Robust curvature extrema detection based on new numerical derivation

International audienceExtrema of curvature are useful key points for different image analysis tasks. Indeed, polygonal approximation or arc decomposition methods used often these points to initialize or to improve their algorithms. Several shape-based image retrieval methods focus also their descriptors on key points. This paper is focused on the detection of extrema of curvature points for a raster-to-vector-conversion framework. We propose an original adaptation of an approach used into nonlinear control for fault-diagnosis and fault-tolerant control based on algebraic derivation and which is robust to noise. The experimental results are promising and show the robustness of the approach when the contours are bathed into a high level speckled noise

Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions

To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents. Pedestrians are particularly challenging to model, as their motion patterns are often uncertain and/or unknown a priori. This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection of changes in intent and online learning of motion patterns not seen in prior training data. The resulting long-term movement predictions demonstrate improved accuracy relative to offline learning alone, in terms of both intent and trajectory prediction. By embedding these predictions within a chance-constrained motion planner, trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware experiments demonstrate that this approach can accurately predict pedestrian motion patterns from onboard sensor/perception data and facilitate robust navigation within a dynamic environment.Comment: Submitted to 2014 International Workshop on the Algorithmic Foundations of Robotic

Model changes in signal processing: state of the art and results of GRECO SARTA

The purpose of this paper is to outline the interest of the so-called "model changes" approach for solving Signal Processing problems. We describe what we think to be the state of the art in this field together with the remaining open problems, and we present the results of the CNRS GRECO SARTA working group on this topic. After an introduction to the change detection and estimation problem, we present three typical examples of situations in which change detection techniques can be used . We then give the state of the art together with the main existing references and we list the open problems . Finally, we describe the contribution of the GRECO SARTA in this area and conclude with some future research works.Le but de ce bref article est de mettre en évidence l'intérêt de l'approche dite « ruptures de modèles » en Traitement du Signal, de présenter ce que l'auteur considère comme étant l'état de l'art ainsi que les problèmes ouverts qui demeurent, et d'indiquer le bilan du GRECO SARTA pour ce thème . Après une introduction au problème, on présente trois exemples typiques de situations qui peuvent être abordées à l'aide de techniques de ruptures de modèles . On précise ensuite l'état de l'art avec les principales références existantes, et on indique les problèmes ouverts . Enfin, on décrit l'apport du GRECO SARTA et en conclusion on propose des perspectives

Aircraft engine fleet monitoring using Self-Organizing Maps and Edit Distance

International audienceAircraft engines are designed to be used during several tens of years. Ensuring a proper operation of engines over their lifetime is therefore an important and difficult task. The maintenance can be improved if efficient procedures for the understanding of data flows produced by sensors for monitoring purposes are implemented. This paper details such a procedure aiming at visualizing in a meaningful way successive data measured on aircraft engines and finding for every possible request sequence of data measurement similar behaviour already observed in the past which may help to anticipate failures. The core of the procedure is based on Self-Organizing Maps (SOM) which are used to visualize the evolution of the data measured on the engines. Rough measurements can not be directly used as inputs, because they are influenced by external conditions. A preprocessing procedure is set up to extract meaningful information and remove uninteresting variations due to change of environmental conditions. The proposed procedure contains four main modules to tackle these difficulties: environmental conditions normalization (ECN), change detection and adaptive signal modeling (CD), visualization with Self-Organizing Maps (SOM) and finally minimal Edit Distance search (SEARCH). The architecture of the procedure and of its modules is described in this paper and results on real data are also supplied

A Methodology for the Diagnostic of Aircraft Engine Based on Indicators Aggregation

Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomaly detection in a way that is understandable by human operators who make the final maintenance decision. The main idea of the method is to generate a very large number of binary indicators based on parametric anomaly scores designed by experts, complemented by simple aggregations of those scores. The best indicators are selected via a classical forward scheme, leading to a much reduced number of indicators that are tuned to a data set. We illustrate the interest of the method on simulated data which contain realistic early signs of anomalies.Comment: Proceedings of the 14th Industrial Conference, ICDM 2014, St. Petersburg : Russian Federation (2014

Model-based fault detection and diagnosis : cases study for vibration monitoring

A signal processing approach is presented for detection and diagnosis of fatigues or failures in vibrating mechanical systems subject to natural excitation . Detection and diagnosis is performed while the system being at work, so that the excitation is usually not observed and may involve turbulent phenomena . This is a short report of a 10 years project which involved more than 2 persons per year in mean . The method is illustrated on the following case studies : offshore structures, and rotating machinery.On présente une approche de traitement du signal pour la détection et le diagnostic des fatigues ou usures dans des systèmes mécaniques soumis à une excitation naturelle ou ambiante . La détection et le diagnostic sont réalisés sur le système en fonctionnement habituel, et donc en général avec une excitation non mesurée et présentant des phénomènes de turbulence . Cet article est un bref rapport sur un projet de recherche d'environ 10 ans qui a mobilisé plus de 2 personnes par an en moyenne . La méthode est illustrée sur les deux cas suivants : structures offshore et turbo-alternateurs (') . (') Ce travail a été soutenu pendant 7 ans par 4 contrats avec IFREMER et pendant 4 ans par 2 contrats avec EDF

Automatic threshold determination for a local approach of change detection in long-term signal recordings

CUSUM (cumulative sum) is a well-known method that can be used to detect changes in a signal when the parameters of this signal are known. This paper presents an adaptation of the CUSUM-based change detection algorithms to long-term signal recordings where the various hypotheses contained in the signal are unknown. The starting point of the work was the dynamic cumulative sum (DCS) algorithm, previously developed for application to long-term electromyography (EMG) recordings. DCS has been improved in two ways. The first was a new procedure to estimate the distribution parameters to ensure the respect of the detectability property. The second was the definition of two separate, automatically determined thresholds. One of them (lower threshold) acted to stop the estimation process, the other one (upper threshold) was applied to the detection function. The automatic determination of the thresholds was based on the Kullback-Leibler distance which gives information about the distance between the detected segments (events). Tests on simulated data demonstrated the efficiency of these improvements of the DCS algorithm

The Minimum Detectable Damage as an Optimization Criterion for Performance-based Sensor Placement

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Improving adaptive bagging methods for evolving data streams

We propose two new improvements for bagging methods on evolving data streams. Recently, two new variants of Bagging were proposed: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. ASHT Bagging uses trees of different sizes, and ADWIN Bagging uses ADWIN as a change detector to decide when to discard underperforming ensemble members. We improve ADWIN Bagging using Hoeffding Adaptive Trees, trees that can adaptively learn from data streams that change over time. To speed up the time for adapting to change of Adaptive-Size Hoeffding Tree (ASHT) Bagging, we add an error change detector for each classifier. We test our improvements by performing an evaluation study on synthetic and real-world datasets comprising up to ten million examples

Subspace-based damage detection on steel frame structure under changing excitation

International audienceDamage detection can be performed by detecting changes in the modal parameters between a reference state and the current (possibly damaged) state of a structure from measured output-only vibration data. Alternatively, a subspace-based damage detection test has been proposed and applied successfully, where changes in the modal parameters are detected, but the estimation of the modal parameters themselves is avoided. Like this, the test can run in an automated way directly on the vibration measurements. However, it was assumed that the unmeasured ambient excitation properties during measurements of the structure in the reference and possibly damaged condition stay constant, which is hardly satisfied by any application. A new version of the test has been derived recently that is robust to such changes in the ambient excitation. In this paper, the robust test is recalled and its performance is evaluated both on numerical simulations and a real application, where a steel frame structure is artificially damaged in the lab