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International audience; Surface electromyography (sEMG) is a valuable technique that helps provide functional and structural information about the electric activity of muscles. As sEMG measures output of complex living systems characterized by multiscale and nonlinear behaviors, Multiscale Permutation Entropy (MPE) is a suitable tool for capturing useful information from the ordinal patterns of sEMG time series. In a previous work, a theoretical comparison in terms of bias and variance of two MPE variants—namely, the refined composite MPE (rcMPE) and the refined composite downsampling (rcDPE), was addressed. In the current paper, we assess the superiority of rcDPE over MPE and rcMPE, when applied to real sEMG signals. Moreover, we demonstrate the capacity of rcDPE in quantifying fatigue levels by using sEMG data recorded during a fatiguing exercise. The processing of four consecutive temporal segments, during biceps brachii exercise maintained at 70% of maximal voluntary contraction until exhaustion, shows that the 10th-scale of rcDPE was capable of better differentiation of the fatigue segments. This scale actually brings the raw sEMG data, initially sampled at 10 kHz, to the specific 0–500 Hz sEMG spectral band of interest, which finally reveals the inner complexity of the data. This study promotes good practices in the use of MPE complexity measures on real data.

Mutual information (MI) has been previously used to select the relevant features for the task of speech emotion recognition (SER). However, the procedure does not deliver the optimal number of relevant features. We propose MI based criterion for estimating this number defined as the minimum number of features that explains the variable of the class indices. In order to minimize the MI estimation errors, we also search the best histogram binning choice considering three formulas: Sturges, Scott and LMSE. Four selection strategies MMI, CMI, JMI and TMI have been implemented and applied on 39-features vectors and on large dimension vectors. The feature selection results have been validated on independent text SER system, based on GMM classifier and evaluated on EMO-db database. Results demonstrate that LMSE bin choice gives the best MI estimation and ensures a minimal number of features with slight performance drop. Particularly, using the proposed stopping criterion, the CMI strategy achieves reduction of 48.72% in the case of the 39-features vectors size and 67.86% in the case of large dimension vectors. Moreover, using the recognition rate criterion, the JMI strategy gives a comparable feature reduction with slight improvement of performance but requiring very high computation capabilities.

This paper addresses the problem of blind stereoscopic image quality assessment (NR-SIQA) using a new multi-task deep learning based-method. In the field of stereoscopic vision, the information is fairly distributed between the left and right views as well as the binocular phenomenon. In this work, we propose to integrate these characteristics to estimate the quality of stereoscopic images without reference through a convolutional neural network. Our method is based on two main tasks: the first task predicts naturalness analysis based features adapted to stereo images, while the second task predicts the quality of such images. The former, so-called auxiliary task, aims to find more robust and relevant features to improve the quality prediction. To do this, we compute naturalness-based features using a Natural Scene Statistics (NSS) model in the complex wavelet domain. It allows to capture the statistical dependency between pairs of the stereoscopic images. Experiments are conducted on the well known LIVE PHASE I and LIVE PHASE II databases. The results obtained show the relevance of our method when comparing with those of the state-of-the-art. Our code is available online on https://github.com/Bourbia-Salima/multitask-cnn-nrsiqa_2021.

International audience; As part of the studies of the University Bachelor of Technology in the University of Orléans, France, second-year students in Logistics and Transport must provide a group project. During the 2018-2019 academic year, a group of four students carried out such a project whose goal was to implement a web documentary aiming at a Virtual Reality (VR) presentation of the Chartres's Institute of Technology (IUT de Chartres, FR). This project, with the acronym PixEL3D, has been successfully introduced to the public using smartphone VR headsets during the University's Open Days event in spring 2019 and has since been linked to the official web pages of the Institute. This paper presents VR technology and material used for this project which was selected by the Call for Innovative Digital Educational Projects of the University of Orléans for the 2018-2019 academic year. Feedback from the project's final users is also being discussed. Next, an experimental course programmed with A-Frame and tested in a real course situation is presented, A-Frame being a free open-source and under MIT license web framework for building 3D, VR, and AR (Augmented Reality) experiences. A discussion on the potential of using virtual reality technology for educational purposes, its advantages as well as drawbacks and constraints of virtual worlds, and on the interaction between VR and learning processes will conclude this work which may figure as a pilot study on educational applications of VR in the ongoing pandemic COVID-19 situation.

