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.     

Model‐based code generation for HLA federates

This paper addresses the problem of automated code generation for a High Level Architecture compliant federate application given its behavior model. The behavior model is a part of the architectural model of a federation that the federate can participate in. The federate behavior model is based on Live Sequence Charts, adopted as the behavioral specification formalism in the Federation Architecture Metamodel (FAMM). FAMM serves as a formal language for describing federation architectures. An objective is to help the testing of federation architecture by means of its prototype implementation early in the development lifecycle. A further objective is to help developers construct complete federate applications that are well modularized. The approach to achieve these objectives is aspect‐oriented in that the generated code, which handles the federate's interactions with a federation execution, serves as the base code, where the computation logic is to be weaved as an aspect. Copyright © 2009 John Wiley & Sons, Ltd.     

Clustering MIT–BIH arrhythmias with Ant Colony Optimization using time domain and PCA compressed wavelet coefficients

In this paper, Ant Colony Optimization (ACO) based clustering analysis of ECG arrhythmias taken from the MIT–BIH Arrhythmia Database is proposed. Both time domain and discrete wavelet transform (DWT) based frequency domain features are used in the analysis. Since the number of wavelet coefficients are huge amount as compared to the time domain parameters, Principal Component Analysis (PCA) based compression is applied on them in order to decrease their number to the number of time domain features. Then, the reduced numbers of frequency parameters are combined with the time domain features, in order to get the total feature sets. Different types of feature sets are tried and the classification results are compared. These are: time domain feature set, frequency domain feature set and the mixture of them. A neural network algorithm is developed in parallel to verify and measure the ACO classifier's success. Moreover, linear discriminant analysis (LDA) is used to show the effect of clustering on the system's results. The method is tested with MIT–BIH database to classify normal beats and five different critical and having vital importance arrhythmia types. Chosen six classes are normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), right bundle branch block (RBBB), ventricular fusion (F) and fusion (f). Comparison results indicate that the mixture feature set gave a better success for the classification.     

Recovery of sensor embedded washing machines using a multi-kanban controlled disassembly line

Product recovery involves the recovery of materials and components from returned or end-of-life products. Disassembly, an element of product recovery, is the systematic separation of an assembly into its components, subassemblies or other groupings. Stricter environmental regulations together with dramatic decrease in natural resources and landfills have increased the importance of disassembly as all product recovery options require some level of disassembly. Due to changes made during the lifetime of a product by customers or service personnel, the number and the version of components prior to disassembly is unknown. Customers may also discriminate between and demand different versions of components. The existence of non-functional components further adds to the uncertainty associated with disassembly yield. Sensors implanted into products during their production can address this uncertainty by providing information on the number, condition and version of components prior to disassembly. In this study, we evaluate the impact of sensor embedded products (SEPs) on the various performance measures of a washing machine (WM) disassembly line controlled by a multi-kanban system, which takes into consideration the highly stochastic behavior of the line while managing material and kanban flows. First, separate design of experiments studies based on orthogonal arrays are performed for conventional products and SEPs. In order to observe the response of each experiment, detailed discrete event simulation (DES) models for both types of products are developed considering the precedence relationships among the components of a WM. Then, pair-wise t-tests are conducted to compare the two cases based on different performance measures. According to the results, SEPs provide significant reductions in all costs (viz., backorder, holding, disassembly, disposal, testing and transportation) while increasing revenue and profit.     

An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSA from electrocardiogram (ECG) recordings is important for clinical diagnosis and treatment. In this study, we proposed an expert system based on discrete wavelet transform (DWT), fast-Fourier transform (FFT) and least squares support vector machine (LS-SVM) for the automatic recognition of patients with OSA from nocturnal ECG recordings. Thirty ECG recordings collected from normal subjects and subjects with sleep apnea, each of approximately 8 h in duration, were used throughout the study. The proposed OSA recognition system comprises three stages. In the first stage, an algorithm based on DWT was used to analyze ECG recordings for the detection of heart rate variability (HRV) and ECG-derived respiration (EDR) changes. In the second stage, an FFT based power spectral density (PSD) method was used for feature extraction from HRV and EDR changes. Then, a hill-climbing feature selection algorithm was used to identify the best features that improve classification performance. In the third stage, the obtained features were used as input patterns of the LS-SVM classifier. Using the cross-validation method, the accuracy of the developed system was found to be 100% for using a subset of selected combination of HRV and EDR features. The results confirmed that the proposed expert system has potential for recognition of patients with suspected OSA by using ECG recordings.     

Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network

Abstract: The electromyographic signals observed at the surface of the skin are the sum of many small action potentials generated in the muscle fibres. After the signals are processed, they can be used as a control source of multifunction prostheses. The myoelectric signals are represented by wavelet transform model parameters. For this purpose, four different arm movements (elbow extension, elbow flexion, wrist supination and wrist pronation) are considered in studying muscle contraction. Wavelet parameters of myoelectric signals received from the muscles for these different movements were used as features to classify the electromyographic signals in a fuzzy clustering neural network classifier model. After 1000 iterations, the average recognition percentage of the test was found to be 97.67% with clustering into 10 features. The fuzzy clustering neural network programming language was developed using Pascal under Delphi.     

A tool for multiobjective evolutionary algorithms

This paper introduces a software tool based on illustrative applications for the development, analysis and application of multiobjective evolutionary algorithms. The multiobjective evolutionary algorithms tool (MOEAT) written in C# using a variety of multiobjective evolutionary algorithms (MOEAs) offers a powerful environment for various kinds of optimization tasks. It has many useful features such as visualizing of the progress and the results of optimization in a dynamic or static mode, and decision variable settings. The performance measurements of well-known multiobjective evolutionary algorithms in MOEAT are done using benchmark problems. In addition, two case studies from engineering domain are presented.