LU factorization with panel rank revealing pivoting and its communication avoiding version

We present the LU decomposition with panel rank revealing pivoting (LU_PRRP), an LU factorization algorithm based on strong rank revealing QR panel factorization. LU_PRRP is more stable than Gaussian elimination with partial pivoting (GEPP). Our extensive numerical experiments show that the new factorization scheme is as numerically stable as GEPP in practice, but it is more resistant to pathological cases and easily solves the Wilkinson matrix and the Foster matrix. We also present CALU_PRRP, a communication avoiding version of LU_PRRP that minimizes communication. CALU_PRRP is based on tournament pivoting, with the selection of the pivots at each step of the tournament being performed via strong rank revealing QR factorization. CALU_PRRP is more stable than CALU, the communication avoiding version of GEPP. CALU_PRRP is also more stable in practice and is resistant to pathological cases on which GEPP and CALU fail.Comment: No. RR-7867 (2012

Multilevel communication optimal LU and QR factorizations for hierarchical platforms

This study focuses on the performance of two classical dense linear algebra algorithms, the LU and the QR factorizations, on multilevel hierarchical platforms. We first introduce a new model called Hierarchical Cluster Platform (HCP), encapsulating the characteristics of such platforms. The focus is set on reducing the communication requirements of studied algorithms at each level of the hierarchy. Lower bounds on communications are therefore extended with respect to the HCP model. We then introduce multilevel LU and QR algorithms tailored for those platforms, and provide a detailed performance analysis. We also provide a set of numerical experiments and performance predictions demonstrating the need for such algorithms on large platforms

T-S Controllers For Photovoltaic-Grid Connected System Through DC-DC Boost Converter and Three Phase Inverter

Ce document présente deux contrôleurs flous TS en ligne pour contrôler l'extraction de puissance optimale et son transfert du système PV via deux convertisseurs statiques vers le réseau public. Le premier contrôleur est appliqué sur le convertisseur élévateur pour calculer, à chaque instant, le rapport cyclique permettant de suivre le point de puissance maximale du panneau sous les variations climatiques et d'atteindre un rendement élevé pour la récolte d'énergie solaire. Alors que le second ajuste les états de commutation des branches de l'onduleur triphasé à deux niveauxtransistors pour un transfert maximal de la puissance active produite par le panneau vers le réseau de distribution avec compensation de puissance réactive lors de l'établissement de la synchronisation.Après présentation de la structure du système de connexion au réseau et modélisation mathématique des convertisseurs côté PV et côté réseau, les contrôleurs flous TS sont détaillés. La synthèse de ces contrôleurs est basée sur la subdivision de l'espace d'états du système non linéaire à contrôler en un ensemble de sous-systèmes linéaires. Pour assurer le rejet des perturbations et garantir la stabilité du contrôleur flou,  Le critère et la fonction de stabilité quadratique de Lyapunov sont considérés. Les gains du contrôleur sont calculés en utilisant la solution d'inégalité de matrice linéaire (LMI). Les résultats de la simulation numérique sur l'environnement Matlab-Simulink montrent l'efficacité et les performances des contrôleurs proposés

Transportation optimization for the collection of end-of-life vehicles

Firms operating in the purchasing of end-of-life vehicles (ELVs) have significant challenges related to the fact that most of the purchased ELVs must be collected efficiently in order to minimize their transportation costs. In this project, we study a reverse logistics problem of a Canadian firm that collects ELVs from a group of dealers and accumulates them at its warehouse for part resale or recycling. This problem can be modeled as a vehicle routing problem (VRP) with different side-constraints. Although prior research has made several contributions to model and solve different variants of the VRP, the specific issue in this project considers solving a VRP with a new combination of constraints, such as customer assignment to the private fleet or an external carrier, time-windows, multi-trip, and loading sequences. We propose a mixed-integer linear programming (MILP) model as well as a heuristic algorithm capable of finding the routes’ planning that minimizes the total transportation costs. The performance of the proposed methods is assessed by generated instances using the data obtained from the company

Gestion de la pollinisation de la variété "Meski" d’olive de table tunisienne auto-incompatible (Olea europaea. L)

