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BCG-VARS: BallistoCardioGraphy vital algorithms for real-time systems
No full textInternational audienceDisorders with a transient signature (e.g., obstructive sleep apnea, drowsy driving, or atrial fibrillation) are difficult to predict. In addition, the aging population is moving towards dependency and medical structures lack caregivers. This paper introduces an innovative and generic vitals monitoring software. This real-time and embedded software, called BCG-VARS, uses ballistocardiography to perform contactless measurements of vital parameters on various types of equipment (e.g. medical bed, car seat, wheelchair). The software consists in two algorithms and was designed to extract three different vital parameters: actigraphy, Breath-to-Breath Interval (BBI), and heart Inter-Beats Interval (IBI). The algorithms were designed to analyze ballistocardiography signals but can be reused for other pseudo-periodic signals. BCG-VARS shows state-of-the-art high performances, even when sensors are deeply integrated in a medical bed. Absolute mean errors of 1.59 beats per minute for IBIs and 1.03 cycles per minute for BBIs were obtained in the deep integration configuration. In addition, all the subjects presences and movements (lie down, roll over or leave) were detected by the algorithms. Thanks to parameter tuning and interval prediction, the system is accurate, reliable in multiple applications and adapts to every new individual
Vers une Classification de Stratégies d'Orchestration de Services Edge Intégrant des Énergies Renouvelables
No full textInternational audienceEdge computing, an emerging decentralized computing paradigm, extends Cloud infrastructure to the network edge to enable low-latency services and emphasizes widespread availability among users. Service orchestration plays a crucial role in optimizing performance and dynamically adapting to the changing context of Edge computing. Due to current environmental considerations and the large number of distributed servers in Edge computing, it is necessary to develop more sustainable and efficient methods for orchestration. To this end, renewable energy sources offer a promising approach to increase system sustainability. In this paper, we present a proposition for a comprehensive classification of edge service orchestration strategies that incorporate renewable energy sources in their infrastructures
Détection d'anomalies dans les séries temporelles déformées - Application à la surveillance des robots industriels
No full textNational audienceThis thesis addresses the problem of detecting time series outliers, focusing on systems with repetitive behavior, such as industrial robots operating on production lines. The research addresses several challenges, notably the significant amount of missing data within the collected datasets that results in irregular sampling of the time series reported by sensors, as well as variations in the duration of each task repetition across the time series.The anomaly detection approach presented in this paper consists of three stages.- The first stage identifies the repetitive cycles in the lengthy time series and segments them into individual time series corresponding to one task cycle, while accounting for possible temporal distortions.- The second stage computes a prototype for the cycles using a GPU-based barycenter algorithm, specifically tailored for very large time series.- The third stage uses the prototype to detect abnormal cycles by computing an anomaly score for each cycle.The overall approach, named WarpEd Time Series ANomaly Detection (WETSAND), makes use of the Dynamic Time Warping algorithm and its variants because they are suited to the distorted nature of the time series.The experiments have been carried out with real robot manipulators of Vitesco Technology plants. Robot manipulators constitute a significant portion of automation in today’s industry. Designed to perform specific, repetitive tasks safely alongside human operators, it is essential to predict and diagnose any deviation from their expected behavior. Consequently, monitoring these robots' behavior is crucial, as it minimizes production line downtime and prolongs the system's lifespan through maintenance schedule adjustments. In the digital era of Industry 4.0, where data collection, storage, and processing are ubiquitous, the parameters of these robots are continuously monitored in real-time, ensuring their tasks are executed flawlessly.The experiments show that WETSAND scales to large signals, computes human-friendly prototypes, works with very little data, and outperforms some recognized neural anomaly detection approaches such as autoencoders. A cloud-based user interface has been designed to deploy WETSAND in the Vitesco Technologies plants and it monitors online different robots in the production chains.This thesis is part of CIFRE program under the “Collaborative AI : Synergistic transformations in model based and data-based diagnosis” chair at ANITI. The research has been conducted through a collaboration between the Laboratory of Analysis and Architecture of Systems (LAAS) and Vitesco Technologies, situated in Toulouse, France.Cette thèse aborde le problème de la détection d’anomalie dans les séries temporelles, en se focalisant sur les systèmes à comportement répétitif, tels que les robots industriels opérant sur des chaînes de production. La recherche traite plusieurs défis, notamment la quantité importante de données manquantes dans les jeux de données, ce qui entraîne un échantillonnage irrégulier des séries temporelles issues des capteurs, ainsi que des variations dans la durée de chaque répétition de la tâche.L'approche de détection d’anomalie présentée se déroule en trois étapes :- La première étape identifie les cycles répétitifs dans les séries temporelles entières et les segmente en sous-séquences correspondant à un cycle de la tâche malgré les éventuelles distorsions temporelles.- La deuxième étape calcule un prototype des cycles à l'aide d'un algorithme de barycentre optimisé par GPU spécifiquement adapté aux très grandes séries temporelles.