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Inducing strong convergence of trajectories in dynamical systems associated to monotone inclusions with composite structure
No full textInternational audienceZeros of the sum of a maximally monotone operator with a single-valued monotone one can be obtained as weak limits of trajectories of dynamical systems, for strong convergence demanding hypotheses being imposed. We extend an approach due to Attouch, Cominetti and coauthors, where zeros of maximally monotone operators are obtained as strong limits of trajectories of Tikhonov regularized dynamical systems, to forward-backward and forward-backward-forward dynamical systems whose trajectories strongly converge towards zeros of such sums of monotone operators under reasonable assumptions
A recurrent approach for predicting Parkinson stage from multimodal videos
No full textInternational audienceParkinson's disease is a neurodegenerative disease that affects more than 6.1 million people worldwide. In the clinical routine, the main tool to diagnose and monitor disease progression is based on motor impairments, such as postural instability, bradykinesia, tremor, among others. Besides, new biomarkers based on motion patterns have emerged to describe disease findings. Nonetheless, this motor characterization has low sensitivity, especially at early stages, and is largely expert-dependent, because protocols are mainly based on visual observations. However, most of these analyses require complex and some invasive systems that additionally only bring global information of complete recordings. This work introduces a multimodal approach that integrates gait and eye motion videos to quantify and predict patient stage on-the-fly. This method starts by computing dense apparent velocity maps that represent the local displacement of the person seen from the gait in a sagittal plane and as micro-movements during the fixation experiment. Then, each frame is described as a covariance descriptor of deep feature activation maps computed over the motion field at each video time. Then, the covariance video manifold is mapped to a recurrent LSTM network to learn higher non-local dependencies and quantify a motion descriptor. Also, an end-to-end scheme allows to lately fuse both modalities (gait and fixational eye) to obtain a more sensitive Parkinson disease descriptor. In a study with 25 subjects, the proposed approach reaches an average F1-score of 0.83 with an average recall of 0.78. In a temporal prediction analysis, the approach reports major correlations with the disease considering swing phase
Combined effects of temperature and of high hydrogen and oxygen contents on the mechanical behavior of a zirconium alloy upon cooling from the βZr phase temperature range
No full textInternational audienceDuring hypothetical loss-of-coolant accidents (LOCA), zirconium-based nuclear fuel claddings can be exposed to high temperatures in the βZr phase domain and absorb substantial amounts of hydrogen (up to about 3000 weight ppm) and oxygen (up to about 1 weight %). This paper provides novel data about the combined effects of high hydrogen and oxygen contents on the mechanical behavior of the (prior-)βZr phase, as a function of temperature, upon cooling from the βZr phase temperature range. A protocol was developed to homogeneously charge Zircaloy-4 cladding tubes at different hydrogen contents, up to 3200 weight ppm, and oxygen contents, between 0.13 and 0.9 weight %. Tensile tests were then performed at various temperatures between 700 and 30°C upon cooling from the βZr domain. The results show that the mechanical behavior strongly depends on the testing temperature and the hydrogen and oxygen contents. Relationships are proposed to describe the macroscopic ductile-to-brittle transition and the mechanical behavior of the material as a function of temperature, hydrogen and oxygen contents. The predictions based on these relationships are compared to selected data from the literature obtained on claddings oxidized at high temperature, including results from semi-integral LOCA tests. Also, it is shown that considering the combined effects of hydrogen and oxygen is necessary to interpret the mechanical behavior of the material, in particular the embrittlement of claddings subjected to LOCA-relevant secondary hydriding
Intelligent Decision Support for Cybersecurity Incident Response Teams: Autonomic Architecture and Mitigation Search
No full textInternational audienceCritical infrastructures must be able to mitigate, at runtime, suspected ongoing cyberattacks that have eluded preventive security measures. To tackle this issue, we first propose an autonomic computing architecture for a Cyber-Security Incident Response Team Intelligent Decision Support System (CSIRT-IDSS) with a precise set of technologies for each of its components. We then zoom in on the component responsible for proposing to the CSIRT, automatically ranked sets of runtime actions to mitigate suspected ongoing cyber-attacks. We formalize its task as a Constraint Optimization Problem (COP). We then propose to implement it by a Constraint Object-Oriented Logic Program (COOLP) deployed as a containerized web service through the integration of three orthogonal extensions of Logic Programming (LP): Web Service Oriented LP (WSOLP), Constraint LP (CLP) and Object-Oriented LP (OOLP). This integration supports seamlessly reusing platform and task independent cybersecurity ontological knowledge to dynamically build a mitigation action search COP that is customized to an input suspected cyberattack action set. This customization then allows the COP, to be solved by a generic CLP engine efficiently enough to propose mitigation actions to the CSIRT team while they can still be effective. To validate this approach, we implemented a prototype called CARMAS (Cyber Attack Runtime Mitigation Action Search) and ran scalability tests on simulated attacks with various COP construction strategies
