Direct adaptive fuzzy control with membership function tuning

In this paper, a direct self‐structured adaptive fuzzy control is introduced for the class of nonlinear systems with unknown dynamic models. Control is accomplished by an adaptive fuzzy system with a fixed number of rules and adaptive membership functions. The reference signal and state errors are used to tune the membership functions and update them instantaneously. The Lyapunov synthesis method is also used to guarantee the stability of the closed loop system. The proposed control scheme is applied to an inverted pendulum and a magnetic levitation system, and its effectiveness is shown via simulation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Synthesis and characterization of hydrophilic nanocomposite coating on glass substrate

Hydrophilic coatings based on 3‐glicidoxy propyl trimethoxy silane (GPTMS) and polyethylene glycol (PEG) were prepared with the incorporation of tetramethoxysilane (TMOS) and silica nanoparticle colloidal suspension by a sol–gel process. Characterization of the coatings has been performed by Fourier Transform Infrared (FTIR) and Attenuated Total Reflectance Infrared (ATR‐IR) techniques. Morphological properties were characterized by Scanning Electron Microscopy (SEM). The distribution of Si atoms in the hybrid system was obtained by Si mapping. The particle size in sol solution of the coating was measured by light scattering analyzer. Optical properties were characterized by using UV–vis spectrophotometer. The hydrophilicity of the coating was determined by contact angle measurements, and also the results have been confirmed by surface energy and water uptake investigations. The obtained results indicate that the surfactants affected the contact angles remarkably but did not change the transparency. It has been found that applying silica nano particles leads to coatings with different properties than those using TMOS, while siloxane contents were the same in these two set of coatings. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102: 5322–5329, 2006     

Alternating active-phase algorithm for multimaterial topology optimization problems: a 115-line MATLAB implementation

Structural and Multidisciplinary Optimization - A new algorithm for the solution of multimaterial topology optimization problems is introduced in the present study. The presented method is based on...     

Semisupervised dimensionality reduction for hyperspectral images based on the combination of semisupervised learning and metric learning

This paper presents a semisupervised dimensionality reduction (DR) method based on the combination of semisupervised learning (SSL) and metric learning (ML) (CSSLML-DR) in order to overcome some existing limitations in HSIs analysis. Specifically, CSSML focuses on the difficulties of high dimensionality of hyperspectral images (HSIs) data, the insufficient number of labelled samples and inappropriate distance metric. CSSLML aims to learn a local metrics under which the similar samples are pushed as close as possible, and simultaneously, the different samples are pulled away as far as possible. CSSLML constructs two local-reweighted dynamic graphs in an iterative two-steps approach: L-step and V-step. In L-step, the local between-class and within-class graphs are updated. In V-step, the transformation matrix and the reduced space are updated. The algorithm is repeated until a stopping criterion is satisfied. Experimental results on two well-known hyperspectral image data sets demonstrate the superiority of CSSLML algorithm compared to some traditional DR methods.     

Deriving operating policies for multi-objective reservoir systems: Application of Self-Learning Genetic Algorithm

Optimal multi-reservoir operation is a multi-objective problem in nature and some of its objectives are nonlinear, non-convex and multi-modal functions. There are a few areas of application of mathematical optimization models with a richer or more diverse history than in reservoir systems optimization. However, actual implementations remain limited or have not been sustained.Genetic Algorithms (GAs) are probabilistic search algorithms that are capable of solving a variety of complex multi-objective optimization problems, which may include non-linear, non-convex and multi-modal functions. GA is a population based global search method that can escape from local optima traps and find the global optima. However GAs have some drawbacks such as inaccuracy of the intensification process near the optimal set.In this paper, a new model called Self-Learning Genetic Algorithm (SLGA) is presented, which is an improved version of the SOM-Based Multi-Objective GA (SBMOGA) presented by Hakimi-Asiabar et al. (2009) [45]. The proposed model is used to derive optimal operating policies for a three-objective multi-reservoir system. SLGA is a new hybrid algorithm which uses Self-Organizing Map (SOM) and Variable Neighborhood Search (VNS) algorithms to add a memory to the GA and improve its local search accuracy. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm can enhance the local search efficiency in the Evolutionary Algorithms (EAs).To evaluate the applicability and efficiency of the proposed methodology, it is used for developing optimal operating policies for the Karoon-Dez multi-reservoir system, which includes one-fifth of Iran's surface water resources. The objective functions of the problem are supplying water demands, generating hydropower energy and controlling water quality in downstream river.     

