Video surveillance for monitoring driver's fatigue and distraction

Fatigue and distraction effects in drivers represent a great risk for road safety. For both types of driver behavior problems, image analysis of eyes, mouth and head movements gives valuable information. We present in this paper a system for monitoring fatigue and distraction in drivers by evaluating their performance using image processing. We extract visual features related to nod, yawn, eye closure and opening, and mouth movements to detect fatigue as well as to identify diversion of attention from the road. We achieve an average of 98.3% and 98.8% in terms of sensitivity and specificity for detection of driver's fatigue, and 97.3% and 99.2% for detection of driver's distraction when evaluating four video sequences with different drivers

Mathematical Modelling and Identification of a Quadrotor

Motivated by the important growth of VTOL vehicles research such as quadrotors and to a small extent autonomous flight, a quadrotor dynamical model is presented in this work. The purpose of this study is to get a better understanding of its flight dynamics. It is an underactuated system. So, a simplified and clear model is needed to implement controllers on these kind of unmanned aerial systems. In addition, a computational tool is used for validation purposes. For future works embedded or intelligent control systems can be developed to control them. Gyroscopic and some aerodynamics effects are neglected

Coffee maturity classification using convolutional neural networks and transfer learning

This work presents a framework for coffee maturity classification from multispectral image data based on Convolutional Neural Networks (CNNs). The system leverages the use of multispectral image acquisition systems that generate large amounts of data, by taking advantage of the ability of CNNs to extract meaningful patterns from very high-dimensional data. We validated the use of five different popular CNN architectures on the classification of cherry coffee fruits according to their ripening stage. The different models were trained on a training dataset balanced in different ways, which resulted in a top accuracy higher than 98% when applied to the classification of 600 coffee fruits in 5 different stages of ripening. This work has the potential of providing the farmer with a high-quality, optimized, accurate and viable method for classifying coffee fruits. In order to foster future research in this area, the data used in this work, which was acquired with a custom-developed multispectral image acquisition system, have been released

Process integration and control tied to economic optimization in biobutanol synthesis

Diferentes esquemas de destilación integrados térmicamente fueron propuestos, y evaluados para la separación de butanol o etanol en el ambiente de simulación. Los requerimientos energéticos y las emisiones obtenidas de CO2 de los esquemas propuestos en este trabajo están entre los más bajos encontrados en la literatura para la producción de butanol. Esquemas de destilación doble-efecto o integración de calor de columnas de alta y baja presión son la opción más económica con requerimientos energéticos entre 30-40% más bajos que los sistemas convencionales. La mejor configuración para la producción de acetona, butanol y etanol fue seleccionada mediante la optimización de sistemas integrados de reacción híbridos. Los reactores integrados con separación alcanzaron requerimientos energéticos totales mayores a los esquemas de separación externa integrados térmicamente. Sin embargo, reactores integrados fueron preferibles porque reducen el consumo de agua y el tamaño del reactor en más de tres y dos veces, respectivamente. Un sistema de reacción y extracción de los inhibidores producidos en la fermentación y en el pretratamiento, fue la opción más económica de producción a partir de lignocelulosa. El esquema y las condiciones óptimas de operación fueron seleccionados para facilitar el control del proceso a condiciones óptimas económicamenteAbstract : Several heat integrated distillation systems were proposed and evaluated for the separation of butanol and ethanol on simulation environment. The fuel requirements of these systems are among the lowest found in the literature for butanol production. Double-effect distillation systems are the most economical choice of external separation, with energy requirements 30-40% lower than that for distillation systems without recovery of condensation heat. The best configuration for the production of acetone, butanol and ethanol (ABE) was selected by optimization. A rigorous kinetic model for ABE production was developed and used in the simulations. Integrated reactor with separation units reached total energy requirements higher than external separation schemes with heat integration. However, integrated reactors were economically preferable because they reduce the water consumption and the size of the reactor for three and two-fold, respectively. A simultaneous reaction system with detoxification of inhibitor from pretreatment was the low-priced option for ABE production from lignocellulose. The optimal operating conditions of the system were selected to facilitate the process control at economic optimal conditionsDoctorad

Modélisation et commande floues de type Takagi-Sugeno appliquées à un bioprocédé de traitement des eaux usées

