Residual stress in polyethylene pipes

This paper deals with residual stress in polyethylene (PE) pipes as an important factor that influences their lifetime. Residual hoop stress distribution in a PE pipe was determined using a methodology previously carried out by the authors on polypropylene pipes. Axial residual stress magnitude was determined by comparison of experimental data and finite element modelling. Based on the obtained results, a new simplified methodology for determining the residual hoop stress is proposed. The method needs only one circular specimen made of pipe, but, unlike older methods, provides a more precise estimation of residual hoop stress distribution taking into account its exponential shape. Some older results from literature are recalculated using this method and residual hoop stress of various PE pipe grades and dimensions are then compared. To provide an idea of residual hoop stress influence on lifetime of a pipe, a lifetime estimation is carried out for the examined pipe.     

MASS TRANSFER EXPERIMENTS ON SINGLE IRREGULAR-SHAPED PARTICLES

Mass transfer from irregular-shaped naphthalene particles (100-200 inn in size) was studitd in an electrodynamic balance. Charged particles were suspended in an electrostatic field directly in line with a calibrated air jet. Mass and size change histories were obtained under ambient conditions, and under steady- and pulsed-flow conditions. For natural convection, the time-averaged Sherwood number was similar to that for spheres. Forced-convection Sherwood number under steady-flow conditions was strongly dependent on particle shape and panicle Reynolds number, and was consistently higher than values predicted for spheres at comparable Reynolds numbers. This study validates the technique and indicates the shape effect on mass transfer form single particles.     

自适应连续多级分区与初始阈值估计的快速模板匹配方法

归一化互相关测度在光照改变时比采用绝对差之和测度（SAD）要稳定，但是归一化互相关测度的缺陷在于它的计算量非常大。为此，提出了一种结合自适应连续多级分区和初始阈值估计的基于归一化互相关（NCC）的快速模板匹配算法。根据模板图像中不同模块的梯度值，将模板图像进行逐级分区，通过分区顺序将互相关之和分为不同的层，得到各层互相关的上界，运用柯西-施不等式得到上界间的关系，形成自适应连续多级分区淘汰方法。同时，为了加快匹配速度，利用初始阈值估计产生一个较大的边界阈值，以淘汰初始搜索时的大量非匹配点，减少搜索点数目。实验结果表明:所提出的算法具有较好的鲁棒性，且算法的执行速度优于传统算法。     

A probabilistic framework for next best view estimation in a cluttered environment

In this article, we present an information gain-based variant of the next best view problem for occluded environment. Our proposed method utilizes a belief model of the unobserved space to estimate the expected information gain of each possible viewpoint. More precise, this belief model allows a more precise estimation of the visibility of occluded space and with that a more accurate prediction of the potential information gain of new viewing positions. We present experimental evaluation on a robotic platform for active data acquisition, however due to the generality of our approach it also applies to a wide variety of 3D reconstruction problems. With the evaluation done in simulation and on a real robotic platform, exploring and acquiring data from different environments we demonstrate the generality and usefulness of our approach for next best view estimation and autonomous data acquisition.     

Unsupervised segmentation of hidden semi-Markov non-stationary chains

The Bayesian segmentation using Hidden Markov Chains (HMC) is widely used in various domains such as speech recognition, acoustics, biosciences, climatology, text recognition, automatic translation and image processing. On the one hand, hidden semi-Markov chains (HSMC), which extend HMC, have turned out to be of interest in many situations and have improved HMC-based results. On the other hand, the case of non-stationary data can pose an important problem in real-life situations, especially when the model parameters have to be estimated. The aim of this paper is to consider these two extensions simultaneously: we propose using a particular triplet Markov chain (TMC) to deal with non-stationary hidden semi-Markov chains. In addition, we consider a recent particular HSMC having the same computation complexity as the classical HMC. We propose a related parameter estimation method and the resulting unsupervised Bayesian segmentation is validated through experiments; in particular, a real radar image segmentations are provided.     

An ALE finite element method for a coupled Stefan problem and Navier–Stokes equations with free capillary surface

In this article, an ALE finite element method to simulate the partial melting of a workpiece of metal is presented. The model includes the heat transport in both the solid and liquid part, fluid flow in the liquid phase by the Navier–Stokes equations, tracking of the melt interface solid/liquid by the Stefan condition, treatment of the capillary boundary accounting for surface tension effects and a radiative boundary condition. We show that an accurate treatment of the moving boundaries is crucial to resolve their respective influences on the flow field and thus on the overall energy transport correctly. This is achieved by a mesh‐moving method, which explicitly tracks the phase boundary and makes it possible to use a sharp interface model without singularities in the boundary conditions at the triple junction. A numerical example describing the welding of a thin‐steel wire end by a laser, where all aforementioned effects have to be taken into account, proves the effectiveness of the approach.Copyright © 2012 John Wiley & Sons, Ltd.     

Maximum likelihood estimation for second level fMRI data analysis with expectation trust region algorithm

The trust region method which originated from the Levenberg–Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare the proposed methods with the conventional expectation-maximization (EM) algorithm based on a series of datasets (synthetic and real human fMRI datasets). From simulation studies, we found a higher damping factor for the LM algorithm is better than lower damping factor for the fMRI data analysis. More importantly, in most cases, the expectation trust region algorithm is superior to the EM algorithm in terms of accuracy if the random effect variance is large. We also compare these algorithms on real human datasets which comprise repeated measures of fMRI in phased-encoded and random block experiment designs. We observed that the proposed method is faster in computation and robust to Gaussian noise for the fMRI analysis. The advantages and limitations of the suggested methods are discussed.     

DOA estimation of wideband sources without estimating the number of sources

In this paper, we propose a new technique to estimate wideband source directions from the sensor snapshots without requiring to know the number of sources present in the scenario. This work is motivated by the fact that the existing model order estimation (number of sources) techniques for wideband source scenario are either inaccurate or computationally expensive. Direction-of-arrival (DOA) estimation is realized using a beamformer framework which imposes nulls in the spatial spectrum along the source directions. The null width along the frequency axis is widened by introducing a new data dependent term into the optimization problem, thus achieving wideband capability. Furthermore, the temporal processing of the data snapshots drastically reduces the number of snapshots required for wideband DOA estimation. The effectiveness of the proposed formulation is studied with simulated experiments.