Design of sliding mode and model reference adaptive control strategies for multivariable tape transport mechanism: a performance comparison

This paper presents sliding mode control and model reference adaptive control strategies for the tape transport mechanism. A nonlinear multivariable MIMO model of the process, consisting of take-up and supply reel servos for tape tension control and capstan servo for speed control is considered. The sliding mode control is applied for the nonlinear dynamic model of the process, while the model reference adaptive control deals with the linearized one. Moreover, in order to associate with the realistic model of system, design of controllers is accomplished with respect to parametric uncertainties. It is shown that both control strategies can guarantee asymptotic stability of the closed-loop system and tracking of desired outputs with the appropriate pace in the presence of uncertainties. Finally, simulation results demonstrate the effectiveness of the proposed controllers.     

Efficient biodegradation of naphthalene by a newly characterized indigenous Achromobacter sp. FBHYA2 isolated from Tehran Oil Refinery Complex

A bacterial strain, FBHYA2, capable of degrading naphthalene, was isolated from the American Petroleum Institute (API) separator of the Tehran Oil Refinery Complex (TORC). Strain FBHYA2 was identified as Achromobacter sp. based on physiological and biochemical characteristics and also phylogenetic similarity of 16S rRNA gene sequence. The optimal growth conditions for strain FBHYA2 were pH 6.0, 30 °C and 1.0% NaCl. Strain FBHYA2 can utilize naphthalene as the sole source of carbon and energy and was able to degrade naphthalene aerobically very fast, 48 h for 96% removal at 500 mg/L concentration. The physiological response of Achromobacter sp., FBHYA2 to several hydrophobic chemicals (aliphatic and aromatic hydrocarbons) was also investigated. No biosurfactant was detected during bacterial growth on any aliphatic/aromatic hydrocarbons. The results of hydrophobicity measurements showed no significant difference between naphthalene- and LB-grown cells. The capability of the strain FBHYA2 to degrade naphthalene completely and rapidly without the need to secrete biosurfactant may make it an ideal candidate to remediate polycyclic aromatic hydrocarbon (PAH)-contaminated sites.     

Roles of Sexual Objectification Experiences and Internalization of Standards of Beauty in Eating Disorder Symptomatology: A Test and Extension of Objectification Theory.

This study extends the literature on eating disorder symptomatology by testing, based on extant literature on objectification theory (B. L. Fredrickson & T. Roberts, 1997) and the role of sociocultural standards of beauty (e.g., L. J. Heinberg, J. K. Thompson, & S. Stormer, 1995), a model that examines (a) links of reported sexual objectification experiences to eating disorder-related variables and (b) the mediating roles of body surveillance, body shame, and internalization of sociocultural standards of beauty. Consistent with hypotheses, with a sample of 221 young women, support was found for a model in which (a) internalization of sociocultural standards of beauty mediated the links of sexual objectification experiences to body surveillance, body shame, and eating disorder symptoms, (b) body surveillance was an additional mediator of the link of reported sexual objectification experiences to body shame, and (c) body shame mediated the links of internalization and body surveillance to disordered eating. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Investigating the effect of nano-silica on efficiency of the foam in enhanced oil recovery

Due to the vast production of crude oil and consequent pressure drops through the reservoirs, secondary and tertiary oil recovery processes are highly necessary to recover the trapped oil. Among the different tertiary oil recovery processes, foam injection is one of the most newly proposed methods. In this regard, in the current investigation, foam solution is prepared using formation brine, C₁₉TAB surfactant and air concomitant with nano-silica (SiO₂) as foam stabilizer and mobility controller. The measurements revealed that using the surfactant-nano SiO₂ foam solution not only leads to formation of stable foam, but also can reduce the interfacial tension mostly considered as an effective parameter for higher oil recovery. Finally, the results demonstrate that there is a good chance of reducing the mobility ratio from 1.12 for formation brine and reservoir oil to 0.845 for foam solution prepared by nanoparticles.