International audience; Point cloud (PC) quality assessment is of fundamental importance to enable the efficient processing, coding and transmission of 3D data for virtual/augmented reality, autonomous driving, cultural heritage, etc. The quality metrics proposed so far aim at quantifying the distortion in the PC geometry and/or attributes with respect to a reference pristine point cloud, using simple features extracted by the points. In this work, we target instead a blind (no-reference) scenario in which the original point cloud is not available. In addition, we learn features from data using deep neural networks. Given the limited availability of subjectively annotated datasets of corrupted point clouds, and the consequent difficulty to learn in an end-to-end fashion PC quality features, in this work we use instead a two-step procedure. First, we extract from local patches three relevant low-level features which have been commonly used in other PC quality metrics, i.e., geometric distance, local curvature and luminance values. Afterwards, we employ a deep neural network to learn, from these low-level features, a mapping to the PC ground truth mean opinion score. Our results on two state-of-the-art PC quality datasets show the potential of the proposed approach. The code is available here : https://github.com/achetouani/NR-CNN-3D-PC

International audience; Permutation Entropy (PE) is a powerful tool for measuring the amount of information contained within a time series. However, this technique is rarely applied directly on raw signals. Instead, a preprocessing step, such as linear filtering, is applied in order to remove noise or to isolate specific frequency bands. In the current work, we aimed at outlining the effect of linear filter preprocessing in the final PE values. By means of the Wiener–Khinchin theorem, we theoretically characterize the linear filter’s intrinsic PE and separated its contribution from the signal’s ordinal information. We tested these results by means of simulated signals, subject to a variety of linear filters such as the moving average, Butterworth, and Chebyshev type I. The PE results from simulations closely resembled our predicted results for all tested filters, which validated our theoretical propositions. More importantly, when we applied linear filters to signals with inner correlations, we were able to theoretically decouple the signal-specific contribution from that induced by the linear filter. Therefore, by providing a proper framework of PE linear filter characterization, we improved the PE interpretation by identifying possible artifact information introduced by the preprocessing steps.

Multiscale Permutation Entropy (MPE) is one of the most common techniques to assess the ordinal information content within a time series. In the present paper we propose an explicit, deterministic function of the MPE of a general ARMA process, as a function only of its parameters and time scale. We compare our theoretical results with the MPE of corresponding simulated signals, which further support our formulation. We also present an exploration of the effects of the ARMA parameters on the MPE curve, where we found a monotonic decrease of entropy for long-term time scales, and highly non-linear effects on short scales. With these results, we aim to provide a benchmark for the MPE of any real time series modelled as an ARMA process.

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International audience; In this paper, we are interested in the Home health care (HHC) scheduling problem. The HHC office needs to minimize traveling costs and to optimize caregiver assignment to patients. Scheduling patient visits have to take into account unexpected situations based on the latest scheduling information. We first analyze the HHC scheduling problem as deterministic, then discuss the dynamic challenges and propose a rescheduling approach based on genetic algorithm. A platform is designed to evaluate the proposed approach. The obtained results showed that the scheduling system is able to compute high quality schedules and can deal with urgent unpredictable situations.

The objective of the present work was to experimentally investigate flow field instantaneous characteristics and their associated heat transfer for pulsating flows representative of engine exhaust flow operating conditions. The experimental apparatus consists of a stationary turbulent hot air flow, with a variable Reynolds number, excited through a pulsating mechanism and exchanging thermal energy with a water cooled steel pipe. Pulsation frequency ranges from 10 to 95 Hz. Simultaneous time-resolved measurements of velocity and temperature were achieved by mean of coupled hot-wire anemometry and micro-unsheathed thermocouples measurements to investigate the impact of the pulsation frequency on heat transfers. Flow pulsation was found to enhance heat transfers in the entire range of frequencies. Significant improvements were observed when the flow was excited with a frequency equal to a resonant mode of the system. With the increase in velocity-amplitude ratio, an increase in the convective heat transfer was observed, with a maximum enhancement corresponding to a factor three and higher, with respect to the relative Nusselt number. Combining experimental results with an analytical formulation, derived from the 1D energy balance equation for a turbulent pulsating flow, the velocity amplitude ratio was identified as the characteristic term representative of the predominant heat-transfer enhancement mechanism.

