Artificial intelligence applications in Permanent Magnet Brushless DC motor drives

Permanent Magnet Brushless DC (PMBLDC) machines are more popular due its simple structure and low cost. Improvements in permanent magnetic materials and power electronic devices have resulted in reliable, cost effective PMBLDC drives, for many applications. Advances in artificial intelligent applications like neural network, fuzzy logic, Genetic algorithm etc. have made tremendous impact on electric motor drives. The brushless DC motor is a multivariable and non-linear system. In conventional PMBLDC drives speed and position sensing of brushless DC motors require high degree of accuracy. Unfortunately, traditional methods of control require detailed modelling of all the motor parameters to achieve this. The Intelligent control techniques like, fuzzy logic control/Neural network control etc. uses heuristic input–output relations to deal with vague and complex situations. This paper presents a literature survey on the intelligent control techniques for PMBLDC motor drives. Various AI techniques for PMBLDC motor drives are described. Attempt is made to provide a guideline and quick reference for the researchers and practicing engineers those are working in the area of PMBLDC motor drives.     

A review of wave-net identical learning &; filling-in in a decomposition space of (JPG-JPEG) sampled images

Continuous flow to send images via encrypted wireless channels may reduce the efficiency of transmission. This is due to the damage or loss of the numerous macro-blocks from these images. Therefore, it is difficult to rebuild these patches from the point of reception. Many algorithms have been proposed in the past decade, particularly error concealment (EC) algorithms. In this paper, we focus on the algorithms that have high efficiency to fill-in the corrupted patches. On the other hand, we also present a new way of detecting the horizontal and vertical gradients especially, in the un-smooth patches. This improves the edge detector filter. Ultimately, a novel scheme for vertical and horizontal interpolation between the corrupted pixels and the non-corrupted adjacent pixels is achieved by improving the efficiency of filling-in. We used a new technique known as the wave-net model. This model combines the wavelet with the neural network classifier (NNC). The neural network was trained in advance to reduce the error extent for the pixels that may occur in the error. The experimental results were convincing and close to the desired. The proposed method is able to enhance image quality in term of both visual perception and the blurriness effects (BE).     

Effective multiplicative updates for non-negative discriminative learning in multimodal dimensionality reduction

Fisher discriminant analysis gives the unsatisfactory results if points in the same class have within-class multimodality and fails to produce the non-negativity of projection vectors. In this paper, we focus on the newly formulated within and between-class scatters based supervised locality preserving dimensionality reduction problem and propose an effective dimensionality reduction algorithm, namely, Multiplicative Updates based non-negative Discriminative Learning (MUNDL), which optimally seeks to obtain two non-negative embedding transformations with high preservation and discrimination powers for two data sets in different classes such that nearby sample pairs in the original space compact in the learned embedding space, under which the projections of the original data in different classes can be appropriately separated from each other. We also show that MUNDL can be easily extended to nonlinear dimensionality reduction scenarios by employing the standard kernel trick. We verify the feasibility and effectiveness of MUNDL by conducting extensive data visualization and classification experiments. Numerical results on some benchmark UCI and real-world datasets show the MUNDL method tends to capture the intrinsic local and multimodal structure characteristics of the given data and outperforms some established dimensionality reduction methods, while being much more efficient.     

Performance analysis of available bandwidth estimation tools for grid networks

Modern large-scale grid computing systems for processing advanced science and engineering applications rely on geographically distributed clusters. In such highly distributed environments, estimating the available bandwidth between clusters is a key issue for efficient task scheduling. We analyze the performance of two well known available bandwidth estimation tools, pathload and abget, with the aim of using them in grid environments. Differently than previous investigations (Jain et al., ; Shriram et al., in Passive and active network measurement: 6th international workshop, PAM 2005. Springer, Berlin, 2005), our experiments consider a series of relevant metrics such as accuracy of the estimation, convergence time, degree of intrusion in the grid links, and ability to handle multiple simultaneous estimations. No previous work has analyzed the use of available bandwidth tools for the derivation of efficient grid scheduling.     

