filtering of nonlinear plants over networks

In this paper, we consider the filtering problem for Lipschitz systems in a networked environment. We assume that the measurements transmitted over the network are subject to quantization, uncertain delays and communication constraints. We first analytically demonstrate how each of the these issues affect the filtering problem. Second, we tackle the filter design as an optimization problem with LMI constraints. The optimization maximizes the Lipschitz constant and thus the region of attraction for which the filter is stable and an bound is satisfied by the error system. Copyright © 2013 John Wiley & Sons, Ltd.     

A robust digital image watermarking technique using lifting wavelet transform and firefly algorithm

In this paper, an optimized and robust digital image watermarking technique based on lifting wavelet transform (LWT) and firefly algorithm is proposed. LWT is newer and faster generation of former wavelet transforms and firefly algorithm is an efficient optimizing algorithms. In current technique, base image decomposed by LWT into 4 sub bands then the first sub band separated into non overlapping blocks. After that blocks are sorted in order of descending based on standard derivation of each block. Selecting suitable blocks for special embedding process seems to be an optimization problem due to existence of a trade-off between imperceptibility and robustness. Firefly algorithm used to solve this trade-off while selecting primary blocks causes high robustness and low imperceptibility and vice versa. For improving security, Arnold transform applied to watermark and achieved scrambled image bits used as condition for embedding process. The proposed technique evaluated by variety of attacks like additive noise, average filter, median filter, sharpening filter and some other geometric and non-geometric attacks and experimental results showed its good imperceptibility and high robustness.     

A high performing meta-heuristic for training support vector regression in performance forecasting of supply chain

The prevalence of the use of third-party logistics (3PL) providers is noticeable. The complexity of the relationships pertinent to 3PL is greater than that of any traditional logistics supplier relationships. Moreover, they can be considered as truly strategic alliances. The use of the mentioned relationships to increase the flexibility of the organization to address the rapid changes occurring in market conditions has become popular while these relationships concentrate on the core competencies as well as the development of long-term growth strategies. A good number of studies have examined the selection of service providers. With respect to the selection of the service providers, the most recent studies approved the better performance of neural networks in comparison with the conventional methods to provide a solution for the real-world engineering problems, one of the sociopolitically inspired optimization strategies named imperialist competitive algorithm (ICA) is used. In order to select the 3PL, integration of the support vector regression (SVR) and self-adaptive ICA (SAICA) has offered a novel model, in which SAICA is utilized to adjust the parameters of the SVR. The suggested model is applied for cosmetics production. Moreover, the comparison of the suggested model and back-propagation neural networks, pure SVR, and ICA–SVR is presented. Higher estimation accuracy is achieved as the results of the proposed model reveal, which leads to the effective prediction.     

Anchoring of Copper(II) Schiff Base Complex into Aminopropyl-Functionalised MCM-41: A Novel,Efficient and Reusable Catalyst for Selective Oxidation of Alcohols

A copper(II) complex containing tetradentate N₂O₂ Schiff base ligand immobilized into aminopropyl-functionalised MCM-41 (mobile crystalline material number 41), was prepared and characterized by Fourier-transform infrared, X-ray diffraction, scanning electron microscopy, thermogravimetric analysis, N₂ adsorption–desorption and inductively coupled plasma analysis techniques. The novel heterogeneous catalyst, MCM-41-pr-NH₂-CuL, can be successfully applied for efficient and selective oxidation of different primary and secondary alcohols to the corresponding carbonyl compounds using hydrogen peroxide as an oxidant in acetonitrile at 60 °C. The effect of reaction parameters such as solvent, amount of catalyst, temperature and kind of oxidant on the oxidation of benzyl alcohol was also studied. The prepared catalyst could be recovered and reused four times without important loss of its catalytic performance. The heterogeneous MCM-41-pr-NH₂-CuL catalyst was found to be catalytically more active in the oxidation of alcohols compared to the similar type of copper(II) Schiff base complex in homogeneous media under the same reaction conditions.     

ZnO nanoparticles and polyaniline blend as an active layer for bulk heterojunction solar cell applications

In this work, a facile and straightforward procedure was introduced to prepare a blend as an active layer for hybrid solar cell applications. The active layer consisting of a blend of ZnO nanoparticles (NPs) and polyaniline (PANI) dispersions was deposited by spin coating on ITO covered glasses. The current density–voltage characteristics were studied under AM1.5G standard illumination, without any encapsulation process. Also, the samples were studied using UV–Vis spectroscopy, energy dispersive X-ray spectroscopy (EDS) and field emission-scanning electron microscopy. The investigation is limited just to the active layer, so the cells were fabricated without any interlayer. The effect of various volume ratios of ZnO–NPs:PANI solutions, thickness and the annealing temperature of the active layer on the open circuit voltage and the short circuit current density of the cells were investigated. Moreover, the blending time of ZnO–NPs:PANI dispersions as a significant factor for achieving the optimum results were studied.     

Strength degradation of sandstone and granodiorite under uniaxial cyclic loading

Change in mechanical properties of rocks under static loading has been widely studied and documented. However, the response of rocks to cyclic loads is still a much-debated topic. Fatigue is the phenomenon when rocks under cyclic loading fail at much lower strength as compared to those subjected to the monotonic loading conditions. A few selected cored granodiorite and sandstone specimens have been subjected to uniaxial cyclic compression tests to obtain the unconfined fatigue strength and life. This study seeks to examine the effects of cyclic loading conditions, loading amplitude and applied stress level on the fatigue life of sandstone, as a soft rock, and granodiorite, as a hard rock, under uniaxial compression test. One aim of this study is to determine which of the loading conditions has a stronger effect on rock fatigue response. The fatigue response of hard rocks and soft rocks is also compared. It is shown that the loading amplitude is the most important factor affecting the cyclic response of the tested rocks. The more the loading amplitude, the shorter the fatigue life, and the greater the strength degradation. The granodiorite specimens showed more strength degradation compared to the sandstone specimens when subjected to cyclic loading. It is shown that failure modes of specimens under cyclic loadings are different from those under static loadings. More local cracks were observed under cyclic loadings especially for granodiorite rock specimens.     

