Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm

This paper proposes a Takagi-Sugeno-Kang (TSK) type Neuro-Fuzzy model tuned by a novel metaheuristic optimization algorithm called Hunting Search (HuS). The HuS algorithm is derived based on a model of group hunting of animals such as lions, wolves, and dolphins when looking for a prey. In this study, the structure and parameters of the fuzzy model are encoded into a particle. Thus, the optimal structure and parameters are achieved simultaneously. The proposed method was demonstrated through modeling and control problems, and the results have been compared with other optimization techniques. The comparisons indicate that the proposed method represents a powerful search approach and an effective optimization technique as it can extract the accurate TSK fuzzy model with an appropriate number of rules.     

A new Peltier sensor for measuring the thermal conductivity offluids

This paper describes a new sensor and a new method to measure the thermal conductivity of many fluids. The principal advantage of the device is self compensation against temperature brought about by an appropriate choice of the materials. Moreover, because the sensor uses both the Peltier and Seebeck effects, measurements can be carried out with accuracy according to an average temperature increase of the device lower than 5 K. Operation of the device brings about a very low Joule power (5 mW). A coherent design rationale is formulated and the various stages in the technical development of the sensor are delineated. Several cases are discussed with a view to increasing the applicability of the method. Notable applications include thermal conductivity gauges for measuring pressures in high-vacuum systems, tank gauging for liquids featuring fire hazards, and low velocity measurements occurring in natural convection mechanisms. It is expected that the versatility of the device will result in a wide number of industrial applications     

Experimental Investigation of Passive Fault Tolerant Control for Induction Machine Using Sliding Mode Approach

This paper presents a passive fault tolerant control approach dedicated to stator inter‐turn short‐circuit fault of an induction machine. This control is based on sliding mode strategy and is implemented for validation on real‐time data acquisition and control platform. The proposed work highlights the integral sliding mode controller benefits during healthy and faulty operations. It can make the induction machine outputs to track their desired reference signals in finite‐time and allows to obtain a better dynamic response performances even in presence of fault. Moreover to avoid the use of a flux sensor, a second order sliding mode observer is used to estimate the rotor flux. Since the used observer converges in finite time, the closed‐loop stability of the proposed system (controller+observer) is proved using the Lyapunov stability theorem. Experimental results are conducted for squirrel cage induction machine to highlight the efficiency and applicability of the proposed fault tolerant control.     

Kinetic characterization of p-tert-butylcalix[4,8,12]arene functionalized gold electrode by electrochemical impedance spectroscopy

Kinetic characterizations of impedimetric sensors having as transducers gold electrodes and as recognizing elements p-tert-butylcalix[4,8,12]arene were accomplished. Calixarene membranes were first studied using contact angle measurements. These revealed that all membranes exhibited hydrophobic and rough behaviours. Besides, based on the Van Oss model, surface energy components were determined and the main difference for the basic energy component was related to the calixarene conformation where hydroxyl groups were pointing towards gold surface.Electrochemical impedance spectroscopy results were modeled by appropriate equivalent circuits for the aim of elucidating electrical properties of calixarene functionalized gold electrodes. As results, a fast ionic transfer took place for p-tert-butylcalix[4] based impedimetric sensor, and a slow ionic transfer occurred for both p-tert-butylcalix[8,12] based impedimetric sensors. These behaviours were well accommodated with potentiometric outcomes that were previously encountered.     

3D brain tumor segmentation in MRI images based on a modified PSO technique

Three-dimensional (3D) brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. This is a challenging task due to variation in type, size, location, and shape of tumors. Several methods such as particle swarm optimization (PSO) algorithm formed a topological relationship for the slices that converts 2D images into 3D magnetic resonance imaging (MRI) images which does not provide accurate results and they depend on the number of input sections, positions, and the shape of the MRI images. In this article, we propose an efficient 3D brain tumor segmentation technique called modified particle swarm optimization. Also, segmentation results are compared with Darwinian particle swarm optimization (DPSO) and fractional-order Darwinian particle swarm optimization (FODPSO) approaches. The experimental results show that our method succeeded 3D segmentation with 97.6% of accuracy rate more efficient if compared with the DPSO and FODPSO methods with 78.1% and 70.21% for the case of T1-C modality.     

