Extraction of voltage harmonics using multi-layer perceptron neural network

This paper presents a harmonic extraction algorithm using artificial neural networks for Dynamic Voltage Restorers (DVRs). The suggested algorithm employs a feed forward Multi Layer Perceptron (MLP) Neural Network with error back propagation learning to effectively track and extract the 3rd and 5th voltage harmonics. For this purpose, two different MLP neural network structures are constructed and their performances compared. The effects of hidden layer, supervisors and learning rate are also presented. The proposed MLP Neural Network algorithm is trained and tested in MATLAB program environment. The results show that MLP neural network enable to extract each harmonic effectively.     

A Polynomial Time Solution to Minimum Forwarding Set Problem in Wireless Networks under Unit Disk Coverage Model

Network-wide broadcast (simply broadcast) is a frequently used operation in wireless ad hoc networks (WANETs). One promising practical approach for energy-efficient broadcast is to use localized algorithms to minimize the number of nodes involved in the propagation of the broadcast messages. In this context, the minimum forwarding set problem (MFSP) (also known as multipoint relay (MPR) problem) has received a considerable attention in the research community. Even though the general form of the problem is shown to be NP-complete, the complexity of the problem has not been known under the practical application context of ad hoc networks. In this paper, we present a polynomial time algorithm to solve the MFSP for wireless network under unit disk coverage model. We prove the existence of some geometrical properties for the problem and then propose a polynomial time algorithm to build an optimal solution based on these properties. To the best of our knowledge, our algorithm is the first polynomial time solution to the MFSP under the unit disk coverage model. We believe that the work presented in this paper will have an impact on the design and development of new algorithms for several wireless network applications including energy-efficient multicast, broadcast, and topology control protocols for WANETs and sensor networks.     

Medical image segmentation with transform and moment based features and incremental supervised neural network

In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.     

ADALINE based robust control in robotics: a Riemann-Liouville fractional differintegration based learning scheme

This paper presents an approach to improve the performance of intelligent sliding model control achieved by the use of a fundamental constituent of soft computing, named Adaptive Linear Element (ADALINE). The proposed scheme is based on the fractional calculus. A previously considered tuning scheme is revised according to the rules of fractional order differintegration. After a comparison with the integer order counterpart, it is seen that the control system with the proposed adaptation scheme provides (1) better tracking performance, (2) suppression of undesired drifts in parameter evolution and (3) a very high degree of robustness and insensitivity to disturbances. The claims are justified through some simulations utilizing the dynamic model of a two degrees of freedom (DOF) direct drive robot arm and overall, the contribution of the paper is to introduce the fractional order calculus into a robust and nonlinear control problem with some outperforming features that are absent when the integer order differintegration operators are adopted.     

Metamodeling live sequence charts for code generation

This article presents a metamodeling study for Live Sequence Charts (LSCs) and Message Sequence Charts (MSCs) with an emphasis on code generation. The article discusses specifically the following points: the approach to building a metamodel for MSCs and LSCs, a metamodel extension from MSC to LSC, support for model-based code generation, and finally action model and domain-specific data model integration. The metamodel is formulated in metaGME, the metamodel language for the Generic Modeling Environment.

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Journal of Signal Processing Systems - Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful...     

Effect of CR and FT-paraffin versus SBS modification in terms of conventional and rheological properties

In this study, the rheological and conventional properties of binders prepared with crumb rubber (CR) and FT-paraffin were compared to styrene-butadiene-styrene (SBS)-modified binders’ properties. The objective of this study was to determine which modified binders in different combinations of the CR and the FT-paraffin exhibited better performance than the SBS-modified binders, in terms of conventional and rheological properties. Results showed that the additives exhibited different performances for different types of tests. Many of the combinations including the common usage of the CR and the FT-paraffin performed better at high temperature than the SBS modification. Overall, the test results show that the binders prepared by 6% CR with 3 and 4% FT-paraffin and those prepared with 8% CR with 3% FT-paraffin were better binders than the 4% SBS-modified binder, in terms of low and high temperature properties.