Investigation of self-heating and dissipative effects in ferroelectric ceramics subjected to compressive mechanical cyclic loading
No full textInternational audienceIn this study the behavior of a soft PZT ceramic subjected to compressive cyclic loadings is investigated with the aim of providing an insight into different dissipative processes which affect the ferroelastic cyclic behavior of the material. Two quantitative imaging techniques, infrared (IR) thermography and digital image correlation (DIC), are employed to measure superficial temperature and in-plane displacement of the sample under mechanical cyclic loadings. Thermal and strain responses are further deduced from these measurements. This allows to quantify the energy dissipated by the material during applied mechanical loadings. Self-heating (SH) and DIC measurements reveal that, even in the piezoelectric regime, the level of dissipation is significant. This suggests that PZT ceramic response under cyclic compression is significantly influenced by domain switching mechanisms. The two imaging techniques are shown to be efficient tools to identify domain wall activity in ferroelectric ceramics and can be advantageously substituted for polarization measurements when polarization variations are too small to be detected. It is also shown that, due to the initial compressive stress experienced by all specimens, the domain wall activity under cyclic uniaxial compressive stress is almost independent of the initial polarization state of the material
Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning
No full textInternational audienceMulti-task learning by robots poses the challenge of the domain knowledge: complexity of tasks, complexity of the actions required, relationship between tasks for transfer learning. We demonstrate that this domain knowledge can be learned to address the challenges in life-long learning. Specifically, the hierarchy between tasks of various complexities is key to infer a curriculum from simple to composite tasks. We propose a framework for robots to learn sequences of actions of unbounded complexity in order to achieve multiple control tasks of various complexity. Our hierarchical reinforcement learning framework, named SGIM-SAHT, offers a new direction of research, and tries to unify partial implementations on robot arms and mobile robots. We outline our contributions to enable robots to map multiple control tasks to sequences of actions: representations of task dependencies, an intrinsically motivated exploration to learn task hierarchies, and active imitation learning. While learning the hierarchy of tasks, it infers its curriculum by deciding which tasks to explore first, how to transfer knowledge, and when, how and whom to imitate
Reduced order modelling and experimental validation of a MEMS gyroscope test-structure exhibiting 1:2 internal resonance
No full textInternational audienceMicro-Electro-Mechanical Systems revolutionized the consumer market for their small dimensions, high performances and low costs. In recent years, the evolution of the Internet of Things is posing new challenges to MEMS designers that have to deal with complex multiphysics systems experiencing highly nonlinear dynamic responses. To be able to simulate a priori and in real-time the behavior of such systems it is thus becoming mandatory to understand the sources of nonlinearities and avoid them when harmful or exploit them for the design of innovative devices. In this work, we present the first numerical tool able to estimate a priori and in real-time the complex nonlinear responses of MEMS devices without resorting to simplified theories. Moreover, the proposed tool predicts different working conditions without the need of ad-hoc calibration procedures. It consists in a nonlinear Model Order Reduction Technique based on the Implicit Static Condensation that allows to condense the high fidelity FEM models into few degrees of freedom, thus greatly speeding-up the solution phase and improving the design process of MEMS devices. In particular, the 1:2 internal resonance experienced in a MEMS gyroscope test-structure fabricated with a commercial process is numerically investigated and an excellent agreement with experiments is found
Low Frequency SAS: Influence of multipaths on Spatial Coherence
No full textInternational audienceMultipath is an issue for the performance of synthetic aperture sonars. In HF-SAS (high frequency SAS) images, multipaths effects manifest in the hiding of targets, the loss of image contrast and the degradation of interferometry bathymetric estimates and degradation of spatial coherence due to a decrease of the SNR. In this study, the behaviour of a LF-SAS (low frequency SAS) equipped with a full 2D receiving array is analysed. This 2D Rx array enables the observation of the 2D spatial coherence. On the vertical axis, the existence of a multipath reflects on the emergence of lobes in the figure of coherence. Intuitively, the lobes can be understood considering that the direct path and a multipath can be seen from the reception array as two sources that act as a dipole antenna. This study aims at evaluating effects of multipaths on these lobes establishing the link between multipaths and lobes). The study builds upon data acquired by the HRLFSAS designed by NATO CMRE. The effect of multipath is highlighted thanks analytical modelisation and a comparison between observed coherence and the one predicted from application of the Van Cittert Zernike theorem to the distribution of energy estimated by a vertical beamforming
