Multi-objective discrete urban road network design

This paper addresses the problem of designing urban road networks in a multi-objective decision making framework. Given a base network with only two-way links, and the candidate lane addition and link construction projects, the problem is to find the optimal combination of one-way and two-way links, the optimal selection of network capacity expansion projects, and the optimal lane allocations on two-way links to optimize the reserve capacity of the network, and two new travel time related performance measures. The problem is considered in two variations; in the first scenario, two-way links may have different numbers of lanes in each direction and in the second scenario, two-way links must have equal number of lanes in each direction. The proposed variations are formulated as mixed-integer programming problems with equilibrium constraints. A hybrid genetic algorithm, an evolutionary simulated annealing, and a hybrid artificial bee colony algorithm are proposed to solve these two new problems. A new measure is also proposed to evaluate the effectiveness of the three algorithms. Computational results for both problems are presented.     

Using class-based feature selection for the classification of hyperspectral data

The rapid advances in hyperspectral sensing technology have made it possible to collect remote-sensing data in hundreds of bands. However, the data-analysis methods that have been successfully applied to multispectral data are often limited in achieving satisfactory results for hyperspectral data. The major problem is the high dimensionality, which deteriorates the classification due to the Hughes Phenomenon. In order to avoid this problem, a large number of algorithms have been proposed, so far, for feature reduction. Based on the concept of multiple classifiers, we propose a new schema for the feature selection procedure. In this framework, instead of using feature selection for whole classes, we adopt feature selection for each class separately. Thus different subsets of features are selected at the first step. Once the feature subsets are selected, a Bayesian classifier is trained on each of these feature subsets. Finally, a combination mechanism is used to combine the outputs of these classifiers. Experiments are carried out on an Airborne Visible/Infrared Imaging Spectroradiometer (AVIRIS) data set. Encouraging results have been obtained in terms of classification accuracy, suggesting the effectiveness of the proposed algorithms.     

Robustification of input redundant feedback systems using robust actuator weighting in the control allocation problem

In this article, a new methodology for robust actuator weighting in the control allocation (CA) problem of input redundant feedback systems is addressed. The methodology is based on the control structural properties of the plant which were previously used for control configuration selection. Robust performance (RP) measures including H _∞ norm and structured singular value of the closed-loop system are used in this article. The capability of the approach is proven with application to lateral dynamics control of the vehicle over-actuated with front and rear steering systems. Employing the RP measures, it is concluded that the vehicle feedback control with front steering angles gives superior RP properties in comparison with the feedback loop of the rear steering angles. Based on these results, the penalty weightings in the cost function of the CA unit are determined. Simulation results based on nonlinear seven degrees of freedom vehicle handling model show that the selection of penalty weightings in the CA unit based on the RP properties of the control inputs (front and rear steering angles) improves the RP of the closed-loop.     

Quantifying nonclassicality of correlations based on the concept of nondisruptive local state identification

A bipartite state is classical with respect to party A if and only if party A can perform nondisruptive local state identification (NDLID) by a projective measurement. Motivated by this we introduce a class of quantum correlation measures for an arbitrary bipartite state. The measures utilize the general Schatten p-norm to quantify the amount of departure from the necessary and sufficient condition of classicality of correlations provided by the concept of NDLID. We show that for the case of Hilbert–Schmidt norm, i.e., \(p=2\), a closed formula is available for an arbitrary bipartite state. The reliability of the proposed measures is checked from the information-theoretic perspective. Also, the monotonicity behavior of these measures under LOCC is exemplified. The results reveal that for the general pure bipartite states these measures have an upper bound which is an entanglement monotone in its own right. This enables us to introduce a new measure of entanglement, for a general bipartite state, by convex roof construction. Some examples and comparison with other quantum correlation measures are also provided.     

Morphology, rheology and dynamic mechanical properties of PP/EVA/clay nanocomposites

This paper reports on morphology, rheology and dynamic mechanical properties of polypropylene (PP)/ethylene vinyl acetate (EVA) copolymer/clay nanocomposite system prepared via a single step melt compounding process using a twin screw micro-compounder. Scanning electron microscopic (SEM) investigations revealed that the dispersed phase droplet size was reduced with incorporation of an organo-modified montmorillonite (OMMT). This reduction was more significant in presence of a maleated PP (PP-g-MAH) used as compatibilizer. Phase inversion in the compatibilized blends caused a further decrease in PP droplet size. The OMMT gallery spacing was higher in nanocomposites with EVA as matrix which could be attributed to higher tendency of OMMT nanoparticles towards EVA rather than PP. This enhanced tendency was confirmed by rheological analysis too. Transmission electron microscopy (TEM) results also showed that the majority of OMMT nanoparticles were localized on the interface and within EVA droplets. According to dynamic mechanical analysis, the compatibilized nanocomposites showed higher storage and loss moduli due to better dispersion of OMMT layers. The modulus enhancement of nanocomposites as a function of OMMT volume fraction was modeled by Halpin-Tsai’s-Nielsen expression of modulus for nanocomposites. The results of modeling suggested that the aspect ratio of the intercalated OMMT, in the form of Einstein coefficient (K _E), plays a determining role in the modulus enhancement of nanocomposites.     

