Formal process algebraic modeling,verification, and analysis of an abstract Fuzzy Inference Cloud Service

In cloud computing, services play key roles. Services are well defined and autonomous components. Nowadays, the demand of using Fuzzy inference as a service is increasing in the domain of complex and critical systems. In such systems, along with the development of the software, the cost of detecting and fixing software defects increases. Therefore, using formal methods, which provide clear, concise, and mathematical interpretation of the system, is crucial for the design of these Fuzzy systems. To obtain this goal, we introduce the Fuzzy Inference Cloud Service (FICS) and propose a novel discipline for formal modeling of the FICS. The FICS provides the service of Fuzzy inference to the consumers. We also introduce four novel formal verification tests, which allow strict analysis of certain behavioral disciplines in the FICS as follows: (1) Internal consistency, which analyzes the service in a strict and delicate manner; (2) Deadlock freeness; (3) Divergence freeness; and (4) Goal reach ability. The four tests are discussed and the FICS is verified to ensure that it can pass all these tests.     

Petrophysical data prediction from seismic attributes using committee fuzzy inference system

This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (S_w) and porosity, are predicted from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.     

Multi-criteria decision making for assembly line balancing

Assembly line balancing often has significant impact on performance of manufacturing systems, and is usually a multiple-objective problem. Neither an algorithmic nor a procedural assembly line balancing methodology is usually effective in solving these problems. This article proposes a data envelopment analysis (DEA) approach to solve an assembly line balancing problem. A computer-aided assembly line balancing tool as Flexible Line Balancing software is used to generate a considerable number of solutions alternatives as well as to generate quantitative decision-making unit outputs. The quantitative performance measures were considered in this article. Then DEA was used to solve the multiple-objective assembly line balancing problem. An illustrative example shows the effectiveness of the proposed methodology.     

Simple,fast, and accurate parametric modeling of the coupling and external quality factor for microstrip filter design using active learning method

In the realization step of any microstrip filter according to the required electrical characteristics, coupling factors and external quality factor (Q_ext) are related to the physical parameters of the structure using time consuming full wave simulations. This paper presents a simple, fast, and accurate parametric model of the coupling between the coupled square open loop resonators (SOLRs) and Q_ext of these resonators versus physical parameters of the structure and substrate characteristics utilizing active learning method (ALM). In the modeling process the multi-dimensional functions of coupling factor and Q_ext are broken down into their simpler aspects, their behaviors are extracted and then final model will be constructed by combining these simpler aspects. ALM allows the overall model for coupling factor and Q_ext to be developed through the use of small number of initial data. Once the modeling process is completed it provides a fast and accurate prediction of the required physical parameters for a given coupling factor and Q_ext. Using the constructed model for a distinct SOLR, which its accuracy was validated by comparison with the full wave simulation results a filter was designed and fabricated. Good agreement between measured and simulated response confirms the accuracy of the modeling procedure.     

Strand Current Distribution Modeling in Turbo-generators Using Finite-element Method Based on Proposed Dual Method

Abstract—The necessity of using a 3D model is one of the main problems for modeling Roebel bar transposition in turbo-generators by the finite-element method. This article proposes a 2D dual model for computing strand current distribution in the Roebel bar of turbo-generators. Considering the leakage and mutual inductances for each strand and the coupling inductances between them, the end winding region is modeled in this 2D dual model. In this article, the Roebel bar structure including 16 strands was considered, and these distributions are calculated for different transpositions using the finite-element method. In the finite-element method model, the circulating currents are calculated by subtracting a load current at each strand from the total of strand current. Also, for different transpositions, such as 90°, 180°, 360°, and 540°, the circulating currents are calculated using an analytical method. The obtained strand current distributions from the 2D dual model have good agreement with the obtained results from the analytical method for the 180° and 360° transposed coil models.     

Influence of monovalent cations and CuO nanoparticles on X-nanozeolite in uranium anionic species separation from contaminated drinking water

