Water temperature modeling in the Garonne River (France)

Stream water temperature is one of the most important parameters for water quality and ecosystem studies. Temperature can influence many chemical and biological processes and therefore impacts on the living conditions and distribution of aquatic ecosystems. Simplified models such as statistical models can be very useful for practitioners and water resource management. The present study assessed two statistical models – an equilibrium-based model and stochastic autoregressive model with exogenous inputs – in modeling daily mean water temperatures in the Garonne River from 1988 to 2005. The equilibrium temperature-based model is an approach where net heat flux at the water surface is expressed as a simpler form than in traditional deterministic models. The stochastic autoregressive model with exogenous inputs consists of decomposing the water temperature time series into a seasonal component and a short-term component (residual component). The seasonal component was modeled by Fourier series and residuals by a second-order autoregressive process (Markov chain) with use of short-term air temperatures as exogenous input. The models were calibrated using data of the first half of the period 1988–2005 and validated on the second half. Calibration of the models was done using temperatures above 20 ◦C only to ensure better prediction of high temperatures that are currently at stake for the aquatic conditions of the Garonne River, and particularly for freshwater migrating fishes such as Atlantic Salmon (Salmo salar L.). The results obtained for both approaches indicated that both models performed well with an average root mean square error for observed temperatures above 20 ◦C that varied on an annual basis from 0.55 ◦C to 1.72 ◦C on validation, and good predictions of temporal occurrences and durations of three temperature threshold crossings linked to the conditions of migration and survival of Atlantic Salmon

Formal Methods for Systems Engineering Behavior Models

International audienceSafety analysis in Systems Engineering (SE) processes, as usually implemented, rarely relies on formal methods such as model checking since such techniques, however powerful and mature, are deemed too complex for efficient use. This paper thus aims at improving the verification practice in SE design: considering the widely-used model of EFFBDs (Enhanced Function Flow Block Diagrams), it formally establishes its syntax and behavioral semantics. It also proposes a structural translation of EFFBDs to transition time Petri nets (TPNs); this translation is then proved to preserve the behavioral semantics (i.e. timed bisimilarity). After proving results on the boundedness of the resulting TPNs, it was possible to extend a number of fundamental properties (such as the decidability of liveness, state-access, etc.) from bounded TPNs to so-called \emph{bounded EFFBDs}. Finally, these results led to implement and integrate an operational formal verification tool within a development platform, used in systems design for defense applications, where the underlying complexity is totally concealed from the end-us

Numerical investigation of ductile damage parameters identification: benefit of local measurements

International audienceIdentification of material parameters is an important issue to improve the accuracy of finite element computations. Identification of these parameters by inverse analysis is based on experimental observables coming from mechanical experiments. In this paper, a simple tensile test is used. Two types of observables are investigated to identify ductile damage law parameters. The first one is a global measurement, such as the load-displacement curve. The second is a local observable based on full field measurements. Our approach, based on response surfaces, allows an efficient analysis of identification issues. Ill conditioned problems and multi-minima can be obtained using only a global observable. Full field measurements are a good way to improve the identification of plastic hardening and damage law parameters. In fact, local measurements combined with global ones, lead to a better formulation of the inverse problem

On the compared expressiveness of arc, place and transition time Petri nets

International audienceIn this paper, we consider safe Time Petri Nets where time intervals (strict and large) are associated with places (TPPN), arcs (TAPN) or transitions (TTPN). We give the formal strong and weak semantics of these models in terms of Timed Transition Systems. We compare the expressiveness of the six models w.r.t. (weak) timed bisimilarity (behavioral semantics). The main results of the paper are : (i) with strong semantics, TAPN is strictly more expressive than TPPN and TTPN ; (ii) with strong semantics TPPN and TTPN are incomparable ; (iii) TTPN with strong semantics and TTPN with weak semantics are incomparable. Moreover, we give a complete classification by a set of 9 relations explained in a figure