Many appliance identification systems use harmonics of current signals as features. However, the choice of the order and number of relevant harmonics for appliance identification task has never been demonstrated. Here, we propose to tackle this issue by analysing the relevance and redundancy of harmonics for this task using feature selection algorithms based on mutual information criterion. Six heuristic strategies based on this criterion were implemented and their selection results from high-dimensional feature vectors were compared. For the choice of a minimal subset of relevant harmonics, we propose a stopping criterion in the selection procedure. In order to validate the selected subset of harmonics, a hidden Markov model based-classifier was used and evaluated on PLAID dataset. Results highlight odd order harmonics relevance. Furthermore, the feature subset {1,2,3,4,5,7,9} was selected by three strategies as strongly relevant since this minimal subset is sufficient for essentially explaining appliances' signatures.

We report an experimental investigation of the separating/reattaching flow over a descending ramp with a $25^{\circ }$ expansion angle. Emphasis is given to mass entrainment through the boundaries of the separated shear layer emanating from the upper edge of the ramp. For this purpose, the turbulent/non-turbulent interface and the separation line inferred from image-based analysis are used respectively to mark the upper and lower bounds of the separated shear layer. The main objective of this study is to identify the physical parameters that scale the development of the separated shear layer, by giving a specific emphasis to the investigation of mass entrainment. Our results emphasise the multiscale nature of mass entrainment through the separated shear layer. The recirculation length $L_{R}$, step height $h$ and free-stream velocity $U_{\infty }$ are the dominant scales that organise the separated flow (and related large-scale quantities as pressure distribution or shear layer growth rate) and set mean mass fluxes. However, local viscous mechanisms seem to be responsible for most of local mass entrainment. Furthermore, it is shown that large-scale mass entrainment is driven by incoming boundary layer properties, since $L_{R}$ scales with $Re_{\unicode[STIX]{x1D703}}$, and in particular by its turbulent state. Surprisingly, the relationships evidenced in this study suggest that these dependencies are established over a large distance upstream of separation and that they might also extend to small scales, at which viscous entrainment is dominant. If confirmed by additional studies, our findings would open new perspectives for designing effective separation control systems.

Transient signals are characteristic of the underlying phenomenon generating them, which makes their analysis useful in many fields. Transients occur as a sudden change between two steady state regimes, subsist for a short period, and tend to decay over time. Hence, superimposed damped sinusoids (SDS) were extensively used for transients modeling as they are adequate for describing decaying phenomena. However, SDS are not adapted for modeling the turn-on transient current of electrical appliances as it tends to decay to a steady state that is different from the one preceding it. In this paper, we propose a new and more suitable model for these signals for the purpose of characterizing appliances. We also propose an algorithm for the model parameter estimation and validate its performance on simulated and real data. Moreover, we give an example on the use of the model parameters as features for the classification of appliances using the Controlled On/Off Loads Library (COOLL) dataset. The results show that the proposed algorithm is efficient and that for real data the network fundamental frequency must be estimated to account for its variations around the nominal value. Finally, real data experiments showed that the model parameters used as features yielded a classification accuracy of 98%.

Multiscale Permutation Entropy (MPE), an extension of Permutation Entropy (PE), was proposed to better capture the information content in long range trends. This technique has been extensively used in biomedical applications for diagnosis purposes. Although PE theory is well established and explored, there is still a lack of theoretical development for MPE. In the present paper, we expand the theory by formulating an explicit MPE model of first order Autoregressive (AR) and Moving Average (MA) processes, which are well known and used in signal modeling. We first build the autocorrelation function of coarse-grained AR and MA models, which are a prerequisite for MPE calculation. Next, we use the resulting autocorrelation functions to establish the theoretical value of MPE as a function of time scale and AR or MA parameters. The theoretical result is tested against MPE measurements from simulations. We found the MPE of the 1o order AR model to converge to the maximum entropy with increasing time scale. Nonetheless, the convergence is not always monotonic. For AR parameter values greater than the Golden Ratio, the MPE curve presents a local minimum at a time scale different than one, which implies a more regular structure than the one measured with PE. The MPE of the 1o order MA model converges rapidly to the maximum entropy with increasing time scales, regardless of the MA parameter value, which is in accordance to our expectations.

