基于三维参照系的摄像机分步定标

基于三维参照系的定标方法,是一种传统的定标方法,需要三维控制场来提供足够的数据信息,定标精度较高,但对图像噪声不够鲁棒,并且非线性算法受出格点影响很大,甚至有时出现优化过程不收敛。本文提出了一种基于三维参照系的摄像机分步定标方法,利用对极几何关系对图像间的特征点进行分类,以剔除出格点,再选择合适的方法进行定标,实验表明分步定标法较好解决了原有技术对噪声敏感和非线性算法不收敛的情况,提高了各种算法的估计精度,并且三维重建误差也减少了30%左右。     

分布式异常检测中隐私保持问题研究

隐私保持是目前数据挖掘领域的一个重要方向,其目标是研究如何在不共享原始数据的条件下,获取准确的数据关系.本文采用现实的多方安全计算模式,结合数据干扰技术,提出了一种保持隐私的异常检测算法.该算法选择那些超出局部阈值距离的两点间距离及其序号进行通讯,为了保持原始数据的隐私,随机抽取一些正常范围内的两点间距离及其序号,在加入干扰后分散在异常信息中.理论分析表明该算法既提供了现实的数据隐私又保障了算法的性能.     

基于MM算法的LAD回归的影响分析

基于Hunter and Lange(2000)提出的MM迭代算法,构造了一个代替L1目标函数的新的目标函数Qε(ββk);在此基础上研究了非线性LAD回归影响分析的若干问题.基于新的目标函数和MM迭代算法,证明了LAD回归模型中数据删除模型和均值漂移模型参数估计的等价性定理,并提出了一种新的影响度量.最后,几个数据实例说明了方法的有效性.     

Progressive censoring from heterogeneous distributions with applications to robustness

Progressively censored order statistics from heterogeneous distributions are introduced and their properties are investigated. After deriving the joint density function, some properties are established. In particular, the case of proportional hazards leads to an interesting connection to the model of generalized order statistics. Finally, the special case of exponential distribution is considered and some known results are generalized to this heterogeneous case, and their implications to robustness are highlighted.     

野值存在情况下组合导航系统的容错技术研究

提出了“故障诊断周期”的概念，并基于一个诊断周期内占多数的数据均超过门限时才认为故障，否则认为是野值的故障原则对故障蜮野值情况分别处理。该容错方法不仅可以避免因野值造成的组成模式的频繁，而且还通过剔除野值对滤波的影响，提高了系统滤波精度和数据的连续性，并提高了系统故障检测的灵敏度。这在仿真中都得到了证明，说明该方法行之有效，在工程实际中也可以应用。     

动态最优滤波系统的粗差探测

观测粗差是影响动态系统最优滤波效果的重要因素，本文以静态平差理论为基础，研究了动态滤波系统粗差探测的理论和方法，简单的应用实例说明，文中介绍的粗差探测方法是有效的     

Applying a Smart Technique for Accurate Determination of Flowing Oil-Water Pressure Gradient in Horizontal Pipelines

Two-phase flow of liquids in pipelines is crucial subject in many industries such as chemical and petroleum. Accurate prediction of pressure gradient will lead to a better design of an energy efficient transportation system. Although numerous studies for prediction of two-phase flowing pressure drop have been reported in the literature, the accurate prediction of this parameter has been a topic of debate in many research areas. In this article, a novel model based on least square support vector (LSSVM) was proposed for calculation of two-phase flowing pressure drop in horizontal pipes. The inputs of this model are oil and water superficial velocities, pipe diameter, pipe roughness, and oil viscosity. To develop and test the model, more than 700 experimental dataset from open literature were utilized. The results of proposed model were compared against the well-known empirical correlations. Statistical error analysis showed that the LSSVM model outperforms existing predictive models. Finally, an outlier diagnosis was performed to detect the doubtful experimental.      

On statistical models for regression diagnostics

In regression diagnostics, the case deletion model (CDM) and the mean shift outlier model (MSOM) are commonly used in practice. In this paper we show that the estimates of CDM and MSOM are equal in a wide class of statistical models, which include LSE, MLE, Bayesian estimate andM-estimate in linear and nonlinear regression models; MLE in generalized linear models and exponential family nonlinear models; MLEs of transformation parameters of explanatory variables in a Box-Cox regression models and so on. Furthermore, we study some models, in which, the estimates are not exactly equal but are approximately equal for CDM and MSOM.     

Estimation of a mean in the presence of an extreme observation

The performance of Anscombe, semi-Winsorization and Winsorization (A, S and W) rules for dealing with extreme observations are investigated for observations from N(μ, σ²) and the simple case where it is assumed that at most one observation in the sample may be biased, arising from N(μ + aσ, σ²) and the primary objective is to estimate μ when σ is unknown. Each of these rules is separately treated in terms of the estimated standard deviation, range and interquartile range. A Monte Carlo method is used to evaluate certain expectation integrals that arise in the computations. We give the results for sample sizes n = 6, 8, 10, 12, 14, 16, 20, 30, 40, 50, 60, 80, 100 of determining the constants necessary to give ‘premiums’ of 0.01 and 0.05 for each of the rules. The performance of the rules is measured in terms of ‘protection’. Features of the resulting tables are discussed.     

Protein folding pathways extracted by OFLOOD: Outlier FLOODing method

The Outlier FLOODing method (OFLOOD) is proposed as an efficient conformational sampling method to extract biologically rare events such as protein folding. In OFLOOD, sparse distributions (outliers in the conformational space) were regarded as relevant states for the transitions. Then, the transitions were enhanced through conformational resampling from the outliers. This evidence indicates that the conformational resampling of the sparse distributions might increase chances for promoting the transitions from the outliers to other meta‐stable states, which resembles a conformational flooding from the outliers to the neighboring clusters. OFLOOD consists of (i) detections of outliers from conformational distributions and (ii) conformational resampling from the outliers by molecular dynamics (MD) simulations. Cycles of (i) and (ii) are simply repeated. As demonstrations, OFLOOD was applied to folding of Chignolin and HP35. In both cases, OFLOOD automatically extracted folding pathways from unfolded structures with ns‐order computational costs, although µs‐order canonical MD failed to extract them. © 2014 Wiley Periodicals, Inc.