The main purpose of this work was the modification of NaX nanozeolite using copper oxide nanoparticles and various monovalent cations such as K~+, Cs~+, and Ag~+in order to make the negatively charged zeolite surface accessible for anionic forms of uranium which are the most dominant species of uranium in the contaminated radioactive waters at natural p H. Various methods such as the X-ray fluorescence(XRF), X-ray powder diffraction(XRD), field emission scanning electron microscopy(FE-SEM), transmission electron microscopy(TEM), Fourier transform infrared spectroscopy(FT-IR), and atomic absorption spectroscopy(AAS) were used to characterize the final synthesized absorbents. Batch technique was used to study the adsorption behavior of uranium ions from polluted drinking water by Na X nanozeolite and its modified forms. In order to better understand the performance of them, the results were compared with those that were obtained for synthesizing bulk NaX zeolite and Na-form of clinoptilolite natural zeolite. Preliminary results indicated that uranium sorption increased as the loading level of CuO nanoparticles on NaX nanozeolite increased from 2.1 wt% to 11.2 wt%. In addition,from the obtained data, an increase in uranium removal efficiency resulted as charge/ionic radius ratio of exchanged cation decreased. Also, the effect of contact time, solid–liquid ratio, initial concentration and temperature on the adsorption process was studied. It is worth mentioning that, in this study, the sorption of uranium was performed under natural conditions of pH and the presence of competing cations and anions which are available in drinking waters.     

A committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data: An example from Kangan and Dalan reservoirs in South Pars Gas Field,Iran

Total organic carbon (TOC) content present in reservoir rocks is one of the important parameters, which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon-bearing units. In general, organic-rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher γ-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into 87 training sets to build the CMIS model and 37 testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC.     

Combination of supercritical fluid extraction with dispersive liquid-liquid microextraction for extraction of organophosphorus pesticides from soil and marine sediment samples

Supercritical fluid extraction (SFE) coupled with dispersive liquid-liquid microextraction (DLLME) and followed by gas chromatography-flame ionization detection (GC-FID) was applied for extraction and determination of ultra-trace amounts of seven organophosphorus pesticides (OPPs) (o,o,o-triethyl phosphorothioate, thionazin, sulfotepp, disulfoton, methyl parathion, parathion, and famphur) in soil and marine sediment samples. Supercritical CO₂ at 150 bar, 60 °C, 10 min static and 30 min dynamic extraction times was used to extract the pesticides. The extracts were collected in 1.0 mL of acetonitrile. Seventeen μL of carbon tetrachloride was dissolved in the collecting solvent and the mixture was then injected rapidly into 5.0 mL of aqueous solution. About (5.0 ± 0.2 μL) of sediment phase was collected after centrifuging and finally 2.0 μL of it was injected into gas chromatography (GC) injection port for analyses. The extraction recoveries for the target analytes were in the range of 44.4% and 95.4% and relative standard deviation (RSD%) for four-replicate measurements was below 7.5%. The limit detections of the method for determining the pesticides were in the range of 0.001-0.009 mg kg⁻¹. The method was successfully applied for analysis of OPPs in real soil and marine sediment samples and satisfactory results were obtained.     

Frame time-hopping patterns in multirate optical CDMA networks using conventional and multicode schemes

A class of generalized optical orthogonal codes (OOCs), namely, frame time-hopping (FTH) patterns with an extremely large cardinality, are studied for implementing multirate and multiservice (MR/MS) optical CDMA (OCDMA) networks. Conventional MR/MS methods, namely variable spreading rate and parallel mapping, are considered. Using FTH patterns, the problem of low OOC code cardinality in conventional MR/MS schemes is removed. Moreover, several new multicode methods, using subcode concatenation scheme, are proposed for MR/MS OCDMA. The proposed multicode schemes present flexibility for supporting MR/MS applications such as lower implementation complexity and cost, less need for service synchronization, and finally lower link power budget. Multiple-access performances of the systems are evaluated using saddle-point approximation methods considering photodetector shot-noise, dark current, and circuit thermal noise. The results show that the conventional parallel mapping outperforms the other schemes in high received powers, and the proposed multicode method, using Walsh subcode along with difference modulation, presents the best performance in low received powers for the cases considered.     

Influence of Co substitution on magnetic properties and thermal expansion of Nd6Fe13Si intermetallic compound

We studied the effect of Co substitution in Nd₆Fe_13?xCo_xSi (x = 0, 1) intermetallic compounds on the crystalline structure, magnetic properties and thermal expansion. The partial Co substitution reduces the lattice parameters of the tetragonal Nd₆Fe₁₃Si structure which enhance (3d–3d) exchange interaction and this causes an increase of the Néel temperature (T_N). The spin reorientation temperature (T_SR) of Nd₆Fe₁₂CoSi occurs at a higher temperature with respect to that of the Nd₆Fe₁₃Si compound. Both the linear thermal expansion and the linear thermal expansion coefficient α(T) show remarkable anomalies at T_SR and the magnetic ordering temperature (T_N). T_SR is associated with a shoulder in the linear thermal expansion and near T_N we observe invar-type of behaviour. Both effects are enhanced for the Co substituted sample, giving clear evidence for the enhanced exchange interactions.