A skewed unscented Kalman filter

State estimation of nonlinear systems is a challenging task, especially when the Gaussian approximation fails. The unscented Kalman filter was proposed to deal with state estimation of nonlinear systems. We modify the traditional unscented Kalman filter to capture the third-order moment (skewness) of the state vector. Methods are also proposed to reduce the computation time of the suggested approach, and showing that the proposed algorithm is as fast as the unscented Kalman filter. Simulation results confirm that the method is better than, or at least as good as, the unscented Kalman filter.     

A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System

This paper presents a new and accurate algorithm for locating faults in a combined overhead transmission line with underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses 10 ANFIS networks and consists of 3 stages, including fault type classification, faulty section detection and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., fundamental component of three phase currents and zero sequence current. Another ANFIS network is used to detect the faulty section, whether the fault is on the overhead line or on the underground cable. Other eight ANFIS networks are utilized to pinpoint the faults (two for each fault type). Four inputs, i.e., the dc component of the current, fundamental frequency of the voltage and current and the angle between them, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on each part of the combined line. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances. Simulation results confirm that the proposed method can be used as an efficient means for accurate fault location on the combined transmission lines.     

Chemical vapor deposition of poly(3‐alkylthiophene) nanoparticles on fabric: Chemical and electrochemical characterization

Chemical vapor deposition of poly(3‐methylthiophene) and poly (3‐hexylthiophene) as conductive polymers on the surface of polyester fabrics was successfully obtained. Fourier transform infrared spectroscopy confirmed the formation of polymers on surface of fabrics (the fingerprint of polythiophenes, υ 600–1500 cm^?1). The uniformity of deposition and nanoparticles (average size of 60 nm) were proved with scanning electron microscopy. Electrochemical impedance spectroscopy showed that P3HT‐coated samples offer higher conductivity in compared to P3MT‐coated samples. The impedance modulus of P3HT‐coated samples was lowered nine times to that of row materials and reached to c8000 Ω. The samples have also shown electrochromic properties under electrical current, changing its color from yellowish green at 0 V to dark green at +12 V for poly (3‐hexylthiophene) samples and from brown at 0 V to red at +12 V for poly(3‐methylthiophene)‐coated fabrics (V = 0 V, λ = 450 nm; V = 12 V, λ = 650 nm). © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40673.     

LaMer diagram approach to study the nucleation and growth of Cu2O nanoparticles using supersaturation theory

Uptake to cuprous oxide (Cu₂O) nanoparticle synthesis with various particle sizes and shapes via supersaturation chemistry approach (LaMer model) has been conducted. Ascorbic acid and maltodextrine as reducing agents and polyvinylpyrrolidone (PVP) as a surfactant were utilized for synthesis of Cu₂O nanoparticles in aqueous solution. The narrow particle size range was achieved by controlling the kinetics of nucleation and growth of particles to satisfy LaMer theory. This mean was performed utilizing different reducing agents (ascorbic acid and maltodextrin) and also, changing the reducing agent addition condition. The results showed the reducing agent addition condition, varying the size of Cu₂O nanoparticles from 89 nm to 74 nm for drop-wisely and at-once routes, respectively. The samples were characterized by XRD, SEM, and UV-Vis spectroscopy. The results indicate the shape of as-prepared cuprous oxide nanoparticles have close relationship with thermodynamic and kinetic conditions, and also reducing addition condition.     

Improved electrochemical performance of nitrocarburised stainless steel by hydrogenated amorphous carbon thin films for bone tissue engineering

In this study, hydrogenated amorphous carbon thin films, structurally similar to diamond‐like carbon (DLC), were deposited on the surface of untreated and plasma nitrocarburised (Nitrocarburizing‐treated) stainless steel medical implants using a plasma‐enhanced chemical vapour deposition method. The deposited DLC thin films on the nitrocarburising‐treated implants (CN+DLC) exhibited an appropriate adhesion to the substrates. The results clearly indicated that the applied DLC thin films showed excellent pitting and corrosion resistance with no considerable damage on the surface in comparison with the other samples. The CN+DLC thin films could be considered as an efficient approach for improving the biocompatibility and chemical inertness of metallic implants.Inspec keywords: tissue engineering, bone, biomedical materials, electrochemistry, amorphous state, carbon, hydrogen, thin films, plasma CVD, adhesion, corrosion resistance, surface hardeningOther keywords: electrochemical performance, plasma nitrocarburised stainless steel medical implants, hydrogenated amorphous carbon thin films, bone tissue engineering, plasma‐enhanced chemical vapour deposition method, adhesion, corrosion resistance, biocompatibility, chemical inertness, metallic implants, C:H