Altran

About: Altran is a company organization based out in Neuilly-sur-Seine, France. It is known for research contribution in the topics: Software development & Formal verification. The organization has 488 authors who have published 512 publications receiving 6395 citations.

Discovery of a large and bright bow shock nebula associated with low-mass X-ray binary SAX J1712.6-3739

Abstract: In a multiwavelength programme dedicated to identifying optical counterparts of faint persistent X-ray sources in the Galactic bulge, we find an accurate X-ray position of SAX J1712.6−3739 through Chandra observations, and discover its faint optical counterpart using our data from EFOSC2 on the ESO 3.6-m telescope. We find this source to be a highly extincted neutron star low-mass X-ray binary (LMXB) with blue optical colours. We serendipitously discover a relatively bright and large bow shock shaped nebula in our deep narrow-band Hα imaging, most likely associated with the X-ray binary. A nebula like this has never been observed before in association with a LMXB, and as such provides a unique laboratory to study the energetics of accretion and jets. We put forward different models to explain the possible ways the LMXB may form this nebulosity, and outline how they can be confirmed observationally.

Integrating Interface Modeling and Analysis in an Industrial Setting

Abstract: Precise specification of system component interfaces enables analysis of component behavior and checking of conformance of an implementation to the interface specification. Very often component interfaces are only defined by their signature and without a formal description of the admissible behavior and timing assumptions. In this paper we present a framework named ComMA (Component Modeling and Analysis) that supports model-based engineering (MBE) of high-tech systems by formalizing interface specifications. ComMA provides a family of domain-specific languages that integrate existing techniques from formal behavioral and time modeling and is easily extensible. It contains tools that support different phases of the development process and can be integrated in the industrial way of working. The framework is applied in the context of the family of interventional X-ray machines developed by Philips.

A new importance sampling Monte Carlo method for a flow network reliability problem

Abstract: The exact evaluation of the probability that the maximum st-flow is greater than or equal to a fixed demand in a stochastic flow network is an NP-hard problem. This limitation leads one to consider Monte Carlo alternatives. In this paper, we propose a new importance sampling Monte Carlo method. It is based on a recursive use of the state space decomposition methodology of Doulliez and Jamoulle during the simulation process. We show theoretically that the resulting estimator belongs to the variance-reduction family and we give an upper bound on its variance. As shown by experimental tests, the new sampling principle offers, in many cases, substantial speedups with respect to a previous importance sampling based on the same decomposition procedure and its best performances are obtained when highly reliable networks are analyzed. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 204–228, 2002; DOI 10.1002/nav.10004

Fractional-order global sliding mode controller for an uncertain quadrotor UAVs subjected to external disturbances

Abstract: In the present paper, the problem of designing a global sliding mode control scheme based on fractional operators for tracking a quadrotor trajectory is investigated. The model of the quadrotor system is given with disturbances and uncertainties. To converge in short finite time of the sliding manifold, a classical quadratic Lyapunov function was used and also a global stabilization of the quadrotor system is ensured. The proposed controller can be ensured the robustness against external disturbances and model uncertainties. Some scenarios are illustrated in this paper. Finally, a comparative study to three other controllers is provided to show the validity and feasibility of the proposed method.

Developing Fine-Grained Actigraphies for Rheumatoid Arthritis Patients from a Single Accelerometer Using Machine Learning.

Abstract: In addition to routine clinical examination, unobtrusive and physical monitoring of Rheumatoid Arthritis (RA) patients provides an important source of information to enable understanding the impact of the disease on quality of life. Besides an increase in sedentary behaviour, pain in RA can negatively impact simple physical activities such as getting out of bed and standing up from a chair. The objective of this work is to develop a method that can generate fine-grained actigraphies to capture the impact of the disease on the daily activities of patients. A processing methodology is presented to automatically tag activity accelerometer data from a cohort of moderate-to-severe RA patients. A study of procesing methods based on machine learning and deep learning is provided. Thirty subjects, 10 RA patients and 20 healthy control subjects, were recruited in the study. A single tri-axial accelerometer was attached to the position of the fifth lumbar vertebra (L5) of each subject with a tag prediction granularity of 3 s. The proposed method is capable of handling unbalanced datasets from tagged data while accounting for long-duration activities such as sitting and lying, as well as short transitions such as sit-to-stand or lying-to-sit. The methodology also includes a novel mechanism for automatically applying a threshold to predictions by their confidence levels, in addition to a logical filter to correct for infeasible sequences of activities. Performance tests showed that the method was able to achieve around 95% accuracy and 81% F-score. The produced actigraphies can be helpful to generate objective RA disease-specific markers of patient mobility in-between clinical site visits.