International audience; Dihydropyrimidine dehydrogenase (DPD) deficiency is the main cause of early severe toxicities induced by fluoropyrimidines (FP). The French Group of Clinical Oncopharmacology (GPCO)-Unicancer and the French Pharmacogenetics Network (RNPGx) initiated two surveys, one addressed to oncologists, the other to biologists, in order to evaluate routine practices regarding DPD deficiency screening at national level, as well as compliance, motivations and obstacles for implementation of these tests. These anonymized online surveys were performed with the logistic assistance of the Francophone Federation of Digestive Oncology (FFCD) and the support of numerous medical and biological societies. The surveys were conducted in 2016-2017 before the creation of the French INCa/HAS expert panel, which contributed to the drafting of rules and recommendations for DPD deficiency screening published in December 2018. In all, 554 questionnaires from clinicians were analyzed (23% participation) and 35 from biologists. The main arguments raised by clinicians for justifying the limited practice of DPD deficiency screening were: the lack of recommendations from medical societies or Health Authorities, delays in obtaining results, and the lack of adequate reimbursement by the health insurance system. The goal of these surveys was to provide the French Health Authorities with an overview on nationwide DPD-deficiency screening practices and thus help to design recommendations for the standardization and improvement of the management and safety of cancer patients receiving FP-based chemotherapy.

In the field of fingerprint identification, local histograms coding is one of the most popular techniques used for fingerprint representation, due to its simplicity. This technique is based on the concatenation of the local histograms resulting in a high dimension histogram, which causes two problems. First, long computing time and big memory capacities are required with databases growing. Second, the recognition rate may be degraded due to the curse of dimensionality phenomenon. In order to resolve these problems, we propose to reduce the dimensionality of histograms by choosing only the pertinent bins from them using a feature selection approach based on the mutual information computation. For fingerprint features extraction we use four descriptors: Local Binary Patterns (LBP), Histogram of Gradients (HoG), Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF). As mutual information based selection methods, we use four strategies: Maximization of Mutual Information (MIFS), minimum Redundancy and Maximal Relevance (mRMR), Conditional Info max Feature Extraction (CIFE) and Joint Mutual Information (JMI). We compare results in terms of recognition rates and number of selected features for the investigated descriptors and selection strategies. Our results are conducted on the four FVC 2002 datasets which present different image qualities. We show that the combination of mRMR or CIFE feature selection methods with HoG features gives the best results. We also show that the selection of useful fingerprint features can surely improve the recognition rate and reduce the complexity of the system in terms of computation cost. The feature selection algorithms may reach 98% of time reduction by considering only 20% of the total number of features while also improving the recognition rate of about 2% by avoiding the curse of dimensionality phenomena.

This work gives tools to overcome the difficulty to determine experimentally physical properties for vegetable oils within the range of temperature typically observed during the injection phase in a diesel engine. Knowing vegetable oils’ physical properties to these ranges of temperature is of fundamental importance when modeling their combustion in diesel engine. However, vegetable oils’ experimental physical properties data are rare in the literature for temperature above 523 K. This paper describes experimental measurements and estimation methods for density, dynamic viscosity, thermal conductivity and heat capacity of vegetable oils for this particular range of temperature. The methodology uses several correlative methods using group contribution approach for each property and compares experimental data with predicted one to select the more accurate model. This work has shown the rapeseed and jatropha oils’ physical properties can be satisfactorily predicted as a function of temperature using group contribution approach.

International audience; Masonry is one of the oldest construction materials and is still commonly used today to build houses or structures because of its strength, solidity, durability, resistance, its elegant appearance, etc. However, masonry, which is not generally thought to be a highly technological material, shows highlycomplex behavior, due in particular, to the interactions between the components (mortar, bricks) and the anisotropy induced by the direction of the joints, which are a source of weakness. Masonry structures were classically designed on the basis of empirical rules. Modern virtual methods of design have been developed only quite recently. Structures built long ago were extremely stable because they were massive. In modern masonry buildings, the walls are very thick, requiring the stability to be studied from a theoretical point of view, especially when wind or earthquakes are a concern. The strength of the masonry is thus critical and it is necessary to study the solidity of the structure using fine models and numerical simulations as in the case of concrete and steel structures. Other problems such as cracks also require more detailed studies on the design of masonry structures. Mortar joints are usually weaker than masonry units, which explains the existence of planes of weakness along which cracks can propagate. Several models have been developed and presented in the literature for studying and predicting the behavior of masonry structures. Depending on the level of accuracy and simplicity required, either macro- or micromodeling strategies can be used for this purpose. [...]

