Archive ouverte HAL-LAAS
Not a member yet
12189 research outputs found
Sort by
Efficient Evaluation of 2-D Collision Probability Derivatives for Uncertain k-scaled Covariances: A PcMax Case Study
No full textInternational audienceThis paper focuses on efficiently evaluating derivatives of the 2-D collision probability, treated as a function of parameters, which appear as linear forms in the entries of the covariance matrix. This boils down to computing moments of the associated Gaussian measure restricted to a disk. Specifically, we propose an optimization-based solution to computing the maximum collision probability when the covariance data is unreliable, implementing an alternative method to the traditional k-scaled covariance approach. Preliminary results indicate our method's potential for improving the understanding of Pc's validity as a measure of conjunction likelihood
Leveraging the Christoffel function for outlier detection in data streams
No full textInternational audienceOutlier detection holds significant importance in the realm of data mining, particularly with the growing pervasiveness of data acquisition methods. The ability to identify outliers in data streams is essential for maintaining data quality and detecting faults. However, dealing with data streams presents challenges due to the non-stationary nature of distributions and the ever-increasing data volume. While numerous methods have been proposed to tackle this challenge, a common drawback is the lack of straightforward parameterization in many of them. This article introduces two novel methods: DyCF and DyCG. DyCF leverages the Christoffel function from the theory of approximation and orthogonal polynomials. Conversely, DyCG capitalizes on the growth properties of the Christoffel function, eliminating the need for tuning parameters. Both approaches are firmly rooted in a well- defined algebraic framework, meeting crucial demands for data stream processing, with a specific focus on addressing low-dimensional aspects and maintaining data history without memory cost. A comprehensive comparison between DyCF, DyCG, and state-of-the-art methods is presented, using both synthetic and real industrial data streams. The results show that DyCF outperforms fine-tuning methods, offering superior performance in terms of execution time and memory usage. DyCG performs less well, but has the considerable advantage of requiring no tuning at all
In Vitro Neurons on a Chip: From Fundamental Neuroscience to Applied Neurotechnology
No full textNational audienc
Toward Next-Generation Bioelectronic Interfaces for In Vitro Neural Network Analysis Using 3D Nanoelectrodes
No full textInternational audienc
III-Nitride MEMS drum resonators on flexible metal substrates
No full textInternational audienceWe present a simple and efficient process for fabricating III-Nitride (III-N) mechanical resonators on flexible metal substrates. This method combines Van der Waals epitaxy of III-N epilayers with the deposition of a thick metal stressor atop the III-N layers. During thermal treatment, the 30 μm thick metal stressor deposited on a 300 nm AlGaN/500 nm GaN layer grown on a 3 nm two-dimensional hexagonal-Boron Nitride (2D h-BN) release layer, initiates a one-step Self-Lift-Off and Transfer (SLOT) process. This process effectively transfers the III-N heterostructure from the h-BN/Sapphire growth wafer to the flexible metal stressor substrate. Additional local etching of the metal stressor and deposition of front electrodes allow for releasing self-standing III-N layers with integrated actuation. Fabricated III-N MEMS drum resonators were analyzed using optical profilometry and laser Doppler vibrometer, enabling the observation of static deflections and distinct vibration modes. Finite element method (FEM) simulations were also performed to further understand experimental observations and assess the mechanical properties of the released III-N layers, particularly enabling the estimation of stress in the GaN and AlGaN released layers. This straightforward approach not only provides a practical solution for cost-effective III-N MEMS resonators but also ensures flexibility, and crack-free structures
Failure Prediction Through Testing Data Using Machine Learning Classification: A Smart Plug Top Case Study
No full textInternational audienceSmart plug tops (SPTs) with sensing capabilities are increasingly important for real-time monitoring and diagnostics in internal combustion engines. However, the proliferation of electronic devices and system complexity can cause failures requiring investigation. This research uses machine learning (ML) to categorize various failures. The method involves collecting sensor data during SPT testing, which is then linked to failures identified through lifetime analysis. ML model uses features such as voltage levels, charge times, current levels, etc. The model is refined using a training and validation method to accurately predict various types of failures, such as electric discharge on the transformer secondary winding, damping diode breakdown, and short circuits between windings. A key challenge is the limited number of failure samples, as failures occurring rarely during the lifetime analysis. Hence, an upsampling technique was applied to improve this imbalanced dataset. The classification algorithm's performance is evaluated by accuracy, precision, recall, and F1-score. The results enable early detection of problem symptoms during acceptance testing and classification of failure probabilities
