Developing neural networks to investigate relationships between lighting quality and lighting glare indices

The present work compares the ability of the two most used glare indices, the Daylight Glare Probability (DGP) and the International Commission on Illumination (CIE) Glare Index (CGI), using Multiple Correspondence Analysis (MCA) and Artificial Neural Networks (ANN). The research investigates the efficiency of indexes in predictive indoor lighting quality. This study was carried out by analyzing data from a survey administered to ninety students in real design classrooms in the city of Biskra, Algeria. The experiment was conducted using three different lighting indoor conditions: natural and artificial lighting and mixed lighting. The true prediction of the Daylight Glare Probability for the variable Comfortable was 60.60%, and for (CIE) Glare Index the prediction values were equal to 44.60% for the same variable

Towards a new model of light quality assessment based on occupant satisfaction and lighting glare indices

This study looks at the effect of daylighting on human performance. It includes a focus on glare index combined with the actual feeling of users of the classroom as a way to assess indoor lighting quality. The main objective of this research is to understand the impact of daylighting from windows on the glare sensation and also to determine which glare index is the closest to human visual sensation under local daylighting conditions in Biskra, Algeria with highly luminous climate. The study used High Dynamic Range (HDR) photography, Evaglare and Aftab Alpha software to calculate the two glare metrics Daylight Glare, Index (DGI) and the Daylight Glare Probability (DGP). A survey was also used with 90 occupants under different lighting conditions (different configurations) in a design classroom. In order to link the mathematical model and the human assessment of glare, statistical regression analysis was used. We established a statistically compelling connection between daylighting and student performance

Beyond thermal comfort in the hospital rooms. Investigation of thermal summer comfort in patients rooms: case of Biskra hospitals

In Algeria, the architectural design of hospitals is unfortunately not subject to clear and specific regulatory thermal strategy, it depends mainly on the architect own approach to the issue of comfort design, materials, etc. This research aims to investigate the present comfort conditions in typical hospital configuration and under specific climate conditions of hot and arid regions. Two different hospital designs have been investigated in Biskra. A main town is southeast of Algeria. The first case study being the oldest hospital in the town, build in the late 30s during the colonial era and renovated recently. The second hospital is a brand new hospital. A series of measurements of ambient air temperature, patient surveys and interviews have been initiated in two typical wards. The results show that thermal comfort sensations in these specific spaces depend not only on architecture, design and typical processes, but also on physiological, psychological and behavioral parameters which widely influence the perception of the patients

Pyramid Scene Parsing Network for Driver Distraction Classification

In recent years, there has been a persistent increase in the number of road accidents worldwide. The US National Highway Traffic Safety Administration reports that distracted driving is responsible for approximately 45 percent of road accidents. In this study, we tackle the challenge of automating the detection and classification of driver distraction, along with the monitoring of risky driving behavior. Our proposed solution is based on the Pyramid Scene Parsing Network (PSPNet), which is a semantic segmentation model equipped with a pyramid parsing module. This module leverages global context information through context aggregation from different regions. We introduce a lightweight model for driver distraction classification, where the final predictions benefit from the combination of both local and global cues. For model training, we utilized the publicly available StateFarm Distracted Driver Detection Dataset. Additionally, we propose optimization techniques for classification to enhance the model’s performanc

Evaluation of the sound environment of the city of Biskra (Algeria)

This research concerns the quantitative evaluation of the urban sound environment of the city of Biskra. The aim was to determine the quality of the soundscape based on in situ measurement, using a Landtek SL5868P sound level meter. 62 points have been identified to represent the whole city. The results show that that the noise level varies from 54.1 to 75.8 dB(A) during the weekdays and from 50.4 to 74.2dB(A) during the weekend. In addition,90% of the results of the weekday measurements and 81% of the results of the weekend measurements exceed the recommended levels given by the World Health Organization. The present urban sound exposure could have a substantial impact on the overall comfort of inhabitants and increase the risk of the syndrome of the sick cities

Un modèle de validation automatique de mécanismes de sécurisation des communications

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal