Laboratoire d'Analyse et d'Architecture des Systèmes

In an industrial maintenance context, degradation diagnosis is the problem of determining the current level of degradation of operating machines based on measurements. With the emergence of Machine Learning techniques, such a problem can now be solved by training a degradation model offline and by using it online. While such models are more and more accurate and performant, they are often black-box and their decisions are therefore not interpretable for human maintenance operators. On the contrary, interpretable ML models are able to provide explanations for the model’s decisions and consequently improves the confidence of the human operator about the maintenance decision based on these models. This paper proposes a new method to quantitatively measure the interpretability of such models that is agnostic (no assumption about the class of models) and that is applied on degradation models. The proposed method requires that the decision maker sets up some high level parameters in order to measure the interpretability of the models and then can decide whether the obtained models are satisfactory or not. The method is formally defined and is fully illustrated on a decision tree degradation model and a model trained with a recent neural network architecture called Multiclass Neural Additive Model.

This paper gives the main definitions relating to dependability, a generic concept including a special case of such attributes as reliability, availability, safety, integrity, maintainability, etc. Security brings in concerns for confidentiality, in addition to availability and integrity. Basic definitions are given first. They are then commented upon, and supplemented by additional definitions, which address the threats to dependability and security (faults, errors, failures), their attributes, and the means for their achievement (fault prevention, fault tolerance, fault removal, fault forecasting). The aim is to explicate a set of general concepts, of relevance across a wide range of situations and, therefore, helping communication and cooperation among a number of scientific and technical communities, including ones that are concentrating on particular types of system, of system failures, or of causes of system failures.

Abstract. We consider the problem of finding the unconstrained global minimum of a realvalued polynomial p(x):R n → R, as well as the global minimum of p(x), in a compact set K defined bypolynomial inequalities. It is shown that this problem reduces to solving an (often finite) sequence of convex linear matrix inequality(LMI) problems. A notion of Karush–Kuhn–Tucker polynomials is introduced in a global optimalitycondition. Some illustrative examples are provided. Key words. global optimization, theoryof moments and positive polynomials, semidefinite programming

The EPIC focal plane imaging spectrometers on XMM-Newton use CCDs to record the images and spectra of celestial X-ray sources focused by the three X-ray mirrors. There is one camera at the focus of each mirror; two of the cameras contain seven MOS CCDs, while the third uses twelve PN CCDs, defining a circular field of view of 30′ diameter in each case. The CCDs were specially developed for EPIC, and combine high quality imaging with spectral resolution close to the Fano limit. A filter wheel carrying three kinds of X-ray transparent light blocking filter, a fully closed, and a fully open position, is fitted to each EPIC instrument. The CCDs are cooled passively and are under full closed loop thermal control. A radio-active source is fitted for internal calibration. Data are processed on-board to save telemetry by removing cosmic ray tracks, and generating X-ray event files; a variety of different instrument modes are available to increase the dynamic range of the instrument and to enable fast timing. The instruments were calibrated using laboratory X-ray beams, and synchrotron generated monochromatic X-ray beams before launch; in-orbit calibration makes use of a variety of celestial X-ray targets. The current calibration is better than 10% over the entire energy range of 0.2 to 10 keV. All three instruments survived launch and are performing nominally in orbit. In particular full field-of-view coverage is available, all electronic modes work, and the energy resolution is close to pre-launch values. Radiation damage is well within pre-launch predictions and does not yet impact on the energy resolution. The scientific results from EPIC amply fulfil pre-launch expectations.

The high alpha-diversity of tropical forests has been amply documented, but beta-diversity-how species composition changes with distance-has seldom been studied. We present quantitative estimates of beta-diversity for tropical trees by comparing species composition of plots in lowland terra firme forest in Panama, Ecuador, and Peru. We compare observations with predictions derived from a neutral model in which habitat is uniform and only dispersal and speciation influence species turnover. We find that beta-diversity is higher in Panama than in western Amazonia and that patterns in both areas are inconsistent with the neutral model. In Panama, habitat variation appears to increase species turnover relative to Amazonia, where unexpectedly low turnover over great distances suggests that population densities of some species are bounded by as yet unidentified processes. At intermediate scales in both regions, observations can be matched by theory, suggesting that dispersal limitation, with speciation, influences species turnover.

A description and analysis of concurrent systems, such as communication systems, whose behavior is dependent on explicit values of time is presented. An enumerative method is proposed in order to exhaustively validate the behavior of P. Merlin's time Petri net model, (1974). This method allows formal verification of time-dependent systems. It is applied to the specification and verification of the alternating bit protocol as a simple illustrative example.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Hybrid systems, which combine both continuous and discrete behavior, are used in many fields, including robotics, biological systems, and control systems. However, due to their complexity, finding an accurate model is a challenge. This paper discusses the usage of symbolic regression to learn hybrid systems from data and specifically analyses learning parameters for a recent algorithm. Symbolic regression is a powerful tool that can automatically discover accurate and interpretable mathematical models in the form of symbolic expressions. Models generated by symbolic regression are a valuable tool for system identification and diagnosis, e.g., to predict future system behavior or detect anomalies. A major opportunity of our approach is the ability to detect transitions between different continuous behaviors of a system directly based on the dynamics. From a diagnosis perspective, this can advantageously be used to detect the system entering fault modes and identify their models. This paper presents a parameter study for a symbolic regression based identification algorithm.

