Synthesis of carbon nanotube,graphene, CoFe<Subscript>2</Subscript>O<Subscript>4</Subscript>, and NiFe<Subscript>2</Subscript>O<Subscript>4</Subscript> polypyrrole nanocomposites and study their microwave absorption

In this experimental work, different conductive polymer nanocomposites were synthesized using polypyrrole as conductive polymer and CoFe₂O₄, NiFe₂O₄, CNT and graphene as fillers. X-ray diffraction pattern was used to study the crystallinity of the products and it was found CoFe₂O₄, NiFe₂O₄, CNT, and graphene were successfully embedded in the polymer matrix. To further approve the synthesis of the nanocomposites, energy dispersive X-ray spectroscopy was served. Surface groups of the synthesized nanocomposites were studied by Fourier transform infrared and Raman spectroscopy. The morphology of the products was examined by scanning electron microscopy and transmission electron microscopy. It was found the fillers were successfully embedded in the polymer matrix and they were in nanometer scales. To investigate the magnetic properties and conductivity of the polymer nanocomposites, alternating gradient force magnetometer and four-point probe were used, respectively. Finally, the microwave absorption properties of the polymer nanocomposites were studied and it was found the fillers have different effects on the polymer microwave absorption value.     

IDFC: A new approach to control bifurcation in TCP/RED

The main objective of this research is to analyze the bifurcation phenomenon in Internet congestion model for Transmission Control Protocol (TCP) with Random Early Detection (RED). This problem can be divided to many categories considering different viewpoints. Different approaches of modeling (continuous and discrete models) and various system structures (control in end node or routers) are some of these categories. The most significant method in control of such systems is Delayed Feedback Controller (DFC). In this paper, a discrete model is considered and a new algorithm Integral DFC (IDFC) is presented that has many preferences over similar algorithms, which are illustrated by simulation results and analytical discussions.     

Online identification of nonlinear multivariable processes using self‐generating RBF neural networks

This paper addresses the problem of online model identification for multivariable processes with nonlinear and time‐varying dynamic characteristics. For this purpose, two online multivariable identification approaches with self‐organizing neural network model structures will be presented. The two adaptive radial basis function (RBF) neural networks are called as the growing and pruning radial basis function (GAP‐RBF) and minimal resource allocation network (MRAN). The resulting identification algorithms start without a predefined model structure and the dynamic model is generated autonomously using the sequential input‐output data pairs in real‐time applications. The extended Kalman filter (EKF) learning algorithm has been extended for both of the adaptive RBF‐based neural network approaches to estimate the free parameters of the identified multivariable model. The unscented Kalman filter (UKF) has been proposed as an alternative learning algorithm to enhance the accuracy and robustness of nonlinear multivariable processes in both the GAP‐RBF and MRAN based approaches. In addition, this paper intends to study comparatively the general applicability of the particle filter (PF)‐based approaches for the case of non‐Gaussian noisy environments. For this purpose, the Unscented Particle Filter (UPF) is employed to be used as alternative to the EKF and UKF for online parameter estimation of self‐generating RBF neural networks. The performance of the proposed online identification approaches is evaluated on a highly nonlinear time‐varying multivariable non‐isothermal continuous stirred tank reactor (CSTR) benchmark problem. Simulation results demonstrate the good performances of all identification approaches, especially the GAP‐RBF approach incorporated with the UKF and UPF learning algorithms. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Efficiency improvement of genetic network programming by tasks decomposition in different types of environments

Genetic Programming and Evolvable Machines - Genetic Network Programming (GNP) is a relatively recently proposed evolutionary algorithm which is an extension of Genetic Programming (GP). However,...     

LADPSO: using fuzzy logic to conduct PSO algorithm

Optimization plays a critical role in human modern life. Nowadays, optimization is used in many aspects of human modern life including engineering, medicine, agriculture and economy. Due to the growing number of optimization problems and their growing complexity, we need to improve and develop theoretical and practical optimization methods. Stochastic population based optimization algorithms like genetic algorithms and particle swarm optimization are good candidates for solving complex problems efficiently. Particle swarm optimization (PSO) is an optimization algorithm that has received much attention in recent years. PSO is a simple and computationally inexpensive algorithm inspired by the social behavior of bird flocks and fish schools. However, PSO suffers from premature convergence, especially in high dimensional multi-modal functions. In this paper, a new method for improving PSO has been introduced. The Proposed method which has been named Light Adaptive Particle Swarm Optimization is a novel method that uses a fuzzy control system to conduct the standard algorithm. The suggested method uses two adjunct operators along with the fuzzy system in order to improve the base algorithm on global optimization problems. Our approach is validated using a number of common complex uni-modal/multi-modal benchmark functions and results have been compared with the results of Standard PSO (SPSO2011) and some other methods. The simulation results demonstrate that results of the proposed approach is promising for improving the standard PSO algorithm on global optimization problems and also improving performance of the algorithm.     