This thesis work is placed at the crossroad of Automatic control, Artificial Intelligence and Biotechnology. It aims at developing a modelling and control methodology based on a fuzzy logic approach. The first part of the work presents an introduction to the principles and techniques used in current wastewater treatment plants. It highlights the difficulty in modelling the multiple phenomena involved. From this fact, in the second part, focused on fuzzy modelling and control, we develop initially the identification of affine fuzzy models of Takagi-Sugeno (TS) type from input-output data. We used several fuzzy clustering methods as well as an agglomerative-competitive method which is robust in presence of noise. This type of "grey box" approach allows a rule-based representation which approximates the nonlinear dynamics as a concatenation of locally linear sub-models in the nonlinear autoregressive form (NARX). Moreover, we have developed a graphical version of the FMID toolbox for the fuzzy modelling of systems. Then, we propose a suboptimal linear-quadratic TS fuzzy control relevant to the structure of the identified fuzzy model, by using the parallel distributed compensation (PDC) technique. Finally, the whole methodology is tested and validated in simulation on an aerobic wastewater treatment bioprocess.Ce travail de thèse s'inscrit au carrefour de l'Automatique, de l'Intelligence Artificielle et des Biotechnologies. Il cherche à développer une méthodologie de modélisation et de commande qui repose sur une approche par logique floue. La première partie du travail présente une introduction aux principes et techniques mises en Suvre dans les stations d'épuration actuelles, et met en évidence la difficulté de modélisation des différents phénomènes mis en jeu. A partir de ce constat, dans une deuxième partie, focalisée sur la modélisation et la commande floues, nous développons d'abord l'identification de modèles flous affines de type Takagi-Sugeno (TS) à partir de données entrées-sorties. Nous considérons différentes méthodes de coalescence floue et une méthode d'agglomération compétitive, robuste en présence de bruit. Ce type d'approche " boîte grise " permet une représentation à base de règles qui approxime la dynamique non linéaire comme une concaténation de sous-modèles localement linéaires sous la forme d'auto-régression non-linéaire (NARX). De plus, nous avons développé une version graphique de la boîte à outils pour la modélisation floue des systèmes (FMIDg). Ensuite, nous proposons une commande floue TS sous-optimale linéaire quadratique adaptée à la structure du modèle flou identifié, en utilisant la philosophie de commande du type compensation parallèle distribuée (PDC). La méthodologie globale est finalement testée et validée en simulation sur un bioprocédé aérobie de dépollution des eaux usées

Separación de butanol a partir de soluciones acuosas diluidas

El butanol es considerado un posible biocombustible, y se espera que su demanda incremente dramáticamente si se produce eficientemente por fermentación. La producción por fermentación tiene bajos rendimientos, bajas concentraciones de producto y bajas productividades, generados principalmente por la alta toxicidad del butanol a concentraciones diluidas ( 20 gL-1). Se puede mejorar significativamente el desempeño de la fermentación, si se utilizan reactores integrados con técnicas de recuperación emergentes como la pervaporación, en los cuales se remueva selectivamente el butanol. En este trabajo, fue estudiada la preparación y caracterización de membranas para la pervaporación; y se realizó una evaluación operacional, económica y energética de un proceso de separación con pervaporación y decantación. Adicionalmente, se optimizó y diseñó mediante simulación, diferentes reactores integrados con pervaporación y suministro de ácidos (acético, butírico o láctico) como cosustratos. El esquema con fermentación previa de ácido láctico es capaz de mejorar la productividad volumétrica del proceso convencional de 0.6 a 10.1 gL-1h-1, y el rendimiento de solventes de 0.32 a 0.46 gg-1 con un consumo de energía 35% más bajoAbstract : Butanol is considered as a potential biofuel and its demand is expected to increase dramatically if its production by fermentation occurs efficiently. Due to the high toxicity of butanol at dilute concentrations ( 20 gL-1), its fermentation has low yields, low product concentrations and low productivities. The performance of the fermentation could be significantly improved using integrated reactors with emerging recovery techniques like pervaporation processes, in which butanol is selectively remove from the reactor. This work presents preparation and characterization of membranes for pervaporation, and operational, economical and energetic evaluation of a separation process integrated with pervaporation and decantation. Different integrated reactors with pervaporation were optimized through simulation, considering several ways of feeding acids as co-substrates (acetic, butyric or lactic). A reactor with previous lactic acid fermentation enhances the volumetric productivity and the yield as compare to the conventional process from 0.6 to 10.1 gL-1 h-1, and from 0.32 to 0.46 g g-1 respectively. Also, it was calculated a 35% saving in power consumptionMaestrí

