Explicit solutions for the response probability density function of nonlinear transformations of static random inputs

This work is the second paper of two companion ones. Both of them show the use of a new version of the Probabilistic Transformation Method (PTM) for finding the probability density function (pdf) of a limited number of response quantities in the transformations of static random inputs. This is made without performing multi-dimensional integrals of the response total joint pdf for saturating the non-interested variables. While in the first paper the linear transformations have been considered, in the present one some nonlinear systems are taken into account. In particular, first the case when the loads on a linear structural system are a nonlinear combination of static random inputs is studied. Then the attention is placed on the case of nonlinear structural systems, for which the new version of the PTM allows to determine approximated, but accurate, results.     

Statistical Analysis for Decision Making

In this article we consider a generalization of the univariate g-and-h distribution to the multivariate situation with the aim of providing a flexible family of multivariate distributions that incorporate skewness and kurtosis. The approach is to modify the underlying random variables and their quantiles, directly giving rise to a family of distributions in which the quantiles rather than the densities are the foci of attention. Using the ideas of multivariate quantiles, we show how to fit multivariate data to our multivariate g-and-h distribution. This provides a more flexible family than the skew-normal and skew-elliptical distributions when quantiles are of principal interest. Unlike those families, the distribution of quadratic forms from the multivariate g-and-h distribution depends on the underlying skewness. We illustrate our methods on Australian athletes data, as well as on some wind speed data from the northwest Pacific.     

经济型数控化改造中抗干扰设计及实现

在进行普通机床经济型数控化改造时，不但要保证改造后机床的功能和性能，还要保证改造后的机床能够可靠、稳定地运行。为了实现改造后机床的可靠性和稳定性，阐述了普通机床经济型数控化改造中的抗干扰设计和实现。     

Improved curvature-based registration methods for high-precision dimensional metrology

Multiple measurements using various data acquisition systems are generally required to substancially enhance measurement accuracy, reliability and holisticity of freeform shapes. The obtained multiple measurement data of the shape are transformed and fused into a common coordinate system within a registration technique involving coarse and fine alignments. Standardized methods have been established for fine registration such as Iterative Closest Points (ICP) and its variants. For coarse registration, no conventional method has been adopted yet despite a significant number of techniques which have been developed in the literature to supply an automatic rough matching between data sets.The work presented in this paper proposes an improvement of registration techniques by the consideration of new discrete curvature parameters. Two main issues are addressed in this paper: the coarse registration and the fine registration. For coarse registration, two novel automated methods based on the exploitation of discrete curvatures are presented: an enhanced Hough Transformation (HT) and an improved Ransac Transformation. The use of curvature features in both methods aims to reduce computational cost. For fine registration, a new variant of ICP method is proposed in order to reduce registration error using curvature parameters. A specific distance considering the curvature similarity is combined with Euclidean distance to define the distance criterion used for correspondences searching. Additionally, the objective function is improved by combining the point-to-point (P-P) minimization and the point-to-plane (P-Pl) minimization with automatic weights. The algorithms are applied on simulated and real data performed by a computed tomography (CT) system. The obtained results reveal the benefit of the proposed improved curvature-based registration methods.     

Geopolymer concrete-filled pultruded composite beams – Concrete mix design and application

The limited research on the geopolymer concrete mix design for targeting a specific strength is identified an obstacle for their effective design and wide use. In this paper, a mix design procedure has been proposed for fly-ash based geopolymer concrete and its use as infill hybrid composite beam is investigated. Then, the structural performance of geopolymer concrete filled hybrid composite beam is investigated to determine their possible application in civil infrastructure. Firstly, a detailed procedure of mix design for fly-ash based geopolymer concrete is presented. Secondly, three hybrid beams filled with geopolymer concrete were prepared and tested in a four-point bending setup to evaluate their flexural modulus and modulus of rupture. Numerical and analytical evaluation of the behaviour of hybrid beam were performed and results showed a good agreement with the experimental investigation. Thirdly, the suitability of the beam for a composite railway sleeper is evaluated and compared with existing timber and composite sleepers. Finally, the beams’ performance in a ballast railway track is analysed using Strand7 finite element simulation software and the results showed that the new concept of using geopolymer concrete as infill to pultruded composite section satisfied the stiffness and strength requirements for a railway sleeper.     

