Mohammadia School of Engineering

Identifying individuals from a facial image is a technique that forms part of computer vision and is used in various fields such as security, digital biometrics, smartphones, and banking. However, it can prove difficult due to the complexity of facial structure and the presence of variations that can affect the results. To overcome this difficulty, in this paper, we propose a combined approach that aims to improve the accuracy and robustness of facial recognition in the presence of variations. To this end, two datasets (ORL and UMIST) are used to train our model. We then began with the image pre-processing phase, which consists in applying a histogram equalization operation to adjust the gray levels over the entire image surface to improve quality and enhance the detection of features in each image. Next, the least important features are eliminated from the images using the Principal Component Analysis (PCA) method. Finally, the pre-processed images are subjected to a neural network architecture (CNN) consisting of multiple convolution layers and fully connected layers. Our simulation results show a high performance of our approach, with accuracy rates of up to 99.50% for the ORL dataset and 100% for the UMIST dataset.

With the growth of the internet of things (IoT) smart objects, managing these objects becomes a very important challenge, to know the total number of interconnected objects on a heterogeneous network, and if they are functioning correctly; the use of IoT objects can have advantages in terms of comfort, efficiency, and cost. In this context, the identification of IoT objects is the first step to help owners manage them and ensure the security of their IoT environments such as smart homes, smart buildings, or smart cities. In this paper, to meet the need for IoT object identification, we have deployed an intelligent environment to collect all network traffic traces based on a diverse list of IoT in real-time conditions. In the exploratory phase of this traffic, we have developed learning models capable of identifying and classifying connected IoT objects in our environment. We have applied the six supervised machine learning algorithms: support vector machine, decision tree (DT), random forest (RF), k-nearest neighbors, naive Bayes, and stochastic gradient descent classifier. Finally, the experimental results indicate that the DT and RF models proved to be the most effective and demonstrate an accuracy of 97.72% on the analysis of network traffic data and more particularly information contained in network protocols. Most IoT objects are identified and classified with an accuracy of 99.21%.

Building Information Modelling (BIM) has gained widespread popularity over the past decade, mainly in the Architecture, Engineering, and Construction (AEC) industry. The adoption of BIM in developed countries is already regulated as opposed to developing and underdeveloped countries. This study aims at filling the knowledge gap as to the adoption and implementation of BIM practices in small and medium enterprises (SMEs), with a focus on the Moroccan AEC industry. The main goals of this study are 1. to determine the BIM awareness level and adoption rate, 2. to assess the familiarity level, 3. to establish the benefits, challenges, and prospects of BIM. The results of this study have shown that BIM awareness is high, but its adoption and implementation are not, though promising as it is exponentially gaining the attention of AEC practitioners.

The technology of electric vehicles is well known. Dozens of vehicles have already been marketed. Nowadays, several models are emerging, whether they are city vehicles or luxury roadsters. Car manufacturers all over the world are developing cars with electric motors allowing lightning accelerations, which require high energy. However, the impact of this new energy demand is still ambiguous with regard to the electricity grid. Since there are no large-scale application examples of electric vehicles, the existing research basically analyzes the traffic laws of ordinary vehicles to infer the possible future charging load characteristics of electric vehicles, and then conduct an impact assessment. According to the evolution of electric vehicles, today, it’s necessary to investigate the behavior of electric vehicles in practice and obtain a more realistic charging load model, in addition, to study charging consequence on the distribution network in order to obtain more accurate results.

Unitcommitment is an optimization problem used to find out the least cost dispatchof obtainable generation resources to meet up a fixed electric power demand over a certain timeperspective under generational, technical and ecological constraints. A power utility’s abilityto satisfy energy demand is influenced undoubtedly by unexpected breakdowns of powergenerating units. In most cases, such unexpected failures of system components are also muchmore expensive to repair than taking planned preventative maintenance action. A number ofmethods for the optimal maintenance scheduling have been proposed up to now. This paperpresents a literature review of solutions proposed to clear up the problem of unit maintenancescheduling in microgrids.