Efficient peerGroup management in JXTA-Overlay P2P system for developing groupware tools

With the fast development of IT technologies, virtual organizations are more and more present in the current collaborative work and learning activity. For instance, many subjects in virtual distance learning are organized as online groups of students, who use groupware tools to complete their learning tasks. In this paper, we address the efficient management of peer groups in JXTA-based P2P systems as a key issue in many P2P applications that use peer group as a unit such as for remote execution of tasks in parallel and distributed applications. From this perspective, we consider peer grouping as the basis in the development of groupware tools in P2P systems.     

Adaptive Variational Method for Restoring Color Images with High Density Impulse Noise

In this paper, a new variational framework of restoring color images with impulse noise is presented. The novelty of this work is the introduction of an adaptively weighting data-fidelity term in the cost functional. The fidelity term is derived from statistical methods and contains two weighting functions as well as some statistical control parameters of noise. This method is based on the fact that impulse noise can be approximated as an additive noise with probability density function (PDF) being the finite mixture model. A Bayesian framework is then formulated in which likelihood functions are given by the mixture model. Inspired by the expectation-maximization (EM) algorithm, we present two models with variational framework in this study. The superiority of the proposed models is that: the weighting functions can effectively detect the noise in the image; with the noise information, the proposed algorithm can automatically balance the regularity of the restored image and the fidelity term by updating the weighting functions and the control parameters. These two steps ensure that one can obtain a good restoration even though the degraded color image is contaminated by impulse noise with large ration (90% or more). In addition, the numerical implementation of this algorithm is very fast by using a split algorithm. Some numerical experimental results and comparisons with other methods are provided to show the significant effectiveness of our approach.     

3-D B-spline Wavelet-Based Local Standard Deviation (BWLSD): Its Application to Edge Detection and Vascular Segmentation in Magnetic Resonance Angiography

Extracting reliable image edge information is crucial for active contour models as well as vascular segmentation in magnetic resonance angiography (MRA). However, conventional edge detection techniques, such as gradient-based methods and wavelet-based methods, are incapable of returning reliable detection responses from low contrast edges in the images. In this paper, we propose a novel edge detection method by combining B-spline wavelet magnitude with standard deviation inside local region. It is proved theoretically and demonstrated experimentally in this paper that the new edge detection method, namely BWLSD, is able to give consistent and reliable strengths for edges with different image contrasts. Moreover, the relationship between the size of local region with non-zero wavelet magnitudes and the scale of wavelet function is established. This relationship indicates that if the scale of the adopted wavelet function is s, then the size of a local region, from which the standard deviation is estimated, should be 2s−1. The proposed edge detection technique is embedded in FLUX, namely, BWLSD-FLUX, for vascular segmentation in MRA image volumes. Experimental results on clinical images show that, as compared with the conventional FLUX, BWLSD-FLUX can achieve better segmentations of vasculatures in MRA images under same initial conditions.     

From Images to Shape Models for Object Detection

We present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models directly from images, and can localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the boundaries of objects, rather than just their bounding-boxes. This is achieved by a novel technique for learning a shape model of an object class given images of example instances. Furthermore, we also integrate Hough-style voting with a non-rigid point matching algorithm to localize the model in cluttered images. As demonstrated by an extensive evaluation, our method can localize object boundaries accurately and does not need segmented examples for training (only bounding-boxes).     

Optimal Error Estimates of the Legendre Tau Method for Second-Order Differential Equations

In this paper, we prove that the Legendre tau method has the optimal rate of convergence in L ²-norm, H ¹-norm and H ²-norm for one-dimensional second-order steady differential equations with three kinds of boundary conditions and in C([0,T];L ²(I))-norm for the corresponding evolution equation with the Dirichlet boundary condition. For the generalized Burgers equation, we develop a Legendre tau-Chebyshev collocation method, which can also be optimally convergent in C([0,T];L ²(I))-norm. Finally, we give some numerical examples.     

The <Emphasis Type="Italic">L</Emphasis><Superscript>2</Superscript>-Optimality of the IIPG Method for Odd Degrees of Polynomial Approximation in 1D

The paper deals with a numerical analysis of the incomplete interior penalty Galerkin (IIPG) method applied to one dimensional Poisson problem. Based on a particular choice of the interior penalty parameter σ (order of O(h ⁻¹)), we derive the optimal error estimate in the L ²-norm for odd degrees of polynomial approximation for locally quasi-uniform meshes. Moreover, we show that only the mentioned choice of the penalty parameter leads to optimal orders of convergence. Finally, presented numerical experiments verify the theoretical results.