Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper, we describe a blood alcohol content estimation prototype based on a comportment analysis performed by artificial neural networks. We asked to subjects that had drunk alcohol to play a video-game after having measured their blood alcohol content with a breathalyser. A racing game was modified so that it could provide various data related to the use of the controls by the player. Using the collected data, we trained our neural network in order to be able to determine whether or not the subject had exceeded a blood alcohol content threshold. We also succeeded in estimating this blood alcohol content with a mean error of 0.1g/l. We could perform those estimations independently of the track played among the two ones used. It was also performed in "real time", e.g., using only the data collected within the last 10 seconds of playing.
Proceedings of the 2011 International Conference on Advances in Computer-Human Interactions
Gosier, Guadeloupe

Droits : info:eu-repo/semantics/OpenAccess
hal-00849971
https://hal.archives-ouvertes.fr/hal-00849971
https://hal.archives-ouvertes.fr/hal-00849971/document
https://hal.archives-ouvertes.fr/hal-00849971/file/article.pdf

Éditeur(s) : HAL CCSD Springer Verlag
Résumé : 17 pages, 11 figures.
International audience
The problem addressed in this paper is the automatic extraction of names from a document image. Our approach relies on the combination of two complementary analyses. First, the image-based analysis exploits visual clues to select the regions of interest in the document. Second, the textual-based analysis searches for name patterns and low-level word textual features. Both analyses are then combined at the word level through a neural network fusion scheme. Reported results on degraded documents such as facsimile and photocopied technical journals demonstrate the interest of the combined approach.
ISSN: 1433-7541

hal-00329459
https://hal.archives-ouvertes.fr/hal-00329459
DOI : 10.1007/s10044-006-0038-6

Éditeur(s) : HAL CCSD Springer
Résumé : International audience
This paper presents a spiking neural network simulator suitablefor biologically plausible large neural networks, named DAMNEDfor “Distributed And Multi-threaded Neural Event-Driven”. The simulatoris designed to run efficiently on a variety of hardware. DAMNEDmakes use of multi-threaded programming and non-blocking communicationsin order to optimize communications and computations overlap.This paper details the even-driven architecture of the simulator. Someoriginal contributions are presented, such as the handling of a distributedvirtual clock and an efficient circular event queue taking into accountspike propagation delays. DAMNED is evaluated on a cluster of computersfor networks from 103 to 105 neurons. Simulation and networkcreation speedups are presented. Finally, scalability is discussed regardingnumber of processors, network size and activity of the simulated NN.
Engineering Applications of Neural Networks: 11th International Conference, EANN 2009
London, United Kingdom

hal-01437753
https://hal.archives-ouvertes.fr/hal-01437753
DOI : 10.1007/978-3-642-03969-0_33

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper describes a blood alcohol content estimation method for car driver, based on a comportment analysis performed within a realistic simulation. An artificial neural network learns how to estimate subject's blood alcohol content. Low-level recording of user actions on the steering wheel and pedals are used to feed a multilayer perceptron, and a breathalyzer is used to build the learning examples set (desired output). Results are compared with a successful previous work based on a simple video game and demonstrate the ''complexity scalability'' of the approach.
The 20 th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Proceedings - Bruges, Belgium from 25 to 27 April 2012 .
Bruges, Belgium

hal-00850136
https://hal.archives-ouvertes.fr/hal-00850136

Éditeur(s) : HAL CCSD
Résumé : International audience
We instrumented a realistic car simulator to extract low level data related to the driver's use of the vehicle controls. After proceeding these data, we generated features that were fed to a Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). Our goal was determine if the driver's Blood Alcohol Content (BAC) was over $0.4g.l^{-1}$ or not, and even estimate the BAC value. Our device process the vehicle's controls data and then outputs the user BAC. We discuss the results of the prototype using the MLP and SVM algorithms in both single-user and multi-user context for detection of drunk drivers and estimation of the BAC value. The prototype performed better with single user base than with multi-user, and provided comparable results with MLP and SVM.
ESANN 2013 proccedings,European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Bruges, Belgium

hal-00850138
https://hal.archives-ouvertes.fr/hal-00850138

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper demonstrates that a human being using an interface can be efficiently evaluated - in real time - by embedding basic measurements in the interface and using a suitable trained artificial neural network. The approach is introduced through video games but is suitable for any machine capable of valuable measurements on user actions. Of course, the quality of the "diagnostic" depends of the learnability of the task and of the size and quality of the learning base. Typical applications include the detection of fatigue, stress, emotions, the influence of a drug or of medical treatments ; screening a deficit or adequateness to a task, etc. Two successful prototypes are presented, one to predict the mental age of children through a set of simple basic games, and the other to detect if a subject is right-handed of left-handed through a racing car simulation.
Proceeding of the Third International Conferences on Advances in Computer-Human Interactions
St. Maarten, Netherlands Antilles

