Modified poly(ether–imide–amide)s with pendent benzazole structures: Synthesis and characterization

Three series of novel modified poly(ether–imide–amide)s (PEIAs) having pendent benzazole units were prepared from diimide–dicarboxylic acids, including 2-[3,5-bis(4-trimellitimidophenoxy) phenyl]benzimidazole, 2-[3,5-bis(4-trimellitimidophenoxy) phenyl]benzoxazole, and 2-[3,5-bis(4-trimellitimidophenoxy) phenyl]benzothiazole, with various diamines by direct polycondensation in N-methyl-2-pyrrolidone with triphenyl phosphite and pyridine as condensing agents. These new diimide–dicarboxylic acids containing ether linkages and benzazole pendent groups were synthesized by the condensation reaction of 5-(2-benzimidazole)-1,3-bis(4-aminophenoxy)benzene, 5-(2-benzoxazole)-1,3-bis(4-aminophenoxy)benzene, or 5-(2-benzothiazole)-1,3-bis(4-aminophenoxy)benzene with trimellitic anhydride, respectively. All of the polymers were obtained in quantitative yields with inherent viscosities of 0.39–0.65 dL/g. For comparative purposes, the corresponding unsubstituted PEIAs were also prepared by the reaction of a diimide–dicarboxylic acid monomer lacking benzazole pendent groups, namely, 3,5-bis(4-trimellitimidophenoxy) phenyl, with the same diamines under similar conditions. The solubilities of the modified PEIAs in common organic solvents and their thermal stability were enhanced compared to those of the corresponding unmodified PEIAs. The glass-transition temperature, 10% weight loss temperature, and char yields at 800°C were 19–31°C, 22–57°C and 4–8% higher, respectively, than those of the unmodified polymers. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

A Study on the Formation of Intermetallics During the Heat Treatment of Explosively Welded Al-Ti Multilayers

Metallic–intermetallic laminate composites are promising materials for many applications, namely, in the aerospace industry. Ti/TiAl₃ laminates are one of the interesting laminate composites, which are mostly used in aerospace applications. In this work, commercially pure aluminum and titanium sheets were explosively joined. The multilayer samples were annealed between 1 and 260 hours at 903 K (630 °C) in ambient atmosphere, and the formation and growth of the intermetallic compound at the Ti/Al interface were monitored. Microstructural investigations were carried out using optical and scanning electron microscopes equipped with energy-dispersive spectroscopy and the X-ray diffraction technique. The microhardness profile of the layers was also determined. The thickness and type of Al-Ti intermetallics were determined. It was found that the only intermetallic phase observed in the interfaces was TiAl₃. It was also shown that two mechanisms for TiAl₃ growth exist: reaction and diffusion controlled. The growth exponent was equal to 0.94 for the reaction-controlled mechanism (first step) and 0.31 for the diffusion-controlled mechanism (second step). These values were in good agreement with theoretical values (1 and 0.5 for the first and second steps, respectively). Based on the results of this research, a kinetic model for the formation and growth of TiAl₃ intermetallic phase was proposed.     

Enhancing Neural Network Traffic Incident-Detection Algorithms Using Wavelets

Researchers have presented freeway traffic incident-detection algorithms by combining the adaptive learning capability of neural networks with imprecision modeling capability of fuzzy logic. In this article it is shown that the performance of a fuzzy neural network algorithm can be improved through preprocessing of data using a wavelet-based feature-extraction model. In particular, the discrete wavelet transform (DWT) denoising and feature-extraction model proposed by Samant and Adeli (2000) is combined with the fuzzy neural network approach presented by Hsiao et al. (1994). It is shown that substantial improvement can be achieved using the data filtered by DWT. Use of the wavelet theory to denoise the traffic data increases the incident-detection rate, reduces the false-alarm rate and the incident-detection time, and improves the convergence of the neural network training algorithm substantially.     

A parallel genetic/neural network learning algorithm for MIMDshared memory machines

A new algorithm is presented for training of multilayer feedforward neural networks by integrating a genetic algorithm with an adaptive conjugate gradient neural network learning algorithm. The parallel hybrid learning algorithm has been implemented in C on an MIMD shared memory machine (Cray Y-MP8/864 supercomputer). It has been applied to two different domains, engineering design and image recognition. The performance of the algorithm has been evaluated by applying it to three examples. The superior convergence property of the parallel hybrid neural network learning algorithm presented in this paper is demonstrated.     

Image steganalysis using improved particle swarm optimization based feature selection

Image steganalysis is the task of discovering the hidden message in a multimedia file in which the steganalysis technique highly depends on the feature elements of the image. Since there is a possibility for a feature vector to contain redundant elements, processing of redundant elements can be harmful in terms of long computation cost and large storage space. This paper proposes a new feature selection approach based on Adaptive inertia weight-based Particle Swarm Optimization (APSO) for the image steganalysis where the inertia weight of PSO is adaptively adjusted using the swarm diameter, average distance of particles around the center and average speed of particles towards the center. Also, the proposed APSO is used with the novel measure of Area Under the receiver operating characteristics Curve (AUC) as the fitness function to enhance the performance of identification of stego-images from the cover images in steganalysis problem. Due to appropriate convergence rate and the regulated search step of APSO, it is able to select the most significant and influential feature elements and so, the performance of steganalysis will be improved. Experimental results of the proposed method on Breaking Out Steganography System (BOSS) benchmark proves the superiority of the proposed method compared to the similar approaches in image steganalysis in terms of detection of stego-image, running time, and diversity measure.     

High‐Performance Computing in Structural Mechanics and Engineering

Recent advances in computer hardware and software have made multiprocessing a viable and attractive technology. This paper reviews high‐performance computing methods in structural mechanics and engineering through the use of a new generation of multiprocessor computers. The paper presents an overview of vector pipelining, performance metrics for parallel and vector computers, programming languages, and general programming considerations. Recent developments in the application of concurrent processing techniques to the solution of structural mechanics and engineering problems are reviewed, with special emphasis on linear structural analysis, nonlinear structural analysis, transient structural analysis, dynamics of multibody flexible systems, and structural optimization.     

Knowledge engineering

Summary Several new and emerging computational paradigms for engineering problem solving automation and processing of various types of knowledge are reviewed. They are artificial intelligence/sybmolic processing, object-oriented programming paradigm, machine learning, evolutionary computing/genetic algorithms, and neurocomputing. Examples of recent research efforts in each area are briefly described.     

Concurrent Genetic Algorithms for Optimization of Large Structures

In a recent article, the writers presented an augmented Lagrangian genetic algorithm for optimization of structures. The optimization of large structures such as high‐rise building structures and space stations with several hundred members by the hybrid genetic algorithm requires the creation of thousands of strings in the population and the corresponding large number of structural analyses. In this paper, the writers extend their previous work by presenting two concurrent augmented Lagrangian genetic algorithms for optimization of large structures utilizing the multiprocessing capabilities of high‐performance computers such as the Cray Y‐MP 8/864 supercomputer. Efficiency of the algorithms has been investigated by applying them to four space structures including two high‐rise building structures. It is observed that the performance of both algorithms improves with the size of the structure, making them particularly suitable for optimization of large structures. A maximum parallel processing speed of 7.7 is achieved for a 35‐story tower (with 1,262 elements and 936 degrees of freedom), using eight processors.