Classification of electrocardiogram signals with support vector machines and extreme learning machine

An Electrocardiogram or ECG is an electrical recording of the heart and is used in the investigation of heart disease. This ECG can be classified as normal and abnormal signals. The classification of the ECG signals is presently performed with the support vector machine. The generalization performance of the SVM classifier is not sufficient for the correct classification of ECG signals. To overcome this problem, the ELM classifier is used which works by searching for the best value of the parameters that tune its discriminant function and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the ECG data from the Physionet arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In this paper, a thorough experimental study was done to show the superiority of the generalization capability of the Extreme Learning Machine (ELM) that is presented and compared with support vector machine (SVM) approach in the automatic classification of ECG beats. In particular, the sensitivity of the ELM classifier is tested and that is compared with SVM combined with two classifiers, and they are the k-nearest Neighbor Classifier and the radial basis function neural network classifier, with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the ELM approach as compared with traditional classifiers.     

Describing Near Surface, Transient Flow Processes in Unconfined Aquifers below Irrigated Lands: Model Application in the Western San Joaquin Valley, California

In order to rigorously examine near surface, field to field interactions between irrigation management regimes and a shallow fluctuating water table, an enhanced deforming finite element (DFE) model was recently developed. The enhanced DFE model, through a process of iteration within each time step, avoids making common assumptions regarding the changing geometry of an aquifer free surface. This paper demonstrates the usefulness and effectiveness of the model by employing it to an irrigated region in the western San Joaquin Valley, Calif., where shallow subsurface tile drains have been installed to control shallow water tables. By virtue of the problems created by the need to dispose off the drainage water, this region has been the focus of several important regional scale modeling exercises, which have evaluated the utility of management strategies, such as source control, groundwater pumping, and land retirement. By refining the focus of the analysis, the enhanced DFE model is found to be able to show that both sources control and managed pumping could be more effective drainage control strategies than predicted based on the results of regional models.     

Object-Oriented Optimization Approach Using Genetic Algorithms for Lattice Towers

A new approach is presented for the optimization of steel lattice towers by combining genetic algorithms and an object-oriented approach. The purpose of this approach is to eliminate the difficulties in the handling of large size problems such as lattice towers. Improved search and rapid convergence are obtained by considering the lattice tower as a set of small objects and combining these objects into a system. This is possible with serial cantilever structures such as lattice towers. A tower consists of panel objects, which can be classified as separate objects, as they possess an independent property as well as inherent properties. This can considerably reduce the design space of the problem and enhance the result. An optimization approach for the steel lattice tower problem using objects and genetic algorithms is presented here. The paper also describes the algorithm with practical design considerations used for this approach. To demonstrate the approach, a typical tower configuration with practical constraints has been considered for discrete optimization with the new approach and compared with the results of a normal approach in which the full tower is considered.     

A literature review, classification and simple meta-analysis on scheduling of batch processors in semiconductor

In this paper, we present a literature review, classification schemes and a simple meta-analysis for scheduling of batch processors (SBP) research in semiconductor manufacturing (SM). This review is based on a study of journals and web-based documents/articles, which include conference materials, lecture notes in computer science, working papers, etc. There are 98 articles published in various publication outlets between 1986 and October 2004. Based on the literature review carried out and the nature of SBP research observed in SM, we have introduced two classification schemes to systematically organize the published articles. The first classification scheme is based on the problem configurations of SBP research in SM and the second one is based on the solution methodology followed by the researchers. These classification schemes indicate that there is much research scope on SBP research in SM. Furthermore, a simple meta-analysis is carried out to enhance understanding on the development and evolution of SBP research in SM and to identify potential research areas for further research and for improvement. The results show that there is an increasing trend in SBP research in SM. A comprehensive list of references is presented. This study is expected to provide a source of reference for other researchers (or readers), who are interested in SBP research particularly in SM and help stimulate further interest.     

POLYBiNN: Binary Inference Engine for Neural Networks using Decision Trees

Journal of Signal Processing Systems - Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) have gained significant popularity in several classification and regression applications....     

MADE3D: Enabling the next generation of high-torque density wind generators by additive design and 3D printing

Forschung im Ingenieurwesen - Direct-drive wind turbine generators are increasing in popularity, thanks to recent project developments—especially offshore, where reliability and efficiency...     

Weld Solidification Cracking Behaviour of AA2195 Al–Cu–Li Alloy

In this study, weld solidification cracking behaviour of AA2195 Al–Cu–Li alloy was studied and compared with conventional AA2219 and AA2014 aluminium alloys. Cracking susceptibility was evaluated using varestraint test and Gleeble^® hot ductility test and the slope of liquidus temperature as function of liquid fraction was also evaluated. Solidification cracking susceptibility of AA2195, AA2219 and AA2014 alloys was ranked based on the above methods. Consistent trend in cracking susceptibility was observed in all the methods where AA2195 and AA2219 alloys showed highest and lowest cracking susceptibility, respectively.     

Influence of Equimolar Concentration on Structural and Optical Properties of Binary Selenides Nanoparticles

Nanoparticles of PbSe, Cu₂Se, and CdSe are prepared for two equimolar concentrations (0.05 M and 0.1 M) by precipitation method at 130°C. The obtained nanoparticles were characterized by x-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, and UV-visible analysis to study the structural, morphological, and optical properties and are discussed in detail.