Fuzzy Logic Applications in Metrology Processes

Three-dimensional metrology is concerned with checking the conformity of machined parts with the geometrical specifications on their definition drawings from the design office. Three-dimensional measurement is a firmly established technique in the industry. For this, we apply the fuzzy logic to solve probing. Probing technology is widely used in three-dimensional metrology. In addition, we measure the very small dimensions, that is, the measurement at the micrometer scales. This chapter presents a new approach to the developing gear curve (CMMs). This method aims to select the most likely contact point for each successive arc by applying geometrical criteria and a fuzzy logic estimator, as you know there are several methods, but the fuzzy logic is more efficient and closer to the profile reel. The fuzzy logic system is particularly suitable for application to the three-dimensional metrology, including applications on a small radius probe as well as probing discontinuities to the flank profile. In addition, the time allowed is 144.09 s. Tests were carried out on gearboxes of agricultural machinery in the factory of my country (Algerian Tractors Company)

Lantana: Lantana camara (Verbenaceae)

peer reviewe

Occupation and mobility in high-mountain agropastoral societies: a proposal for an ethnoarchaeological study in the Jbel Sirwa (Anti-Atlas, Morocco)

Desde los orígenes de la domesticación, las sociedades agropastoriles de alta montaña han sido un ejemplo excepcional de adaptación y resiliencia a territorios inhóspitos, ya que requieren de un delicado equilibrio entre las actividades de trashumancia y cultivo para poder asegurar la continuidad del grupo. Los trabajos arqueológicos sobre estas sociedades han dejado tras de sí numerosas preguntas sobre la relación entre los humanos y el medio de montaña. Todos estos interrogantes nos han llevado a estudiar la materialidad de la vida cotidiana de las poblaciones agropastoriles del Jbel Sirwa. Así pues, desde el proyecto ARCHEOMOBAS, ofrecemos esta propuesta metodológica interdisciplinar para llevar a cabo estudios etnoarqueológicos de este tipo de comunidades en territorios montañosos.Since the first days of domestication, high mountain agropastoral societies have provided an exceptional example of adaptation and resilience to inhospitable territories, as the maintenance of population groups requires a delicate balance between transhumance and cultivation activities. Archaeological research into these societies has left numerous questions about the relationship between humans and the mountain environment unresolved. These questions have motivated the authors to explore the materiality of the daily life of the agropastoral populations of the Jbel Sirwa. Thus, with this proposal, the ARCHEOMOBAS project outlines an interdisciplinary methodological approach intended to carry out ethnoarchaeological studies of this kind of community in mountainous territories

Genetic variability, chemotype distribution, and aggressiveness of Fusarium culmorum on durum wheat in Tunisia

Fusarium culmorum is the most commonly reported root rot pathogen in Tunisian durum wheat. Isolates of the pathogen from four durum wheat growing areas in the north of Tunisia were analyzed for their chemotypes. Two chemotypes were detected at unequal abundance (96% of 3-ADON and 4% of NIV). Distribution of a SNP mutation located at the position 34 bp after the first exon of the EF-1\u3b1 partial sequence was analysed, to verify whether the haplotype was specifically associated to Fusarium root rot. A and T haplotypes were homogeneously distributed in three different Tunisian regions (Mateur, Beja and Bousalem) but not for the region of Bizerte, from which greatest number of A haplotype strains were detected. The isolates were tested for their virulence under glasshouse conditions, and a mean of 91% of crown and root infection was observed. Chemotype influenced virulence, but there was no significant influence of the geographical origin or haplotype on virulence. The distribution of three inter simple sequence repeats (ISSR) was examined, to better understand the structure of F. culmorum populations in Tunisia. A total of 27 fragments were obtained with eight polymorphic bands. Cluster analysis showed a high level of similarity between isolates. Analysis of molecular variance confirmed that there was little genetic differentiation among F. culmorum strains from different locations

