International Professional University of Technology in Nagoya Repository
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Evaluer des solutions de mobilité à déployer dans des contextes géographiques hétérogènes : un outil d’aide à la décision en deux phases
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Alteration of physico-chemical characteristics of coconut endocarp — Acrocomia aculeata — by isothermal pyrolysis in the range 250–550 °C
No full textInternational audienceCharacteristics of the endocarp of Acrocomia aculeata fruit samples were evaluated before and after 2 h of isothermal pyrolysis in the range 250-550 degrees C. Differential thermogravimetric (DTG) curves from the char, obtained at 300 degrees C, confirm that degradation of hemicellulose and cellulose was complete and resulted in approximately 42.5% oxygen loss. The micrographs obtained from scanning electron microscopy with a field emission gun (SEM/FEG) confirmed a softened phase from the chars treated at 250 degrees C. The van Krevelen analysis shows that energy intensification of the sample transferred from peat to charcoal as the treatment intensity increased; this resulted in a 71% mass loss at 550 degrees C. The surface area of the treated sample increased exponentially with a factor of 1.2 per percentage of mass loss, from 450 degrees C and reached 216 m(2)/g at 550 degrees C as a consequence of the development of microporous structures. The water-vapor-sorption properties were strongly affected by the treatment, with a pronounced type V isotherm curve for the char at 550 degrees C. These results show the evolution in chemical and structural properties of coconut endocarp during its isothermal pyrolysis. In particular, the improved char properties indicate that this material may be used as solid fuel or as raw material for the gasification process
Monitoring dynamic mobile ad-hoc networks: a fully distributed hybrid architecture
No full textInternational audienceThe mobile ad-hoc networks (MANETs) represent a broad area of study and market interest. They provide a wide set of applications in multiple domains. In that context, the functional and non-functional monitoring of these networks is crucial. For that purpose, monitoring techniques have been deeply studied in wired networks using gossip-based or hierarchical-based approaches. However, when applied to a MANET, several problematics arise mainly due to the absence of a centralized administration, the inherent MANETs constraints and the nodes mobility. In this paper, we present a hybrid distributed monitoring architecture for mobile adhoc networks in context of mobility pattern. We get inspired of gossip-based and hierarchical-based algorithms for query dissemination and data aggregation. We define gossip-based mechanisms that help our virtual hierarchical topology to complete the data aggregation, and then ensure the stability and robustness of our approach in dynamic environments. Further, we propose a fully distributed monitoring protocol that ease the nodes communications. We evaluate our approach through a simulated testbed by using NS3 and Docker, and illustrate the efficiency of our mechanism
Experimental and numerical investigation of intermittent drying of timber
No full textInternational audienceIntermittent drying may be of interest in the future to address energy issues. Such drying conditions are also likely to help stress relaxation through mechanosorptive creep. The influence of applying oscillations of temperature and relative humidity during the drying of beech timber on time and drying stresses is discussed in the paper by means of nonsymmetrical and loaded drying. Experimental data were used to validate a numerical model in the case of intermittent drying. The model was then used to perform a numerical investigation of the possibility of using an intermittent energy source to dry wood
Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment
No full textInternational audienceMulti-State Physics Modeling (MSPM) provides a physics-based semi-Markov modeling framework for a more detailed reliability assessment. In this work, a three-loop Monte Carlo (MC) simulation scheme is proposed to operationalize the MSPM approach, quantifying and controlling the uncertainty affecting the system reliability model. The proposed MC simulation scheme involves three steps: (i) the identification of the system components that deserve MSPM, (ii) the quantification of the uncertainties in the MSPM component models and their propagation onto the system-level model, and (iii) the selection of the most suitable modeling alternative that balances the computational demand for the system model solution and the robustness of the system reliability estimates. A Reactor Protection System (RPS) of a Nuclear Power Plant (NPP) is considered as case study for numerical evaluation
Weighted-feature and cost-sensitive regression model for component continuous degradation assessment
No full textInternational audienceConventional data-driven models for component degradation assessment try to minimize the average estimation accuracy on the entire available dataset. However, an imbalance may exist among different degradation states, because of the specific data size and/or the interest of the practitioners on the different degradation states. Specifically, reliable equipment may experience long periods in low-level degradation states and small times in high-level ones. Then, the conventional trained models may result in overfitting the low-level degradation states, as their data sizes overwhelm the high-level degradation states. In practice, it is usually more interesting to have accurate results on the high-level degradation states, as they are closer to the equipment failure. Thus, during the training of a data-driven model, larger error costs should be assigned to data points with high-level degradation states when the training objective minimizes the total costs on the training dataset. In this paper, an efficient method is proposed for calculating the costs for continuous degradation data. Considering the different influence of the features on the output, a weighted-feature strategy is integrated for the development of the data-driven model. Real data of leakage of a reactor coolant pump is used to illustrate the application and effectiveness of the proposed approach
Compromis efficacité énergétique et efficacité spectrale pour les objets communicants autonomes
No full textTechnological advances have led to the develop-ment of wireless sensor applications. These sensorsare generally deployed with reduced energy resourceswhere replacing a battery can be costly. Energy ef-ficiency is an important constraint to ensure a highlevel of autonomy. The current trend towards high-throughput applications requires not only high spectralefficiency but also reduced energy consumption. It istherefore essential to study the trade-off between spec-tral efficiency and energy efficiency for wireless sensornetworks (WSNs). In this thesis we concentrate on thedifferent techniques adopted at the level of the phys-ical layer. At first, the various aspects characterizingthe WSNs are introduced. Then, the efforts made tooptimize the conservation of energy in these networksare summarized while highlighting the link between theenergy consumption and the spectral efficiency. Then,different energy models are introduced and classifiedin order to study the evolution of the consumed energyas a function of the spectral efficiency. Secondly, wefocus on the choice of modulation in order to find theoptimal scheme that minimizes energy. We then stud-ied the tradeoff between energy and spectral efficiency,taking into account the constraints imposed by the sys-tem. Finally, we are interested in coding strategy anderror control protocol to study their impact on the en-ergy efficiency and spectral efficiency tradeoff.Les progrès technologiques ont permis le développement d’applications de capteurs sans fil. Ces capteurs sont généralement déployés avec des ressources énergétiques réduites où le remplacement d’une batterie peut être coûteux. L’efficacité énergétique est une contrainte importante pour assurer une grande autonomie. La tendance actuelle vers des applications à haut débit demande non seulement une grande efficacité spectrale mais aussi une consommation réduite de l’énergie. Étudier le compromis entre l’efficacité spectrale et l’efficacité énergétique pour les réseaux de capteurs sans fil (RCSFs) est donc primordial. Nous nous concentrons dans cette thèse sur l’étude destechniques adoptées au niveau de la couche physique. D’abord, les différents aspects caractérisant les RCSFs sont introduits. Puis les approches courantes pour réduire l’énergie dans ces réseaux sont rappelés tout en soulignant le lien entre la consommation d’énergie et l’amélioration de l’efficacité spectrale. Des modèles courants de consommation d’énergie sont introduits et classés afin d’étudier l’évolution de l’énergie consommée en fonction de l’efficacité spectrale. En second lieu, nous nous sommes focalisés sur le choix de la modulation du point de vue énergétique et spectrale afin de trouver le schéma de modulation optimal qui minimise l’énergie. Nous avons étudié ensuite le compromis entre l’efficacité énergétique et spectrale en tenant compte des contraintes imposées par le système. Enfin, nous nous sommes intéressés à l’intégration du codage et du protocole de contrôle d’erreurs dont nous avons étudié l’impact sur le compromis efficacité énergétique et efficacité spectrale
A Survey of Some Methods for Real Quantifier Elimination, Decision, and Satisfiability and Their Applications
No full textInternational audienceEffective quantifier elimination procedures for first-order theories provide a powerful tool for genericallysolving a wide range of problems based on logical specifications. In contrast to general first-order provers, quantifierelimination procedures are based on a fixed set of admissible logical symbolswith an implicitly fixed semantics. Thisadmits the use of sub-algorithms from symbolic computation. We are going to focus on quantifier elimination forthe reals and its applications giving examples from geometry, verification, and the life sciences. Beyond quantifierelimination we are going to discuss recent results with a subtropical procedure for an existential fragment of thereals. This incomplete decision procedure has been successfully applied to the analysis of reaction systems inchemistry and in the life sciences
Prediction of protein function using a deep convolutional neural network ensemble
No full textInternational audienceBackground. The availability of large databases containing high resolution three-dimensional (3D) models of proteins in conjunction with functional annotation allows the exploitation of advanced supervised machine learning techniques for automatic protein function prediction. Methods. In this work, novel shape features are extracted representing protein structure in the form of local (per amino acid) distribution of angles and amino acid distances, respectively. Each of the multi-channel feature maps is introduced into a deep convolutional neural network (CNN) for function prediction and the outputs are fused through support vector machines or a correlation-based k-nearest neighbor classifier. Two different architectures are investigated employing either one CNN per multi-channel feature set, or one CNN per image channel. Results. Cross validation experiments on single-functional enzymes (n = 44,661) from the PDB database achieved 90.1% correct classification, demonstrating an improvement over previous results on the same dataset when sequence similarity was not considered. Discussion. The automatic prediction of protein function can provide quick annotations on extensive datasets opening the path for relevant applications, such as pharmacological target identification. The proposed method shows promise for structure-based protein function prediction, but sufficient data may not yet be available to properly assess the method's performance on non-homologous proteins and thus reduce the confounding factor of evolutionary relationships
Algorithmes de crible pour le logarithme discret dans les corps finis de moyenne caractéristique
No full textThe security of public-key cryptography relies mainly on the difficulty to solve some mathematical problems, among which the discrete logarithm problem on finite fields GF(p^n). In this thesis, we study the variants of the number field sieve (NFS) algorithm, which solve the most efficiently this problem, in the case where the characteristic of the field is medium. The NFS algorithm can be divided into four main steps: the polynomial selection, the relation collection, the linear algebra and the computation of an individual logarithm. We describe these steps and focus on the relation collection, one of the most costly steps. A way to perform it efficiently is to make use of sieve algorithms. Contrary to the classical case for which the relation collection takes place in a two-dimensional space, the finite fields we target require the enumeration of elements in a higher-dimensional space to reach the best theoretical complexity. There exist efficient sieve algorithms in two dimensions, but only a few in higher dimensions. We propose and study two new sieve algorithms allowing us to treat any dimensions, with an emphasis on the three-dimensional case. We have provided a complete implementation of the relation collection for some variants of the NFS in three dimensions. This implementation relies on our new sieve algorithms and is distributed in the CADO-NFS software. We validated its performances by comparing with examples from the literature. We also establish two new discrete logarithm record computations, one in a 324-bit GF(p^5) and one in a 422-bit GF(p^6)La sécurité des systèmes cryptographiques à clef publique repose sur la difficulté de résoudre certains problèmes mathématiques, parmi lesquels se trouve le problème du logarithme discret sur les corps finis GF(p^n). Dans cette thèse, nous étudions les variantes de l’algorithme de crible algébrique, number field sieve (NFS) en anglais, qui résolvent le plus rapidement ce problème, dans le cas où la caractéristique du corps est dite moyenne. NFS peut être divisé en quatre étapes principales : la sélection polynomiale, la recherche de relations, l’algèbre linéaire et le calcul d’un logarithme individuel. Nous décrivons ces étapes, en insistant sur la recherche de relations, une des étapes les plus coûteuses. Une des manières efficaces de réaliser cette étape est d’utiliser des algorithmes de crible. Contrairement au cas classique où la recherche de relations est réalisée dans un espace à deux dimensions, les corps finis que nous ciblons requièrent une énumération d’éléments dans un espace de plus grande dimension pour atteindre la meilleure complexité théorique. Il existe des algorithmes de crible efficaces en deux dimensions, mais peu pour de plus grandes dimensions. Nous proposons et analysons deux nouveaux algorithmes de crible permettant de traiter n’importe quelle dimension, en insistant particulièrement sur la dimension trois. Nous avons réalisé une implémentation complète de la recherche de relations pour plusieurs variantes de NFS en dimensions trois. Cette implémentation, qui s'appuie sur nos nouveaux algorithmes de crible, est diffusée au sein du logiciel CADO-NFS. Nous avons pu valider ses performances en nous comparant à des exemples de la littérature. Nous avons également été en mesure d’établir deux nouveaux records de calcul de logarithmes discrets, l'un dans un corps GF(p^5) de taille 324 bits et l'autre dans un corps GF(p^6) de taille 422 bit