Search results for "machine learning."

Using self-deferral to achieve fairness between Wi-Fi and NR-U in downlink and uplink scenarios

2022

Wireless networks operating in unlicensed bands generally use one of two channel access paradigms: random access (e.g., Wi-Fi) or scheduled access (e.g., LTE License Assisted Access, LTE LAA and New Radio-Unlicensed, NR-U). The coexistence between these two paradigms is based on listen before talk (LBT), which was, however, designed for random access. Meanwhile, scheduled systems require that their transmissions start at the beginning of a slot boundary. Synchronizing this boundary to the end of LBT usually requires transmitting a reservation signal (RS) to block the channel. Since the RS is a waste of channel resources, we investigate an alternative self-deferral approach (gap-based access…

Connections Between Topology and Macroscopic Mechanical Properties of Three-Dimensional Open-Pore Materials

2018

This work addresses a number of fundamental questions regarding the topological description of materials characterized by a highly porous three-dimensional structure with bending as the major deformation mechanism. Highly efficient finite-element beam models were used for generating data on the mechanical behavior of structures with different topologies, ranging from highly coordinated bcc to Gibson–Ashby structures. Random cutting enabled a continuous modification of average coordination numbers ranging from the maximum connectivity to the percolation-cluster transition of the 3D network. The computed macroscopic mechanical properties–Young's modulus, yield strength, and Poisson's ratio–co…

Reports and other PDF Documents

2014

Stata users often need to combine text, tables, and figures. The author's command, lpdf, generates reports and other PDF documents. lpdf compiles text stored in global macros, tables stored as dataset tables or LATEX table input files, and figures stored as Stata graphs or PDF figure files. LATEX must be installed, but familiarity with LATEX is not necessary. lpdf performs every step through Stata and with Stata syntax. It generates documents in report or article style and portrait or landscape orientation. The default author name, document title, and date can be modified. Further format options include the font and margin sizes. For each table and figure, the width and layout can be adapt…

Adaptive Learning Process for the Evolution of Ontology-Described Classification Model in Big Data Context

2016

International audience; One of the biggest challenges in Big Data is to exploit value from large volumes of variable and changing data. For this, one must focus on analyzing the data in these Big Data sources and classify the data items according to a domain model (e.g. an ontology). To automatically classify unstructured text documents according to an ontology, a hierarchical multi-label classification process called Semantic HMC was proposed. This process uses ontologies to describe the classification model. To prevent cold start and user overload, the classification process automatically learns the ontology-described classification model from a very large set of unstructured text documen…

Overlapping community detection versus ground-truth in AMAZON co-purchasing network

2015

International audience; Objective evaluation of community detection algorithms is a strategic issue. Indeed, we need to verify that the communities identified are actually the good ones. Moreover, it is necessary to compare results between two distinct algorithms to determine which is most effective. Classically, validations rely on clustering comparison measures or on quality metrics. Although, various traditional performance measures are used extensively. It appears very clearly that they cannot distinguish community structures with different topological properties. It is therefore necessary to propose an alternative methodology more sensitive to the community structure variations in orde…

Une approche Web sémantique et combinatoire pour un système de recommandation sensible au contexte appliqué à l'apprentissage mobile

2014

National audience; Au vu de l'émergence rapide des nouvelles technologies mobiles et la croissance des offres et besoins d'une société en mouvement en formation, les travaux se multiplient pour identifier de nouvelles plateformes d'apprentissage pertinentes afin d'améliorer et faciliter le processus d'apprentissage à distance. La prochaine étape de l'apprentissage à distance est naturellement le port de l'e-learning (apprentissage électronique) vers les nouveaux systèmes mobiles. On parle alors de m-learning (apprentissage mobile). La recherche d'informations dans le domaine du m-learning peut être définie comme une activité dont la fi-nalité est de localiser et de délivrer des contenus d'a…

Construction de Modèles Prédictifs pour l'Analyse des Relations Oiseaux-Paysage

2013

National audience; Cet article présente une comparaison de trois méthodes (Modèles Linéaires Généralisés, Réseaux de Neurones, Machines Vecteurs Supports) et de différentes combinaisons de prétraitements de données (filtrage, arrondi, analyse factorielle, sélection de paramètres). L'objectif de cette comparaison est de définir quel est le processus qui permet de construire le meilleur modèle prédictif, dans le cadre de la prédiction d'abondances d'espèces d'oiseaux à partir de variables décrivant le paysage. Nous comparerons les modèles grâce à l'erreur moyenne absolue et à l'information mutuelle. Cette comparaison a montré qu'aucune technique étudiée ne permet de construire des modèles pré…

Numerical models contribute to expand the sweet taste chemical space

2021

A Neural Network Meta-Model and its Application for Manufacturing

2015

International audience; Manufacturing generates a vast amount of data both from operations and simulation. Extracting appropriate information from this data can provide insights to increase a manufacturer's competitive advantage through improved sustainability, productivity, and flexibility of their operations. Manufacturers, as well as other industries, have successfully applied a promising statistical learning technique, called neural networks (NNs), to extract meaningful information from large data sets, so called big data. However, the application of NN to manufacturing problems remains limited because it involves the specialized skills of a data scientist. This paper introduces an appr…

Système de sécurité biométrique multimodal par imagerie, dédié au contrôle d’accès

2019

Research of this thesis consists in setting up efficient and light solutions to answer the problems of securing sensitive products. Motivated by a collaboration with various stakeholders within the Nuc-Track project, the development of a biometric security system, possibly multimodal, will lead to a study on various biometric features such as the face, fingerprints and the vascular network. This thesis will focus on an algorithm and architecture matching, with the aim of minimizing the storage size of the learning models while guaranteeing optimal performances. This will allow it to be stored on a personal support, thus respecting privacy standards.