A hybrid genetic algorithm–adaptive network-based fuzzy inference system in prediction of wave parameters

An important issue in application of fuzzy inference systems (FISs) to a class of system identification problems such as prediction of wave parameters is to extract the structure and type of fuzzy if–then rules from an available input–output data set. In this paper, a hybrid genetic algorithm–adaptive network-based FIS (GA–ANFIS) model has been developed in which both clustering and rule base parameters are simultaneously optimized using GAs and artificial neural nets (ANNs). The parameters of a subtractive clustering method, by which the number and structure of fuzzy rules are controlled, are optimized by GAs within which ANFIS is called for tuning the parameters of rule base generated by GAs. The model has been applied in the prediction of wave parameters, i.e. wave significant height and peak spectral period, in a duration-limited condition in Lake Michigan. The data set of year 2001 has been used as training set and that of year 2004 as testing data. The results obtained by the proposed model are presented and analyzed. Results indicate that GA–ANFIS model is superior to ANFIS and Shore Protection Manual (SPM) methods in terms of their prediction accuracy.     

Memristive fuzzy edge detector

Fuzzy inference systems always suffer from the lack of efficient structures or platforms for their hardware implementation. In this paper, we tried to overcome this difficulty by proposing a new method for the implementation of the fuzzy rule-based inference systems. To achieve this goal, we have designed a multi-layer neuro-fuzzy computing system based on the memristor crossbar structure by introducing a new concept called the fuzzy minterm. Although many applications can be realized through the use of our proposed system, in this study we only show how the fuzzy XOR function can be constructed and how it can be used to extract edges from grayscale images. One main advantage of our memristive fuzzy edge detector (implemented in analog form) compared to other commonly used edge detectors is it can be implemented in parallel form, which makes it a powerful device for real-time applications.     

Heat Transfer from Pulsating Laminar Impingement Slot Jet on a Flat Surface with Inlet Velocity: Sinusoidal and Square Wave

Heat transfer from a pulsating laminar impingement slot jet on a flat surface was investigated numerically and experimentally. Inlet velocity was considered sinusoidal velocity and square wave velocity. Experimental studies were done only for the sinusoidal velocity state. An inverse heat conduction method, conjugated gradient method with adjoint equation, was used for the experimental estimation of the local heat transfer coefficient along the target surface. Effect of the square wave velocity of the laminar impingement slot jet was studied numerically. The results show pulsations in flow change flow patterns and the thermal boundary layer thickness because of the newly forming thermal boundary layer is extremely small each time the flow is resumed. Heat transfer rate in this state enhances due to pulsating inlet velocity in comparison with steady state. Heat transfer increases with increasing pulsation amplitude. Enhancement in mean heat transfer on the target plate for sinusoidal velocity is rather than square wave velocity.     

Biosorption of Pb(II) using Gundelia tournefortii: Kinetics,equilibrium, and thermodynamics

In this paper, the feasibility of Gundelia tournefortii was studied as a novel, high-capacity biosorbent for removing lead ions from synthetic wastewater in a batch system. The effects of various parameters such as temperature, initial concentration, initial pH, biosorbent dosage, and contact time were investigated. Based on batch results, the optimum operating conditions were found to be pH 5, biosorbent dosage of 25 mg, and temperature of 20°C in the range of lead initial concentrations from 5 to 100 mg/L. The equilibrium contact time was 60 min. The biosorption mechanism can be well described by the Langmuir isotherm with a monolayer maximum adsorption capacity of 144.928 (mg/g) at 20°C and a pseudo-second-order kinetic model. Thermodynamic studies proved that the sorption process was physical, spontaneous, feasible, random, and exothermic. In the second step, the ability of artificial neural network (ANN) to predict the adsorption capacity of Gundelia tournefortii for the removal of Pb(II) from aqueous solution was examined. The model was developed using a three-layer feed-forward back-propagation (BP) network with 5, 12, and 1 neurons in the first, second, and third layers, respectively. The Levenberg–Marquardt BP training algorithm (LMA) was found to be the best BP algorithm with a minimum mean squared error of 0.000867 and a minimum relative squared error of 0.032771. The comparison between the results of ANN and experimental data showed that ANN has a superior performance (R²= of 0.998) in the prediction of the Pb(II) removal process.     

Experimental and theoretical investigations on the carbon dioxide gas hydrate formation kinetics at the onset of turbidity regarding CO2 capture and sequestration processes

The carbon dioxide gas hydrate formation kinetics at the onset of turbidity is experimentally and theoretically investigated. It is shown that the time-dependent heterogeneous nucleation and growth kinetics are simultaneously governing the hydrate formation process at the onset of turbidity. A new approach is also presented for determination of gas hydrate-liquid interfacial tension. The CO₂ hydrate-liquid interfacial tension according to the suggested heterogeneous nucleation mechanism is found to be about 12.7 mJ/m². The overall average absolute deviation between predicted and measured CO₂ molar consumption is about 0.61%, indicating the excellent accuracy of the proposed model for studying the hydrate-based CO₂ capture and sequestration processes over wide ranges of pressures and temperatures.     