hal-00601476
https://hal.archives-ouvertes.fr/hal-00601476

Résumé : La forte variabilité de la vitesse du vent fait que l'énergie produite par un parc éolien n'est pas constante dans le temps. Le gestionnaire ne peut donc pas dimensionner son réseau électrique en prenant intégralement ce type de production en compte. L' une des solutions préconisées pour permettre le développement de l' éolien et son intégration avec une plus grande sureté aux réseaux, est de développer et d'améliorer les outils de prévisions. Le travail de thèse consiste à améliorer les performances d'un outil de prédiction basé sur les réseaux de neurones bayesiens, permettant la prédiction de la puissance à très court terme . Le prédicteur fonctionne notamment par J'ajustement de paramètres, certain se détermine « automatiquement » via le mécanisme des réseaux de neurones bayesiens d' autres, que nous nommerons paramètres temporels, sont à l' appréciation de l'utilisateur. Le travail mené consiste à établir un protocole pour la fixation de ces paramètres tout en améliorant les performances du prédicteur . Nous avons donc décidé de conditionner leurs valeurs en fonction de la variabilité des séquences de puissance précédent l'instant de prévision. Tout d'abord nous avons classifié des séquences de puissance en fonction de leurs coefficients de variation en appliquant la méthode des C-moyennes floues. Puis, chaque classe formée a été testée sur plusieurs valeurs de paramètres, les valeurs associées aux meilleures prédictions ont été retenues. Enfin, ces résultats couplés au formalisme des Chaines de Markov, par le biais de la matrice de transition , ont perm is d'obtenir des taux d'amélioration par rapport à la persistance allant de 7,73 à 23,22 % selon l'horizon de prédiction considéré
The high variability of the wind speed has for conse quences that the energy produced by a wind farm is not constant over time. Therefore, the manager can't size the electrical network by takin g into account this type of production. One solution advocated for the development of wind energy and its integrati on with greater security at network, is to develop and improve fore casting tools. The thesi s objective is to improve the performance of a predi ction tool based on Bayesian neural networks, allowing the predi ction of wind power for short timescales. The predictor works, in part icular by the adjustment of parameters, sorne is determined "automatically" through the mechan ism of neural networks Bayesian other , which we cali temporal parameters are at the discretion of the user. The work involves establishing a protocol for the determination of these parameters and improving the performance of the predictor. So, we decided to condition their values depending on the sequence variability of wind power previous the moment of the forecast. First we classified sequences of power according to their coefficients of variation using the method of fuzzy C-means. Then, each formed class was tested for several parameters values, the values associated with the best predictions were selected. Finally , these result s coupled with the formalism of Markov chains , through the transition matrix allowed to obtain rates of improvement over the persistence ranging from 7.73 to 23.22 % depending on the prediction horizon considered

http://www.theses.fr/2013AGUY0679/document

Éditeur(s) : HAL CCSD Ecological Society of America
Résumé : Reliable assessment of fish origin is of critical importance for exploited species, since nursery areas must be identified and protected to maintain recruitment to the adult stock. During the last two decades, otolith chemical signatures (or "fingerprints") have been increasingly used as tools to discriminate between coastal habitats. However, correct assessment of fish origin from otolith fingerprints depends on various environmental and methodological parameters, including the choice of the statistical method used to assign fish to unknown origin. Among the available methods of classification, Linear Discriminant Analysis (LDA) is the most frequently used, although it assumes data are multivariate normal with homogeneous within-group dispersions, conditions that are not always met by otolith chemical data, even after transformation. Other less constrained classification methods are available, but there is a current lack of comparative analysis in applications to otolith microchemistry. Here, we assessed stock identification accuracy for four classification methods (LDA, Quadratic Discriminant Analysis [QDA], Random Forests [RF], and Artificial Neural Networks [ANN]), through the use of three distinct data sets. In each case, all possible combinations of chemical elements were examined to identify the elements to be used for optimal accuracy in fish assignment to their actual origin. Our study shows that accuracy differs according to the model and the number of elements considered. Best combinations did not include all the elements measured, and it was not possible to define an ad hoc multielement combination for accurate site discrimination. Among all the models tested, RF and ANN performed best, especially for complex data sets (e. g., with numerous fish species and/or chemical elements involved). However, for these data, RF was less time-consuming and more interpretable than ANN, and far more efficient and less demanding in terms of assumptions than LDA or QDA. Therefore, when LDA and QDA assumptions cannot be reached, the use of machine learning methods, such as RF, should be preferred for stock assessment and nursery identification based on otolith microchemistry, especially when data set include multispecific otolith signatures and/or many chemical elements.
ISSN: 1051-0761

hal-00644549
https://hal.archives-ouvertes.fr/hal-00644549
DOI : 10.1890/09-1887.1

