Modelling of aluminium foam core sandwich panels under impact perforation

Aluminium foam core sandwich panels are good energy absorbers for impact protection applications, such as light-weight structural panels, packing materials and energy absorbing devices. In this study, the high-velocity impact perforation of aluminium foam core sandwich structures was analysed. Sandwich panels with 1100 aluminium face-sheets and closed-cell A356 aluminium alloy foam core were modelled by three-dimensional finite element models. The models were validated with experimental tests by comparing numerical and experimental damage modes, output velocity, ballistic limit and absorbed energy. By this model the influence of foam core and face-sheet thicknesses on the behaviour of the sandwich panel under impact perforation was evaluated.     

Fusion Rules Effects on Lifetime Maximization of Multi-channel Cooperative Spectrum Sensing

Cooperative spectrum sensing (CSS) is an efficient method to detect the reliability of the free spectrum, however, with great overhead and energy consumption. In this paper, we study a much broader application of CSS, where the sensors are used to sense multiple channels. On the one hand, since the tiny and low-cost sensors do not have high-speed analog-to-digital-convertors, they cannot simultaneously sense more than one channel. Therefore, the simultaneous multi-channel CSS is an important issue in a cognitive sensor network (CSN). On the other hand, these tiny sensors do not have high-power batteries, which makes the network lifetime as an important metric. In this paper, node selection is proposed for the multi-channel CSS to maximize the lifetime of a CSN under some detection constraints with lower overhead than cooperative sensing by all the sensors simultaneously. We analyze the problem for the OR and the AND rules, which can be implemented at the fusion center. The problem is solved by using convex optimization methods where assignment indices for every sensor are assumed. We provide a performance analysis through simulations using MATLAB, which shows that the sensor selection scheme provides a significant long lifetime for a CSN compared to the case where sensors are selected randomly and where all sensors are just classified to sense the channels simultaneously.

     

An enhanced shuffled Shepherd Optimization Algorithm for optimal design of large-scale space structures

Shuffled Shepherd Optimization Algorithm (SSAO) is a swarm intelligence-based optimizer inspired by the herding behavior of shepherds in nature. SSOA may suffer from some shortcomings, including being trapped in a local optimum and starting from a random population without prior knowledge. This study aims to enhance the performance of the SSOA by incorporating two efficient devices. The first device is utilized from the Opposition-Based Learning (OBL) approach to improve the initialization phase of the algorithm. The second device is incorporated a solution generator in the cyclic body of the SSOA based on the statistical results of the solutions. This feature is the so-called statistically regenerated stepsize. The proposed devices provide a good balance between exploration and exploitation capability of the algorithm and reduce the probability of getting tapped in a local optimum. The viability of the proposed Enhanced Shuffled Shepherd Optimization Algorithm (ESSOA) is demonstrated through three large-scale design examples. ESSOA is compared to the standard SSOA and some other existing metaheuristic algorithms. The optimization results reveal the competence and robustness of the ESSOA for optimal design of the large-scale space structures.

     

Recognition of communication signal types using genetic algorithm and support vector machines based on the higher order statistics

Automatic recognition of communication signal type plays an important role in various applications. Most of the existing recognizers can only identify a few types of communication signal. This paper presents a novel intelligent technique that identifies a variety of digital signal types. Here, a hierarchical support vector machine based structure is proposed as the multiclass classifier. A proper set of the higher order moments (up to eighth) and higher order cumulants (up to eighth) are proposed as the effective features for recognizing of the digital communication signal. A genetic algorithm is used for selecting the suitable parameters of support vector machines. This idea improves the performance of the recognizer, efficiently. Simulation results show that the proposed recognizer has a high success rate for recognition of the different modulations even at very low SNRs.     

Glutaraldehyde‐crosslinking for improved copper absorption selectivity and chemical stability of polyethyleneimine coatings

Nano‐thin coatings of glutaraldehyde (GA)‐crosslinked polyethyleneimine (PEI) are extremely selective and effective in binding copper from seawater. Here it was demonstrated that GA‐PEI performs significantly different from PEI. The copper‐selectivity of self‐assembled PEI coatings on silicon substrates was greatly improved by GA‐crosslinking. After submersion in artificial seawater containing 200 ppb copper and equimolar amounts of 11 competing ions only copper and trace amounts of Zn were detected in the GA‐crosslinked coatings, while for non‐crosslinked PEI there was about 30% Zn present relative to copper. The coatings were demonstrated to be highly stable under acidic conditions and retained their copper‐binding selectivity after repeated cycles of binding and acid‐mediated elution. After self‐assembly of the GA‐crosslinked coating on mesoporous diatomaceous earth particles, significant amounts of copper could be extracted from 200 ppb in artificial seawater and eluted under acidic pH. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43954.