Formal Modelling and Verification of Security Policies in Cloud Computing

This thesis tackles the problem of formal modelling and verification of security policies in cloud computing. Indeed, our research focuses on the modelling and the verification of access control policies using Coloured Petri Nets (CPNs). Due to its ability to reduce complexity, Role Based Access Control (RBAC) model was one of the predominant models for access control and the specification of security policies. In its original version, RBAC does not consider several important events, thus, TRBAC (Temporal RBAC) was proposed as an RBAC extension. This thesis provides three basic contributions. In the first contribution, HTCPNs (Hierarchical Timed Colored Petri Nets) formalism is used to model the TRBAC (Temporal Role Based Access Control) policy, and then the CPN-tool is exploited to analyse the obtained models. Indeed, the timed aspect in HTCPN allows us to deal with temporal constraints in TRBAC. The hierarchical aspect of HTCPN makes the TRBAC model “manageable”, despite the complexity of the policy. RBAC as well as TRBAC suffer from several drawbacks in large scale networks as the case of cloud environment. Although Attribute Based Access Control (ABAC) model handles some RBAC drawbacks, ABAC misses RBAC advantages. Hence, as a second contribution, we propose an access control model FRABAC (Fine-Grained Role Attribute Based Access Control) that provides scalability, flexibility, and fine granularity in the cloud environment. FRABAC combines and extends, basically, two models RBAC and ABAC. In order to demonstrate the advantages of the new proposed model, an empirical study is realised. In this study, the new proposed model is compared versus three existing models, using specific metrics. The results demonstrate that the new proposed model is more suitable than existing ones. As a third contribution, we provide a formal specification/ verification of FRABAC using HTCPN formalism and CPN-tool

Robustification of Nonlinear Model Predictive Control - Application to sustainable development processes.

Les dernières années ont permis des développements très rapides, tant au niveau de l’élaboration que de l’application, d’algorithmes de commande prédictive non linéaire (CPNL), avec une gamme relativement large de réalisations industrielles. Un des obstacles les plus significatifs rencontré lors du développement de cette commande est lié aux incertitudes sur le modèle du système. Dans ce contexte, l’objectif principal de cette thèse est la conception de lois de commande prédictives non linéaires robustes vis-à-vis des incertitudes sur le modèle. Classiquement, cette synthèse peut s’obtenir via la résolution d’un problème d’optimisation min-max. L’idée est alors de minimiser l’erreur de suivi de la trajectoire optimale pour la pire réalisation d'incertitudes possible. Cependant, cette formulation de la commande prédictive robuste induit une complexité qui peut être élevée ainsi qu’une charge de calcul importante, notamment dans le cas de systèmes multivariables, avec un nombre de paramètres incertains élevé. Pour y remédier, une approche proposée dans ces travaux consiste à simplifier le problème d’optimisation min-max, via l’analyse de sensibilité du modèle vis-à-vis de ses paramètres afin d’en réduire le temps de calcul. Dans un premier temps, le critère est linéarisé autour des valeurs nominales des paramètres du modèle. Les variables d’optimisation sont soit les commandes du système soit l’incrément de commande sur l’horizon temporel. Le problème d’optimisation initial est alors transformé soit en un problème convexe, soit en un problème de minimisation unidimensionnel, en fonction des contraintes imposées sur les états et les commandes. Une analyse de la stabilité du système en boucle fermée est également proposée. En dernier lieu, une structure de commande hiérarchisée combinant la commande prédictive robuste linéarisée et une commande par mode glissant intégral est développée afin d’éliminer toute erreur statique en suivi de trajectoire de référence. L'ensemble des stratégies proposées est appliqué à deux cas d'études de commande de bioréacteurs de culture de microorganismes.The last few years have led to very rapid developments, both in the formulation and the application of Nonlinear Model Predictive Control (NMPC) algorithms, with a relatively wide range of industrial achievements. One of the most significant challenges encountered during the development of this control law is due to uncertainties in the model of the system. In this context, the thesis addresses the design of NMPC control laws robust towards model uncertainties. Usually, the above design can be achieved through solving a min-max optimization problem. In this case, the idea is to minimize the tracking error for the worst possible uncertainty realization. However, this robust approach tends to become too complex to be solved numerically online, especially in the case of multivariable systems with a large number of uncertain parameters. To address this shortfall, the proposed approach consists in simplifying the min-max optimization problem through a sensitivity analysis of the model with respect to its parameters, in order to reduce the calculation time. First, the criterion is linearized around the model parameters nominal values. The optimization variables are either the system control inputs or the control increments over the prediction horizon. The initial optimization problem is then converted either into a convex optimization problem, or a one-dimensional minimization problem, depending on the nature of the constraints on the states and commands. The stability analysis of the closed-loop system is also addressed. Finally, a hierarchical control strategy is developed, that combines a robust model predictive control law with an integral sliding mode controller, in order to cancel any tracking error. The proposed approaches are applied through two case studies to the control of microorganisms culture in bioreactors

Robustification de la commande prédictive non linéaire - Application à des procédés pour le développement durable.