This article aims to propose a real-time compatible strategy for the energy management of a compression-ignition Hybrid Electric Vehicle (HEV), under pollutants emissions constraints. First, an offline method is applied in order to obtain a performance reference. Then, an online-oriented approach based on Model Predictive Control (MPC) is described, and a projected gradient algorithm used to solve the successive optimal control problems is explicitly presented. Both approaches are compared through simulation and the results are validated through experimentation.

International audience; This paper provides multi-level modeling of microcracked viscoelastic masonries generally present in historical masonries or refractory linings. It is an extension of the Cecchi and Tralli (2012) or Cecchi & Barbieri (2008) and Cecchi & Taliercio (2013) works for the typical Burgers and Modified Maxwell models followed by both undamaged and microcracked masonries. For the sake of simplicity and in order to provide rigorous analytical global estimates, only the mortar is assumed to be viscoelastic and microcracked. Bricks are assumed to be undamaged, elastic or quasi-rigid. The distribution of microcracks is assumed to be isotropic. The effective behavior of the viscoelastic microcracked masonry is provided by two steps. The first one relies on the coupling between the Griffith’s brittle fracture theory and linear mean-field homogenization scheme in order to account for the effect of microcracks on the macroscopic deformation of the mortar and establishes a linear relation between apparent macroscopic stress and strain. This step allows to easily and fast determine the effective creep function of the microcracked mortar without recourse to ’complex’ or heavy numerical inversion of the Laplace–Carson transform. The second step is based on the coupling between asymptotic analysis and homogenization theory applied for a periodic masonry. The proposed models provide analytical solutions - explicit functions of the crack density parameter - for the effective orthotropic behavior of a microcracked viscoelastic periodic masonry cell. This study proves that the Cecchi & Tralli’s and Cecchi & Taliercio’s extension estimates are close and that the later are softer. Such overall properties are used to perform finite element computations on a compressed masonry panel as a first application. These models allow then the prediction of mostly stressed and deformed areas in microcracked masonry structures. This study demonstrates that modeling a mortar (at its undamaged or microcracked state) with this Burgers formulation is only suitable for a masonry with too high values of the Maxwell’s relaxation time otherwise it yields to vanishing effective properties with the increase of time and crack density leading thus to a premature collapse of the masonry. On the other hand, the Modified Maxwell model permits the masonry to preserve a certain resistance for every range value of Maxwell’s relaxation time. These conclusions are valid for masonry either with elastic or quasi-rigid undamaged bricks.

The fourth industrial revolution is obviously well underway. Every day, we have the demonstration through new modes of commercial and industrial organizations. We are faced with major breaks as well related to the evolution of technologies as to the globalization of the economy with the emergence and the maturity of new actors: the challenge of the energy transition, the digital revolution, the protection of our planet, the convergences between human sciences and sciences. This paper presents different applications implemented for “Internet of Things” with Profinet network. These applications can be used on all of the company's processes as part of the Industry of the Future. The OPC standard appeared in the mid-1990s to facilitate exchanges between the world of automation and that of PC-based supervision. The latest OPC UA (Unified Architecture) specifications are now validated. With this new version, the OPC Foundation is revolutionizing its standard of communication between industrial equipment. OPC UA breaks the links that made it inseparable from Microsoft Windows. It can be deployed on all types of platforms. A Programmable Logic Controller (PLC) manages a manufacturing process. Seven operator panels also manage the various production recipes. Client n°1 makes the link to other Clients (reporting, E-mail, opening up to a scientific platform). The results are presented and detailed for numerous opportunities for industrial developments.

International audience; This paper presents a new approach to enhance a random bearing vibration signal (repetitive character with a random part) corrupted by an oscillatory gear vibration signal. This approach, called the Taylor-H∞ (TH) estimator, is proposed in a variable speed condition. The latter is designed using the mathematical modeling of the raw signal in the state space. It is used to remove the oscillatory signal from the raw signal. The obtained residual signal is the enhanced signal of the bearing contribution. Different from the Vold-Kalman (VK) estimator, which results from the minimization of the mean squared error, the proposed method is based on the minimax optimization. The minimax approach for the TH estimator leads to the minimization of the estimation error for the worst possible amplification of the random signals. No prior knowledge about the statistical properties of the random signals is required. The proposed approach is assessed using both synthetic and experimental nonstationary vibration data. In addition, the squared envelope spectrum of the residual signal is analyzed to diagnose the bearings state.

