintenance purposes, which can be a low‐cost data source for reliability estimation. Field‐failure data are very difficult to evaluate using parametric inferential methods due to their small and highly censored samples, quite often representing mixed modes of failure. In this paper a method for reliability estimation using field failure data is proposed. The proposal is based on the use of non‐parametric inferential methods, associated with resampling techniques to derive confidence intervals for the reliability estimates. Test results show the adequacy of the proposed method to calculate reliability estimates and their confidence interval for different populations, including cases with highly right‐censored failure data. The method is shown to be particularly useful when the sampling distribution is not known, which happens to be the case in a large number of practical reliability evaluations. Copyright © 2006 John Wiley & Sons, Ltd.     

基于形变模型的三维人脸重建方法及其改进

形变模型（morphable model）是近几年出现的三维人脸建模新方法．该方法使用原型人脸的组合表示新的人脸,对于特定人脸图像,通过模型匹配实现了三维人脸的自动重建．虽然形变模型具有自动化、真实感好等优点,但现有形变模型的建立依赖于不稳定的人脸图像对应光流算法,模型匹配只考虑了一般光照环境下的人脸重建问题,且建模计算量大．针对以上问题,文章对形变模型进行了改进：提出了网格重采样的方法,实现了模型人脸数据的精确对应;建立了多分辨率的三维人脸模型;在模型匹配过程中采用了多光源光照模型,使模型可适用于复杂光照环境下的人脸重建．实验结果表明,上述改进可以有效提高模型匹配的效率和准确性以及模型对光照的适应性．     

State estimation comparison for a high-dimensional nonlinear system by particle-based filtering methods

The sequential filtering scheme provides a suitable framework for estimating and tracking geophysical states of systems as new data become available online. Mathematical foundations of sequential Bayesian filtering are reviewed with emphasis on practical issues for both particle filters and Kalman-based filters. In this study, we further investigate the study of Kim (2005) such that the sequential Importance resampling method (SIR), Ensemble Kalman Filter (EnKF), and the Maximum Entropy Filter (MEF) are tested in a relatively high dimensional ocean model that conceptually represents the Atlantic thermohaline circulation. The model exhibits large-amplitude transitions between strong (thermo-dominated) and weak (salinity-dominated) circulations that represent climate states between ice-age and normal climate.The performance of the particle-based schemes is compared with the convergent results from SIR based on measurement errors, observation locations, and particle sizes in various sets of twin experiments. The sensitivity analysis shows strength and weakness of each filtering method when applied to multimodal non-linear systems. As the number of particles is increased, SIR achieves the convergent results that are mathematically optimal solutions. EnKF shows suboptimal results regardless of sample sizes, and MEF achieves the optimal solution even with a small sample size. Both EnKF, and MEF produces robust results with a relatively small sample size or increased measurement locations. Small measurement errors or short intervals of observations (or, more frequent observations) significantly improve the performances of SIR and EnKF, and MEF still show robust results even with a relatively small sample size or sparse measurement locations when the system experiences the transition between one region to the other region.