The last few years have led to very rapid developments, both in the formulation and the application of Nonlinear Model Predictive Control (NMPC) algorithms, with a relatively wide range of industrial achievements. One of the most significant challenges encountered during the development of this control law is due to uncertainties in the model of the system. In this context, the thesis addresses the design of NMPC control laws robust towards model uncertainties. Usually, the above design can be achieved through solving a min-max optimization problem. In this case, the idea is to minimize the tracking error for the worst possible uncertainty realization. However, this robust approach tends to become too complex to be solved numerically online, especially in the case of multivariable systems with a large number of uncertain parameters. To address this shortfall, the proposed approach consists in simplifying the min-max optimization problem through a sensitivity analysis of the model with respect to its parameters, in order to reduce the calculation time. First, the criterion is linearized around the model parameters nominal values. The optimization variables are either the system control inputs or the control increments over the prediction horizon. The initial optimization problem is then converted either into a convex optimization problem, or a one-dimensional minimization problem, depending on the nature of the constraints on the states and commands. The stability analysis of the closed-loop system is also addressed. Finally, a hierarchical control strategy is developed, that combines a robust model predictive control law with an integral sliding mode controller, in order to cancel any tracking error. The proposed approaches are applied through two case studies to the control of microorganisms culture in bioreactors.Les dernières années ont permis des développements très rapides, tant au niveau de l’élaboration que de l’application, d’algorithmes de commande prédictive non linéaire (CPNL), avec une gamme relativement large de réalisations industrielles. Un des obstacles les plus significatifs rencontré lors du développement de cette commande est lié aux incertitudes sur le modèle du système. Dans ce contexte, l’objectif principal de cette thèse est la conception de lois de commande prédictives non linéaires robustes vis-à-vis des incertitudes sur le modèle. Classiquement, cette synthèse peut s’obtenir via la résolution d’un problème d’optimisation min-max. L’idée est alors de minimiser l’erreur de suivi de la trajectoire optimale pour la pire réalisation d'incertitudes possible. Cependant, cette formulation de la commande prédictive robuste induit une complexité qui peut être élevée ainsi qu’une charge de calcul importante, notamment dans le cas de systèmes multivariables, avec un nombre de paramètres incertains élevé. Pour y remédier, une approche proposée dans ces travaux consiste à simplifier le problème d’optimisation min-max, via l’analyse de sensibilité du modèle vis-à-vis de ses paramètres afin d’en réduire le temps de calcul. Dans un premier temps, le critère est linéarisé autour des valeurs nominales des paramètres du modèle. Les variables d’optimisation sont soit les commandes du système soit l’incrément de commande sur l’horizon temporel. Le problème d’optimisation initial est alors transformé soit en un problème convexe, soit en un problème de minimisation unidimensionnel, en fonction des contraintes imposées sur les états et les commandes. Une analyse de la stabilité du système en boucle fermée est également proposée. En dernier lieu, une structure de commande hiérarchisée combinant la commande prédictive robuste linéarisée et une commande par mode glissant intégral est développée afin d’éliminer toute erreur statique en suivi de trajectoire de référence. L'ensemble des stratégies proposées est appliqué à deux cas d'études de commande de bioréacteurs de culture de microorganismes

The Analytical Solution of Telegraph Equation of Space-Fractional Order Derivative by the Aboodh Transform Method

In this article, an analytical solution based on the series expansion method is proposed to solve the telegraph equation of space - fractional order (TESFO), namely the Aboodh transformation method (ATM) subjected to the appropriate initial condition. Using ATM, it is possible to find exact solution or a closed approximate solution of a differential equation. Finally, several numerical examples are given to illustrate the accuracy and stability of this method