Early diagnosis of osteoporosis can efficiently predict fracture risk. There is a great demand to prevent this disease. The goal of this study was to distinguish osteoporotic cases from healthy controls on 2D bone radiograph images, using texture analysis and genetic algorithms (GAs). Gray Level Co-occurrence Matrix (GLCM), Run length Matrix (RLM) and Binarized Statistical Image Features (BSIF) were used for texture analysis. Features are numerous and parameter-dependent. The related experts can pick out the useful input features for the classifier. It however remains a difficult task and may be inefficient or even harmful as the data pattern is not clear. In this paper, GAs were used to optimize the two parameters of the co-occurrence matrix (distance parameter or pixel separation, orientation or direction) and the number of gray levels used in the preprocessing quantification step. GAs were also used to select the best combination of features extracted from GLCM and RLM matrices. Experiments were conducted on two populations composed of Osteoporotic Patients and Control Subjects. Results show that GAs combined with GLCM and BSIF features can improve the classification rates (ACC = 87.50%) obtained using GLCM (ACC = 77.8%) alone.
Experimental studies confirm that the obtained electrical power by a conventional photovoltaic PV system is progressively degraded when the temperature of its cells is increased. The water-cooled photovoltaic thermal PVT system is therefore proposed to avoid the voltage drop at high temperature. The use of single diode PV/PVT models in simulation software becomes indispensable to analyze its performances where several climatic conditions such as environmental temperature and solar radiation variations should be considered. An optimal set of PV/PVT model parameters are determined through experimental data using two evolutionary computation algorithms; genetic algorithm and particle swarm optimization algorithm. Furthermore, the robustness of the given PV/PVT model should be analyzed. The predicted electrical properties by the proposed PVT model are compared with those given by the conventional PV model at its operating cell conditions and also at several rigid atmospheric conditions.
The current paper investigates Backstepping controller using Particle Swarm Optimization for Photovoltaic "PV"/Wind hybrid system. The tested system was connected to the grid by three-phase inverter commissioned to address current depending on the grid parameters and still deliver its reactive power to zero. Backstepping control is a recursive methodology that uses Lyapunov function which can ensure the system stability. The best selection of Lyapunov function gains values should give a good result. In most of the literatures, the choice was based on the expertise of the studied system using hurwitzienne method considered as heuristic choice. The aim of this work is to propose an optimization using a powerful method commonly called Particle Swarm Optimization "PSO" able to calculate the gains values depending on the grid parameters by minimizing a selected criterion. The simulation results show that the PSO Backstepping controller gives good results shown in the current injected to grid with a small harmonic distortion despite climate change in the wind speed and the irradiation, which also shows the robustness of the applied control.
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