3D customized silica-based AFM probes fabricated by selective laser etching
No full textInternational audienceAtomic force microscopy (AFM) cantilevers are essential components that function both as force sensors and nanoscale interaction tools that plays a critical role in AFM capabilities, sensitivity and precision. Conventional fabrication techniques for probes, that rely on silicon or silicon nitride bulk micro-machining, generally requires complex fabrication processes associated to low throughput and limited geometric flexibility. Here we explore the development of innovative AFM cantilevers made of silica glass through a novel approach based on selective laser etching, that offers cantilever and tip design flexibility, condense the process into three steps, and reduces the fabrication time and cost while minimizing reliance on complex equipment and clean room facilities. We demonstrate the fabrication and characterization of functional glass cantilevers with thicknesses ranging from 1 to 50 µm and spring constants spanning from 0.02 to 80 N.m-1. The fabricated glass probes show excellent performance in both AFM imaging and force spectroscopy applications. The simple and fast fabrication approach, highlight the potential of selective laser etching to produce innovative versatile silica-based probes for AFM
Advancing High-Throughput Cellular Atomic Force Microscopy with Automation and Artificial Intelligence
No full textInternational audienceAtomic force microscopy (AFM) has reached a significant level of maturity in biology, demonstrated by the diversity of modes for obtaining not only topographical images but also insightful mechanical and adhesion data by performing force measurements on delicate samples with a controlled environment (e.g., liquid, temperature, pH). Numerous studies have applied AFM to describe biological phenomena at the molecular and cellular scales, and even on tissues. Despite these advances, AFM is not established as a diagnostic tool in the biomedical field. This article describes the reasons for this gap, focusing on one of the main weaknesses of bio-AFM: its low data throughput. We review current efforts to improve the automation of AFM measurements in particular on living cells, as well as the developments in automating data analysis. For the latter, artificial intelligence (AI) is progressively employed to classify data to distinguish healthy and diseased cells or tissues. Finally, we propose a roadmap to foster the application of bio-AFM into medical diagnostics
Control and maximum power extraction of a switched reluctance generator with low resolution pulse-based position estimation
No full textInternational audienceThis paper presents a control approach for switched reluctance generators (SRGs), based on single-pulse-per-revolution position sensors (e.g., Hall-effect sensors or single pulse encoders, as opposed to high-resolution encoders). The framework comprises a multi-level control architecture, based on the assumption of a single-pulse SRG control mode, which adjusts the generator firing angles to pursue control of the average speed (measured once per cycle) and maximization of the power yield via an extremum seeking approach.To consistently formulate the control law in terms of the cyclic average speed, we use a recasting of the SRG dynamic equations via the hybrid dynamical formalism. We then assess the performance of the proposed control scheme in simulation, using a traditional four-phase 8/6 SRG as a target for the investigation. The simulation results demonstrate the effectiveness of the proposed control approach, even in complex scenarios in which the SRG is coupled with a small-scale wind turbine operating in realistic wind conditions
A Neural Double Observer Scheme Based on LSTMs for Air Data Fault Detection and Isolation
No full textInternational audienceThere is an increasing interest in developing algorithmic fault detection and isolation (FDI) of aircraft air data sensors without relying on the existing hardware redundancy. This is a complex problem, as the available state equations have non-observable states in the event of a complete loss of all redundant sensors that measure a flight variable. Furthermore, FDI approaches commonly require surrogate models and the analytical relations they use are strongly subject to external disturbances. Trying to tackle these difficulties, we introduce the Neural Double Observer Scheme based on Long Short Term Memory units (LSTMs), a new estimation framework for FDI that allows for fault isolation in systems with intense coupling of physical equations. This framework is inspired by classic observer schemes for FDI and powered by LSTMs units. Evaluated on real flight data from an Airbus aircraft, it demonstrated promising performance compared to previously used model-driven methods