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Prediction of industrial equipment Remaining Useful Life by fuzzy similarity and belief function theory
No full textInternational audienceWe develop a novel prognostic method for estimating the Remaining Useful Life (RUL) of industrial equipment and its uncertainty. The novelty of the work is the combined use of a fuzzy similarity method for the RUL prediction and of Belief Function Theory for uncertainty treatment. This latter allows estimating the uncertainty affecting the RUL predictions even in cases characterized by few available data, in which traditional uncertainty estimation methods tend to fail. From the practical point of view, the maintenance planner can define the maximum acceptable failure probability for the equipment of interest and is informed by the proposed prognostic method of the time at which this probability is exceeded, allowing the adoption of a predictive maintenance approach which takes into account RUL uncertainty. The method is applied to simulated data of creep growth in ferritic steel and to real data of filter clogging taken from a Boiling Water Reactor (BWR) condenser. The obtained results show the effectiveness of the proposed method for uncertainty treatment and its superiority to the Kernel Density Estimation (KDE) and the Mean-Variance Estimation (MVE) methods in terms of reliability and precision of the RUL prediction intervals
Leveraging deep neural networks with nonnegative representations for improved environmental sound classification
No full textInternational audienceThis paper introduces the use of representations based on non-negative matrix factorization (NMF) to train deep neural networks with applications to environmental sound classification. Deep learning systems for sound classification usually rely on the network to learn meaningful representations from spectrograms or hand-crafted features. Instead, we introduce a NMF-based feature learning stage before training deep networks , whose usefulness is highlighted in this paper, especially for multi-source acoustic environments such as sound scenes. We rely on two established unsupervised and supervised NMF techniques to learn better input representations for deep neural networks. This will allow us, with simple architectures, to reach competitive performance with more complex systems such as convolutional networks for acoustic scene classification. The proposed systems outperform neu-ral networks trained on time-frequency representations on two acoustic scene classification datasets as well as the best systems from the 2016 DCASE challenge
Proving Soundness of Extensional Normal-Form Bisimilarities
No full textInternational audienceNormal-form bisimilarity is a simple, easy-to-use behavioral equivalence that relates terms in λ-calculi by decomposing their normal forms into bisimilar subterms. Besides, they allow for powerful up-to techniques, such as bisimulation up to context, which simplify bisimulation proofs even further. However, proving soundness of these relations becomes complicated in the presence of η-expansion and usually relies on ad-hoc proof methods which depend on the language. In this paper, we propose a more systematic proof method to show that an extensional normal-form bisimilarity along with its corresponding bisimulation up to context are sound. We illustrate our technique with the call-by-value λ-calculus, before applying it to a call-by-value λ-calculus with the delimited-control operators shift and reset. In both cases, there was previously no sound bisimulation up to context validating the η-law. Our results have been formalized in the Coq proof assistant
Modèles probabilistes d’ordonnancement partiel pour les systèmes distribués
No full textDistributed systems have managed to extend technology to a broader audience, in both terms of location and numbers. However these geo-replicated systems need to be scalable in order to meet the ever growing demands. Moreover, the system has to be able to process messages in an equivalent order that they were created to avoid unwanted side effects. Partial order enforcement provides an ordering of events that all nodes will follow therefore processing the messages in an adequate order. A system that enforces a partial order simplifies the challenge of developing distributed applications, and ensures that the end-user will not observe causality defying behaviors. In this thesis we present models for different partial order enforcements, using different latency model distributions. While a latency model, which yields the time it takes for a message to go from one node to another, our model builds on it to give the additional time that it takes to enforce a given partial order. We have proposed the following models. First, in a one to many nodes communication, the probability for the message to be delivered in all the nodes before a given time. Second, in a one to many nodes communication from the receivers, the probability that all the other nodes have delivered the message after a given time of him receiving it. Third, in a one to many nodes communication, the probability that the message has arrived to at least a subset of them before a given time. Fourth, applying either