Search results for "Method"
showing 10 items of 13253 documents
Multidomain BEM for crack analysis in stiffened anisotropic plates.
2014
The present paper is concerned with the application of a boundary element model for the analysis of cracks in stiffened composite panels. The panel stiffeners are reduced to equivalent strips and the multidomain technique is used to model panel zones presenting different properties (skin and stiffeners equivalent strip). Also the crack is modeled exploiting the multidomain formulation. Evaluation of stress intensity factors is performed for representative problems.
Position-sensitive neutron detector
2002
Abstract A position-sensitive neutron detector has been developed for use in nuclear physics research. The detector consists of a ∅5.5 cm×100 cm long quartz tube filled with liquid scintillator viewed from both ends by photomultipliers and enclosed in a light-tight titanium container. The properties of the detector were determined both experimentally and by Monte Carlo simulations (EFEN code). A time resolution of 0.4 ns was reached resulting in the position resolution of less than 4 cm. The neutron registration efficiency varies from 36% to 20% within neutron energy range 1–10 MeV and is practically independent of the position along the detector length. Good n–γ separation is achieved for …
Processing of rock core microtomography images: Using seven different machine learning algorithms
2016
The abilities of machine learning algorithms to process X-ray microtomographic rock images were determined. The study focused on the use of unsupervised, supervised, and ensemble clustering techniques, to segment X-ray computer microtomography rock images and to estimate the pore spaces and pore size diameters in the rocks. The unsupervised k-means technique gave the fastest processing time and the supervised least squares support vector machine technique gave the slowest processing time. Multiphase assemblages of solid phases (minerals and finely grained minerals) and the pore phase were found on visual inspection of the images. In general, the accuracy in terms of porosity values and pore…
Alternating model trees
2015
Model tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes. In this paper, we propose a method for growing alternating model trees, a form of option tree for regression problems. The motivation is that alternating decision trees achieve high accuracy in classification problems because they represent an ensemble classifier as a single tree structure. As in alternating decision trees for classification, our alternating model trees for regression contain splitter and prediction nodes, but we use simple linear regression functions as opposed to constant predicto…
Evaluation of Record Linkage Methods for Iterative Insertions
2009
Summary Objectives: There have been many developments and applications of mathematical methods in the context of record linkage as one area of interdisciplinary research efforts. However, comparative evaluations of record linkage methods are still underrepresented. In this paper improvements of the Fellegi-Sunter model are compared with other elaborated classification methods in order to direct further research endeavors to the most promising methodologies. Methods: The task of linking records can be viewed as a special form of object identification. We consider several non-stochastic methods and procedures for the record linkage task in addition to the Fellegi-Sunter model and perform an e…
Bagging and Boosting with Dynamic Integration of Classifiers
2000
One approach in classification tasks is to use machine learning techniques to derive classifiers using learning instances. The co-operation of several base classifiers as a decision committee has succeeded to reduce classification error. The main current decision committee learning approaches boosting and bagging use resampling with the training set and they can be used with different machine learning techniques which derive base classifiers. Boosting uses a kind of weighted voting and bagging uses equal weight voting as a combining method. Both do not take into account the local aspects that the base classifiers may have inside the problem space. We have proposed a dynamic integration tech…
Real-time flaw detection on a complex object: comparison of results using classification with a support vector machine, boosting, and hyperrectangle-…
2006
We present a classification work performed on industrial parts using artificial vision, a support vector machine (SVM), boost- ing, and a combination of classifiers. The object to be controlled is a coated heater used in television sets. Our project consists of detect- ing anomalies under manufacturer production, as well as in classi- fying the anomalies among 20 listed categories. Manufacturer speci- fications require a minimum of ten inspections per second without a decrease in the quality of the produced parts. This problem is ad- dressed by using a classification system relying on real-time ma- chine vision. To fulfill both real-time and quality constraints, three classification algorit…
Emergence of statistically validated financial intraday lead-lag relationships
2014
According to the leading models in modern finance, the presence of intraday lead-lag relationships between financial assets is negligible in efficient markets. With the advance of technology, however, markets have become more sophisticated. To determine whether this has resulted in an improved market efficiency, we investigate whether statistically significant lagged correlation relationships exist in financial markets. We introduce a numerical method to statistically validate links in correlation-based networks, and employ our method to study lagged correlation networks of equity returns in financial markets. Crucially, our statistical validation of lead-lag relationships accounts for mult…
Many-body Green's function theory of electrons and nuclei beyond the Born-Oppenheimer approximation
2020
The method of many-body Green's functions is developed for arbitrary systems of electrons and nuclei starting from the full (beyond Born-Oppenheimer) Hamiltonian of Coulomb interactions and kinetic energies. The theory presented here resolves the problems arising from the translational and rotational invariance of this Hamiltonian that afflict the existing many-body Green's function theories. We derive a coupled set of exact equations for the electronic and nuclear Green's functions and provide a systematic way to approximately compute the properties of arbitrary many-body systems of electrons and nuclei beyond the Born-Oppenheimer approximation. The case of crystalline solids is discussed …
Managing Human Factors to Reduce Organisational Risk in Industry
2018
[EN] Human factors are intrinsically involved at virtually any level of most industrial/business activities, and may be responsible for several accidents and incidents, if not correctly identified and managed. Focusing on the significance of human behaviour in industry, this article proposes a multi-criteria decision-making (MCDM)-based approach to support organizational risk assessment in industrial environments. The decision-making trial and evaluation laboratory (DEMATEL) method is proposed as a mathematical framework to evaluate mutual relationships within a set of human factors involved in industrial processes, with the aim of highlighting priorities of intervention. A case study relat…