Sharif Digital Repository

One of the most important requirements for the operation and planning activities of an electrical utility is the prediction of load for the next hour to several days out, known as short term load forecasting (STLF). This paper presents STLF of Iran national power system (INPS) using artificial neural network (ANN). The developed program is based on a four-layered perceptron ANN building block. The optimum inputs were selected for the ANN considering historical data of the INPS. Instead of conventional back propagation (BP) methods, Levenberg-Marquardt BP (LMBP) method has been used for the ANN training to increase the speed of convergence. A data analyzer and a temperature forecaster are... 

A hydrogen-selective nano-composite ceramic membrane was prepared by depositing a dense layer composed of SiO2 and Al2O 3 on top of a graded multilayer substrate using co-current chemical vapor deposition (CVD) method. The multilayer substrate was made by dip-coating a macroporous α-alumina tubular support by a series of boehmite solutions to get a graded structure. Using DLS analysis, it was concluded that decreasing hydrolysis time and increasing acid concentration lead to smaller particle size of boehmite sols. XRD analysis was carried out to investigate the structure of intermediate layer and an optimized calcination temperature of 973 K was obtained. SEM images indicated the formation... 

Co/Cu multilayers were deposited in a sulfate solution by controlling the current and potential for the deposition of cobalt and copper layer respectively. The electrochemical behavior of these multilayers was studied by cyclic voltammetry and current transients. In addition, a mathematical analysis was used to characterize the electrodeposition system. Simultaneously, the nucleation and growth mechanisms were monitored by these techniques. In this case, the results clearly showed that electrodeposition of cobalt layers was a kinetically controlled process while the reduction of copper ions was a diffusion-control process. Although nucleation mechanism of the single Co deposit was found as a... 

We introduce a theoretical method for simultaneous measurement of refractive index and thickness of multilayer systems using Fourier domain optical coherence tomography (FD-OCT) without any auxiliary arrangement. The input data to the formalism are the FD-OCT measured optical path lengths (OPLs) and properly selected spectral components of FD-OCT interference spectrum. The outputs of the formalism can be affected significantly by uncertainty in measuring the OPLs. An optimization method is introduced to deal with the relatively large amount of uncertainty in measured OPLs and enhance the final results. Simulation result shows that by using the optimization method, indices can be extracted... 

We introduce a theoretical framework for simultaneous refractive index and thickness measurement of multilayer systems using the Fourier domain optical coherence tomography (FD-OCT) system without any previous information about the item under investigation. The input data to the new formalism are the FD-OCT measured optical path lengths and properly selected spectral components of the FD-OCT interference spectrum. No additional arrangement, reference reflector, or mechanical scanning is needed in this approach. Simulation results show that the accuracy of the extracted parameters depends on the index contrast of the sample while it is insensitive to the sample's thickness profile. For... 

In this study, a multiplate shear model is developed for dynamic analysis of multilayer graphene sheets with arbitrary shapes considering the interlayer shear effect. By utilizing the model, then some free-vibration analysis is presented. According to the experimental results, the weak interlayer van der Waals interaction cannot maintain the integrity of carbon atoms in adjacent layers. Therefore, it is required that the interlayer shear effect is accounted to study multilayer graphene mechanical behavior. The governing differential equation of motion is derived for the multilayer graphene sheets utilizing a variational approach based on the Kirchhoff plate model. The essential and natural... 

The Ni-P/Zn-Ni compositionally modulated multilayer coatings CMMCs were electrodeposited from a single bath by switching the deposition current density. The corrosion resistance of the deposits was studied and compared with that of monolayers of Ni-P and Zn-Ni alloys via Tafel polarization, EIS and salt spray tests. Characterization of corrosion products by means of EDS and XRD revealed more details from the corrosion mechanism of the monolayers and multilayers. The corrosion current density of Ni-P/Zn-Ni CMMCs were around one tenth of Zn-Ni monolayer. The CMMC with incomplete layers performed lower polarization resistance and higher corrosion current density compared to the CMMC with... 

