Landsat ETM+ imaging for mineral potential mapping: application to Avaj area,Qazvin, Iran

The Avaj area is located in the northwestern part of Iran, on the boundary between the Orumieh-Dokhtar and Sanandaj-Sirjan geological zones. It corresponds to two different geological sub-zones:Abe-Garm, to the north, which is related to the volcanic belt of Orumieh-Dokhtar; and the Razan sub-zone, to the south, which is related to the Sanandaj-Sirjan zone. Lithological aspects of different rock units indicate the presence of four distinct volcanic groups of andesite, basalt, tuff, and dacite. The present research utilizes various Landsat Enhanced Thematic Mapper Plus (ETM+) image-processing techniques, including false colour composite images, colour composite ratio images, and standard principal component (PC) image analysis on six bands and colour composite selective PC images, filtering and supervised classification. Nonetheless, colour composite selective images may be the most reliable method for enhancement of areas with hydrothermal alteration. All the techniques used clearly show the pervasive alteration of kaolinite-argillite in the area. These alterations are mainly related to the andesite, and tuffs have the same trend as fault directions.     

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.     

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.     

The Effect of Hydraulic Fracturing on the Performance of Gas Condensate Systems

Gas condensate reservoirs usually exhibit complex flow behaviors due to the buildup of condensate bank around the wellbore when the bottom-hole pressure falls below the dew point. Hydraulic fracturing improves well productivity by delaying the onset of dew point pressure and also reducing the condensate saturation near the wellbore area. The authors present the results of a study that compared the near wellbore behaviors and productivities of a vertical nonfractured well and a vertical hydraulically fractured well in a low permeability lean gas condensate reservoir. Results show that there is no indication of capillary number effects in this low permeability reservoir because of low production rates.     

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.     

Development of degradation model for urban asphalt pavement

There is a lack of a profound understanding of urban pavement deterioration pattern. This is due to the complexity of traffic conditions and the variety of pavement structures in urban roads. The lack of a suitable deterioration model for the urban pavements limits the possibility of making any scientific and cost-effective repair and maintenance strategy. There is a need for a better understanding of the long-term behaviour of urban pavements by which predictive pavement condition models can be derived and consequently a suitable maintenance management system can be built. In response to this need, a comprehensive field study was performed in three Iranian cosmopolitan cities. Pervasive pavement damages were defined and an urban pavement condition index was established. A deterioration model was developed by monitoring and analysing the conditions of road pavements in a period of four years. This model varies as the structural and loading conditions of the pavement change. The efficiency and practicability of the model in predicting the conditions of the pavements were illustrated.