A weakly supervised large margin domain adaptation method for isolated handwritten digit recognition

Learning handwriting categories fail to perform well when trained and tested on data from different databases. In this paper, we propose a novel large margin domain adaptation algorithm which is able to learn a transformation between training and test datasets in addition to adapting the parameters of classifier using a few or even no training labeled samples from target handwriting dataset. Additionally, we developed a framework of ensemble projection feature learning for datasets representation as a front end for our algorithm to utilize the abundant unlabeled samples in target domain. Experiments on different handwritten digit datasets adaptations demonstrate that the proposed large margin domain adaptation algorithm achieves superior classification accuracy comparing with the state of the art methods. Quantitative evaluation of the proposed algorithm shows that semi-supervised adaptation utilizing one sample per class of target domain set reduces the error rates by 64.72% comparing with a corresponding SVM classifier.     

Offshore wind turbine blades measurement using Coherent Laser Radar

To maximize aerodynamic efficiency, large-scale offshore wind turbine blades require inspection during the production stage to ensure strict tolerance requirements are met. During production, the blade is fixed at the root, restricting movement in the Z direction. X, Y, Rx, Ry and Rz remain unconstrained causing blade flex due to gravity. This deforms the blade away from the theoretical CAD blade location, causing measurement results that do not accurately represent the blade profile. Measurement error can be minimized using rigorous B-spline data alignment. Such alignment compensates for blade flex by varying the constrained Degrees of Freedom (DoF), and provides manufacturers with confidence in the design process. This paper used Coherent Laser Radar and Spatial Analyzer to establish the optimal constrained DoF variation, giving the most accurate data alignment solution. Of the constraints investigated, the optimal data transformation solution was found with a double B-spline alignment method, whilst constraining movement in Y, Z and Ry.     

(Y vs. X) or (log Y vs. X)?

The problem of choosing between the models Y = α + βX and log Y = α + βX is considered. The maximum likelihood method of Box and Cox is compared with the method of using correlation coefficients to make the choice between the two models. The methods are applied to some data from the field of metallurgy. Operating characteristics of the two methods are estimated by simulation. Over a wide range of parameters there is not much difference between the two methods, although there are parameter values for which the correlation method appears markedly inferior to the likelihood method. The likelihood method seems preferable in that it is based on a probability model; this enables the statistician to make a confidence statement about the choice of model.     

Halal status of ingredients after physicochemical alteration (Istihalah)

BackgroundHalal products are the only permissible products that can be consumed by Muslim population all over the world. Halal certification process is becoming critical due to processing advancements and conflict of opinions among various Islamic schools of thought. Multiple Halal certification standards are framed by regulatory bodies in different countries, but there is inconsistency about few issues among these standards. One such conflict is Istihalah, which deals with the alteration in physicochemical nature of food and render a non-permissible food (Haram) to an acceptable form (Halal) for Muslims. There is dire need to understand the grey areas, such as the concept of Istihalah, to develop unanimous International Halal Standards complying with the rulings of all Islamic schools of thought.Scope and approachTo understand the Istihalah, scientific and Islamic literature from various schools of thought, has been reviewed. The primary objective of this study is to explain the concept of Istihalah with respect to the physicochemical alteration of food matrix and rulings of various Islamic schools, leading to generate a consensus for international Halal certification standards.Key findings and conclusionsIstihalah is an agreed concept among Islamic schools of thought but the definition for the extent of such alteration (to be considered as Istihalah) is not agreed. This concept should not be used for Halal Certification as it may create ambiguous situation for the global acceptability and credibility of a certificate.