The cross-pollination of olive self-incompatible variety Meski (Tunisia) was accomplished by interplanting Picholine variety (France) in an irrigated field in the central and continental location of Sidi Bouzid with a density of 204 trees ha-1 (7 m / 7 m). Olive production was averaged for each tree Meski for 2006-2015 period. Trees were grouped according to the minimum distance from the first pollenizer tree and to the number of pollenizers in the first three squares around the tree Meski. The relationships between olive production and the distance from Picholine pollenizer as well as the relationships of Meski olive production and the number of Picholine pollenizer were analyzed with regression models. No significant effects were observed. Thus, the first pollenizer could be placed in the second row (14 m). The pollenizer number might be 0 in the first square around Meski, 1 in the second square and 3 in the third square. In total, the three first squares should totalize a maximum of 4 pollenizer trees. Results showed an optimum olive production with 8 % of pollenizers in Meski plantations. Thus, the low productive performance of Meski is not due to low autopollination but other factors could be involved. Key words: Olive, Meski variety, Pollination, Tunisia.La pollinisation croisée de la variété d’olive auto-incompatible Meski (Tunisie) a été réalisée par l’interplantation de la variété Picholine (France) dans un champ irrigué dans la zone centrale et continentale de Sidi Bouzid avec une densité de 204 arbres ha-1 (7 m / 7 m). La production d’olives moyenne a été calculée pour chaque arbre Meski sur la période 2006 à 2015. Les arbres ont été regroupés en fonction de la distance minimale par rapport au premier arbre pollinisateur et le nombre de polinisateurs dans les trois premières carrés autour de l’arbre Meski. Les relations de régression entre la production d’olives Meski et sa distance du pollinisateur Picholine d’une part, et entre la production d’olive Meski et le nombre de pollinisateurs Picholine d’autre part, étaient toutes non significatives. Ainsi, le premier arbre pollinisateur pourrait être placé dans le second carré (14 m). Le nombre de pollinisateurs pourrait être 0 dans le premier carré autour de Meski, 1 dans le deuxième carré et 3 dans le troisième carré. Au total, les trois premiers carrés devraient totaliser un maximum de 4 arbres pollinisateurs. Les résultats ont montré une production optimale d’olive avec 8 % des polinisateurs dans les plantations Meski. Ainsi, la faible performance productive de Meski n’est pas seulement due à la faible autopollinisation mais d’autres facteurs seraient également impliqués. Mots clés: Olive, variété Meski, Pollinisation, Tunisi

Soft sensor approach based on magnetic Barkhausen noise by means of the forming process punch-hole-rolling

The relevance of the magnetic Barkhausen noise (MBN) and the non-destructive characterization of material properties in near surface layers, has increased in recent years.With the development of new signal processing techniques, the method was further developed into a powerful evaluation technique and is used in various areas of online and offline measurement. In addition to the established use in the detection of grinding burn, the method is increasingly used in the context of soft sensors for property controlled processes, due to its short analysis times. By a detailed description of a soft sensor concept for the novel forming process punch-hole-rolling this work focuses on the offline characterization of the process specific cause-effect relationships. This is done by analyzing the process interactions as well as the surface layer state by a metallographic investigation. Additionally a non-destructive characterization by means of MBN was done and correlated with the surface layer state. This provides important findings for the use of a MBN-sensor in a soft sensor concept and the potential integration into the forming process

Impact of inoculation with single and mixed species of arbuscular mycorrhizal fungi on the soil fertility and the nutrient uptake of young olive plants

The current study aimed to determine the effect of single and dual inoculation with arbuscular mycorrhizal fungi (AMF) on soil quality and mineral content of young olive plants. One-year-old self-rooted olive plants (Olea europaea L.) of the cultivar Chetoui were inoculated with different AMF: (i) Glomus deserticola (AMF1); (ii) Gigaspora margarita (AMF2) or (iii) a 1:1 mixture of G. deserticola and G. margarita (AMF3). After one year of symbiosis, the obtained results showed that AMF played an important role in improving the fertility of the experimental soil by increasing the organic matter and the micro-nutrients contents (Nt, P and K+), as compared to control soil. Such effect induced an improvement in marco- and micro-nutrient contents in leaves and roots of all inoculated olive plants. The beneficial effect of mycorrhizal association was more important under inoculation with mixed species of AMF

Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers

Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification, to provide healthcare of higher standards. The purpose of this study is to investigate a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare. This method classifies wearable sensor acceleration time series data of human movement using efficient classifier combination of feature engineering-based and feature learning-based data representation. Leave-one-subject-out cross-validation of the method with data acquired from 44 subjects wearing a single waist-worn accelerometer on a smart textile, and engaged in a variety of 10 activities, yields an average recognition rate of 90%, performing significantly better than individual classifiers. The method easily accommodates functional and computational parallelization to bring execution time significantly down