Biometric systems are currently widely used in many applications to control and verify individual's identity. Keystroke dynamics modality has been shown as a promising solution that would be used in many applications such as e-payment and banking applications. However, such systems suffer from several performance limitations (such as cross-devices problem) that prevent their widespread of use in real applications. The objective of this paper is to provide researchers and developers with a public touchscreen-based benchmark collected using a mobile phone and a tablet (both portrait and landscape orientation each). Such a benchmark can be used to assess keystroke-based matching algorithms. Furthermore, It is mainly developed to measure the robustness of keystroke matching algorithms vis-'a-vis cross-devices and orientation variations. An online visualizer for the database is also given to researchers allowing them to visualize the acquired keystroke signals.

International audience; Biometrics is an emerging technology more and more present in our daily life. However, building biometric systems requires a large amount of data that may be difficult to collect. Collecting such sensitive data is also very time consuming and constrained, s.a. GDPR legislation. In the case of keystroke dynamics, existing databases have less than 200 users. For these reasons, we aim at generating a keystroke dynamics synthetic dataset. This paper presents the generation of keystroke data from known users as a first step towards the generation of synthetic datasets, and could also be used to impersonate users' identity. Results were also found for user generation, but are not part of this paper.

International audience; We present an approach allowing us to compare the energy consumption of an electric vehicle (EV) in different maneuvers of driving scenarios. This approach is used in order to estimate the potential of Eco-driving for reducing EV consumption, and also to analyze where this gain comes from. Drivability constraints were taken into account in the study. We present energy loss distribution among the different components of the vehicle. In simulations, we considered cruising, acceleration and deceleration scenarios in urban driving. Results seem to show that reducing driver aggressiveness on accelerations is the main factor in reducing EV consumption in urban settings.

Permutation Entropy has been used as a robust and fast approach to calculate complexity of time series. There have been extensive studies on the properties and behavior of Permutation Entropy on known signals. Similarly, Multiscale Permutation Entropy has been used to analyze the structures at different time scales. Nevertheless, the Permutation Entropy is constrained by signal length, a problem which is accentuated with Multiscaling. We have analyzed the fractional Gaussian noise under a Multiscale Permutation Entropy analysis, taking into account the effect of finite-length signals across all scales. We found the Permutation Entropy value of Fractional Gaussian noise to be invariant to time scale. Nonetheless, a finite-length linear approximation for scale dependency is found as a result solely from the finite-length constrains of the method.

The discrete Teager-Kaiser operator and its generalized versions (GTKO) have found many applications in various fields of signal processing. However, few studies have focused on their statistical properties and most of them were limited to the mean and variance expressed under Gaussian assumptions. We aim at filling the lack of distribution laws of these operators in the case of uniformly distributed random signals. To that end, the GTKO definition is reformulated as the determinant of a 2×2 embedding square matrix. Based on the determinant theory of independent uniform random matrix, we first introduce the probability density function (pdf) of the GTKO under the assumption of identically distributed matrix entries. We then derive the GTKO pdf under the assumption of nonidentically distributed matrix entries. The former assumption is motivated by the case where the matrix entries are the raw signal samples whereas the latter assumption is motivated by the case where the matrix entries are the signal samples multiplied by a window function, Hanning or Hamming widow for example. We also derive for the first time the explicit expressions of the high order moments of the considered operators. An application to a real signal is then presented.

International audience; Integration of active compliant joints in a robotic system contributes to enhance safety in physical human-robot interactions. In this paper we study the behavior of a variable stiffness actuator -V2SOM-, proposed in the context of the SISCob project. We describe the working principle and design of the proposed system. Furthermore, the dynamic modeling, including its nonlinear stiffness law, is presented. A preliminary study is conducted to evaluate the safety performance of a collaborative robot using the proposed mechanism. Thus, a dynamic simulator has been developed to integrate the compliant behavior of V2SOM in two joints of a Kuka iiwa robot. In order to evaluate the safety performance of the system, some following-trajectory tasks are executed in a planar workspace whereas unexpected collisions are produced with an unknown environment. The results presented in this paper show the effectiveness of V2SOM, enhancing the safety performance of the robot.