Un algoritmo de selección de variables de enfoque híbrido basado en información mutua para aplicaciones de sensores blandos industriales basados en datos

The development of virtual sensors predicting the desired output requires a careful selection of input variables for model construction. In an industrial environment, datasets contain many instrumentation system measures; however, these variables are often non-relevant or excessive information. This paper proposes a variable selection algorithm based on mutual information examination, redundancy analysis, and variable reduction for soft-sensor modeling. A relevance calculation is performed in the first stage to select important variables using the mutual information criterion. Then, the detection and exclusion of redundant variables are carried out, penalizing undesired variables. Finally, the most relevant variables subset is determined through a wrapper method using Mallowssans' Cp metric to assess the fitting prediction performance. The approach was successfully applied to estimate the ethanol concentration for a distillation column process using an adaptive network-based fuzzy inference system architecture as a non-linear dynamic regression model. A comparative study was performed considering the application of correlation analysis and the method proposed in this study. Simulation results show the effectiveness of the proposed approach in the variable selection providing a reduction in search of suitable models that achieve faster results for developing soft sensors oriented to industrial applications.El desarrollo de sensores virtuales que predicen el resultado o producto deseado requie- re una cuidadosa selección de variables de entrada para la construcción del modelo. En un entorno industrial, los conjuntos de datos contienen muchas medidas del sistema de instrumentación; sin embargo, estas variables suelen ser información no relevante o excesiva. Este artículo propone un algoritmo de selección de variables basado en el examen de información mutua, el análisis de re- dundancia y la reducción de variables para el modelado de sensores blandos. En la primera etapa se realiza un cálculo de relevancia para seleccionar variables importantes utilizando el criterio de infor- mación mutua. Luego, se realiza la detección y exclusión de variables redundantes, penalizando las variables no deseadas. Finalmente, el subconjunto de variables más relevante se determina a través de un método de envoltura utilizando la métrica Cp de Mallows para evaluar el rendimiento de la pre- dicción de ajuste. El enfoque se aplicó con éxito para estimar la concentración de etanol para un pro- ceso de columna de destilación utilizando una arquitectura de sistema de inferencia difusa basada en red adaptativa como un modelo de regresión dinámica no lineal. Se realizó un estudio comparativo considerando la aplicación del análisis de correlación y el método propuesto en este estudio. Los re- sultados de la simulación muestran la efectividad del enfoque propuesto en la selección de variables proporcionando una reducción en la búsqueda de modelos adecuados que logren resultados más rápidos para el desarrollo de sensores blandos orientados a&nbsp

Online Data Stream Clustering: Proposal

Pattern recognition methods, specially classification, has been growing in popularity because its ability to adapt in a changing environment. This paper proposes a classification of such techniques found on a literature review of dynamic classification techniques collected among a variety of fields, including fault detection, identification of moving objects, bank customer segmentation, among others. Based on how the dynamic behavior is incorporated in the classification process (data, classifier structure or both), three main categories are detected: Methods for classifying static objects using dynamic classifiers, methods classifying dynamic objects with static classifiers and finally methods classifying dynamic objects with dynamic classifiers

A novel algorithm for dynamic clustering: properties and performance

International audienceIn this paper, we present a dynamic clustering algorithm that efficiently deals with data streams and achieves several important properties which are not generally found together in the same algorithm. The dynamic clustering algorithm operates online in two different timescale stages, a fast distance-based stage that generates micro-clusters and a density-based stage that groups the micro-clusters according to their density and generates the final clusters. The algorithm achieves novelty detection and concept drift thanks to a forgetting function that allows micro-clusters and final clusters to appear, drift, merge, split or disappear. This algorithm has been designed to be able to detect complex patterns even in multi-density distributions and making no assumption of cluster convexity. The performance of the dynamic clustering algorithm is assessed theoretically through complexity analysis and empirically through a set of experiments