FUZZY LOGIC VELOCITY OPTIMIZATION OF AUTONOMOUS VEHICLES BASED ON ROAD BUMP GEOMETRY

In today’s world of fast developing technologies, semi-autonomous vehicles are becoming a common sight and increasing in numbers. Some feature autopilot systems that drive the vehicle without any intervention from the driver in certain situations or warn the driver of dangers ahead. A fully autonomous vehicle is yet to come due to old fashioned road infrastructures that would convey challenging scenarios for it. From speed bumps and traffic lights to road lanes and warning signs, current autopilot systems will have to cope with all. A system that ensures safe crossing of speed bumps when in autopilot mode is discussed in this paper. Crossing a speed bump at high speeds may result in loss of control, suspension failure, onboard cargo damage, and/or compromised passenger comfort. The proposed system can detect a speed bump from a distance, calculate the suitable crossing speed by studying vertical acceleration disturbances, and apply the brakes automatically using a Fuzzy Logic Controller (FLC) to reduce the speed before the car reaches the speed bump

SPEED BUMP DETECTION FOR AUTONOMOUS VEHICLES USING SIGNAL-PROCESSING TECHNIQUES

Autonomous vehicle (AV) is one of the emerging technologies that have far-reaching applications and implications in smart cities. Among the current challenges of the Smart City, Traffic management is of utmost importance. AV technologies can decrease transportation cost and can be used for efficient management and control of traffic flows. Traffic management strongly depends on the road surface condition. Abnormalities in the road, such as manholes and potholes, can cause accidents when not identified by the drivers. Furthermore, human-induced abnormalities, such as speed bumps, could also cause accidents. Detecting road abnormalities provide safety to human and vehicles. Current researches on speed bump detection are based on using sensors, accelerometer and GPS. This makes them vulnerable to GPS error, network overload, delay and battery draining. To overcome these problems, we propose a novel method for speed bump detection that combines both image and signal processing techniques. The advantage of the proposed approach consists in detecting speed bumps accurately without using any special sensors, hardware, Smartphone and GPS

HEMATOLOGICAL PARAMETERS OF LEBANESE AND SYRIAN REFUGEES LIVING IN PROXIMITY OF DEIR KANOUN RAS EL AIN DUMP IN LEBANON

Anemia is one of the most common diseases that are associated with pollutants exposure. A complete blood count test (CBC) can determine the presence of abnormal hematological parameters and diagnose many serious preventable disorders including anemia. To assess the associations between exposure to pollutants and hematologic parameters among Lebanese inhabitants and Syrian refugees, who were exposed to toxic fumes emanating from Deir Kanoun Ras El Ain dump, a population-based study involving 679 Lebanese and Syrian Refugees living in the three villages Deir Kanoun, Klayleh and Smaiyeh was carried out. Blood samples were collected in EDTA tubes. CBC tests were performed and differences were statistically analyzed between different villages, sexes, nationalities, and age groups. Many blood parameters showed abnormal levels indicating hematological disorders including anemia, infections, allergy, and inflammation. Similar trends of abnormal CBC parameters were observed among the three villages. The highest percentage of abnormal erythrocyte parameters was found in Klayleh, while for leukocyte parameters, the highest was in Smaiyeh. Significant differences were observed between sexes and nationalities that may be associated with low income, environmental pollution and poor hygiene. This paper investigates and highlights the associations of living in a polluted area and the abnormal trends of CBC parameters. They emphasize the damaging effect of Deir Kanoun Dump on all inhabitants of the surrounding region calling for immediate intervention from the Lebanese government to find solutions

Apprentissage statistique pour l'évaluation et le contrôle non destructifs : application à l'estimation de la durée de vie restante des matériaux par émission acoustique sous fluage

