Automatisation du processus de construction des structures de données floues

Notion de base sur la logique floue -- Problématique et motivation de la recherche -- Systèmes à base de connaissances -- Génération automatique de bases de connaissances floues -- Généralités sur les algorithmes génétiques -- Généralités sur le procédé de pâtes thermomécanique -- Recherche proposée -- algorithmes génétiques hybride et binaire pour la génération automatique de bases de connaissances -- Stratégies multicombinatoires pour éviter la convergence prématurée dans les algorithmes génétiques -- Prédiction en ligne de la blancheur ISO de la pâte thermomécanique -- Real/binary-like coded versus binary coded genetic algorithms to automatically generate fuzzy knowledge bases : a comparative study -- Fuzzy decision support system -- Automatic generation of fuzzy knowledge bases using GAs -- Learning process -- Validation results -- Multi-combinative strategy to avoid premature convergence in genetically-generated fuzzy knowledge bases -- Introduction and problem definition -- Real/binary like coded genetic algorithm -- Performance criteria -- Evolutionary strategy -- Application to experimental data -- Online prediction of pulp brightness using fuzzy logic models -- The Chips management system -- Experiment plan for data collection -- Selection of the influencing variables -- Genetic-based learning process -- Performance criterion -- Evolutionary strategy -- Learning the FKBs for brightness prediction -- Learning the FKBs using laboratory variables

Génération automatique de bases de connaissances floues pour les systèmes d'aide à la décision

Préparation de la base de connaissances pour le SAD "Fuzzy-Flow" -- Fuzzy decision support system knowledge base generation using a genetic algorithm -- Genetic-based learning process -- Numerical validation -- Influence des paramètres d'optimisation et de sélection -- Système d'aide à la décision -- Paramètres de l'AG -- Paramètres de sélection et d'optimisation -- Tool wear monitoring using genetically-generated fuzzy knowledge bases -- Monitoring systems

Machine tool volumetric error features extraction and classification using principal component analysis and K-means

Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how to use principal component analysis (PCA) to extract the features of VE and how to use the K-means method for machine tool accuracy state classification. The proposed data processing methods have been tested with the VE data acquired from a five-axis machine tool with different states of malfunction. The results indicate that the PCA and K-means are capable of extracting the VE feature information and classifying the fault states including the C axis encoder fault, uncalibrated C axis encoder fault, and pallet location fault from the machine tool normal states. This research provides a new way for VE features extraction and classification

A Fuzzy-based Framework to Support Multicriteria Design of Mechatronic Systems

Designing a mechatronic system is a complex task since it deals with a high number of system components with multi-disciplinary nature in the presence of interacting design objectives. Currently, the sequential design is widely used by designers in industries that deal with different domains and their corresponding design objectives separately leading to a functional but not necessarily an optimal result. Consequently, the need for a systematic and multi-objective design methodology arises. A new conceptual design approach based on a multi-criteria profile for mechatronic systems has been previously presented by the authors which uses a series of nonlinear fuzzy-based aggregation functions to facilitate decision-making for design evaluation in the presence of interacting criteria. Choquet fuzzy integrals are one of the most expressive and reliable preference models used in decision theory for multicriteria decision making. They perform a weighted aggregation by the means of fuzzy measures assigning a weight to any coalition of criteria. This enables the designers to model importance and also interactions among criteria thus covering an important range of possible decision outcomes. However, specification of the fuzzy measures involves many parameters and is very difficult when only relying on the designer's intuition. In this paper, we discuss three different methods of fuzzy measure identification tailored for a mechatronic design process and exemplified by a case study of designing a vision-guided quadrotor drone. The results obtained from each method are discussed in the end

Semi-Synthetic Dataset Augmentation for Application-Specific Gaze Estimation

Although the number of gaze estimation datasets is growing, the application of appearance-based gaze estimation methods is mostly limited to estimating the point of gaze on a screen. This is in part because most datasets are generated in a similar fashion, where the gaze target is on a screen close to camera's origin. In other applications such as assistive robotics or marketing research, the 3D point of gaze might not be close to the camera's origin, meaning models trained on current datasets do not generalize well to these tasks. We therefore suggest generating a textured tridimensional mesh of the face and rendering the training images from a virtual camera at a specific position and orientation related to the application as a mean of augmenting the existing datasets. In our tests, this lead to an average 47% decrease in gaze estimation angular error.Comment: 5 pages, 5 figure