Etude par résonnance paramagnétique électronique de sédiments volcaniques du Trapp d'Ethiopie

Des sédiments du Trapp d'Ethiopie, contenant quatre séquences de cendres ont été étudiés par RPE (4K-1200K). Notre objectif est d'identifier les signaux magnétiques et de déterminer l'origine de ces quatre évènements. Des informations complémentaires ont été fournies par mesures d'aimantation, détermination des phases chimiques et des morphologies des cendres. L'analyse des résultats suggère une même origine des événements et une forte contribution ferromagnétique coexistant avec un signal paramagnétique dans les cendres. Les résultats en température confirment l'étude à 300K et permettent d'identifier la nature et l'origine des signaux magnétiques présents dans les spectres RPE: 1) un signal paramagnétique, à g = 4.3, attribué à des ions isolés Fe3+ dispersés dans les cendres; 2) un signal paramagnétique, à g = 2.03, attribué à des ions Mn2+ des carbonates; 3) un signal principal, ferromagnétique à g = 2.14, attribué aux ions de Fe2+/Fe3+ localisés dans les nanoinclusions des cendres.AIX-MARSEILLE3-BU Sc.St Jérô (130552102) / SudocSudocFranceF

An Advanced Arrhythmia Recognition Methodology Based on R-waves Time-Series Derivatives and Benchmarking Machine-Learning Algorithms

International audienceIn this paper, we propose an automated decision-making approach to improve the efficiency of arrhythmia recognition. In particular, we focus on recognizing Normal Sinus Rhythms (NSR) from Abnormal Heart Rhythms (AHR). AHR include atrial fibrillation, atrial flutter, sinus bradycardia, and supraventricular tachyarrhythmia. Arrhythmia recognition approaches generally involve a feature extraction step designed to describe the heart rhythms and lead the decision-making process. Indeed, we develop an improved feature extraction strategy employing five dynamic patterns, defined as R-R intervals time series, and its first four absolute derivatives. The R-R intervals refer to the time interval separating two successive R-waves. Therefore, to describe each dynamic pattern, we use 13 feature measures. These measures comprise four time-domain features, six geometric features, and three non-linear features. As a result, a set of 65-features is built and evaluated to determine the most appropriate consistent combination of features. First, we implement a univariate statistical-based feature selection to remove irrelevant features. Then, we construct a model evaluation and selection process composed of dimensionality reduction strategies and machine learning algorithms. The latter serves to define the most suitable model based on its ability to discriminate between NSR and AHR. The findings underscore the benefits of this proposed approach, which could serve as valuable decision-making support in the detection of arrhythmias

An Effective Data-Driven Diagnostic Strategy for Cardiac Pathology Screening

International audienceIn this research, we propose an effective data-driven diagnostic strategy to identify atrial fibrillation (AF) episodes. Published research so far has targeted AF detection through univariate and multivariate analysis of R-R interval. As a potential enhancement, we suggested an advanced diagnostic methodology based on three dynamic patterns, namely the R-R interval and its first and second derivatives. Accordingly, we have targeted 11 metrics to describe each dynamic pattern, including four time-domain features, and seven non-linear features, yielding 33 detectors. Therefore, to conduct a suitable detection strategy for pathological AF screening, a dimensionality reduction process using a factor analysis technique is implemented to provide a homogeneous combination of the most relevant detectors with only 14 inputs. To demonstrate the effectiveness of the proposed approach, support vector classification algorithm trained on the 14-reduced-features has achieved, an average precision of 98.77% for validation and 98.78% for testing, calculated with 10-fold cross-validation

Regularized regression models to predict the product quality in multistep manufacturing

International audienceno abstrac

Regression methods for predicting the product's quality in the semiconductor manufacturing process

International audienceno abstrac

8th IFAC Conference on Manufacturing Modelling, Management and Control

International audienceno abstrac

A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques

International audienceIn the health care and medical domain, it has been proven challenging to diagnose correctly many diseases with complicated and interferential symptoms, including arrhythmia. However, with the evolution of artificial intelligence (AI) techniques, the diagnosis and prognosis of arrhythmia became easier for the physicians and practitioners using only an electrocardiogram (ECG) examination. This review presents a synthesis of the studies conducted in the last 12 years to predict arrhythmia’s occurrence by classifying automatically different heartbeat rhythms. From a variety of research academic databases, 40 studies were selected to analyze, among which 29 of them applied deep learning methods (72.5%), 9 of them addressed the problem with machine learning methods (22.5%), and 2 of them combined both deep learning and machine learning to predict arrhythmia (5%). Indeed, the use of AI for arrhythmia diagnosis is emerging in literature, although there are some challenging issues, such as the explicability of the Deep Learning methods and the computational resources needed to achieve high performance. However, with the continuous development of cloud platforms and quantum calculation for AI, we can achieve a breakthrough in arrhythmia diagnosis

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

Sliding Mode Observers for the Estimation of Vehicle Parameters, Forces and States of the Center of Gravity

Abstract-In this paper, sliding mode (SM) observers are proposed to replace expensive sensors used for the measurement of tires forces, vehicle side slip angle and vehicle velocity. These estimations are done for two important purposes: The first is for the estimation of the forces and parameters needed for vehicle control, while the second is for the diagnosis preview based on the safety region for each parameter and state. For that purpose, the model of the vehicle is divided in two parts, in the first, the dynamical equations of the wheels are used to estimate their longitudinal forces and angular velocities. The estimations of the longitudinal forces are realized using a second order SM observer based on a super-twisting algorithm. In the second part, the estimated longitudinal forces are injected in the reduced state space equations representing the vehicle, which are the equations of side slip angle, yaw rate and the velocity of the vehicle. Estimations in this part are based on the principles of the classical SM observer. In this part the observability of the model is studied. The model takes as input the yaw rate and estimates the side slip angle and the velocity. Validation with the simulator VE-DYNA, at each step, pointed out the good performance and the robustness of the proposed observers