The composite materials are characterized by a high dispersion of their lifetime, which may extend from several minutes to several weeks in a creep test. When tested in creep of these materials we distinguish three phases, each characterized by its own acoustic activity. In the first phase, the occurrence rate of the AE signals is important, and then the rate drops to a relatively low constant value during the second phase, then this occurrence rate accelerate announcing the third phase which ends by a rupture. The characteristics of the acoustic emission (AE) signals in the phase preceding the rupture are different from those of other phases.The first part of this study is to use learning methods from artificial intelligence (neural networks, support vector machines and Bayesian classifier) to predict if the signals collected from the material under test in the pre-rupture or not. These are methods which, when applied to acoustic emission, identify among a large number of signals, characterized by key parameters, classes of signals having similar parameters and thus probably from the same phase. These methods have proved highly effective in classification; we reach the SVM with a sensitivity of 82 % and a specificity of 84 % for cross-validation results, and a sensitivity of 90 % and a specificity of 94 % for test results, with an acceptable calculation time.The second part of the study in the framework of this thesis concerns the estimation of the remaining life of composites. Standardization of signals accumulated acoustic emission curves as a function proves that the responses of the creep test pieces are set perfectly similar. A model was developed to characterize the behavior of this material during this test. Two approaches are used to determine the time of rupture. Compared to the literature, the first proposed approach improves the detection time of transition phases. This approach also provides a better correlation with the rupture time. The second approach is based on the correlation of rupture time with the reference time corresponding to the decrease of the speed by a percentage. The results of this latter approach is very interesting : the estimation of the rupture time for a test piece having a life of one hour may be possible from the first 15 seconds, with an error of about 4 %.Les matériaux composites se caractérisent par une forte dispersion de leur durée de vie qui peut s'étendre de quelques minutes à plusieurs semaines lors d'un test de fluage. Lors d'un essai en fluage de ces matériaux nous distinguons trois phases de temps caractérisées chacune par une activité acoustique propre. Dans la première phase, le taux d'apparition des signaux d'EA est important, puis le taux diminue et atteint une valeur constante relativement faible durant la seconde phase, ensuite ce taux d'apparition s'accélère annonçant la troisième phase qui se termine par la rupture. Les caractéristiques des signaux d'émission acoustique (EA) émis dans la phase précédant la rupture sont différentes de celles des autres phases. Le premier volet de cette étude consiste à utiliser des méthodes d'apprentissage relevant de l'intelligence artificielle (réseaux de neurones, machines à vecteurs de support et classifieurs bayésiens) afin de prédire si les signaux recueillis à partir d'un matériau sous test se trouve dans la phase de pré-rupture ou non. Ce sont des méthodes qui, appliquées à l'émission acoustique, permettent d'identifier parmi un grand nombre de signaux, caractérisés par des paramètres principaux, des classes de signaux ayant des paramètres voisins et donc provenant probablement de la même phase. Ces méthodes se sont avérées très performantes en classification, nous atteignons avec les SVM une sensibilité de 82 % et une spécificité de 84% pour les résultats en validation croisée, et une sensibilité de 90 % et une spécificité de 94 % pour les résultats en test, avec un temps de calcul acceptable.Le deuxième volet de l'étude effectué dans le cadre de cette thèse concerne l'estimation de la durée de vie restante des les matériaux composites. La normalisation des courbes cumulées des signaux d'émission acoustique en fonction du temps prouve que les réponses en fluage des éprouvettes mises en test sont parfaitement ressemblantes. Un modèle a été établi pour caractériser le comportement de ce matériau lors de ce test. Deux approches sont utilisées pour déterminer le temps de rupture. Par rapport à la littérature, la première approche proposée améliore la détection des temps de transition des différentes phases. Cette approche fournit également une meilleure corrélation avec le temps de rupture. La deuxième approche est fondée sur la corrélation du temps de rupture avec le temps de référence correspondant à la diminution de la vitesse d'un certain pourcentage. Les résultats de cette dernière approche sont très intéressants : l'estimation du temps de rupture pour une éprouvette ayant une durée de vie de 1 heure peut être possible dès les 15 premières secondes, avec une erreur de l'ordre de 4 %

Statistical learning for evaluation and non-destructive testing : application in estimating the remaining lifetime of materials by acoustic emission under creep test