Structural translation from time petri nets to timed automata

International audienceIn this paper, we consider Time Petri Nets (TPN) where time is associated with transitions. We give a formal semantics for TPNs in terms of Timed Transition Systems. Then, we propose a translation from TPNs to Timed Automata (TA) that preserves the behavioral semantics (timed bisimilarity) of the TPNs. For the theory of TPNs this result is two-fold: i) reachability problems and more generally TCTL model-checking are decidable for bounded TPNs; ii) allowing strict time constraints on transitions for TPNs preserves the results described in i). The practical appli- cations of the translation are: i) one can specify a system using both TPNs and Timed Automata and a precise semantics is given to the composition; ii) one can use existing tools for analyzing timed automata (like Kronos, Uppaal or Cmc) to analyze TPNs. In this paper we describe the new feature of the tool Romeo that implements our translation of TPNs in the Uppaal input format. We also report on experiments carried out on various examples and compare the result of our method to state-of-the-art tool for analyzing TPNs

How to coexist with fire ants: The roles of behaviour and cuticular compounds

tBecause territoriality is energetically costly, territorial animals frequently respond less aggressively toneighbours than to strangers, a reaction known as the “dear enemy phenomenon” (DEP). The contrary,the “nasty neighbour effect” (NNE), occurs mainly for group-living species defending resource-basedterritories. We studied the relationships between supercolonies of the pest fire ant Solenopsis saevissimaand eight ant species able to live in the vicinity of its nests plus Eciton burchellii, an army ant predatorof other ants. The workers from all of the eight ant species behaved submissively when confrontedwith S. saevissima (dominant) individuals, whereas the contrary was never true. Yet, S. saevissima weresubmissive towards E. burchellii workers. Both DEP and NNE were observed for the eight ant species, withsubmissive behaviours less frequent in the case of DEP. To distinguish what is due to chemical cues fromwhat can be attributed to behaviour, we extracted cuticular compounds from all of the nine ant speciescompared and transferred them onto a number of S. saevissima workers that were then confronted withuntreated conspecifics. The cuticular compounds from three species, particularly E. burchellii, triggeredgreater aggressiveness by S. saevissima workers, while those from the other species did not

Detection of phase singularities with a Shack-Hartmann wavefront sensor

While adaptive optical systems are able to remove moderate wavefront distortions in scintillated optical beams, phase singularities that appear in strongly scintillated beams can severely degrade the performance of such an adaptive optical system. Therefore, the detection of these phase singularities is an important aspect of strong scintillation adaptive optics. We investigate the detection of phase singularities with the aid of a Shack-Hartmann wavefront sensor and show that, in spite of some systematical deficiencies inherent to the Shack-Hartmann wavefront sensor, it can be used for the reliable detection of phase singularities, irrespective of their morphologies. We provide full analytical results, together with numerical simulations of the detection process.Comment: 23 pages, 9 figure

Spectral Dimensionality Reduction

In this paper, we study and put under a common framework a number of non-linear dimensionality reduction methods, such as Locally Linear Embedding, Isomap, Laplacian Eigenmaps and kernel PCA, which are based on performing an eigen-decomposition (hence the name 'spectral'). That framework also includes classical methods such as PCA and metric multidimensional scaling (MDS). It also includes the data transformation step used in spectral clustering. We show that in all of these cases the learning algorithm estimates the principal eigenfunctions of an operator that depends on the unknown data density and on a kernel that is not necessarily positive semi-definite. This helps to generalize some of these algorithms so as to predict an embedding for out-of-sample examples without having to retrain the model. It also makes it more transparent what these algorithm are minimizing on the empirical data and gives a corresponding notion of generalization error. Dans cet article, nous étudions et développons un cadre unifié pour un certain nombre de méthodes non linéaires de réduction de dimensionalité, telles que LLE, Isomap, LE (Laplacian Eigenmap) et ACP à noyaux, qui font de la décomposition en valeurs propres (d'où le nom "spectral"). Ce cadre inclut également des méthodes classiques telles que l'ACP et l'échelonnage multidimensionnel métrique (MDS). Il inclut aussi l'étape de transformation de données utilisée dans l'agrégation spectrale. Nous montrons que, dans tous les cas, l'algorithme d'apprentissage estime les fonctions propres principales d'un opérateur qui dépend de la densité inconnue de données et d'un noyau qui n'est pas nécessairement positif semi-défini. Ce cadre aide à généraliser certains modèles pour prédire les coordonnées des exemples hors-échantillons sans avoir à réentraîner le modèle. Il aide également à rendre plus transparent ce que ces algorithmes minimisent sur les données empiriques et donne une notion correspondante d'erreur de généralisation.non-parametric models, non-linear dimensionality reduction, kernel models, modèles non paramétriques, réduction de dimensionalité non linéaire, modèles à noyau