Sharif Digital Repository

This paper presents a method for estimation of road profile for automotive research applications with more accuracy and higher speed. Dynamic response of a car equipped with position and velocity sensors and driving on a sample road is used as basic data. A feed-forward neural network, trained with outputs from a car model in ADAMS, is used as the car inverse model. The neural network is capable of estimating the road roughness from the car response during test drives. The ADAMS model is corrected and validated using a series of dynamic experiments on the car, performed on a hydro-pulse test rig. The only problem in this approach, like other identification and optimization methods, is the... 

Single nucleotide polymorphism (SNP) in human genomes is considered to be highly associated with complex genetic diseases. As a consequence, obtaining all SNPs from human populations is one of the primary goals of recent studies on human genomics. The two sequences of SNPs in diploid human organisms are called haplotypes. In this paper, the problem of haplotype reconstruction from SNP fragments with and without genotype information is studied. Minimum error correction (MEC) is an important model for this problem but only effective when the error rate of the fragments is low. MEC/GI, as an extension to MEC model, employs the related genotype information besides the SNP fragments and,... 

Breast cancer is regarded as one of the most frequent mortality causes among women. As early detection of breast cancer increases the survival chance, creation of a system to diagnose suspicious masses in mammograms is important. In this paper, two automated methods are presented to diagnose mass types of benign and malignant in mammograms. In the first proposed method, segmentation is done using an automated region growing whose threshold is obtained by a trained artificial neural network (ANN). In the second proposed method, segmentation is performed by a cellular neural network (CNN) whose parameters are determined by a genetic algorithm (GA). Intensity, textural, and shape features are... 

In this paper, the application of neural networks for simulation and optimization of the cogeneration systems has been presented. CGAM problem, a benchmark in cogeneration systems, is chosen as a case study. Thermodynamic model includes precise modeling of the whole plant. For simulation of the steady sate behavior, the static neural network is applied. Then using dynamic neural network, plant is optimized thermodynamically. Multi- layer feed forward neural networks is chosen as static net and recurrent neural networks as dynamic net. The steady state behavior of Excellent CGAM problem is simulated by MFNN. Subsequently, it is optimized by dynamic net. Results of static net have excellent... 

A Co-Mo/graphene oxide (GO) catalyst has been synthesized for the first time for application in a defined hydrodesulfurization (HDS) process to produce sulfur free naphtha. An intelligent model based upon the neural network technique has then been developed to estimate the total sulfur output of this process. Process operating variables include temperature, pressure, LHSV and H2/feed volume ratio. The three-layer, feed-forward neural network developed consists of five neurons in a hidden layer, trained with Levenberg–Marquardt, back-propagation gradient algorithm. The predicted amount of residual total sulfur is in very good agreement with the corresponding experimental values revealing a... 

Purpose – The purpose of this paper is to propose a Meta modeling based on regression, neural network, and clustering to analyze the job satisfaction factors and improvement policy making. Design/methodology/approach – Since any job satisfaction evaluation supposes to improve the status by prescribing specific strategies to be performed in the organization, proposing applicable strategies is decisively important. Task demand, social structure and leader-member exchange (LMX) are general applications easily conceptualized while proposing job satisfaction improvement strategies. Findings – On the basis of these empirical findings, the authors first aim to identify relationships between LMX,... 

Nowadays users of the Web are encouraged to generate content on the Web by themselves. Weblogs are the most popular tools to do this. In fact Weblogs are one kind of social networks and they are one of the most important components in Web 2.0. Blogs are often situated within a Blog community of similar interest. These communities can be a useful way for readers to access space slice of millions of Weblogs. In this paper we introduce a new algorithm to infer communities based on their link structure. This algorithm uses self organizing maps to identify communities among Weblogs. The link pattern of each Weblog is the input for SOM. © 2007 IADIS  

The critical micelle concentration (CMC) of a set of 58 alkylsulfates, alkylsulfonates, alkyltrimethyl ammonium and alkylpyridinium salts were predicted using an artificial neural network (ANN). The multiple linear regression (MLR) technique was used to select the important descriptors that act as inputs for artificial neural network. These descriptors are Balaban index, heat of formation, maximum distance between the atoms in the molecule, Randic index and volume of the molecule. Designed artificial neural network is a fully connected back-propagation network that has a 5-5-1 architecture. The results obtained using neural network were compared with those obtained using MLR technique.... 

This paper concerns the design of a neural state observer for nonlinear dynamic systems with noisy measurement channels and in the presence of small model errors. The proposed observer consists of three feedforward neural parts, two of which are MLP universal approximators, which are being trained off-line and the last one being a Linearly Parameterized Neural Network (LPNN), which is being updated on-line. The off-line trained parts are able to generate state estimations instantly and almost accurately, if there are not catastrophic errors in the mathematical model used. The contribution of the on-line adapting part is to compensate the remainder estimation error due to uncertain parameters... 

In this paper a novel method for the design and optimization of cooling systems is presented. The numerical solution of free convection from a heated horizontal cylinder confined between adiabatic walls obtained from a finite element solver is used to propose a non-linear heat transfer model of GMDH type approach. In the context of GMDH model, three different methods depending on the structure of neural network are implemented. The system of orthogonal equations is solved using a SVD scheme. The coefficients of second order polynomials are computed and their behavior is discussed. In addition, to demonstrate the performance of the predicted model, the numerical data are divided into trained... 

