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STRUCTURAL HEALTH MONITORING (SHM) OF SMART SANDWICH COMPOSITES DURING THREE-POINT BENDING TESTS
No full textInternational audienceThis article presents the Structural Health Monitoring (SHM) study conducted on the smart sandwich composites under three-point bending tests. Piezoelectric transducers (PZT and P(VDF-TrFE)/CP) were integrated inside the Polymer-Matrix Composite (PMC) skins to make them "smart". Several Non-Destructive Testing (NDT) techniques have been used: the classical ones (external Acoustic Emission (AE), thermocouples, strain gauges…) and new ones using the embedded transducers (internal Acoustic Emission (AE), Electrical Capacitance and Ultrasonic testing (US)) were combined simultaneously. The monotonic and load/unload three-point bending tests introduced progressive damages in sandwich composite specimens and the combined NDT techniques made it possible to monitor, in real-time and in situ, the different damage signatures before, during and after the mechanical tests. The obtained results of the non-destructive approach show a multiphysics (thermal/mechanical/acoustic and ultrasonic) coupling in the detection and monitoring of the different damage mechanisms of sandwich composite materials
Synthesis and Integration of Hybrid Metal Nanoparticles Covered with a Molecularly Imprinted Polymer Nanolayer by Photopolymerization
No full textInternational audienceInterfacing recognition materials with transducers has consistently presented a challenge in the development of sensitive and specific chemical sensors. In this context, a method based on nearfield photopolymerization is proposed to functionalize gold nanoparticles, which are prepared by a very simple process. This method allows in situ preparation of a molecularly imprinted polymer for sensing by surface-enhanced Raman scattering (SERS). In a few seconds, a functional nanoscale layer is deposited by photopolymerization on the nanoparticles. In this study, the dye Rhodamine 6G was chosen as a model target molecule to demonstrate the principle of the method. The detection limit is 500 pM. Due to the nanometric thickness, the response is fast, and the substrates are robust, allowing regeneration and reuse with the same performance level. Finally, this method of manufacturing has been shown to be compatible with integration processes, allowing the future development of sensors integrated in microfluidic circuits and on optical fibers
Combined Surface Electromyography and Motion Capture for Quantitative Analysis of Facial Movements
No full textInternational audienceResearch ObjectivesThe aim of this work was to test the feasibility of integrating EMG into our motion capture protocol for quantitative analysis of facial movements, in order to measure at the same time the action potentials by sEMG and the markers displacement amplitudes for a determined movement.DesignA prospective study for quantitative analysis of facial movements by the simultaneous use of surface electromyography (sEMG) was conducted.SettingThis study was approved by the local independent ethics committees (CPP Nord Ouest II, Amiens, France; references: ID-RCB 2011-A00532-39; CPP 2011-23), registered at clinicaltrials.gov (NCT02002572).ParticipantsThis feasibility trial was performed on two healthy male volunteers without history of facial pathology.InterventionsWe used a motion capture system consisting of 10 Vantage optoelectronic cameras (Vicon Ltd, Oxford, UK). In addition, we used wireless bipolar PicoEMG sensors (Cometa Systems, Milan, Italy). They were placed on the subject's face, at the motor points of frontalis and zygomatic major muscles.Main Outcome MeasuresKinematic and EMG data was exported as a .csv file that combined EMG signals and markers displacement amplitudes over time. A custom algorithm was developed for processing and analyzing both signals.ResultsWe obtained noiseless, rectified and filtered EMG signals, which allowed a readable visual graphical analysis. EMG signals analysis between sensors of each side of the face showed similar signal patterns between right and left muscles, for each acquisition and subject. Regarding motion capture data, the markers’ displacements amplitudes were compared between each side of the face according to the muscular zone, and we observed symmetry of the results between muscles on each side of the face.ConclusionsHere we showed the feasibility of using motion capture and electromyography for quantitative analysis of facial movements in one single acquisition. We obtained facial expression indicators that can be used for a simultaneous multimodal analysis
Asymptotic properties of conditional U -statistics using delta sequences
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Synthesis and evaluation of new vanillin derivatives for crop protection against phytopathogens (VANIFUN)
No full textInternational audienceIn the context of the valorization of plant biomass for the substitution of fungicides and antibacterialagents, lignin derivatives such as vanillin have gained a lot of interest[1]. In previous studies, molecularcompounds synthesized from vanillin by reductive amination have shown antimicrobial activity againstcontaminants of cosmetic products[2]. In this thesis, these compounds will be studied for their potentialantimicrobial effect, as well as their ability to induce a defensive response against crop plant pathogens.Chemical synthesis of vanillin derivatives using green chemistry conditions will be performed first[3],followed by inhibition tests on phytopathogenic microorganisms affecting wheat and rapeseed. The bestmolecular candidates, that lead in vitro to a strong inhibition of fungal pathogens development without anytoxicity to human cells, will be determined. These candidates will then be assessed on plants, in vitro ondetached plant tissues or in vivo on entire plants according to pathogens. A final study will determine therelationship between the molecular structure and its anti-fungal potency
Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models
No full textInternational audienceStress is an increasingly prevalent mental health condition across the world. In Europe, for example, stress is considered one of the most common health problems, and over USD 300 billion are spent on stress treatments annually. Therefore, monitoring, identification and prevention of stress are of the utmost importance. While most stress monitoring is carried out through self-reporting, there are now several studies on stress detection from physiological signals using Artificial Intelligence algorithms. However, the generalizability of these models is only rarely discussed. The main goal of this work is to provide a monitoring proof-of-concept tool exploring the generalization capabilities of Heart Rate Variability-based machine learning models. To this end, two Machine Learning models are used, Logistic Regression and Random Forest to analyze and classify stress in two datasets differing in terms of protocol, stressors and recording devices. First, the models are evaluated using leave-one-subject-out cross-validation with train and test samples from the same dataset. Next, a cross-dataset validation of the models is performed, that is, leave-one-subject-out models trained on a Multi-modal Dataset for Real-time, Continuous Stress Detection from Physiological Signals dataset and validated using the University of Waterloo stress dataset. While both logistic regression and random forest models achieve good classification results in the independent dataset analysis, the random forest model demonstrates better generalization capabilities with a stable F1 score of 61%. This indicates that the random forest can be used to generalize HRV-based stress detection models, which can lead to better analyses in the mental health and medical research field through training and integrating different models
Optimization of material distribution applied to magnet-free rotors of synchronous motors
No full textInternational audienceIn a relentless desire to continuously improve the performance of electric actuators, topological optimal design methods present interesting possibilities. In particular, this work applies an optimization approach of material distribution in order to define new configurations of material arrangements (air, steel, copper) for internal parts of machines, that potentially provide better performance. This methodology is applied to different design configurations of magnet-free synchronous machine rotors
DECISION SUPPORT FRAMEWORK USING KNOWLEDGE BASED DIGITAL TWIN FOR SUSTAINABLE PRODUCT DEVELOPMENT AND END OF LIFE
No full textInternational audienceAbstract In order to have a sustainable disassembly process, a successful decision-making based on reliable and up-to-date information should be made while taking into consideration sustainability indicators. In this context, The aim of this paper is to introduce a decision support system based on knowledge based and digital twin in order to help stakeholders to choose the most sustainable disassembly scenario .In this research, firstly, we presented the state of art of disassembly process, digital twin, knowledge based system and the merging of knowledge based system and digital twin for disassembly. Secondly, we presented the knowledge based digital twin (KBDTw) system framework for a sustainable disassembly process. Thirdly, a case study is presented about the use of KBDTw in the end-of-life of internet boxes. Finally, a conclusion and future work are conducted
Integration method of at least one piezoelectric transducer within polymer and composite parts manufactured using 3d printing techniques
No full textA method of manufacturing a part having at least one piezoelectric measurement device integrated inside, the method comprising the following successive steps:Using an additive method to obtain at least a first portion of the part, with at least one cavity being at least partly formed in said portion;Stopping the additive method;Placing at least one piezoelectric transducer of the piezoelectric measurement device in said cavity; andUsing an additive method to create at least a second portion of the part so that the at least one transducer is held captive between at least the first and second portions
A Threat-Aware and Efficient Wireless Charging Scheme for IoT Networks
No full textInternational audienceThe Internet of Things (IoT) is a breakthrough that enables many applications and improves our daily life. Since IoT networks deploy many devices which are generally energy or computationally-constrained, it is necessary to efficiently manage their energy to maximize network lifetime. Moreover, IoT networks may face multiple security threats that must be dealt with defense mechanisms. However, these defense mechanisms reduce network lifetime. Hence, it is necessary to design solutions that reduce the impacts of defense solutions on network lifetime. In this paper, we propose a solution based on wireless Mobile Chargers (MCs) which proactively and preventively charge devices that may need energy for the execution of security services that protect the IoT network. By using Deep Reinforcement Learning (DRL), especially Deep-Q learning, our solution determines, from the current threat level, the remaining energy of the devices, and the distance from the charger, the next device to charge. Compared to approaches that are not threat-aware, our solution improves the lifetime of a rechargeable IoT network of 16 devices by 21.59%