ParisTech

About: ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Residual stress & Finite element method. The organization has 1888 authors who have published 1965 publications receiving 55532 citations. The organization is also known as: Paris Institute of Technology & ParisTech Développement.

Towards an abstraction layer for security assurance measurements: (invited paper)

Abstract: Measurement of any complex, operational system is challenging due to the continuous independent evolution of the components. Security risks introduce another dimension of dynamicity, reflected to risk management and security assurance activities. The availability of different measurements and their properties will vary during the overall system lifecycle. To be useful, a measurement framework in this context needs to be able to adapt to both the changes in the target of measurement and in the available measurement infrastructure. In this study, we introduce a taxonomy-based approach for relating the available and attainable measurements to the measurement requirements of security assurance plans by providing an Abstraction Layer that makes it easier to manage these dynamic features. The introduced approach is investigated in terms of a security assurance case example of firewall functionality in a Push E-mail service system.

Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction

Abstract: Compressed sensing is a signal compression technique with very remarkable properties. Among them, maybe the most salient one is its ability of overcoming the Shannon–Nyquist sampling theorem. In other words, it is able to reconstruct a signal at less than 2Q samplings per second, where Q stands for the highest frequency content of the signal. This property has, however, important applications in the field of computational mechanics, as we analyze in this paper. We consider a wide variety of applications, such as model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction. Examples are provided for all of them that show the potentialities of compressed sensing in terms of CPU savings in the field of computational mechanics.

Balanced Proper Orthogonal Decomposition Applied to Magnetoquasi-Static Problems Through a Stabilization Methodology

Abstract: Model order reduction (MOR) methods are applied in different areas of physics in order to reduce the computational time of large scale systems. It has been an active field of research for many years, in mechanics especially, but it is quite recent for magnetoquasi-static problems. Although the most famous method, the proper orthogonal decomposition (POD) has been applied for modeling many electromagnetic devices, this method can lack accuracy for low-order magnitude output quantities, like flux associated with a probe in regions where the field is low. However, the balanced POD (BPOD) is an MOR method, which takes into account these output quantities in its reduced model to render them accurately. Even if the BPOD may lead to unstable reduced systems, this can be overcome by a stabilization procedure. Therefore, the POD and the stabilized BPOD will be compared on a 3-D linear magnetoquasi-static field problem.

High speed heterodyne infrared thermography applied to thermal diffusivity identification

Abstract: We have combined InfraRed thermography and thermal wave techniques to perform microscale, ultrafast (microsecond) temperature field measurements. The method is based on an IR camera coupled to a microscope and synchronized to the heat source by means of phase locked function generators. The principle is based on electronic stroboscopic sampling where the low IR camera acquisition frequency f(acq) (25 Hz) undersamples a high frequency thermal wave. This technique permits the measurement of the emissive thermal response at a (microsecond) short time scale (microsecond) with the full frame mode of the IR camera with a spatial thermal resolution of 7 μm. Then it becomes possible to study 3D transient heat transfer in heterogeneous and high thermal conductive thin layers. Thus it is possible for the first time in our knowledge to achieve temperature field measurements in heterogeneous media within a wide range of time domains. The IR camera is now a suitable instrument for multiscale thermal analysis.