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International audience; En cas d'incendie, les matériaux composites exposés à un flux de chaleur peuvent émettre des fumées et des vapeurs toxiques contribuant à la croissance du feu. Dans la présente étude, les émissions gazeuses et les limites inférieures d'inflammabilité (LII) sont évaluées lors de la dégradation de deux composites à base de fibres de carbone. La résine polyétherkétone-kétone (PEKK), connu pour sa stabilité à haute température, a été comparé à une résine phénolique classique, bien connu et utilisé comme protection thermique Les émissions gazeuses ont été mesurées à l'aide d'un pyrolyseur couplé à un chromatographe en phase gazeuse et à un spectromètre de masse (Py-GC-MS) pour trois températures différentes. À partir des espèces identifiées par les expériences de pyrolyse, les LII ont été déterminées au moyen d'une relation quantitative structure-propriété. La classification des trois matériaux a ensuite révélé des performances supérieures des composites PEKK.

Various volatile organic compounds (VOCs) are known to form in forest fires. They may also be formed in oxidation and pyrolysis of biomass. Prior to the building of detailed chemical kinetic models for biomass combustion, thermodynamic data of species are needed. Structures for α-pinene, limonene, camphene, filifolone, β-pinene, myrcene, p-cymene, thuja-2,4-(10)-diene, verbenone, terpinolene, sabinene, α-terpinene, γ-terpinene and camphor were computed at the B3LYP/6-31G(d,p) level of theory. Thermochemical properties, ΔfH°(298), S(T) and Cp(T) of target molecules were calculated using density functional theory. Enthalpies of formation were determined at the B3LYP/6-31G(d,p) level using two semiempirical atomization methods. Because these methods failed to agree with known values for limonene and camphene, higher level composite methods [G4 and the correlation consistent composite approach (ccCA)] were considered.

International audience; This study focuses on the actual exhaust emissions of regulated and unregulated pollutantsfrom earthwork machines (EM). In addition to providing a realistic assessment of gaseousand particulate emissions from key EM and filling critical data gaps in emission inventories,it emphasizes the importance of breaking down the average operating emission factors intotask-related emission factors (TEF). The latter serve many goals, namely by: categorizingEM in relation to their emission dynamics, finding emission determinants and proposingon-site emission mitigation strategies. These have also led to a more refined analysis ofboth the effectiveness of EU and US emissions standards and the accuracy ofNONROAD2008 model. Ultimately, by using task-related emission factors in conjunctionwith data available from the literature and with the methodology developed by US-EPA,a lower bound was estimated for gaseous exhaust emissions from the major constructionvehicles operable at earthwork sites.

International audience; When an explosion occurs in a tunnel, the study of the blast wave quickly becomes complicated, owing to the multiple propagation patterns of the blast wave (Incident wave, regular and Mach reections) and to the geometrical conditions. Considering this problem, two patterns can be revealed. Near the explosive, the well known free-eld pressure wave can be observed. After multiple reections on the tunnel's walls, this overpressure behaves like a one-dimensional (1D) wave. One aim of this paper is to determine the position of this transition spherical-to-planar wave propagation in a tunnel using both numerical and reduced-scale experiments, and thereby validate the dedicated law established in a previous work. For this purpose, a detonation of TNT in a tunnel with a cross-section of up to 55 m 2 is considered. Results show good agreement between the numerical simulations and experiments. The transition zone between the three-dimensional (3D) and the 1D wave is well detected. An application to a simplied subway station is also investigated which shows that signicant planar waves can be transmitted to the neighboring stations via the junction tunnels.

International audience; In order to support the optimization of mortarless brick linings of steel producing furnaces it is proposed in this paper to investigate only the compressibility of dry refractory joints. To this end, optical measurements based on the Digital Image Correlation method were carried out during compression of Magnesia-Carbon brick samples with dry joints. The second main objective is to assess the accuracy of the secant linearization schemes (the classical secant procedure and its modified extension) to reproduce the reference local and global behaviour of refractory mortarless linings accounting for the identified inelastic convex powerlaw behaviour of the Magnesia-Carbon dry joint. The reference nonlinear solution is obtained by means of finite elements method. Under normal compressive loading, unlike for usual (concave) power-law viscoplastic composites for which the secant schemes are known to provide too stiff results, it was found that the modified secant scheme leads to good overall predictions. The classical secant procedure underestimates the reference local and overall behaviour. To improve the latter result, an empirical improved secant formulation was proposed and implemented. It leads to better estimates at local and global levels.