Structured singular value of a repeated complex full-block uncertainty

The structured singular value (SSV), or $\mu$, is used to assess the robust stability and performance of an uncertain linear time-invariant system. Existing algorithms compute upper and lower bounds on the SSV for structured uncertainties that contain repeated (real or complex) scalars and/or nonrepeated complex full-blocks. This paper presents algorithms to compute bounds on the SSV for the case of repeated complex full-blocks. This specific class of uncertainty is relevant for the input-output analysis of many convective systems, such as fluid flows. Specifically, we present a power iteration to compute the SSV lower bound for the case of repeated complex full-blocks. This generalizes existing power iterations for repeated complex scalars and nonrepeated complex full-blocks. The upper bound can be formulated as a semi-definite program (SDP), which we solve using a standard interior-point method to compute optimal scaling matrices associated with the repeated full-blocks. Our implementation of the method only requires gradient information, which improves the computational efficiency of the method. Finally, we test our proposed algorithms on an example model of incompressible fluid flow. The proposed methods provide less conservative bounds as compared to prior results, which ignore the repeated full-block structure.     

Kinetic studies on extra heavy crude oil upgrading using nanocatalysts by applying CFD techniques

Regarding the growth of global energy consumption and the paucity of light crude oil,extracting and using heavy and extra heavy crude oil has received much more attention,but the application of this kind ofoil is complicated due to its very high molecular weight.High viscosity and low flowability complicate the transportation of heavy and extra heavy crude oil.Accordingly,it is essential to reduce the viscosity of heavy and extra heavy crude oil through in-situ operations or immediate actions after extraction to reduce costs.Numerical simulations are influential methods,because they reduce calculation time and costs.In this study,the cracking of extra heavy crude oil using computational fluid dynamics is simulated,and a unique kinetic model is proposed based on experimental procedures to predict the behavior of extra heavy crude oil cracking reaction.Moreover,the hydrodynamics and heat transfer of the system and influence of nanocatalysts and temperature on the upgrading of crude oil are studied.The geometry of a reactor is produced using commercial software,and some experiments are performed to examine the validity and accuracy of the numerical results.The findings reveal that there is a good agreement between the numerical and experimental results.Furthermore,to investigate the main factors affecting the process,sensitivity analysis is adopted.Results show that type of catalyst and concentration of catalyst are the parameters that influence the viscosity reduction of extra heavy crude oil the most.The findings further revealed that when using a 25 nm SiO2 nanocatalyst,a maximum viscosity reduction of 98.67％ is observed at 623 K.Also,a catalyst concentration of 2.28wt％ is best for upgrading extra heavy crude oil.The results obtained through sensitivity analysis,simulation model,and experiments represent effectual information for the design and development of high performance upgrading processes for energy applications.     

Experimental investigation into glass fiber/epoxy composite laminates subjected to single and repeated high-velocity impacts of ice

Many engineering components in aerospace structures which are made from polymer composite materials are often damaged during service life due to hail ice and bird impact. This study examines the damage which may be incurred by a single and repeated high-velocity impact of 11.7 g cylindrical-shaped ice on glass fiber/epoxy laminated composite panels carried out on a 20-mm diameter smooth barrel gas gun. The laminates were made from E-glass fiber/epoxy resin with 0/90, ±45, chopped strand mat (CSM) and unidirectional fiber orientation and in different stacking sequence. The impact velocity was in the range of 130–140 m/s and the resulting damage extension zones from ice projectile impacts were measured. Damage extension was successfully identified in all specimens subjected to high-velocity ice projectile impact. Results showed specimens with ±45 orientation and CSM fiber exhibited the lowest damage extension. The results also revealed that specimens with plain weave 0/90 lay-up of glass woven roving show the highest damage extension. Extended damages were observed in composite panels under repeated ice projectile impacts. Study of the stacking sequence effect indicated significant role played by presence of ±45 reinforcement in reducing the damage extension in the laminated plates. Delamination constituted the major damage mechanism for most specimens tested followed by matrix and fiber fracture.     

A software technique to improve lifetime of caches containing ultra-leaky SRAM cells caused by within-die Vth variation

Exceptionally leaky transistors are increasingly more frequent in nanometer-scale technologies due to lower threshold voltage and its increased variation. SRAM cells containing such transistors suffer from accelerated aging due to electromigration intensified by higher currents continuously flowing through thin metals such as vias and contacts. Such cells do not violate target delay since leaky transistors are faster than ideal ones, and hence they are not faulty to be worth replacing with redundant rows and columns, which may also themselves contain exceptionally leaky transistors. Moreover, their number is growing so fast that makes redundancy ineffective. We show that in SRAM cells leakage current depends on the value stored in the cell and propose a software-based runtime technique that suppresses such abnormal leakages in the standby mode by storing safe values in the corresponding cache lines. Consequently, the lifetime of such caches is restored when used in long-standby applications. Moreover, energy dissipation in the standby mode is reduced by this technique if the standby duration is more than a few seconds. Analysis proves the performance penalty is, in the worst case, linearly dependent to the number of so-cured cache lines.