FRand: Toolbox de MATLAB para Simulación de Números Aleatorios Difusos

Context: This paper presents a MATLAB code implementation and the GUI (General User Interface) for fuzzy random variable generation. Based on previous theoretical results and applications, a MATLAB toolbox has been developed and tested for selected membership functions. Method: A two–step methodology was used: i) a MATLAB toolbox was implemented to be used as interface and ii) all .m functions are available to be used as normal code. The main goal is to provide graphical and code–efficient tools to users. Results: The main obtained results are the MATLAB GUI and code. In addition, some experiments were ran to evaluate its capabilities and some randomness statistical tests were successfully performed. Conclusions: Satisfactory results were obtained from the implementation of the MATLAB code/toolbox. All randomness tests were accepted and all performed experiments shown stability of the toolbox even for large samples (>10.000). Also, the code/toolbox are available online. Acknowledgements: The authors would like to thank to the Prof. M Sc. Miguel Melgarejo and Prof. Jos´e Jairo Soriano–Mendez sincerely for their interest and invaluable support, and a special gratefulness is given to all members of LAMIC.Contexto: Este trabajo presenta una implementaci´on de c´odigo de MATLAB y un GUI (interfaz de usuario) para la generaci´on de variable aleatoria difusa. Basados en resultados te´oricos y aplicaci´on previos, un toolbox de MATLAB fu´e desarrollado y validado para diferentes funciones de pertenencia. M´etodo: Una metodolog´ıa de dos pasos ha sido implementada: i) un toolbox de MATLAB es implementado para usarse como interfaz y ii) todas las funciones .m est´an disponibles para usarse como c´odigo normal. La meta principal es proveer herramientas gr´aficas y de c´odigo a los usuarios Resultados: Los resultados principales de este trabajo son el MATLAB GUI y el c´odigo subyacente. Adicionalmente, algunos experimentos fueron realizados para evaluar las capacidades del toolbox, y algunas pruebas estad´ısticas de aleatoriedad fueron realizadas con ´exito. Conclusiones: Resultados satisfactorios de la implementaci´on del c´odigo/toolbox de MATLAB fueron obtenidos. Todos los tests estad´ısticos fueron aceptados y todos los experimentos realizados mostraron que el toolbox es estable a´un para tama˜nos de muestra grande (>10.000). Adicionalmente, el toolbox/c´odigo est´a disponible online. Agradecimientos: Los autores agradecen sinceramente a los Prof. M Sc. Miguel Melgarejo y Prof. Jos´e Jairo Soriano–Mendez por su inter´es e invaluable apoyo, y agradecen de manera especial a todos los miembros del Grupo LAMIC

FRand: MATLAB Toolbox for Fuzzy Random Number Simulation

Context: This paper presents a MATLAB code implementation and the GUI (General User Interface) for fuzzy random variable generation. Based on previous theoretical results and applications, a MATLAB toolbox has been developed and tested for selected membership functions. Method: A two–step methodology was used: i) a MATLAB toolbox was implemented to be used as interface and ii) all .m functions are available to be used as normal code. The main goal is to provide graphical and code–efficient tools to users. Results: The main obtained results are the MATLAB GUI and code. In addition, some experiments were ran to evaluate its capabilities and some randomness statistical tests were successfully performed. Conclusions: Satisfactory results were obtained from the implementation of the MATLAB code/toolbox. All randomness tests were accepted and all performed experiments shown stability of the toolbox even for large samples (>10.000). Also, the code/toolbox are available online. Acknowledgements: The authors would like to thank to the Prof. M Sc. Miguel Melgarejo and Prof. Jos´e Jairo Soriano–Mendez sincerely for their interest and invaluable support, and a special gratefulness is given to all members of LAMIC