The Ni-P/Zn-Ni compositionally modulated multilayer coatings CMMCs were electrodeposited from a single bath by switching the cathodic current density. The composition, surface morphology, roughness, layers growth pattern as well as the phase structure of deposits were extensively studied via SEM, EDS, AFM and XRD analysis. Effects of bath ingredients on the electrodeposition behavior were analyzed through cathodic linear sweep voltammetry. Although the concentration of Zn2+ in bath was 13 times higher than Ni2+, the Zn-Ni deposition potential was much nearer to Ni deposition potential rather than that of Zn. Addition of NaH2PO2 to the Ni deposition bath considerably raised the current... 

Multilayer perceptron (MLP) neural networks have been used extensively for estimation/regression of parameters. Moreover, recent studies have shown that learning algorithms of MLP which are based on Gaussian function are more accurate. In this paper, the mass attenuation coefficient (MAC) of gamma radiation for light-weight materials (e.g. O-8), mid-weight materials (e.g. Al-13), and heavy-weight materials (e.g. Pb-82) is modelled using Gaussian function based regularization of MLP (i.e. Bayesian regularization (BR)) and by a modular estimator. The results are compared with the Reference results. To show better performance of the utilized algorithm, the results of the different supervised... 

In this study, the estimation of the uranium price as one of the most important factors affecting the fuel cost of nuclear power plants (NPPs) is investigated. Supervised learning algorithms, especially, multilayer feed-forward neural network (FFNN) are used extensively for parameters estimation. Similar to other supervised methods, FFNN can suffer from overfitting (i.e. imbalance between memorization and generalization). In this study, different regularization techniques of FFNN are discussed and the most appropriate regularization technique (i.e. Bayesian regularization) is selected for estimation of the uranium price. The different methods including different learning algorithms of FFNN,... 

Up to now, different methods have been developed for estimation of buildup factor (BF). However, either expensive estimation or time-consuming estimation are major restrictions/challenges of these methods. In this study a new technique utilizing combination of Monte Carlo method and the Bayesian regularization (BR) learning algorithm of multilayer feed-forward neural network (FFNN) is employed for estimation of BFs. First, the BFs of the different elements (i.e. Al, Cu, and Fe) at different energies and different mean free paths (MFPs) are modeled by the MCNP code. The results show that the calculated BFs by MCNP code are in good agreement with the reported values of American nuclear society... 

In this study, accurate estimation of nuclear power plant (NPP) parameters is done using the new and simple technique. The proposed technique using the genetic algorithm (GA) in combination with the Bayesian regularization (BR) and Levenberg- Marquardt (LM) learning algorithms identifies the appropriate architecture for estimation of the target parameters. In the first step, the input patterns features are selected using the features selection (FS) technique. In the second step, the appropriate number of hidden neurons and hidden layers are investigated to provide a more efficient initial population of the architectures. In the third step, the estimation of the target parameter is done using... 

Up to now, different methods have been developed for estimation of buildup factor (BF). However, either expensive estimation or time-consuming estimation are major restrictions/challenges of these methods. In this study a new technique utilizing combination of Monte Carlo method and the Bayesian regularization (BR) learning algorithm of multilayer feed-forward neural network (FFNN) is employed for estimation of BFs. First, the BFs of the different elements (i.e. Al, Cu, and Fe) at different energies and different mean free paths (MFPs) are modeled by the MCNP code. The results show that the calculated BFs by MCNP code are in good agreement with the reported values of American nuclear society... 

This paper considers multi-biasing conditions for graphene patterns in multi-layer structures. The term “Multi Bias” is introduced as a different bias for parts of a single graphene layer in shape of disks. This technique is developed to control device behavior versus THz radiation. Exploiting the multi-bias configuration, a unique structure acts as both narrowband absorber and wideband reflector. This feature can pave the way to realize complex THz sensors, detectors, and optical systems. Also, the proposed structure is modeled using circuit modeling by considering the multi bias effects. Accuracy of the circuit model is validated via the finite element method which exhibits less than 1%... 

In this paper we will address two major problems in mammogram analysis for breast cancer in MR-images. The first is classification between normal and abnormal cases and then, discrimination between benign and malignant in cancerous cases. Our proposed method extracts textural and statistical descriptive features that are fed to a learning engine based on the use of Support Vector Machine learning framework to categorize them. The obtained results show excellent accuracy in both classification problems, that proves the appropriate combination of our features and selecting powerful classifier i.e. Support Vector Machine leads us to a brilliant outcome