- La troisième étape utilise le prototype pour détecter les cycles anormaux en calculant un score d'anomalie pour chaque cycle.L'approche globale, nommée WarpEd Time Series ANomaly Detection (WETSAND), utilise l'algorithme Dynamic Time Warping (Déformation Temporelle Dynamique) et ses variantes qui gèrent la nature déformée des séries temporelles.Les expériences ont été menées sur des robots manipulateurs réels des usines de Vitesco Technologies. Les robots manipulateurs représentent une part importante de l'automatisation dans l'industrie actuelle. Conçus pour effectuer des tâches spécifiques et répétitives en toute sécurité aux côtés des opérateurs humains, il est essentiel de prédire et de diagnostiquer toute déviation par rapport à leur comportement attendu. Par conséquent, surveiller le comportement de ces robots est crucial, car cela minimise les temps d'arrêt des chaînes de production et prolonge la durée de vie des systèmes en permettant d’ajuster les calendriers de maintenance. Dans l'ère numérique de l'Industrie 4.0, où la collecte, le stockage et le traitement des données sont omniprésents, les paramètres de ces robots sont surveillés en temps réel, garantissant ainsi l'exécution parfaite de leurs tâches.Les expériences montrent que WETSAND s'adapte à des signaux de grande taille, calcule des prototypes faciles à interpréter, fonctionne avec très peu de données et surpasse certaines approches de détection d'anomalie neuronales reconnues telles que les autoencodeurs. Une interface utilisateur basée sur le cloud a été conçue pour déployer WETSAND* dans les usines de Vitesco Technologies, où elle permet de surveiller en ligne différents robots sur les chaînes de production.Cette thèse fait partie du programme **CIFRE** sous la chaire « AI Collaborative: Transformations Synergiques en in diagnostic basé sur des modèles et sur des » au sein de l’institut interdisciplinaire d’intelligence artificielle ANITI (Artificial and Natural Intelligence Toulouse Institute). La recherche a été menée en collaboration entre le Laboratoire d'Analyse et d'Architecture des Systèmes (LAAS-CNRS) et Vitesco Technologies, situés à Toulouse, France
An Epistemic Human-Aware Task Planner which Anticipates Human Beliefs and Decisions
No full textInternational audienceWe present a substantial extension of our Human-Aware Task Planning framework, tailored for scenarios with intermittent shared execution experiences and significant belief divergence between humans and robots, particularly due to the uncontrollable nature of humans. Our objective is to build a robot policy that accounts for uncontrollable human behaviors, thus enabling the anticipation of possible advancements achieved by the robot when the experience is not shared, e.g., when humans are briefly absent from the shared environment to complete a subtask. But, this anticipation is considered from the perspective of humans who have access to an estimated robot's model. To this end, we propose a novel planning framework and build a solver based on AND/OR search, which integrates knowledge reasoning, including situation assessment by perspective taking. Our approach dynamically models and manages the expansion and contraction of potential advances while precisely keeping track of when (and when not) agents share the task execution experience. It systematically assesses the situation and ignores worlds that it has reason to think are impossible for humans. Overall, our new solver can estimate the distinct beliefs of the human and the robot along potential courses of action, enabling the synthesis of plans where the robot selects the right moment for communication, i.e. telling or replying to an inquiry, or defers ontic actions until the execution experiences can be shared. Preliminary experiments in two domains -one novel and one adapted -demonstrate the framework's effectiveness
Optical Long Base Hydrostatic Tiltmeter for Slow Earthquake Detection
No full textInternational audienceWe present an optical long base hydrostatic tiltmeter (OLBT) designed to be able to detect slow earthquakes in this work. Interrogation is carried out by a high precision fiber-based Fabry-Perot interferometer that detects variations of the surface liquid level in a hydrostatic leveling vessel. Field results from a 41 m long OLBT installed along the Gulf of Corinth, Greece, near the Psathopyrgos fault since mid-February 2024 demonstrate a tilt resolution of 0.2 nanorad and a stability of better than 0.15 µrad per month. This can potentially enable the instrument to be employed for detecting and monitoring slow slip events over the long term in earthquake precursor research
Imaging the 3D cellular effects of confinement in oncology: Application in pancreatic cancer
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Clip-Chips: 3D printed microfluidic and characterization modules for cell culture applications
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A High Resolution Phase Shift Detection System for a Differential Multimode Fiber Refractometer
No full textInternational audienceIn this work, we present a phase shift detection system to instrument a differential multimode fiber (MMF) refractometer designed for methane (CH4) detection in aqueous environments. The concentration of CH4 can typically be directly obtained by observing the variation of the transmitted optical power along a functionalized MMF using a sensitised polymeric thin film compared to a reference MMF. Here, using a lock-in amplifier and modulating the laser drive current, we propose to indirectly monitor this concentration variation via phase shift measurement of a second order filter implemented using the photo diode parasitic capacitance. We show that the sensitivity of the system can be amplified by the quality factor of the circuit compared to a first order filter. Simulations show that refractive index (RI) change down to 20 nRIU can be detected. In addition, a closed-loop approach based on delay locked-loop is designed to improve both the linearity and dynamic range
Detecting Traffic Engineering from public BGP data
No full textInternational audienceRouting is essential to the Internet functioning. However, more and more functions are added to BGP, the inter-AS routing protocol. In addition to providing connectivity for best effort service, it carries flow specification rules and blackholing signals to react to DDoS, routes for virtual private networks, IGP link-state database information among other uses. One such addition is the tweaking of BGP advertisements to engineer the traffic, to direct it on some preferred paths. In this paper we aim to estimate the impact of Traffic Engineering (TE) on the BGP ecosystem. We develop a method to detect the impact in space, that is, to find which traffic engineering technique impacts which prefix and which AS. We design a methodology to pinpoint TE events to quantify the impact on time. We find that on average, a BGP vantage point sees 35% of the announced prefixes impacted by TE. Quantifying the impact of TE on BGP stability, we find that TE events contribute to 39% of BGP updates and 44% of the BGP convergence time, and that prefixes belonging to hypergiants contribute the most to TE