Adaptive multi-feature tracking in particle swarm optimization based particle filter framework

This paper proposes a particle swarm optimization（PSO） based particle filter（PF） tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage,and simultaneously incorporates the newest observations into the proposal distribution in the update stage.In the proposed approach,likelihood measure functions involving multiple features are presented to enhance the performance of model fitting.Furthermore,the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process.There are three main contributions.Firstly,the PSO algorithm is fused into the PF framework,which can efficiently alleviate the particles degeneracy phenomenon.Secondly,an effective convergence criterion for the PSO algorithm is explored,which can avoid particles getting stuck in local minima and maintain a greater particle diversity.Finally,a multi-feature weight self-adjusting strategy is proposed,which can significantly improve the tracking robustness and accuracy.Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.

Hierarchical 3D mechanical parts matching based-on adjustable geometry and topology similarity measurements

A hierarchical scheme of feature-based model similarity measurement was proposed,named CSG_D2,in which both geometry similarity and topology similarity were applied.The features of 3D mechanical part were constructed by a series of primitive features with tree structure,as a form of constructive solid geometry(CSG) tree.The D2 shape distributions of these features were extracted for geometry similarity measurement,and the pose vector and non-disappeared proportion of each leaf node were gained for topology similarity measurement.Based on these,the dissimilarity between the query and the candidate was accessed by level-by-level CSG tree comparisons.With the adjustable weights,our scheme satisfies different comparison emphasis on the geometry or topology similarity.The assessment results from CSG_D2 demonstrate more discriminative than those from D2 in the analysis of precision-recall and similarity matrix.Finally,an experimental search engine is applied for mechanical parts reuse by using CSG_D2,which is convenient for the mechanical design process.

Rheological behavior of hydrolyzed polyacrylamide solution flowing through a molecular weight adjusting device with porous medium

The separate-layer injection in different interlayers and the injection of the same-molecular-weight polymer so- lution in a layer are necessary in the polymer flooding process because of heterogeneous multilayer sandstone reservoirs in EOR projects. To alleviate the matching problems between the layer permeability and the injected polymer molecular weight, a molecular weight adjusting device with porous medium was designed on the basis of mechanical degradation principle. In terms of four variables （polymer concentration, pore diameter, length of shear component and flow rate ）, the theological behavior of hydrolyzed polyacrylamide （HPAM） solu- tion flowing through the device was investigated in detail. The change of these variables is able to control the shear rate of HPAM solutions through ceramic foam, and achieve the desired degree of shear degradation and the final theological parameters-viscosity loss, viscoelasticity and pressure drop. Therefore, a linear relationship between viscosity loss and shearing rate was established so as to obtain the targeted viscosity easily. Field tests in the Daqing Oil Field showed that the polymer molecular weight could drop 20% to 50%. In a word, the results could guide the industrial application of the novel device and the further study of polymer degradation flowing through the porous medium.

The Importance of Selection Bias in Internet Surveys

Nowadays, internet-based surveys are increasingly used for data collection, because their usage is simple and cheap. Also they give fast access to a large group of respondents. There are many factors affecting internet surveys, such as measurement, survey design and sampling selection bias. The sampling has an important place in selection bias in internet survey. In terms of sample selection, the type of access to internet surveys has several limitations. There are internet surveys based on restricted access and on voluntary participation, and these are characterized by their implementation according to the type of survey. It can be used probability and non-probability sampling, both of which may lead to biased estimates. There are different ways to correct for selection biases;poststratification or weighting class adjustments, raking or rim weighting, generalized regression modeling and propensity score adjustments. This paper aims to describe methodological problems about selection bias issues and to give a review in internet surveys. Also the objective of this study is to show the effect of various correction techniques for reducing selection bias.

EMPIRICAL LIKELIHOOD CONFIDENCE REGION FOR PARAMETERS IN LINEAR ERRORS-IN-VARIABLES MODELS WITH MISSING DATA

The multivariate linear errors-in-variables model when the regressors are missing at random in the sense of Rubin （1976） is considered in this paper. A constrained empirical likelihood confidence region for a parameter β0 in this model is proposed, which is constructed by combining the score function corresponding to the weighted squared orthogonal distance based on inverse probability with a constrained region of β0. It is shown that the empirical log-likelihood ratio at the true parameter converges to the standard chi-square distribution. Simulations show that the coverage rate of the proposed confidence region is closer to the nominal level and the length of confidence interval is narrower than those of the normal approximation of inverse probability weighted adjusted least square estimator in most cases. A real example is studied and the result supports the theory and simulation＇s conclusion.

Parameter Estimation of Varying Coefficients Structural EV Model with Time Series

In this paper, the parameters of a p-dimensional linear structural EV(error-in-variable)model are estimated when the coefficients vary with a real variable and the model error is time series.The adjust weighted least squares(AWLS) method is used to estimate the parameters. It is shown that the estimators are weakly consistent and asymptotically normal, and the optimal convergence rate is also obtained. Simulations study are undertaken to illustrate our AWLSEs have good performance.