Model predictive control for continuous lactide ring‐opening polymerization processes

Polylactic acid (PLA) is an attractive environment‐friendly thermoplastic that is bio‐sourced and biodegradable. PLA is industrially produced by the ring‐opening polymerization of lactide. This reaction is sensitive to drifts in the operating conditions and impurities in the raw materials that may affect the reaction rate as well as the polymer properties, which can be very costly in continuous processes. It is therefore crucial to employ a control strategy that allows recovering the nominal conditions and maintaining the desired properties and conversion level in case of drift. Three control strategies are discussed in this paper: proportional‐integral (PI) controller, dynamic optimization, and model predictive control (MPC). The proposed approaches are validated by simulation of a continuous PLA process constituted of three cascade reactors including one loop reactor in the middle. Besides the coupling of inputs and outputs, the process model is highly nonlinear, and the control is done only on the boundaries. The results show that the open‐loop optimization strategy provides better performance compared to the PI controller if the disturbance is assumed to be measured. The MPC also shows superior performances provided that the disturbance is first estimated. A polynomial model is developed to predict the nonmeasured disturbance based on the measured outputs.     

Surface modification of Si/SiO2 by polymeric anion-exchanging membrane: Effect on interfacial morphology and electrochemical properties

PolyTHF as anion exchanger membrane was deposited by dip-coating on SiO₂/Si samples. Wettability measurements allowed monitoring hydrophobicity and conformation of polymeric chains on the silicon-based electrode, during swelling of PolyTHF polymer in water. The reduction of the contact angle after immersion in de-ionized water has shown the possibility for a conformational reorganization of the layer characterized by a reversal of the ionic extremities of the polymeric chains toward the outer surface as the polymer equilibrated with the aqueous medium.

Impedance spectroscopy allowed to model the PolyTHF coating by equivalent electrical circuits, and led to a better understanding of the different processes: swelling of the membrane, morphology of the surface and ionic charge transfer during ionic exchanging.

Variation of R_tc toward ionic potential of NO₃⁻ and AB25 reflect clearly ionic charge transfer and presents a sigmoid shape. The observed dynamic range are 10^− 5 M to 10^− 3.2 M for NO₃⁻ and 10^− 4.8 M to 10^− 3.8 M for AB25. A narrower dynamic range and a weaker sensitivity (10 times) are observed for AB25 detection compared to NO₃⁻ detection.     

Ag‐nanocomposite based on carboxymethylcellulose for humidity detection: Green synthesis and sensing performances

A novel highly sensitive Ag‐nanocomposite for humidity detection has been successfully prepared. Initially, cellulose isolated from Tunisian palm date petiole was converted to carboxymethyl cellulose (CMC) as biomatrix under heterogeneous conditions. The synthesized product was thoroughly characterized by means of FT‐IR spectroscopy, viscosity analysis, and high performance size exclusion chromatography multiangle laser light scattering. CMC was used as reducing and stabilizing agent to prepare CMC‐stabilized silver nanoparticles via a rapid green method. The bioreduction of silver ions under different experimental conditions, including Ag⁺ concentration and pH, was investigated. Optimal experimental conditions provided a long‐term stable colloidal suspension and well‐dispersed spherical shape Ag NPs with a size ranging from 13 to 28 nm. Ag‐nanocomposite coated quartz microbalance crystal was used as sensitive layer for humidity detection. A comparative study showed that the immobilized metallic nanostructures greatly reduced changes in visco‐elastic properties, increased surface area as well as surface local charge density of the CMC. Consequently, sensor performances were greatly enhanced: better stability even at higher relative humidity (RH), good reproducibility and linearity (11–98% RH), low hysteresis characteristics, and rapid response and recovery times (14 and 6 s, respectively) were obtained. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43686.     

A new images segmentation method based on modified particle swarm optimization algorithm

The partitioning of an image into several constituent components is called image segmentation. Many approaches have been developed; one of them is the particle swarm optimization (PSO) algorithm, which is widely used. PSO algorithm is one of the most recent stochastic optimization strategies. In this article, a new efficient technique for the magnetic resonance imaging (MRI) brain images segmentation thematic based on PSO is proposed. The proposed algorithm presents an improved variant of PSO, which is particularly designed for optimal segmentation and it is called modified particle swarm optimization. The fitness function is used to evaluate all the particle swarm in order to arrange them in a descending order. The algorithm is evaluated by performance measures such as run time execution and the quality of the image after segmentation. The performance of the segmentation process is demonstrated by using a defined set of benchmark images and compared against conventional PSO, genetic algorithm, and PSO with Mahalanobis distance based segmentation methods. Then we applied our method on MRI brain image to determinate normal and pathological tissues. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 265–271, 2013