This article discusses a method for complete control of the dynamics of a rotating shaft on magnetic bearings under the effects of mass imbalance. The electromagnetic bearings used in this research are 2 four-pole bearing at the two ends of the rotor, which are actuated by differential currents. Full dynamic behavior of 3- dimensional rigid rotor and its effects on dynamic model are included. The effects of rotating mass unbalance are also included in the equations. The geometric couplings between electromagnetic forces of the coils are included as uncertainty. By using sliding mode controller and a neural network to estimate the system nonlinear-coupled equations, in a way suitable for... 

Process Fault Diagnosis (PFD) involves interpreting the current status of the plant given sensor readings and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for PFD. Neural networks have been used to solve PFD problems in chemical processes, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks (HANN) in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks (SANN). The lower efficiency of HANN, in comparison to SANN, in PFD is elaborated and analyzed. Also, the concept... 

Due to inherent potential of General Regression Neural Network(GRNN) to approximate nonlinear functions, it can be implemented as a nonlinear state-feedback controller. In this paper the methodology of direct collocation and nonlinear programming is utilized in combination with GRNN to determine the optimal control of dynamical systems. Application of this method to the design of an optimal controller for a chaotic flexible beam as a non-autonomous, nonlinear system is investigated. © 2002 The American Institute Aeronautics and Astronautics Inc. All rights reserved  

In this paper, a new control strategy for optimal attitude tracking control of a multivariable satellite system has been presented. The approach is based on Radial Basis Function Neural Network (RBFNN) and classical PD Controller for its initial stabilization. It is shown how the network can be employed as a multivariable self- organizing and self learning controller in conjunction with a PD controller for attitude control of a satellite. By using four reaction wheels and quaternion for kinematics representation, the attitude dynamics of the satellite has been presented. In contrast to the previous classical approaches, it is shown how this controller can be carried out in an on-line manner... 

This paper presents a new method to solve the problem of finding the best configuration of fuel assemblies in a pressurized water reactor (PWR) core. Finding the optimum solution requires a huge amount of calculations in classical methods. It has been shown that the application of continuous Hopfield neural network accompanied by simulated annealing method to this problem not only reduces the amount of calculations, but also guarantees finding the best solution. In this study flattening of neutron flux inside the reactor core of Bushehr NPP is considered as the objective function. The result is the optimum configuration which is in agreement with the pattern proposed by the designer. © 2001... 

Interactions between the members of an imaginary universe, where all the members are adaptive and have learning capability, is simulated numerically by using artificial neural networks. The universe is called λ-universe for brevity. It is shown here that in such a universe, rules governing the behavior of the members might be formed inside the universe by its own members and the randomness which is observed in the behavior of its members is a direct result of the learning capability of the members. Although this is not a simulation of the real universe, some fundamental concepts of astrophysics have been implemented in it  

Advanced model-based control of pH processes is noticeably a chemical modeling issue, because it can have a profound effect on the attainable control quality. This is especially the case when the pH regulation of streams, consisting of hundreds of constituents with varying concentrations, is encountered. The severe non-linear behavior of pH processes is reflected in the titration curve of the process stream. The performances of all model-based controllers are highly dependent on the accuracy of the model. Considering a great number of parameters such as dissociation constants, solubility products and characteristic concentrations places the designer in a dilemma of choosing between... 

In this study, the effect of protection methods regarding the corrosion decrement of steel in concrete was simulated by artificial neural networks (ANNs) and fuzzy logic (FL) approaches. Hot dip galvanizing as a protective coating, Ferrogard 901 corrosion inhibitor, a pozzolanic component, such as fly ash (FA) and micro-silica (MS), and eventually rebar AISI-304 were employed in concrete. Reinforced concrete samples were held under impressed voltage of 30 V in 3.5 % NaCl electrolyte for 350 hours toward a stainless-steel auxiliary electrode. Corrosion currents have been modelled using feed forward back propagation ANNs and FL methods. The results demonstrate good consistency between... 

Since the recent on-demand applications need more sophisticated circuits and subsystems, components with configurable capabilities attract attention more than before in commercial systems, specifically the fifth generation (5G). Power dividers play a crucial role in 5G phased array systems, and their role becomes more significant if the output powers ratio is adjustable. Here, we suggest a design methodology by which planar power splitters with arbitrary output power levels can be designed in light of very simple perturbations, i.e., vias. Through our design procedure, we find an optimized pattern for hybrid vias-some of them are made of PEC, and others are dielectric, e.g., air,... 

This paper focuses on enhancing MFCC features using a set of MLP NN in order to improve phoneme recognition accuracy under different noise types and SNRs. A NN can be used in different domains (between any pair of MFCC feature extraction blocks). It includes FFT, MEL, LOG, DCT and DELTA domains. Various domains have different complexities and achieve different degrees. A comparative study is done in this paper in order to find the best domain. Furthermore, a set of MLP NNs, instead of one NN, is used to enhance various noise types with different levels of SNRs. In this case, each NN is trained with a special noise type and SNR. The database used in the simulations is created by artificially...