A set theoretical shuffled shepherd optimization algorithm for optimal design of cantilever retaining wall structures

Engineering with Computers - In this paper, a recently developed meta-heuristic algorithm, shuffled shepherd optimization algorithm (SSOA), is employed for optimal design of reinforced concrete...     

Selective harmonic elimination method for wide range of modulation indexes in multilevel inverters using ICA

Selective harmonic elimination（SHE） in multilevel inverters is an intricate optimization problem that involves a set of nonlinear transcendental equations which have multiple local minima. A new advanced objective function with proper weighting is proposed and also its efficiency is compared with the objective function which is more similar to the proposed one. To enhance the ability of the SHE in eliminating high number of selected harmonics, at each level of the output voltage, one slot is created. The SHE problem is solved by imperialist competitive algorithm（ICA）. The conventional SHE methods cannot eliminate the selected harmonics and satisfy the fundamental component in some ranges of modulation indexes. So, to surmount the SHE defect, a DC-DC converter is applied. Theoretical results are substantiated by simulations and experimental results for a 9-level multilevel inverter. The obtained results illustrate that the proposed method successfully minimizes a large number of identified harmonics which consequences very low total harmonic distortion of output voltage.     

Detection of premature ventricular contractions using MLP neural networks: A comparative study

This paper proposes a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising module, a feature extraction module and a classification module. In the first module we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. The feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptron (MLP) neural networks with different number of layers and nine training algorithms are designed. The performances of the networks for speed of convergence and accuracy classifications are evaluated for seven files from the MIT–BIH arrhythmia database. Among the different training algorithms, the resilient back-propagation (RP) algorithm illustrated the best convergence rate and the Levenberg–Marquardt (LM) algorithm achieved the best overall detection accuracy.     

Optimal design of a modular axial-flux permanent-magnet synchronous generator for gearless wind turbine applications

Air-cored axial-flux permanent-magnet synchronous generators (AFPMSGs) are potential candidates for gearless direct-coupled wind turbines (DCWTs) owing to providing high efficiency and power density. The design of a DCWT generator is so complicated since the generator cost, dimension, and weight affected by gear elimination. Therefore, it is essential to find an optimal AFPMSG design at rated conditions. In this paper, an accurate procedure for the optimal design of an air-cored AFPMSG applicable for DCWTs is proposed. The genetic algorithm (GA) is used for multi-objective design optimization to reach the optimal configuration as well as system dimension in order to decrease the weight, increase the power density and enhance the effectiveness of the generator. To validate the efficiency of the suggested optimization proceducer, a 30 kW AFPMSG has been considered as a case study. The results of optimization have been investigated by finite element analysis (FEA). A prototype generator is also fabricated, and the test results are offered and compared with the numerical study. The outcomes show that there exists an acceptable agreement between FEA and experimental outcomes with the error percentage about of 1.35%.     

Recognition of the Communication Signals Using Particle Swarm Optimization and Support Vector Machine Based on the Multi-Resolution Wavelet Analysis

Automatic recognition of the communication signals plays an important role for various applications. This paper presents a novel intelligent system for recognition of digital communication signals. This system includes three main modules: feature extraction module, classifier module and optimization module. In the feature extraction module, multi-resolution wavelet analysis is proposed for extraction the suitable features. In the classifier module, a multi-class support vector machine (SVM) based classifier is proposed as the multi-class classifier. For optimization module, a particle swarm optimization algorithm is proposed to improve the generalization performance of the recognizer. In this module, it is optimized the SVM classifier design 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. Simulation results show that the proposed hybrid intelligent system has high performance even at very low signal to noise ratios (SNRs).     

