Sharif Digital Repository

Purpose: The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits. Design/methodology/approach: In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and... 

In this study, a reliable and predictive model namely, least-squares support vector machine (LS-SVM) was developed to predict dye removal efficiency. Four LS-SVM models have been developed and tested using more than 630 series of experimental data which were obtained from our previous paper. These data consist of adsorbate type, adsorbent dosage, initial dye concentration, salt, absorbance time and dye removal efficiency. Direct Red 31 (DR31), Direct Green 6 (DG6) and Acid Blue (AB92) were used as a model dyes. The results show that the developed model is more accurate and reliable with the average absolute relative deviation of 0.678%, 0.877%, 0.581% and 0.978% for single systems and... 

Localization is a crucial problem in wireless sensor networks and most of the localization algorithms given in the literature are non-adaptive and designed for fixed sensor networks. In this paper, we propose a learning based localization algorithm for mobile wireless sensor networks. By this technique, mobility in the network will be discovered by two crucial methods in the beacons: position and distance checks methods. These two methods help to have accurate localization and constrain communication just when it is necessary. The proposed method localizes the nodes based on connectivity information (hop count), which doesn't need extra hardware and is cost efficient. The experimental... 

Viscosity of crude oil is a fundamental factor in simulating reservoirs, forecasting production as well as planning thermal enhanced oil recovery methods which make its accurate determination necessary. Experimental determination of reservoir oil viscosity is costly and time consuming. Hence, searching for quick and accurate determination of reservoir oil viscosity is inevitable. The objective of this study is to present a reliable, and predictive model namely, Least-Squares Support Vector Machine (LSSVM) to predict reservoir oil viscosity. To this end, three LSSVM models have been developed for prediction of reservoir oil viscosity in the three regions including, under-saturated, saturated... 

Prediction of accident frequency based on traffic and roadway characteristics has been a very significant tool in the field of traffic management. The accident frequencies on 185 roadway segments of the city of Mashhad, Iran, for the year 2007, were used to develop accident prediction models. Negative Binomial Regression, Zero Inated Negative Binomial Regression, Support Vector Machine and Back-Propagation Neural Network models were used to fit the accident data. Both fitting and predicting abilities of the models were evaluated through computing error values. Results show that the NBR model is the most effective model for predicting the number of accidents because of its low prediction and... 

Active Learning Method (ALM) is a powerful fuzzy soft computing tool, developed originally in order to promote an engineering realization of human brain. This algorithm, as a macro-level brain imitation, has been inspired by some behavioral specifications of human brain and active learning ability. ALM is an adaptive recursive fuzzy learning algorithm, in which a complex Multi Input, Multi Output system can be represented as a fuzzy combination of several Single-Input, Single-Output systems. SISO systems as associative layer of algorithm capture partial spatial knowledge of sample data space, and enable a granular knowledge resolution tuning mechanism through the learning process. The... 

Demand forecasting plays an important role as a decision support tool in power system management, especially in smart grid and liberalized power market. In this paper, a demand forecasting method is presented by using support vector regression (SVR). The proposed method is applied to practical hourly data of the Greater Tehran Electricity Distribution Company. The SVR parameters are selected by using a grid optimization process and an investigation on different kernel functions. Moreover, correlation analysis is used to find exogenous variables. Acceptable accuracy of load prediction is shown by comparing the result of SVR model to that of the artificial neural networks and the actual data,... 

SrFe12-xTix/2Zn x/2O19 (x = 0-2.5) powders were synthesized by use of chlorides through co-precipitation method. The obtained powders were then milled by high energy ball mill to crash hard agglomerates and achieve nanoparticles. In order to evaluate microwave absorption versus frequency, composites including ferrite, as a filler, and matrix of polyvinylchloride (PVC) with weight ratio of 70% ferrite were prepared. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FE-SEM), vibrating sample magnetometer (VSM) and vector network analyzer (VNA) were employed to study the structural, magnetic and microwave absorption properties... 

In this paper, a fast phoneme recognition system is introduced based on sparse representation. In this approach, the phoneme recognition is fulfilled by Viterbi decoding on support vector machines (SVM) output probability estimates. The candidate classes for classification are adaptively pruned by a k-dimensional (KD) tree search followed by a sparse representation (SR) based class selector with adaptive number of classes. We applied the proposed approach to introduce a phoneme recognition system and compared it with some well-known phoneme recognition systems according to accuracy and complexity issues. By this approach, we obtain competitive phoneme error rate with promising computational... 