Les matériaux composites se caractérisent par une forte dispersion de leur durée de vie qui peut s'étendre de quelques minutes à plusieurs semaines lors d'un test de fluage. Lors d'un essai en fluage de ces matériaux nous distinguons trois phases de temps caractérisées chacune par une activité acoustique propre. Dans la première phase, le taux d'apparition des signaux d'EA est important, puis le taux diminue et atteint une valeur constante relativement faible durant la seconde phase, ensuite ce taux d'apparition s'accélère annonçant la troisième phase qui se termine par la rupture. Les caractéristiques des signaux d'émission acoustique (EA) émis dans la phase précédant la rupture sont différentes de celles des autres phases. Le premier volet de cette étude consiste à utiliser des méthodes d'apprentissage relevant de l'intelligence artificielle (réseaux de neurones, machines à vecteurs de support et classifieurs bayésiens) afin de prédire si les signaux recueillis à partir d'un matériau sous test se trouve dans la phase de pré-rupture ou non. Ce sont des méthodes qui, appliquées à l'émission acoustique, permettent d'identifier parmi un grand nombre de signaux, caractérisés par des paramètres principaux, des classes de signaux ayant des paramètres voisins et donc provenant probablement de la même phase. Ces méthodes se sont avérées très performantes en classification, nous atteignons avec les SVM une sensibilité de 82 % et une spécificité de 84% pour les résultats en validation croisée, et une sensibilité de 90 % et une spécificité de 94 % pour les résultats en test, avec un temps de calcul acceptable.Le deuxième volet de l'étude effectué dans le cadre de cette thèse concerne l'estimation de la durée de vie restante des les matériaux composites. La normalisation des courbes cumulées des signaux d'émission acoustique en fonction du temps prouve que les réponses en fluage des éprouvettes mises en test sont parfaitement ressemblantes. Un modèle a été établi pour caractériser le comportement de ce matériau lors de ce test. Deux approches sont utilisées pour déterminer le temps de rupture. Par rapport à la littérature, la première approche proposée améliore la détection des temps de transition des différentes phases. Cette approche fournit également une meilleure corrélation avec le temps de rupture. La deuxième approche est fondée sur la corrélation du temps de rupture avec le temps de référence correspondant à la diminution de la vitesse d'un certain pourcentage. Les résultats de cette dernière approche sont très intéressants : l'estimation du temps de rupture pour une éprouvette ayant une durée de vie de 1 heure peut être possible dès les 15 premières secondes, avec une erreur de l'ordre de 4 %.The composite materials are characterized by a high dispersion of their lifetime, which may extend from several minutes to several weeks in a creep test. When tested in creep of these materials we distinguish three phases, each characterized by its own acoustic activity. In the first phase, the occurrence rate of the AE signals is important, and then the rate drops to a relatively low constant value during the second phase, then this occurrence rate accelerate announcing the third phase which ends by a rupture. The characteristics of the acoustic emission (AE) signals in the phase preceding the rupture are different from those of other phases.The first part of this study is to use learning methods from artificial intelligence (neural networks, support vector machines and Bayesian classifier) to predict if the signals collected from the material under test in the pre-rupture or not. These are methods which, when applied to acoustic emission, identify among a large number of signals, characterized by key parameters, classes of signals having similar parameters and thus probably from the same phase. These methods have proved highly effective in classification; we reach the SVM with a sensitivity of 82 % and a specificity of 84 % for cross-validation results, and a sensitivity of 90 % and a specificity of 94 % for test results, with an acceptable calculation time.The second part of the study in the framework of this thesis concerns the estimation of the remaining life of composites. Standardization of signals accumulated acoustic emission curves as a function proves that the responses of the creep test pieces are set perfectly similar. A model was developed to characterize the behavior of this material during this test. Two approaches are used to determine the time of rupture. Compared to the literature, the first proposed approach improves the detection time of transition phases. This approach also provides a better correlation with the rupture time. The second approach is based on the correlation of rupture time with the reference time corresponding to the decrease of the speed by a percentage. The results of this latter approach is very interesting : the estimation of the rupture time for a test piece having a life of one hour may be possible from the first 15 seconds, with an error of about 4 %