Blending polypropylene with glycidyl methacrylate‐containing polymer to improve adhesion to elastomers

BACKGROUND: Polypropylene (PP) is one of the most widely used polyolefins but gets restricted in surface applications due to its non‐polar nature. Surface properties of films made of PP were modified to improve their adhesion to elastomeric polymers such as thermoplastic polyurethanes (TPU), especially to Pebax^® [poly(ether‐block‐amide)]. RESULTS: Surface modification of PP was brought about by blending it with glycidyl methacrylate (GMA)‐containing polymer to increase its surface energy. Films of modified PP were analyzed to determine the blending efficiency and characterized using contact angle measurements, differential scanning calorimetry, X‐ray photoelectron spectroscopy and scanning electron microscopy. Molecular dynamics simulations were done to determine surface and bulk properties of PP blended with GMA. The computational results correlated very well with the experimental data and revealed that the changes in the surface energy can be linked to the position of the functional group within the sample. T‐Peel tests indicated a 2.4 times increase in adhesion to Pebax^® and only 1.7 times increase in adhesion to TPU compared to unmodified PP. CONCLUSION: The surface energy and enhanced adhesion proved that PP was successfully modified and its surface made more polar. Copyright © 2008 Society of Chemical Industry     

Clustering and selection of multiple criteria alternatives using unsupervised and supervised neural networks

There are decision-making problems that involve grouping and selecting a set of alternatives. Traditional decision-making approaches treat different sets of alternatives with the same method of analysis and selection. In this paper, we propose clustering alternatives into different sets so that different methods of analysis, selection, and implementation for each set can be applied. We consider multiple criteria decision-making alternatives where the decision-maker is faced with several conflicting and non-commensurate objectives (or criteria). For example, consider buying a set of computers for a company that vary in terms of their functions, prices, and computing powers. In this paper, we develop theories and procedures for clustering and selecting discrete multiple criteria alternatives. The sets of alternatives clustered are mutually exclusive and are based on (1) similar features among alternatives, and (2) preferential structure of the decision-maker. The decision-making process can be broken down into three steps: (1) generating alternatives; (2) grouping or clustering alternatives based on similarity of their features; and (3) choosing one or more alternatives from each cluster of alternatives. We utilize unsupervised learning clustering artificial neural networks (ANN) with variable weights for clustering of alternatives, and we use feedforward ANN for the selection of the best alternatives for each cluster of alternatives. The decision-maker is interactively involved by comparing and contrasting alternatives within each group so that the best alternative can be selected from each group. For the learning mechanism of ANN, we proposed using a generalized Euclidean distance where by changing its coefficients new formation of clusters of alternatives can be achieved. The algorithm is interactive and the results are independent of the initial set-up information. Some examples and computational results are presented.     

Hydrogen production in a zigzag and straight catalytic wall coated micro channel reactor by CFD modeling

Hydrogen production from steam reforming of methanol for fuel cell application was modeled in a wall coated micro channel reactor by CFD approach. Heat of steam reforming (SR) was supplied from catalytic total oxidation (TOX) of methanol on Cu/ZnO/Al₂O₃ catalyst and Heat conducts from TOX to SR zone through Steel divider wall between two channels. Heat integration was compared in zigzag and straight geometry of microreactor by CFD modeling. The model is two dimensional, steady state and containing five zones: TOX fluid, TOX catalyst layer, steel wall of the channel, SR catalyst layer and SR fluid. Set of partial differential equations (PDEs) including x and y momentum balance, continuity, partial mass balances and energy balance was solved by finite volume method. Stiff reaction rates were considered for methanol total oxidation (TOX), methanol steam reforming (SR), water gas shift (WGS) and methanol decomposition (MD) reactions. The results show that zigzag geometry is better than straight one because heat and mass transfer in zigzag reactor are more than straight. Conversion of methanol in zigzag geometry is greater than straight one. In the outlet of zigzag micro channels, carbon monoxide selectivity is less and hydrogen mole fraction is more than straight one.     

A mixed-integer linear programming model along with an electromagnetism-like algorithm for scheduling job shop production system with sequence-dependent set-up times

In this paper, we consider the job shop scheduling problem (JSS) with non-anticipatory, per-machine, sequence-dependent setup times (SDST). The contributions of this paper are twofold. First, we propose a formulation in the form of a mixed-integer linear programming (MILP) model to modelize the aforementioned problem. Second, we play a pioneering effort for the effective adaptation of a novel metaheuristic known as electromagnetism-like algorithm (EMA) to solve the foregoing problem under the minimization of makespan. Afterwards, we evaluate the performance of the proposed MILP model, the EMA, and other effective metaheuristic algorithms from the literature on two different sets of benchmarks: small-sized and large-sized instances. The rationale behind applying the MILP model and the other algorithms at the small-sized instances is to compare the solutions obtained by the metaheuristic algorithms and the optimal solutions obtained by the MILP model (optimality gap analysis). Subsequently, to demonstrate the competitiveness of the EMA against some effective algorithms in the literature, we conduct an experimental design based on Taillard's benchmark, which is considered as large-sized instances. The purpose of conducting this very experiment is to show whether the acceptable performance of the EMA is transferrable to large-sized instances. The computational evaluations simply manifest the superiority of our proposed algorithm vs the other high-performing algorithms over both small and large instances.