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Proportional-integral observer design for switching systems-bond graph approach
No full textInternational audienc
Improvements of the PLANET System for Real-Time Satellite Data Transmission During the HAIC-HIWC Darwin Field Campaign
No full textInternational audienceThe PLANET System was used for real-time satellite data transmission during the HAIC-HIWC Darwin field campaign (January to March 2014). The basic system was initially providing aircraft tracking, chat, weather text messages (METAR, TAF, etc.), and aeronautical information (NOTAMs) in a standalone application. In the framework of the HAIC project, many improvements were made in order to fulfill requirements of the onboard and ground science teams for the field campaign. The aim of this paper is to present the main improvements of the system that were implemented for the Darwin field campaign. New features of the system are related to the hardware component, the communication protocol, weather and tracking display, geomarkers on the map, and image processing and compression before onboard transfer
Sea level monitoring and sea state estimate using a single geodetic receiver
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Uma visão geral das teorias do humor: aplicação da incongruência e da superioridade ao sarcasmo
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"Cosa può un'anima ? Corporeismo e saggezza nello stoicismo antico"
No full textInternational audienceThis paper aims to show that the Stoic physicalist conception of the soul as a specific body is original and still interesting for contemporary philosophy, as witnessed by some of its ethical consequences, which are shown to be similar to some of Deleuze’s claims drawn from Spinoza. The paper focuses on the Stoic analysis of the relationships between the soul and the body and of the nature of virtue and its capacity to resist various bodily threats, like drunkenness, taking into account the disagreements between Cleanthes and Chrysippus about the physical «strength» of wisdom
A new model for VMMP dealing with execution time uncertainty in a multi-clouds system
No full textInternational audienceThe Virtual Machine Mapping Problem (VMMP) arise by the development of cloud computing. Focusing on a novel approach for cloud broker model in multi-clouds environment, we present a new model to deal with VMMP that takes into account the uncertainty of the VM execution time, hence allowing to obtain robust assignment solutions. The uncertainty of the VM execution time is modeled by (i) relying on a truncated normal distribution for constructing mapping instances, and (ii) by using the expected value of the generating truncated normal distribution. The proposed methods, for the optimization for the VMMP, are conducted on the Grid5000 in order to bring a detailed results comparison between the obtained results from the experimental study with different benchmarks
Parallel Branch-and-Bound using private IVM-based work stealing on Xeon Phi MIC coprocessor
No full textInternational audienceMany combinatorial optimization problems are modeled in practice as permutation-based ones. We have recently proposed a new data structure called IVM dedicated to those problems. IVM is memory efficient in terms of size and management time for solving large permutation problems using Branch-and-Bound (B&B) algorithm. We believe that those memory properties make IVM well-suited for Many Integrated Cores (MIC) architecture. This paper deals with the parallel design and implementation of the B&B algorithm on MIC architectures using private IVM-based work stealing. The proposed approach has been extensively experimented on an Intel Xeon Phi 5110P using several instances of the Flow-Shop scheduling permutation problem. The reported results show that the IVM-based work stealing approach is about 10 times faster than the linked-list traditionally used for parallel B&B
Inverse Reinforcement Learning in Relational Domains
No full textInternational audienceIn this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in relational domains. IRL has been used to recover a more compact representation of the expert policy leading to better generalization performances among different contexts. On the other hand, rela-tional learning allows representing problems with a varying number of objects (potentially infinite), thus provides more generalizable representations of problems and skills. We show how these different formalisms allow one to create a new IRL algorithm for relational domains that can recover with great efficiency rewards from expert data that have strong generalization and transfer properties. We evaluate our algorithm in representative tasks and study the impact of diverse experimental conditions such as : the number of demonstrations, knowledge about the dynamics, transfer among varying dimensions of a problem, and changing dynamics
Global vs local search on multi-objective NK-landscapes: contrasting the impact of problem features
No full textBest paper award (ECOM track)International audienceComputationally hard multi-objective combinatorial optimization problems are common in practice, and numerous evolutionary multi-objective optimization (EMO) algorithms have been proposed to tackle them. Our aim is to understand which (and how) problem features impact the search performance of such approaches. In this paper, we consider two prototypical dominance-based algorithms: a global EMO strategy using an ergodic variation operator (GSEMO) and a neighborhood-based local search heuristic (PLS). Their respective runtime is estimated on a benchmark of combinatorial problems with tunable ruggedness, objective space dimension, and objective correlation (MNK-landscapes). In other words, benchmark parameters define classes of instances with increasing empirical problem hardness; we enumerate and characterize the search space of small instances. Our study departs from simple performance comparison to systematically analyze the correlations between runtime and problem features, contrasting their association with search performance within and across instance classes, for both chosen algorithms. A mixed-model approach then allows us to further generalize from the experimental design, supporting a sound assessment of the joint impact of instance features on EMO search performance