FIFO or Causal ordering determining if a message is ready for being delivered, in one node or many. All of this furthers the understanding of how distributed systems with partial orders behave. Furthermore using this knowledge we have built an algorithm that uses the insight of network behavior to provide a reliable causal delivery system. In order to validate our models, we developed a simulation tool that allows to run scenarios tailored to our needs. We can define the different parameters of the latency model, the number of clients, and the clients workloads. This simulation allows us to compare the randomly generated values for each specific configuration with the predicted outcome from our model. One of the applications that can take advantage of our model, is a reliable causal delivery algorithm. It uses causal information to detect missing elements and removes the need of message acknowledgment by contacting other replicas only when the message is assumed missing. This information is provided by our model, that defines waiting timers according to the network statistics and resource consumption. Finally this application has been both tested in the same simulator as the models, with promising results, and then evaluated in a real-life experiment using Amazon EC2 for the platformLes systèmes distribués ont réussi à étendre la technologie de l’information à un public plus large, en termes d’emplacement et de nombre. Cependant, ces systèmes géo-répliqués doivent être évolutifs afin de répondre aux demandes toujours croissantes. De plus, le système doit pouvoir traiter les messages dans un ordre équivalent à celui de leur création afin d’éviter des effets indésirables. L’exécution suivant des ordres partiels fournit un ordonnancement d’événements que tous les nœuds suivront, ce qui permet donc le traitement des messages dans un ordre adéquat. Un système qui applique un ordre partiel simplifie le développement des applications distribuées et s’assure que l’utilisateur final n’observera pas des comportements défiant la causalité. Dans cette thèse, nous présentons des modèles statistiques pour différentes contraintes d’ordre partiel, en utilisant différentes distributions de modèles de latence. Étant donné un modèle de latence, qui donne le temps qu’il faut pour qu’un message passe d’un nœud à un autre, notre modèle s’appuie sur lui pour donner le temps supplémentaire qu’il faut pour appliquer un ordre partiel spécifique. Nous avons proposé les modèles suivants. Tout d’abord, dans une communication entre un et plusieurs nœuds, la probabilité que le message soit délivré dans tous les nœuds avant un temps donné. Deuxièmement, après la réception d’un message, la probabilité que tous les autres nœuds aient exécuté ce message avant temps donné. Troisièmement, dans une communication de un à plusieurs nœuds, la probabilité que le message soit arrivé à au moins un sous-ensemble d’entre eux avant un temps donné. Quatrièmement, l’ordre FIFO ou causal qui détermine si un message est prêt à être livré, dans un nœud ou plusieurs. Tout cela favorise la compréhension du comportement des systèmes distribués en présence d’ordres partiels. En outre, en utilisant cette connaissance, nous avons construit un algorithme qui utilise ces modèles de comportement du réseau pour établir un système de livraison causal fiable. Afin de valider nos modèles, nous avons développé un outil de simulation qui permet d’exécuter des scénarios adaptés à nos besoins. Nous pouvons définir les différents paramètres du modèle de latence, le nombre de clients et les charges de travail des clients. Cette simulation nous permet de comparer les valeurs générées de façon aléatoire pour chaque configuration spécifique avec les résultats prévus de notre modèle. Une des applications qui peuvent tirer profit de notre modèle, est un algorithme de livraison causale fiable. Il utilise l’information causale pour détecter les éléments manquants et réduit le besoin d’acquittement de message en contactant d’autres répliques seulement lorsque le message est supposé manquant. Cette information est fournie par notre modèle, qui définit les temporisateurs d’attente en fonction des statistiques du réseau et de la consommation des ressources. Enfin, cette application a été testée dans le même simulateur que les modèles, avec des résultats prometteurs, puis évaluée dans une expérience réelle utilisant Amazon EC2 comme plate-form
Response of a Transcritical Coaxial Flame to Fuel Injection Rate Modulations: Analysis and Low-Order Modeling of the Generation of Unsteady Heat Release Rate
No full textInternational audienceAcoustic pressure fluctuations within the combustion chamber or the plenums of a liquid-rocket engine can induce temporal modulations of the propellants injection velocities. The dynamic response of a tran-scritical coaxial flame to such velocity modulations is here investigated as it is thought to be one of the mechanisms that can promote high-frequency combustion instabilities. A low-order model for this process is proposed, assuming that the modulation induces fluctuating flame stretch rates traveling along the flame front. This question is then addressed through Large-Eddy Simulation (LES). The annular fuel stream of a single element LOx/GCH 4 injector is acoustically modulated at several frequencies. The proposed model is in reasonable agreement with the flame response extracted from the LES in terms of both gain and phase