Synthesis of hybrid free and nanoporous silica aerogel‐anchored polystyrene chains via in situ atom transfer radical polymerization

Polystyrene nanocomposite with mixed free and anchored chains was synthesized by atom transfer radical polymerization. Attachment of 3‐(trimethoxysilyl)propyl methacrylate with a double bond on the nanoporous silica aerogel surface results in a double bond grafted silica aerogel which could be incorporated into the polystyrene chains by a grafting‐through process. Conversion and molecular weight evaluation was carried out using gas chromatography and gel permeation chromatography, respectively. Double bond containing silica aerogel has an inconsiderable effect on conversion. There is no considerable discrepancy between the molecular weights of the free and anchored chains. Addition of silica aerogel with pendant CC bonds leads to increase of apparent rate constant of polymerization and also molecular weights. This is mainly because of initiator trapping in silica aerogel pores. Every percent of double bond containing silica aerogel with respect to styrene results in trapping of about 0.08 mol of ethyl alpha‐bromoisobutyrate among the silica pores. POLYM. COMPOS., 34:1648–1654, 2013. © 2013 Society of Plastics Engineers     

Dissolution and rheological behaviour of hematite and quartz particles in aqueous media at pH 1

In this study we investigate isothermal, atmospheric acid dissolution behaviour of quartz and hematite minerals which constitute two of the predominant host gangue phases of typical low grade limonitic laterite ores. Batch dissolution tests were carried out on 57 wt.% solid dispersions for 4 h at pH 1 and 25 and 70 °C to establish the influential role of oxide mineralogy/chemistry on rheology and leaching behaviour. The results show that the two minerals displayed a weakly temperature and time-independent, non-Newtonian rheological behaviour with low shear yield stresses (<4 Pa) and viscosities (9–17 mPa s). Hematite dissolution rate was significantly higher compared with that of quartz under similar conditions. Quartz dissolution mechanism was substantially volume diffusion controlled at lower agitation rate (600 rpm) whilst for hematite it was both volume diffusion and chemical reaction controlled. These mechanisms reflected activation energies of 17.7 ± 0.9 and 28.5 ± 1.4 kJ/mol, respectively, for quartz and hematite. At 800 and 1000 rpm, dissolution of both minerals was chemical reaction-controlled with similar activation energies (32.6 ± 1.7 and 32.2 ± 1.6 kJ/mol). The findings underscore the need for higher agitation rates and elevated temperatures, to overcome both volume diffusion and chemical reaction limitations for enhanced acid leaching of these two fairly refractory oxides studied herein.     

Recognition of control chart patterns using swarm intelligence and neural networks based on the statistical and shape features

Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. In this paper, a hybrid intelligent system is proposed for the recognition of control chart patterns. In this system, we have used a proper set of shape features and statistical features as the efficient characteristics of the patterns. Then we proposed a hybrid heuristic recognition system based on particle swarm optimization to improve the generalization performance of the radial basis function neural network classifier. For this purpose, we have optimized the classifier design by searching for the best value of the parameters that tune its discriminate function. The obtained results show that the proposed technique has high recognition accuracy in comparison with other techniques. This recognition accuracy is achieved with fewer training samples. IEEJ Trans 2010 DOI: 10.1002/tee.20610     

Investigation of ex-vessel core catcher for SBO accident in VVER-1000/V528 containment using MELCOR code

Nuclear Science and Techniques - An S-band high-gradient accelerating structure is designed for a proton therapy linear accelerator (linac) to accommodate the new development of compact,...     

Modeling of a convective-infrared kiwifruit drying process

This paper aims to evaluate the experimental performance of a convective-infrared system with heat recovery (CIRHR) at different drying temperatures (40, 45, 50 and 55 °C) and 0.5 m/s air velocity and also to discuss and predict the performance of system on energy consumption and drying kinetics of sliced kiwifruit using artificial neural networks (ANNs). The energy efficiency values were obtained between 2.85% and 32.17%. The ANN model was used to predict the energy consumption of the system and moisture content of the kiwifruit. The back-propagation learning algorithm with Levenberg–Marquardt (LM) and Fermi transfer function were used in the network. The coefficient of determination (R²), the root means square error (RMSE) and the mean absolute percentage error (MAPE) were calculated as 0.99, 0.001 and 0.34, respectively. It can be concluded that predicted values are in good agreement with experimental results.