Type-2 Takagi-Sugeno-Kang Fuzzy Logic System and Uncertainty in Machining

RÉSUMÉ: Plusieurs méthodes permettent aujourd’hui d’analyser le comportement des écoulements qui régissent le fonctionnement de systèmes rencontrés dans l’industrie (véhicules aériens, marins et terrestres, génération d’énergie, etc.). Pour les écoulements transitoires ou turbulents, les méthodes expérimentales sont utilisées conjointement avec les simulations numériques (simulation directe ou faisant appel à des modèles) afin d’extraire le plus d’information possible. Dans les deux cas, les méthodes génèrent des quantités de données importantes qui doivent ensuite être traitées et analysées. Ce projet de recherche vise à améliorer notre capacité d’analyse pour l’étude des écoulements simulés numériquement et les écoulements obtenus à l’aide de méthodes de mesure (par exemple la vélocimétrie par image de particules PIV ). L’absence, jusqu’à aujourd’hui, d’une définition objective d’une structure tourbillonnaire a conduit à l’utilisation de plusieurs méthodes eulériennes (vorticité, critère Q, Lambda-2, etc.), souvent inadaptées, pour extraire les structures cohérentes des écoulements. L’exposant de Lyapunov, calculé sur un temps fini (appelé le FTLE), s’est révélé comme une alternative lagrangienne efficace à ces méthodes classiques. Cependant, la méthodologie de calcul actuelle du FTLE exige l’évaluation numérique d’un grand nombre de trajectoires sur une grille cartésienne qui est superposée aux champs de vitesse simulés ou mesurés. Le nombre de noeuds nécessaire pour représenter un champ FTLE d’un écoulement 3D instationnaire atteint facilement plusieurs millions, ce qui nécessite des ressources informatiques importantes pour une analyse adéquate. Dans ce projet, nous visons à améliorer l’efficacité du calcul du champ FTLE en proposant une méthode alternative au calcul classique des composantes du tenseur de déformation de Cauchy-Green. Un ensemble d’équations différentielles ordinaires (EDOs) est utilisé pour calculer simultanément les trajectoires des particules et les dérivées premières et secondes du champ de déplacement, ce qui se traduit par une amélioration de la précision nodale des composantes du tenseur. Les dérivées premières sont utilisées pour le calcul de l’exposant de Lyapunov et les dérivées secondes pour l’estimation de l’erreur d’interpolation. Les matrices hessiennes du champ de déplacement (deux matrices en 2D et trois matrices en 3D) nous permettent de construire une métrique optimale multi-échelle et de générer un maillage anisotrope non structuré de façon à distribuer efficacement les noeuds et à minimiser l’erreur d’interpolation.----------ABSTRACT: Several methods can help us to analyse the behavior of flows that govern the operation of fluid flow systems encountered in the industry (aerospace, marine and terrestrial transportation, power generation, etc..). For transient or turbulent flows, experimental methods are used in conjunction with numerical simulations ( direct simulation or based on models) to extract as much information as possible. In both cases, these methods generate massive amounts of data which must then be processed and analyzed. This research project aims to improve the post-processing algorithms to facilitate the study of numerically simulated flows and those obtained using measurement techniques (e.g. particle image velocimetry PIV ). The absence, even until today, of an objective definition of a vortex has led to the use of several Eulerian methods (vorticity, the Q and the Lambda-2 criteria, etc..), often unsuitable to extract the flow characteristics. The Lyapunov exponent, calculated on a finite time (the so-called FTLE), is an effective Lagrangian alternative to these standard methods. However, the computation methodology currently used to obtain the FTLE requires numerical evaluation of a large number of fluid particle trajectories on a Cartesian grid that is superimposed on the simulated or measured velocity fields. The number of nodes required to visualize a FTLE field of an unsteady 3D flow can easily reach several millions, which requires significant computing resources for an adequate analysis. In this project, we aim to improve the computational efficiency of the FTLE field by providing an alternative to the conventional calculation of the components of the Cauchy-Green deformation tensor. A set of ordinary differential equations (ODEs) is used to calculate the particle trajectories and simultaneously the first and the second derivatives of the displacement field, resulting in a highly improved accuracy of nodal tensor components. The first derivatives are used to calculate the Lyapunov exponent and the second derivatives to estimate the interpolation error. Hessian matrices of the displacement field (two matrices in 2D and three matrices in 3D) allow us to build a multi-scale optimal metric and generate an unstructured anisotropic mesh to efficiently distribute nodes and to minimize the interpolation error. The flexibility of anisotropic meshes allows to add and align nodes near the structures of the flow and to remove those in areas of low interest. The mesh adaptation is based on the intersection of the Hessian matrices of the displacement field and not on the FTLE field

Sensor Reduction for Backing-Up Control of a Vehicle With Triple Trailers

This paper presents a cost-effective design based on sensor reduction for backing-up control of a vehicle with triple trailers. To realize a cost-effective design, we newly derive two linear-matrix-inequality (LMI) conditions for a discrete Takagi-Sugeno fuzzy system. One is an optimal dynamic output feedback design that guarantees desired control performance. The other is an avoidance of jackknife phenomenon for the use of the optimal dynamic output feedback controller. Our results demonstrate that the proposed LMI-based design effectively achieves the backing-up control of the vehicle with triple trailers while avoiding the jackknife phenomenon. More importantly, we demonstrate that the designed optimal control can achieve the backing-up control without, at least, two potentiometers that were employed to measure the relative angles (of a vehicle with triple trailers) in our previous experiments. Since the relative angles directly relate to the jackknife phenomenon, the successful control results without two potentiometers are very interesting and important from the cost-effective design point of view

Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis

The subject of machine condition monitoring and fault diagnosis as a part of system maintenance has gained a lot of interest due to the potential benefits to be learned from reduced maintenance budgets, enhanced productivity and improved machine availability. Artificial intelligence (AI) is a successful method of machine condition monitoring and fault diagnosis since these techniques are used as tools for routine maintenance. This chapter attempts to summarize and review the recent research and developments in the field of signal analysis through artificial intelligence in machine condition monitoring and fault diagnosis. Intelligent systems such as artificial neural network (ANN), fuzzy logic system (FLS), genetic algorithms (GA) and support vector machine (SVM) have previously developed many different methods. However, the use of acoustic emission (AE) signal analysis and AI techniques for machine condition monitoring and fault diagnosis is still rare. In the future, the applications of AI in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature

Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

Traditionally, fuzzy neural networks have parametric clustering methods based on equally spaced membership functions to fuzzify inputs of the model. In this sense, it produces an excessive number calculations for the parameters’ definition of the network architecture, which may be a problem especially for real-time large-scale tasks. Therefore, this paper proposes a new model that uses a non-parametric technique for the fuzzification process. The proposed model uses an autonomous data density approach in a pruned fuzzy neural network, wich favours the compactness of the model. The performance of the proposed approach is evaluated through the usage of databases related to the Optical Interconnection Network. Finally, binary patterns classification tests for the identification of temporal distribution (asynchronous or client–server) were performed and compared with state-of-the-art fuzzy neural-based and traditional machine learning approaches. Results demonstrated that the proposed model is an efficient tool for these challenging classification tasks