About vocabulary adaptation for automatic speech recognition of video data
No full textInternational audienceThis paper discusses the adaptation of vocabularies for automatic speech recognition. The context is the transcriptions of videos in French, English and Arabic. Baseline automatic speech recognition systems have been developed using available data. However, the available text data, including the GigaWord corpora from LDC, are getting quite old with respect to recent videos that are to be transcribed. The paper presents the collection of recent textual data from internet for updating the speech recognition vocabularies and training the language models, as well as the elaboration of development data sets necessary for the vocabulary selection process. The paper also compares the coverage of the training data collected from internet, and of the GigaWord data, with finite size vocabularies made of the most frequent words. Finally, the paper presents and discusses the amount of out-of-vocabulary word occurrences, before and after update of the vocabularies, for the three languages
Detection of Side Channel Attacks Based on Data Tainting in Android Systems
No full textPart 4: Operating System and Firmware SecurityInternational audienceMalicious third-party applications can leak personal data stored in the Android system by exploiting side channels. TaintDroid uses a dynamic taint analysis mechanism to control the manipulation of private data by third-party apps [9]. However, TaintDroid does not propagate taint in side channels. An attacker can exploit this limitation to get private data. For example, Sarwar et al. [2] present side channel class of attacks using a medium that might be overlooked by the taint-checking mechanism to extract sensitive data in Android system. In this paper, we enhance the TaintDroid system and we propagate taint in side channels using formal policy rules. To evaluate the effectiveness of our approach, we analyzed 100 free Android applications. We found that these applications use different side channels to transfer sensitive data. We successfully detected that 35% of them leaked private information through side channels. Also, we detected Sarwar et al. [2] side channel attacks. Our approach generates 9% of false positives. The overhead given by our approach is acceptable in comparison to the one obtained by TaintDroid (9% overhead)
A Case Study on the Parametric Occurrence of Multiple Steady States
No full textInternational audienceWe consider the problem of determining multiple steady states for positive real values in models of biological networks. Investigating the potential for these in models of the mitogen-activated protein kinases (MAPK) network has consumed considerable effort using special insights into the structure of corresponding models. Here we apply combinations of symbolic computation methods for mixed equality/inequality systems, specifically virtual substitution, lazy real triangularization and cylindrical algebraic decomposition. We determine multistationarity of an 11-dimensional MAPK network when numeric values are known for all but potentially one parameter. More precisely, our considered model has 11 equations in 11 variables and 19 parameters, 3 of which are of interest for symbolic treatment, and furthermore positivity conditions on all variables and parameters
FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network
No full textInternational audienceClustering is an efficient approach in a wireless sensor network (WSN) to reduce the energy consumption of nodes and to extend the lifetime of the network. Unfortunately, this approach requires that all cluster heads (CHs) transmit their data to the base station (BS), which gives rise to the long distance communications problem, and in multi-hop routing, the CHs near the BS have to forward data from other nodes that lead those CHs to die prematurely, creating the hot zones problem. Unequal clustering has been proposed to solve these problems. Most of the current algorithms elect CH only by considering their competition radius, leading to unevenly distributed cluster heads. Furthermore, global distances values are needed when calculating the competition radius, which is a tedious task in large networks. To face these problems, we propose a flooding tree uneven clustering protocol (FTUC) suited for large networks. Based on the construction of a tree type sub-network to calculate the minimum and maximum distances values of the network, we then apply the unequal cluster theory. We also introduce referenced position circles to evenly elect cluster heads. Therefore, cluster heads are elected depending on the node’s residual energy and their distance to a referenced circle. FTUC builds the best inter-cluster communications route by evaluating a cluster head cost function to find the best next hop to the BS. The simulation results show that the FTUC algorithm decreases the energy consumption of the nodes and balances the global energy consumption effectively, thus extending the lifetime of the network