Éditeur(s) : HAL CCSD
Résumé : International audience
We instrumented a car simulator by gathering low level data and fed it to an artificial neural network in order to perform blood alcohol content (BAC) estimations. The results depend on the quality of the data extraction and processing, and also on the selected inputs. We explain our data extraction and processing methodology, and how we used it to generate reliable and comparable features. At last, we describe the performances of individual features and how they combine. In the end, the prototype was able to accurately estimate the BAC value of a subject after being trained with driving samples of this subject with various BAC values.
Proceedings of ACHI 2013, The Sixth International Conference on Advances in Computer-Human Interactions
Nice, France

hal-00850137
https://hal.archives-ouvertes.fr/hal-00850137

Éditeur(s) : HAL CCSD
Résumé : International audience
BIO-Inspired Systems. Computational and Ambient Intelligence

hal-00515730
https://hal.archives-ouvertes.fr/hal-00515730

Éditeur(s) : HAL CCSD Wuerz Publishing Ltd
Mathematics applied to Biology and Medicine,

hal-00690152
https://hal.archives-ouvertes.fr/hal-00690152

Éditeur(s) : HAL CCSD
Résumé : International audience
Modelling techniques for river hydrologic forecasting systems have taken advantage of machine learning methods especially for flood prediction. But current solutions, mostly based on artificial neural networks do not always meet end users requirements on the readability and the understandability of predictive models. In this paper, we present a new version of our original solution based on the concept of aggregate variables in order to predict flash flood events from observed water level and/or rain measurements, particularly in the context of caribbean watersheds in which flash flood are much uncertain. We combine aggregate variables in juries. Juries of aggregate variables are trained and tested using a typical 10-fold cross validation scheme. Best juries are searched through an evolutionary approach that is optimized. Different parameters are set up like aggregation periods and jury sizes to prove the efficiency of the proposed approach compared to classical solutions.
Congress on Evolutionary Computation (CEC) 2013
Cancun, Mexico

hal-00847582
https://hal.archives-ouvertes.fr/hal-00847582
DOI : 10.1109/CEC.2013.6557656

Résumé : Les fourmis sont des organismes cibles appropriés pour les études environnementales. Au cours de notre étude, nous nous sommes focalisés sur les fourmis de la litière via l'application de deux méthodes de récolte complémentaires selon le protocole all (ants of leaf litter). Dans les forêts naturelles de Guyane française, l'hétérogénéité environnementale et les perturbations structurent les communautés de fourmis de la litière en influençant la richesse, la diversité, l'abondance et la densité en espèces ainsi que les compositions taxonomique et fonctionnelle. Chaque type de formation végétale possède une communauté spécifique. La fragmentation et la conversion des forêts en plantations ont entrainé une altération plus ou moins profonde des communautés; cette variabilité est fonction du type d’agriculture et des espèces d'essences cultivées. Bien que l'altération des communautés se soit révélée maximale dans la plupart des monocultures, les plantations de cacaoyers ont un potentiel de conservation réel. Enfin, dans un contexte ou il est urgent de simplifier l'intégration des arthropodes dans les études de conservation, de contrôle et de suivi de la santé des écosystèmes terrestres, la méthode des réseaux de neurones est apparue comme un outil puissant pour mettre en évidence et analyser les patrons des communautés de fourmis.
Ants are reliable and relevant target organisms for environmental surveys. In our study, we focused on litter-dwelling ants through the use of two complementary sampling methods that were implemented according to the ants of leaf litter (all) protocol. In pristine forests of french guiana, leaf-litter ant communities are structured by environmental heterogeneity and natural perturbations - which influence species richness, diversity, abundance and density, as well as taxonomic and functional composition. This results in habitat-specific communities for each vegetal formation. Forest fragmentation and conversion into monocultures have led to a more or less deep alteration of the ant communities; this variability depends on the type of agricultural system and cultivated tree species. Although the intensity of community alteration peaked in tree monocultures, cocoa plantation exhibited a real potential for native species conservation. Finally, in the current context where simplifying the integration of arthropods into conservation programs as well as into surveys designed to monitor and manage the environment is critical, the use of neural networks appears to be a powerful tool for reliably highlighting and analyzing ant communities patterns.

http://www.theses.fr/2011AGUY0475/document

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance.
ISSN: 2072-4292

meteo-01304579
https://hal-meteofrance.archives-ouvertes.fr/meteo-01304579
https://hal-meteofrance.archives-ouvertes.fr/meteo-01304579/document
https://hal-meteofrance.archives-ouvertes.fr/meteo-01304579/file/remotesensing-07-15831.pdf
DOI : 10.3390/rs71215831