Many important applications in global optimization, algebra, probability and statistics, applied mathematics, control theory, financial mathematics, inverse problems, etc. can be modeled as a particular instance of the Generalized Moment Problem (GMP) . This book introduces a new general methodology to solve the GMP when its data are polynomials and basic semi-algebraic sets. This methodology combines semidefinite programming with recent results from real algebraic geometry to provide a hierarchy of semidefinite relaxations converging to the desired optimal value. Applied on appropriate cones,

Context.The Crab nebula was observed with the HESS stereoscopic Cherenkov-telescope array between October 2003 and January 2005 for a total of 22.9 h (after data quality selection). This period of time partly overlapped with the commissioning phase of the experiment; observations were made with three operational telescopes in late 2003 and with the complete 4 telescope array in January–February 2004 and October 2004–January 2005.

The very large collection area of ground-based gamma-ray telescopes gives them a substantial advantage over balloon or satellite based instruments in the detection of very-high-energy (>600 GeV) cosmic-ray electrons. Here we present the electron spectrum derived from data taken with the High Energy Stereoscopic System (H.E.S.S.) of imaging atmospheric Cherenkov telescopes. In this measurement, the first of this type, we are able to extend the measurement of the electron spectrum beyond the range accessible to direct measurements. We find evidence for a substantial steepening in the energy spectrum above 600 GeV compared to lower energies.

Conventional neuro-computing architectures and artificial neural networks have often been developed with no or loose connections to neuroscience. As a consequence, they have largely ignored key features of biological neural processing systems, such as their extremely low-power consumption features or their ability to carry out robust and efficient computation using massively parallel arrays of limited precision, highly variable, and unreliable components. Recent developments in nano-technologies are making available extremely compact and low power, but also variable and unreliable solid-state devices that can potentially extend the offerings of availing CMOS technologies. In particular, memristors are regarded as a promising solution for modeling key features of biological synapses due to their nanoscale dimensions, their capacity to store multiple bits of information per element and the low energy required to write distinct states. In this paper, we first review the neuro- and neuromorphic computing approaches that can best exploit the properties of memristor and scale devices, and then propose a novel hybrid memristor-CMOS neuromorphic circuit which represents a radical departure from conventional neuro-computing approaches, as it uses memristors to directly emulate the biophysics and temporal dynamics of real synapses. We point out the differences between the use of memristors in conventional neuro-computing architectures and the hybrid memristor-CMOS circuit proposed, and argue how this circuit represents an ideal building block for implementing brain-inspired probabilistic computing paradigms that are robust to variability and fault tolerant by design.

This note addresses the design of antiwindup gains for obtaining larger regions of stability for linear systems with saturating inputs. Considering that a linear dynamic output feedback has been designed to stabilize the linear system (without saturation), a method is proposed for designing an antiwindup gain that maximizes an estimate of the basin of attraction of the closed-loop system. It is shown that the closed-loop system obtained from the controller plus the antiwindup gain can be modeled by a linear system with a deadzone nonlinearity. A modified sector condition is then used to obtain stability conditions based on quadratic Lyapunov functions. Differently from previous works these conditions are directly in linear matrix inequality form. Some numerical examples illustrate the effectiveness of the proposed design technique when compared with the previous ones.