Three-stage hybrid osmotic–intermittent microwave–convective drying of apple at low temperature and short time

ABSTRACT

In recent years, intermittent microwave coupled with hot air-drying has been used increasingly, thanks to considerable improvements observed in drying properties. The aim of this study was to investigate the effect of process of drying apple pretreated osmotically with sucrose solution at five concentrations of 0 (control), 10, 30, 50, and 70% (w/w), using intermittent microwave at four power levels of 0 (control), 360, 600, and 900?W, four pulse ratios of 1, 2, 3, and 4, and convective hot air (40°C) on drying kinetics, effective moisture diffusion coefficient, shrinkage, bulk density, rehydration ratio, and energy consumption. Results showed that the three-stage hybrid osmotic–intermittent microwave–convective drying of apple at low temperature yielded higher drying rates (with 41.5% decrease in drying time) and improved quality of final product. The effective moisture diffusion coefficient increased with an increase in power, pulse ratio, and the concentration of osmotic solution. Furthermore, shrinkage, bulk density, and energy consumption of the samples decreased with an increase in power, pulse ratio, and the concentration of osmotic solution. In summary, the use of intermittent microwave coupled with forced convection of hot air (at low temperature) in drying of apple pretreated by sucrose osmotic solution led to products with improved properties in terms of both quality and quantity.     

Polyacrolein/mesoporous silica nanocomposite: Synthesis,thermal stability and covalent lipase immobilization

In this work, new polyacrolein/MCM-41 nanocomposites with good phase mixing behavior were prepared through an emulsion polymerization technique. Mesoporous silica was synthesized by in situ assembly of tetraethyl orthosilicate (TEOS) and cetyl trimethyl ammonium bromide (CTAB). The structure and properties of polyacrolein containing nanosized MCM-41 particle (5 and 10 wt%), were investigated by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction, Dynamic light scattering (DLS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), N₂ adsorption techniques, and thermogravimetric (TGA) analyses. The SEM images from the final powder have revealed good dispersion of the MCM-41 nanoparticles throughout polymeric matrix with no distinct voids between two phases. The results indicated that the thermal properties of the nanocomposite were enhanced by addition of MCM-41. Thermomyces lanuginosa lipase (TLL) was used as a model biocatalyst and successfully immobilized with polyacrolein and the nanocomposite via covalent bonds with the aldehyde groups. The activity between free enzyme, polyacrolein, and MCM-41 nanocomposite (10 wt%)-immobilized TLL was compared. The immobilized lipase with the nanocomposite shows better operational stability such as pH tolerance, thermal and storage stability. In addition, the immobilized lipase with the nanocomposite can be easily recovered and retained at 74% of its initial activity after 15 time reuses.     

Adaptive fault-tolerant DVFS with dynamic online AVF prediction

Advances in silicon technology and shrinking the feature size to nanometer levels make random variations and low reliability of nano-devices the most important concern for fault-tolerant design. Design of reliable and fault-tolerant embedded processors is mostly based on developing techniques that compensate reliability shortcomings by adding hardware or software redundancy. The recently-proposed redundancy adding techniques are generally applied uniformly to all parts of a system and lead to heavy overheads and inefficiencies in terms of performance, power, and area. Efficient employment of non-uniform redundancy becomes possible when a quantitative analysis of a system behavior while encountering transient faults is provided. In this work, we present a quantitative analysis of the behavior of an embedded processor regarding transient faults and propose a new approach that accurately predicts the architecture vulnerability factor (AVF) in real-time. Another critical concern in design of new-silicon processors is power consumption issue. Dynamic voltage and frequency scaling (DVFS) is an effective method for controlling both energy consumption and performance of a system. Since rate of radiation-induced transient faults depends on operating frequency and supply voltage, DVFS techniques are recently shown to have compromising effects on electronic system reliability. Therefore, ignoring the effects of voltage scaling on fault rate could considerably degrade the system reliability. Here, by exploiting the proposed online AVF prediction methodology and based on analytic derivation, we propose a reliability-aware adaptive dynamic voltage and frequency scaling (DVFS) approach in case study of Multi-Processor System on Chip (MPSoC) with Multiple Clock Domain (MCD) pipeline architectures in which the frequency and voltage are scaled by simultaneously considering all three of power consumption, reliability, and performance. Comparing to the traditional methods of reliability-aware DVFS systems, the proposed reliability-aware DVFS method yields 50% better power saving at the same reliability level.     

Controller design based on μ analysis and PSO algorithm

In this paper an evolutionary algorithm is employed to address the controller design problem based on μ analysis. Conventional solutions to μ synthesis problem such as D–K iteration method often lead to high order, impractical controllers. In the proposed approach, a constrained optimization problem based on μ analysis is defined and then an evolutionary approach is employed to solve the optimization problem. The goal is to achieve a more practical controller with lower order. A benchmark system named two-tank system is considered to evaluate performance of the proposed approach. Simulation results show that the proposed controller performs more effective than high order H_∞ controller and has close responses to the high order D–K iteration controller as the common solution to μ synthesis problem.