In this research work a Shape from Shading (SFS) technique which incorporates a Lambertian model is implemented for the automatic generation of a digital terrain model (DTM) using a single view aerial image. The developed algorithm is tested on both simulated and real data. The estimated accuracy of the generated DTM from the simulated data, which has a bilinear surface, is about ±3 cm. The real data is a scanned aerial photograph taken over a low texture hilly terrain. The generated DTM by the SFS technique is compared with a DTM acquired by the manual measurement of the stereo image of the same area using a photogram metric plotter. The estimated rimes of the discrepancies between the grid nodes of the measured and the automatically generated DTM is about ±4 meters. The unsuccessful reconstruction of the terrain surface for the real data is due to the fact that a simple Lambertian model does not take into account, in a perfect way, different nondeterministic influential factors such as the terrain alb Edo variations and the random noise. The influence of the latter case was reduced by a low pass filter applied as a preprocessing stage.

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Industrial Radiography is one of the most important methods to detect weld defects such as porosity, slag and crack. The interpretation of radiographic films characterizes the weld defects. The defect detection depends on radiographic film quality and the expert of interpreter. If the provided images of the industrial radiography are not clear, the detection of the defect can be difficult. But the radiographic image is usually noisy and has low quality. Thus, it is requred to use methods that can detect the defect accurately. The use of image processing techniques is available to achieve this aim. It is noticed that human eye is used to see objects in three dimensions and can also detect the depth. Thus, in this research, the Shape from the Shading (SFS) method is applied for two-dimensional radiographic images to extract three-dimensional ones. For the evaluation, the experts’ opinions in radiography have also been considered. The results of comments indicate that using the SFS method is valuable to improve the defects detection in weld radiography.

server ways of calculating non-Hermitian Shape functions are considered. Also, disabilities of these techniques in generating triangular elements are mentioned. A direct method of behaviour understanding for finding Shape functions of triangular element with side nodes is suggested. In addition, this technique is generalized to include internal nodes. Finally, suggested elements with these kinds of Shape functions are evaluated numerically

Most of the partial Shading maximum power point tracking methods have been designed for the static Shading pattern of the partial Shading conditions, however, the irradiance pattern may change further when in partial Shading mode. Therefore, to cover this research gap, a global maximum power point control under varying irradiance (GCVI) algorithm is proposed in this paper. The algorithm does not use any sensors to detect the change in the irradiance, instead, the change in the current values of the modules are continuously monitored to detect the change. The reference voltages across which the peaks on the power curve are scanned are obtained from the reference voltage generation process, the consideration of these reference points avoids the excessive power losses in the system. The verification of the working of the proposed algorithm is carried out by simulating the photovoltaic system model on SIMULINK in MATLAB software. Simulations are carried out in various scenarios to show the effectiveness of the control. The simulation results illustrate that with the change in the global maximum under partial Shading, the system successfully retunes to the new maximum point; the maximum point retunes from 10 kW to 9.2 kW and from 13.8 kW to 11.5 kW for two different case scenarios. Further, the comparisons are also carried out with the previously reported methods.

Hats are used to protect the head and for safety or fashion in different cultures and nations. Identifying the fabric, designs, and sewing the hat can give us good information about the history of fashion in different nations. In this study, five hats belonging to the Tavichi family in Italy were investigated by radiography. The design and construction, morphology, internal structure and damaged regions have been considered. Image processing algorithms can increase the quality of radiography images. In this research, total variation (TV) and Shape from Shading (SFS) algorithms have been used to enhance the quality of the images. The TV method is based on minimizing the changes and is used to eliminate noise in images. In the 3D method, a 3D image is created from a 2D image based on the light reflected. The results show that radiography testing is an effective method for identifying the structure of old hats and can show the internal structure and connections of the components without splitting the fabric. The processed images also have better contrast and can be used to identify components and structures. The radiography and restoration experts have evaluated the reconstructed images. They have confirmed the effectiveness of processing methods in extracting efficient information from radiographs. Also, the profile lines of the images show that the contrast changes in the reconstructed images are greater than the original radiographs, and the components of the reconstructed images are clearer.

