Renewable energy systems have replaced systems that use fossil fuels in many applications in diff... more Renewable energy systems have replaced systems that use fossil fuels in many applications in different regions of the world. This is seen in the increasing use of solar and wind energy as the two most important sources for producing environment-friendly and economically convenient electrical energy. The fluctuating and unstable nature of renewable energy sources makes this type of energy complex to exploit, and related research has therefore mainly focused on Control and optimization. This work proposes an optimized configuration of two hybrid systems designed for a microgrid network with the aim to improve the power supply in isolated areas and provide a low cost, more reliable, and sustainable source of electricity for rural communities that may have limited access to traditional power grids. These hybrid setups consist of an initial system that caters for 10 houses which is then extended to serve 20 houses. Both setups utilize solar and wind energy sources, energy storage batteries, and a diesel generator. Real data collected in the Biskra region in the southeast of Algeria, is used. Particle Swarm Optimization algorithm is applied to achieve the optimal size of the hybrid system components through the weighted sum multi-objective approach, whereby three factors, namely, Cost of Electricity, Loss of Power Supply Probability, and Dummy Excess are combined into one objective function. Results of simulation show that the proposed approach achieves highly satisfactory values for the electricity prices in the 10-house and 20house scenarios, with estimates of 0.
This article addresses the problem of fault detection in robot manipulator systems. In the produc... more This article addresses the problem of fault detection in robot manipulator systems. In the production field, online detection and prevention of unexpected robot stops avoids disruption to the entire manufacturing line. A number of researchers have proposed fault diagnosis architectures for electrical systems such as induction motor, DC motor, etc..., utilising the technique of discrete wavelet transform. The results obtained from the use of this technique in the field of diagnosis are very encouraging. Inspired by previous work, The objective of this paper is to present a methodology that enables accurate fault detection in the actuator of a two-degree of freedom robot arm to avoid system performance degradation. A partial reduction in joint torque constitutes the actuator fault, resulting in a deviation from the desired end-effector motion. The actuator fault detection is carried out by analysing the torques signals using the wavelet transform. The stored energy at each level of the transform contains information which can be used as a fault indicator. A Matlab/Simulink simulation of the manipulator robot demonstrates the effectiveness of the proposed technique.
In this paper, we propose a new methodology for crack monitoring in concrete structures. This app... more In this paper, we propose a new methodology for crack monitoring in concrete structures. This approach is based on a n this paper, we propose a new methodology for monitoring cracks in concrete structures. This approach is based on a multi-resolution analysis of a sample or a specimen of the studied material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processing by a dedicated wavelet will be analyzed according to several scales in order to detect internal cracks and crack initiation. The ultimate goal of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible on the concrete surface. The key idea allowing such a performance is the combination of two major data analysis tools which are wavelets and Deep Learning. This original p...
Segmentation of brain tumor images is a major research topic in medical imaging to have a refined... more Segmentation of brain tumor images is a major research topic in medical imaging to have a refined detection and understanding of abnormal masses in the brain. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block to classify it as tumor or not. A thorough evaluation of the proposed algorithm is performed using the MRI database provided by the Kouba imaging center in Algie...
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Papers by Madina Hamiane