GPS tracking system for autonomous vehicles

This paper presents a proposed design of a mechatronics system for autonomous vehicles. The proposed design is able to memorize a route based on Global Positioning System (GPS) rather than using pre-saved maps that are infrequently updated and do not include all roads of all countries. Moreover, it can autonomously avoid obstacles and detect bumps. Experimental tests are conducted using a small-scale car equipped with the proposed mechatronics system. The results show that the proposed system operates with minor errors and slips. The proposed autonomous vehicle can serve normal, disabled, and elderly people. It can be used on roads and even inside facilities like campuses, airports, and factories to transport passengers or loads thus reducing workmanship and costs. Keywords: Autonomous vehicles, GPS, Collision avoidance, Tracking syste

Prediction of blood transfusion donation

International audienceThe goal of the present study was to develop and evaluate machine learning algorithms for the prediction of blood transfusion donation. The machine learning algorithms studied included multilayer perceptrons (MLPs) and support vector machines (SVMs). The methods were evaluated retrospectively in a group of 600 patients and validated prospectively in a group of 148 patients. We reach a sensitivity of 65.8% and a specificity of 78.2% in the prospective group. This discrimination is very interesting because it could allow to propose to the patients, classified as non-donators, to give their blood in the future. Furthermore, the blood transfusion donation UCI corpus used, has been processed in a different manner than the initial marketing one. Therefore, this recent corpus could give a new training set for testing and improving machine learning methods in the future

Effect of Successive Impact Loads From a Drop Weight on a Reinforced Concrete Flat Slab

Lebanon is one of the countries which are at high risk of experiencing rock falls. In order to ensure public safety, engineers must take into consideration this risk. In the past years, numerous researches were conducted on the behavior of horizontal structural elements, slabs, of different types under dynamic impact load. Reinforced concrete flat slabs are commonly used slabs in residential buildings. To build a profound understanding of the structural behavior of the slabs under such loadings, it is important to investigate the effect of energy dissipation on the equivalent impact force, mid-span deflection and damage pattern. In this study a sample reinforced concrete slab of 500 x 1000 x 100 mm dimensions is considered. The aim of this paper is to find how these factors vary with the increase in energy as the drop load resembling the real rock fall is left to drop freely from different heights 0.6 m and 1 m

Creep-rupture prediction by naive bayes classifiers

International audienceThe purpose of this study was to predict the failure of composite materials by developing and evaluating an artificial learning algorithm that could predict their life time. This will be done by predicting whether a specimen will break within 30 seconds or not. Specimens were tested according to the creep test by the traction method. Naive Bayesian classifiers have been developed retrospectively in a group of 90 samples and tested prospectively in a group of 30 samples to evaluate and ensure the performance of this learning method. Each sample was characterized by a number of relevant parameters. During the five cross-validations, the learning machine achieved a mean sensitivity of 78% and a mean specificity of 82%. The mean area under the ROC curve (Receiver Operating Curves) reached 0.88. The study can be regarded as a very important step in the term of prediction of composite material time life remaining

A normalization method for life-time prediction of composite materials

International audienceThe objective of this study is to predict the rupture of specimens of composite materials. During a creep experiment, with traction method, the specimens have different time of rupture (130 seconds, 159 seconds, 539 seconds...). The acoustic activity during the test involves three phases (phase 1, phase 2 and phase 3). When we apply our normalization method (cumulative acoustic emission vs. time), we can notice that all tests look very similar, and we can see that there is a proportionality relation between the transition time $t_{m}$ (phase 1 -> phase 2) and the time of rupture $t_{r}$. The technique works significantly better than other recent works. To validate this technique we have achieved a K-cross validation, on 7 specimens. The proportionality between $t_{m}$ and $t_{r}$ of the 7-cross validations had a mean value of 0.1218 and a standard deviation of 0.0018. The mean errors that we got is about 8.58% ($\pm$ 4.65). It is a very important result in life-time prediction