This paper considers the problem of motion planning for a car-like robot (i.e., a mobile robot with a nonholonomic constraint whose turning radius is lower-bounded). We present a fast and exact planner for our mobile robot model, based upon recursive subdivision of a collision-free path generated by a lower-level geometric planner that ignores the motion constraints. The resultant trajectory is optimized to give a path that is of near-minimal length in its homotopy class. Our claims of high speed are supported by experimental results for implementations that assume a robot moving amid polygonal obstacles. The completeness and the complexity of the algorithm are proven using an appropriate metric in the configuration space R/sup 2//spl times/S/sup 1/ of the robot. This metric is defined by using the length of the shortest paths in the absence of obstacles as the distance between two configurations. We prove that the new induced topology and the classical one are the same. Although we concentrate upon the car-like robot, the generalization of these techniques leads to new theoretical issues involving sub-Riemannian geometry and to practical results for nonholonomic motion planning.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The authors address the problem of validating the dependability of fault-tolerant computing systems, in particular, the validation of the fault-tolerance mechanisms. The proposed approach is based on the use of fault injection at the physical level on a hardware/software prototype of the system considered. The place of this approach in a validation-directed design process and with respect to related work on fault injection is clearly identified. The major requirements and problems related to the development and application of a validation methodology based on fault injection are presented and discussed. Emphasis is put on the definition, analysis, and use of the experimental dependability measures that can be obtained. The proposed methodology has been implemented through the realization of a general pin-level fault injection tool (MESSALINE), and its usefulness is demonstrated by the application of MESSALINE to the experimental validation of two systems: a subsystem of a centralized computerized interlocking system for railway control applications and a distributed system corresponding to the current implementation of the dependable communication system of the ESPRIT Delta-4 Project.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In order to understand its environment, a mobile robot should be able to model consistently this environment, and to locate itself correctly. One major difficulty to be solved is the inaccuracies introduced by the sensors. The approach proposed in this paper to cope with this problem relies on 1) defining general principles to deal with uncertainties : the use of a multisensory system, favo ring of the data collected by the more accurate sensor in a given situation, averaging of different but consistent measurements of the same entity weighted with their associated uncertainties, and 2) a methodology enabling a mobile robot to define its own reference landmarks while exploring its environment. These ideas are presented together with an example of their application on the mobile robot HILARE.

Microfluidics is an emerging field that has given rise to a large number of scientific and technological developments over the last few years. This review reports on the use of various materials, such as silicon, glass and polymers, and their related technologies for the manufacturing of simple microchannels and complex systems. It also presents the main application fields concerned with the different technologies and the most significant results reported by academic and industrial teams. Finally, it demonstrates the advantage of developing approaches for associating polymer technologies for manufacturing of fluidic elements with integration of active or sensitive elements, particularly silicon devices.

The anti-windup technique which can be used to tackle the problems of stability and performance degradation for linear systems with saturated inputs is dealt with. The anti-windup techniques which can be found in the literature today have evolved from many sources and, even now, are diverse and somewhat disconnected from one another. In this survey, an overview of many recent anti-windup techniques is provided and their connections with each other are stated. The anti-windup technique is also explained within the context of its historical emergence and the likely future directions of the field are speculated. The focus is on so-called `modern' anti-windup techniques which began to emerge during the end of the 20th century and which allow a priori guarantees on stability to be made. The survey attempts to provide constructive LMI conditions for the synthesis of anti-windup compensators in both global and local contexts. Finally, some interesting extensions and open problems are discussed, such as nested saturations, the presence of time delays in the state or the input, and anti-windup for non-linear systems.

Abstract. An eight-year long reanalysis of atmospheric composition data covering the period 2003–2010 was constructed as part of the FP7-funded Monitoring Atmospheric Composition and Climate project by assimilating satellite data into a global model and data assimilation system. This reanalysis provides fields of chemically reactive gases, namely carbon monoxide, ozone, nitrogen oxides, and formaldehyde, as well as aerosols and greenhouse gases globally at a horizontal resolution of about 80 km for both the troposphere and the stratosphere. This paper describes the assimilation system for the reactive gases and presents validation results for the reactive gas analysis fields to document the data set and to give a first indication of its quality. Tropospheric CO values from the MACC reanalysis are on average 10–20% lower than routine observations from commercial aircrafts over airports through most of the troposphere, and have larger negative biases in the boundary layer at urban sites affected by air pollution, possibly due to an underestimation of CO or precursor emissions. Stratospheric ozone fields from the MACC reanalysis agree with ozonesondes and ACE-FTS data to within ±10% in most seasons and regions. In the troposphere the reanalysis shows biases of −5% to +10% with respect to ozonesondes and aircraft data in the extratropics, but has larger negative biases in the tropics. Area-averaged total column ozone agrees with ozone fields from a multi-sensor reanalysis data set to within a few percent. NO2 fields from the reanalysis show the right seasonality over polluted urban areas of the NH and over tropical biomass burning areas, but underestimate wintertime NO2 maxima over anthropogenic pollution regions and overestimate NO2 in northern and southern Africa during the tropical biomass burning seasons. Tropospheric HCHO is well simulated in the MACC reanalysis even though no satellite data are assimilated. It shows good agreement with independent SCIAMACHY retrievals over regions dominated by biogenic emissions with some anthropogenic input, such as the eastern US and China, and also over African regions influenced by biogenic sources and biomass burning.

None of the classical access control models such as DAC, MAC, RBAC, TBAC or TMAC is fully satisfactory to model security policies that are not restricted to static permissions but also include contextual rules related to permissions, prohibitions, obligations and recommendations. This is typically the case of security policies that apply to the health care domain. We suggest a new model that provides solutions to specify such contextual security policies. This model, called organization based access control, is presented using a formal language based on first-order logic.

We describe a major update of our Matlab freeware GloptiPoly for parsing generalized problems of moments and solving them numerically with semidefinite programming.