This paper presents a model-based approach for the global maximum power point (GMPP) tracking of solar strings under partial Shading conditions. In the proposed method, the GMPP voltage is estimated without any need to solve numerically the implicit and nonlinear equations of the photovoltaic (PV) string model. In contrast to the existing methods in which first the locations of all the local peaks on the P-V curve are estimated and next the place of the GMPP is selected among them, the suggested method estimates directly the GMPP without any need for the evaluation of the other local peaks. The obtained GMPP voltage is then given as a reference value to the input voltage controller of a DC-DC boost converter to regulate the output voltage of the solar string at the GMPP voltage in various irradiation conditions. Furthermore, the values of the temperature and irradiation level of each PV module within the PV string are estimated, and therefore, the proposed method does not need to thermometers and pyranometers. This makes it as a reliable and low-cost GMPP tracking method. The theoretical aspects on which the proposed GMPP algorithm is established are also discussed. The comparison of the numerical results of the suggested GMPP tracking scheme with the existing methods at different environmental conditions shows the satisfactory operation of the proposed technique from the speed and accuracy point of views.

Dynamic reconfi guration of photovoltaic arrays is one of the effective ways to decrease partial Shading effects. In this paper, by using auxiliary modules and after a suitable fi xed reconfi guration, an optimizer and economical method based on dynamic reconfi guration is presented. In this method, Auxiliary modules are arranged next to the photovoltaic array and replaced with shaded modules to maximize the energy delivery. The best connection between the auxiliary modules and the array is determined by an optimal decision process. The objective function for this decision process is energy delivery of the solar array in shadow conditions, which is maximized by the genetic algorithm. Signifi cant improvement in the output power of the photovoltaic array and smaller number of switches than the other dynamic reconfi guration methods are the main advantages of the proposed method. Benefi ts and effectiveness of this method are compared with other recently dynamic confi guration approaches, and the results confi rm power enhancement of the photovoltaic arrays in various shadow patterns.

With the advent of Shape memory alloys (SMAs), several commercial and industrial applications were proposed due to their superior mechanical and biological properties. Among these materials, Nickel-Titanium (NiTi) alloys are widely applied and well-researched since they are characterized not only by good thermal and mechanical properties but also by excellent biocompatibility compared to other SMAs. In most of the applications, the structural components and devices made of NiTi SMAs work under cyclic thermo mechanical loading and one of the major limitations facing the industrial use of this alloy is the degradation of the material when subjected to cyclic loadings (i.e., training). In this study, pseudoelastic training procedure in NiTi Shape memory alloy and the resultant two-way Shape memory effect are studied using in-situ electric resistivity measurement. At first, variations in the residual strain and in the electric resistivity during pseudoelastic training method are revealed. Then, by measuring the electric resistivity after training procedure (upon specified thermal cycling at stress-free condition) as well as the induced two-way Shape memory strain, the effects of residual martensite and dislocation (plastic deformation) on the residual strain are investigated. The obtained results show that about 33% of the residual strain accumulated in 100 pseudoelastic cycles can be ascribed to the residual martensite and about 67% of the residual stain is attributed to the dislocations (plasticity).

This paper proposes an efficient meta-heuristic method called expert groups' optimization algorithm. The method strategy relies on four principles and starts from a random initial population. The population members are divided into two expert groups: the free group and the guided group. Each group has specific tasks for effective domain search, but with one new operator. This operator has an intelligent mechanism so that exploration and exploitation of the population can lead the members to the global optimum. The new method is validated through a standard test function. Then its performance is evaluated in the application of an inverse geometric reconstruction and the results are compared with a genetic algorithm, particle swarm optimization, and mean-variance mapping optimization. Results show that the new method outperforms the alternative methods in convergence rate and reaching the global optimum. Finally, the expert groups' optimization algorithm performance is evaluated in an engineering problem with high computational cost. In this case, the goal is drag coefficient minimization of the RAE 2822 airfoil in transonic flow at a fixed lift coefficient with constraints on the pitching moment and airfoil area. An unstructured grid Navier-Stokes flow solver with a two-equation turbulence model is used to evaluate the aerodynamic objective function. The results show that the optimal solutions obtained by the new method outperform those of mean-variance mapping optimization with considerably faster convergence.