Vapor liquid equilibrium (VLE) data has significant role in designing processes which include vapor and liquid in equilibrium. Since it is impractical to measure equilibrium data at any desired temperature and pressure, particularly near critical region, thermodynamic models based on equation of state (EOS) are usually used for VLE estimating. In recent years due to the development of numerical tools like artificial intelligence methods, VLE prediction has been find new alternatives. In the present study a novel method called Least-Squares Support Vector Machine (LSSVM) used for predicting bubble/dew point pressures of binary mixtures containing carbon dioxide (CO 2) + cyclic compounds as... 

Demands for hydrocarbon production have been increasing in recent decades. As a tertiary production processes, chemical flooding is one of the effective technologies to increase oil recovery of hydrocarbon reservoirs. Retention of surfactants is one of the key parameters affecting the performance and economy of a chemical flooding process. The main parameters contribute to surfactant retention are mineralogy of rock, surfactant structure, pH, salinity, acidity of the oil, microemulsion viscosity, co-solvent concentration, and mobility. Despite various theoretical studies carried out so far, a comprehensive and reliable predictive model for surfactant retention is still found lacking. In this... 

This paper introduces the first pattern-based Persian Temporal Relation Classifier (PTRC) that finds the type of temporal relations between pairs of events in the Persian texts. The proposed system uses support vector machines (SVMs) equipped by combinations of simple, convolution tree, and string subsequence kernels (SSK). In order to evaluate the algorithm, we have developed a Persian TimeBank (PTB) corpus. PTRC not only increases the performance of the classification by applying new features and SSK, but also alleviates the probable adverse effects of the Free Word Orderness (FWO) of Persian on temporal relation classification. We have also applied our proposed algorithm to two standard... 

In this study, the authors investigate the performance of optical code-division multiple access (OCDMA) systems with belief propagation (BP)-based receivers. They propose three receivers for the optical fibre channel that provide a trade-off between detecting complexity and system performance. The first proposed receiver achieves a performance very close to the so-called known interference lower bound. The second receiver exhibits a considerably less complexity at the expense of a slight degradation in performance. They show that the third BP-based receiver, which is a simplified version of the second receiver, is surprisingly the same as the so-called multistage detector in OCDMA systems.... 

This paper is aimed at proposing a current based vector control model of the brushless doubly fed induction generator, modelling the presented control method, as well as implementing the proposed algorithm by DSP. In order to achieve the purpose, by presenting a detailed coupled circuit model of BDFIG, the vector model and then the current based vector control algorithm of the mentioned machine are acquired. The way of independent control of torque and power, and also the structure of speed controller amongst the proposed control model are discussed. Additionally, the concepts behind the proposed structure of the speed control system and the way of determining the model parameters are... 

Integrity verification or forgery detection of an image is a difficult procedure, since the forgeries use various transformations to create an altered image. Pixel mapping transforms, such as contrast enhancement, histogram equalisation, gamma correction and so on, are the most popular methods to improve the objective property of an altered image. In addition, fabricators add Gaussian noise to the altered image in order to remove the statistical traces produced because of pixel mapping transforms. A new method is introduced to detect and classify four various categories including original, contrast modified, histogram-equalised and noisy images. In the proposed method, the absolute value of... 

In this paper the direct power control methods of doubly fed induction generator in wind turbine applications are studied. In the methods under study, the proper voltage space vector of the rotor side converter is selected using a switching table which is derived from flux position and the difference between the measured and reference stator active and reactive powers. Various simulations are performed in Matlab/Simulink software on a DFIG system in order to investigate the dynamic performance and robustness of the proposed control methods against machine internal parameters variations  

In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper... 

Background: The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification.Results: Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection... 

Precipitation of asphaltene leads to rigorous problems in petroleum industry such as: wettability alterations, relative permeability reduction, blockage of the flow with additional pressure drop in wellbore tubing, upstream process facilities and surface pipelines. Experimentally determination of the asphaltene precipitation is costly and time consuming. Therefore, searching for some other quick and accurate methods for determination of the asphaltene precipitation is inevitable. The objective of this communication is to present a reliable and predictive model namely, the least - squares support vector machine (LSSVM) to predict the asphaltene precipitation. This model has been developed and... 

Dew-point pressure is one of the most important quantities for characterizing and successful prediction of the future performance of gas condensate reservoirs. The objective of this study is to present a reliable, computer-based predictive model for prediction of dew-point pressure in gas condensate reservoirs. An intelligent approach based on least square support vector machine (LSSVM) modeling was developed for this purpose. To this end, the model was developed and tested using a total set of 562 experimental data points from different retrograde gas condensate fluids covering a wide range of variables. Coupled simulated annealing (CSA) was employed for optimization of hyper-parameters of...