1,476 research outputs found
The quaternion-based three-dimensional beam theory
This paper presents the equations for the implementation of rotational quaternions in the geometrically exact three-dimensional beam theory. A new finite-element formulation is proposed in which the rotational quaternions are used for parametrization of rotations along the length of the beam. The formulation also satisfies the consistency condition that the equilibrium and the constitutive internal force and moment vectors are equal in its weak form. A strict use of the quaternion algebra in the derivation of governing equations and for the numerical solution is presented. Several numerical examples demonstrate the validity, performance and accuracy of the proposed approach. (C) 2009 Elsevier B.V. All rights reserved
Single Slice Grouping Mechanism for Recognition of Cursive Handwritten Courtesy Amounts of Malaysian Bank Cheques
Mechanism to group single slice for recognition involves the process of cutting
vertically across an image slice by slice, group every slice at a certain width and
tested for recognition using a trained Neural network. The image contains
cursive handwritten courtesy Amounts of Malaysian bank cheques. A three layer
neural Network architecture with the new error function of Backpropagation
learning algorithm is used. This approach yields good recognition results with
faster convergence rates
High-fidelity Multidisciplinary Sensitivity Analysis and Design Optimization for Rotorcraft Applications
A multidisciplinary sensitivity analysis of rotorcraft simulations involving tightly coupled high-fidelity computational fluid dynamics and comprehensive analysis solvers is presented and evaluated. A sensitivity-enabled fluid dynamics solver and a nonlinear flexible multibody dynamics solver are coupled to predict aerodynamic loads and structural responses of helicopter rotor blades. A discretely consistent adjoint-based sensitivity analysis available in the fluid dynamics solver provides sensitivities arising from unsteady turbulent flows and unstructured dynamic overset meshes, while a complex-variable approach is used to compute structural sensitivities with respect to aerodynamic loads. The multidisciplinary sensitivity analysis is conducted through integrating the sensitivity components from each discipline of the coupled system. Accuracy of the coupled system is validated by conducting simulations for a benchmark rotorcraft model and comparing solutions with established analyses and experimental data. Sensitivities of lift computed by the multidisciplinary sensitivity analysis are verified by comparison with the sensitivities obtained by complex-variable simulations. Finally the multidisciplinary sensitivity analysis is applied to a constrained gradient-based design optimization for a HART-II rotorcraft configuration
Using generative models for handwritten digit recognition
We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable B-splines with Gaussian ``ink generators'' spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. (1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. (2) During the process of explaining the image, generative models can perform recognition driven segmentation. (3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. (4) Unlike many other recognition schemes it does not rely on some form of pre-normalization of input images, but can handle arbitrary scalings, translations and a limited degree of image rotation. We have demonstrated our method of fitting models to images does not get trapped in poor local minima. The main disadvantage of the method is it requires much more computation than more standard OCR techniques
Handwritten Digit Recognition and Classification Using Machine Learning
In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy
Classification of reduction invariants with improved backpropagation
Data reduction is a process of feature extraction that transforms
the data space into a feature space of much lower dimension
compared to the original data space, yet it retains most of the
intrinsic information content of the data. This can be done by
using a number of methods, such as principal component analysis
(PCA), factor analysis, and feature clustering. Principal
components are extracted from a collection of multivariate cases
as a way of accounting for as much of the variation in that
collection as possible by means of as few variables as possible.
On the other hand, backpropagation network has been used
extensively in classification problems such as XOR problems,
share prices prediction, and pattern recognition. This paper
proposes an improved error signal of backpropagation network for
classification of the reduction invariants using principal
component analysis, for extracting the bulk of the useful
information present in moment invariants of handwritten digits,
leaving the redundant information behind. Higher order
centralised scale- invariants are used to extract features of
handwritten digits before PCA, and the reduction invariants are
sent to the improved backpropagation model for classification
purposes
- …