Design of Minimal and Tolerant Sensor Networks for Observability of Vehicle Active Suspension

International audienc

Virtual metrology based on relevant feature extraction and just-in-time learning approach

International audienc

Multi-Dynamics Analysis of QRS Complex for Atrial Fibrillation Diagnosis

International audienceThis paper presents an effective atrial fibrillation (AF) diagnosis algorithm based on multi-dynamics analysis of QRS complex. The idea behind this approach is to produce a variety of heartbeat time series features employing several linear and nonlinear functions via different dynamics of the QRS complex signal. These extracted features from these dynamics will be connected through machine learning based algorithms such as Support Vector Machine (SVM) and Multiple Kernel Learning (MKL), to detect AF episode occurrences. The reported performances of these methods were evaluated on the Long-Term AF Database which includes 84 of 24-hour ECG recording. Thereafter, each record was divided into consecutive intervals of one-minute segments to feed the classifier models. The obtained sensitivity, specificity and positive classification using SVM were 96.54%, 99.69%, and 99.62%, respectively, and for MKL they reached 95.47%, 99.89%, and 99.87%, respectively. Therefore, these medical-oriented detectors can be clinically valuable to healthcare professional for screening AF pathology

A Novel Method to Identify Relevant Features for Automatic Detection of Atrial Fibrillation

International audienceThe selection of an appropriate subset of predictors from a large set of features is a major concern in clinical diagnosis research. The purpose of this study is to demonstrate that the Multiple Kernel Learning (MKL) approach could be successfully applied as a feature selection process for machine learning pipelines. Furthermore, we suggest a multi-dynamic analysis of heartbeat signal to characterize the most common sustained arrhythmia, Atrial Fibrillation (AF). Indeed, we have targeted six different dynamics of QRS time series, where each one will be associated with 12 linear and nonlinear functions to yield a set of 72 features. Afterward, a feature selection process is implemented using the MKL to evaluate the relevant features allowing AF diagnosis. Hence, a subset of only 13 features has been selected. To demonstrate the effectiveness of the proposed approach, Support Vector Classification (SVC) model has been conducted, first, on all features, and then on the features issued from the MKL selection feature process. The obtained results showed that the SVC model trained by 13 features outperformed the one trained by 72 features. This approach has reached 99.77% of success rate in the discrimination between Normal Sinus Rhythm (NSR) and AF. The proposed selection feature method holds several interesting properties in dimensionality reduction which makes it a suitable choice for several applications

Virtual Metrology applied in Run-to-Run Control for a Chemical Mechanical Planarization process

International audienc

The Impact of the Virtual Metrology on a Run-to-Run control for a Chemical Mechanical Planarization process * *This work is part of the European projects "INTEGRATE" and "MAGE", and carried by STMicroelectronics Fab of Rousset

International audienc

Towards the development of a methodology for designing helicopter flight control laws by integrating handling qualities requirements from the first stage of tuning

International audiencePiloting a helicopter is a demanding task for human operators: autopilots have been introduced by manufacturers to assist pilots in their piloting task. Designing the gains of the associated control laws – taking into account Handling Qualities requirements from standards such as the ADS-33 – is a difficult industrial problem. NASA has already led many studies on this area. These works have led to the development of CONDUIT© (Control Designer's Unified Interface), a computer aided design tool for rotary and fixed wing aircrafts control laws using interactive optimization techniques. The tool has demonstrated its benefits in terms of time of development reduction. ONERA has been working for several years to the establishment of another process. One of the first ideas was to lead local sensitivity studies, and use the results as design guidance. Then these results have been confronted with some analytical developments led on simplified models. This paper shows how these analytical studies can be used to initialize the gain tuning process efficiently, taking into account the structure of the system and the requirements from handling qualities standards (ADS-33). A tool has been developed: it generates charts of Flying Qualities. Thanks to the charts generated for each case of study, the gains of the control laws can be efficiently initialized for the simplified models, with Flying Qualities objectives. The expected results are compared with those obtained with the full linear model. Sensitivities can then be used to help in designing more precisely the gains. Finally, full nonlinear simulations are led in order to compare the results with the expected Flying Qualities. As a conclusion, all these studies seem to lead to a full and efficient process of gain tuning taking into account the constraints from the control law structures and the requirements from the handling qualities standard. Furthermore, the procedure can be applied from the very first stage of tuning: the initialization of the gains

Over or Under Pressure Detection of Tire

National audienc

A Model Predictive Control Approach for Energy Management in Micro-Grid Systems

International audienceMG systems integrate renewable energy sources (RES) together with storage devices for being connected to the traditional electrical grid in order to supply the power to the building's loads. However, management and control approaches are required for seamless integration of these systems in energy efficient buildings. In this paper, a model predictive control approach is introduced for efficient MG operation. The proposed approach allows minimizing the usage of the traditional electric grid for supplying the power to the building's loads by using as much as possible the power generated by RES while optimizing the storage devices operations. A model predictive control (MPC) strategy is proposed in order to balance the power flow in MG system. The deployed strategy controls the charge/discharge current of the battery depending on RES production, and load consumption variability. Simulations have been conducted using real dataset generated from our deployed MG system, and preliminary results show the usefulness of predictive control principles for the efficient operation of MG systems