International Professional University of Technology in Nagoya Repository
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A Millimeter Wave Transparent Transmitarray Antenna Using Meshed Double Circle Rings Elements
No full textInternational audienceA novel transparent millimeter wavetransmitarray antenna (TA) using three layers of meshedmetal double circle rings elements is presented in this paper. Atransparent plastic material, polymethylmethacrylate (PMMA)is used as the substrate. In order to promote the transparency,a meshed double circle rings unit-cell is designed using several50 μm width metal grid lines. Good agreement between meshedand non-meshed unit-cells is reached at Ka-band. Atransparent TA with diameter of 9.9 wavelengths is designedand simulated by the commercial software Ansys HFSS at Kabandusing the proposed elements. By moving the feed alongthe focal plane of TA, beam scanning up to 30° with low sidelobe level, low cross polarization level and widebandperformance is reached
Learning Functional Causal Models with Generative Neural Networks
No full textInternational audienceWe introduce a new approach to functional causal modeling from observational data, called Causal Generative Neural Networks (CGNN). CGNN leverages the power of neural networks to learn a generative model of the joint distribution of the observed variables, by minimizing the Maximum Mean Discrepancy between generated and observed data. An approximate learning criterion is proposed to scale the computational cost of the approach to linear complexity in the number of observations.The performance of CGNN is studied throughout three experiments.Firstly, CGNN is applied to cause-effect inference, where the task is to identify the best causal hypothesis out of and . Secondly, CGNN is applied to the problem of identifying v-structures and conditional independences. Thirdly, CGNN is applied to multivariate functional causal modeling: given a skeleton describing the direct dependences in a set of random variables , CGNN orients the edges in the skeleton to uncover the directed acyclic causal graph describing the causal structure of the random variables.On all three tasks, CGNN is extensively assessed on both artificial and real-world data, comparing favorably to the state-of-the-art. Finally, CGNN is extended to handle the case of confounders, where latent variables are involved in the overall causal model
A Statistical Study of DORT Method for Locating Soft Faults in Complex Wire Networks
No full textInternational audienceDecomposition of the time-reversal (TR) operator (DORT), a recently applied TR method to transmission lines, has proven to be effective in detecting and locating soft faults in complex wire networks. In this paper, we will propose a fault location criterion which will form a later basis for a statistical study investigating the influence of several parameters, namely, the number of testing ports and the position of the fault, on the performance of DORT technique. Notably, this would allow a closer inspection of the method's practicability for future implementation in real-life networks. Index Terms— Complex wire networks, decomposition of the time-reversal (TR) operator (DORT) method, soft fault location, statistical study
Electromagnetic imaging of damages in fibered layered laminates via equivalence theory
No full textInternational audienceAbstract—Electromagnetic non-destructive testing of damaged multi-layer fiber-reinforced laminates is of concern. Each layer involves periodically positioned circular cylindrical fibers embedded within a matrix medium. Missing, displaced, shrunk, expanded fibers as well as circular inclusions inside a fiber are modeled as properly designed equivalent sources inside the undamaged structure. Then, the location of damages is retrieved via simply searching for such equivalent sources. A sparsity-constrained solution as well as classical algorithms including truncated singular value decomposition (TSVD), multiple signal classification (MUSIC) and basic matching pursuit (BMP) are considered, and their pros and cons are illustrated by numerical results in various configurations
Time-series averaging using constrained dynamic time warping with tolerance
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Lyapunov stabilization of discrete-time feedforward dynamics
No full textInternational audienceThis paper represents a first attempt toward an alternative way of computing reduction-based feedback à la Arstein for input-delayed systems. To this end, we first exhibit a new reduction state evolving as a new dynamics which is free of delays. Then, feedback design is carried out by enforcing passivity-based arguments in the reduction time-delay scenario. The case of strict-feedforward dynamics serves as a case study to discuss in details the computational advantages. A simulated exampled highlights performances
Characterizations of idempotent discrete uninorms
No full textInternational audienceIn this paper we provide an axiomatic characterization of the idempotent discrete uninorms by means of three conditions only: conservativeness, symmetry, and nondecreasing monotonicity. We also provide an alternative characterization involving the bisymmetry property. Finally, we provide a graphical characterization of these operations in terms of their contour plots, and we mention a few open questions for further research
Wasserstein Discriminant Analysis
No full textInternational audienceWasserstein Discriminant Analysis (WDA) is a new supervised method that canimprove classification of high-dimensional data by computing a suitable linearmap onto a lower dimensional subspace. Following the blueprint of classical Lin-ear Discriminant Analysis (LDA), WDA selects the projection matrix that maxi-mizes the ratio of two quantities: the dispersion of projected points coming fromdifferent classes, divided by the dispersion of projected points coming from thesame class. To quantify dispersion, WDA uses regularized Wasserstein distances,rather than cross-variance measures which have been usually considered, notablyin LDA. Thanks to the the underlying principles of optimal transport, WDA is ableto capture both global (at distribution scale) and local (at samples scale) interac-tions between classes. Regularized Wasserstein distances can be computed usingthe Sinkhorn matrix scaling algorithm; We show that the optimization of WDAcan be tackled using automatic differentiation of Sinkhorn iterations. Numericalexperiments show promising results both in terms of prediction and visualizationon toy examples and real life datasets such as MNIST and on deep features ob-tained from a subset of the Caltech dataset