IntroductionSurgery is the standard curative treatment for lung cancer but is only possible in patients with local tumour and preserved exercise capacity. Improving fitness before surgery can reduce postoperative complications and mortality. However, preoperative rehabilitation remains difficult to implement for several reasons. We aim to investigate the effectiveness of an intensive 3-week home-based preoperative exercise training programme on hospital discharge ability, postoperative complications and physical performance in patients with chronic obstructive pulmonary disease (COPD) who are eligible for lung cancer surgery.Methods and analysisWe designed a multicentre randomised controlled trial. The randomisation sequence will be generated and managed electronically by a research manager independent of assessments or interventions. We will recruit 90 patients with COPD and a diagnosis of lung cancer from four university hospitals. The rehabilitation group (R group) will receive a standardised preoperative home exercise programme for 3 weeks, combining both high-intensity training and usual physical therapy. The R group will perform 15 training sessions over 3 weeks on a cycloergometer. A physical therapist experienced in pulmonary rehabilitation will visit the patient at home and supervise one session a week. The R group will be compared with a control group receiving preoperative usual physical therapy only. The primary outcome will be hospital discharge ability assessed with a 10-item list. Secondary outcomes will be postoperative course (complication rate and mortality) as well as pulmonary function, exercise capacity and quality of life assessed 1 month before and the day before surgery.Ethics and disseminationThis protocol has been approved by the French health authority for research (2016-A00622-49) and the research ethics committee/institutional review board (AU1267). Adverse events that occur during the protocol will be reported to the principal investigator. The results will be published in an international peer-reviewed journal.Trial registration numberNCT03020251.

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This paper deals with the survey of kinematic structures adapted to specific medical robots: minimally invasive surgery (MIS) and tele-echography. The large diversity of kinematic architectures that can be found in medical robotics leads us to perform a statistical analysis to inform and guide design of medical robots. Safety constraints and some considerations in design evolution of medical robots are presented in this paper. First, we describe the spectrum of medical robots in minimally invasive surgery and tele-echography applications and particularly the variety of kinematic architectures used. We present the robots and their kinematic architectures and highlight differences that occur in each medical application. We perform a statistical analysis which can serve as a resource in topological synthesis for each specific medical application. Safety is an important specification in medical robotics, and for that reason we show the means used to take into account this constraint. This study demonstrates that the nature of medical robots implies specific requirements leading to different kinematic structures. The statistical analysis gives information on choice of kinematic structures for medical applications (minimally invasive surgery and echography). The safety constraint as well as the interaction between doctor and robot leads to investigate new mechanical solutions to enhance medical robot safety and compliance. We expect that this paper will serve as a significant resource and help the design of future medical robots.

To reduce the levelized cost of energy, the energy production, robustness and lifespan of horizontal axis wind turbines (HAWTs) have to be improved to ensure optimal energy production and operational availability during periods longer than 15–20 years. HAWTs are subject to unsteady wind loads that generate combinations of unsteady mechanical loads with characteristic time scales from seconds to minutes. This can be reduced by controlling the aerodynamic performance of the wind turbine rotors in real time to compensate the overloads. Mitigating load fluctuations and optimizing the aerodynamic performance at higher time scales need the development of fast-response active flow control (AFC) strategies located as close as possible to the torque generation, i.e., directly on the blades. The most conventional actuators currently used in HAWTs are mechanical flaps/tabs (similar to aeronautical accessories), but some more innovative concepts based on fluidic and plasma actuators are very promising since they are devoid of mechanical parts, have a fast response and can be driven in unsteady modes to influence natural instabilities of the flow. In this context, the present paper aims at giving a state-of-the-art review of current research in wind turbine-oriented flow control strategies applied at the blade scale. It provides an overview of research conducted in the last decade dealing with the actuators and devices devoted to developing AFC on rotor blades, focusing on the flow phenomena that they cause and that can lead to aerodynamic load increase or decrease. After providing some general background on wind turbine blade aerodynamics and on the atmospheric flows in which HAWTs operate, the review focuses on flow separation control and circulation control mainly through experimental investigations. It is followed by a discussion about the overall limitations of current studies in the wind energy context, with a focus on a few studies that attempt to provide a global efficiency assessment and wind energy-oriented energy balance.

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In a robotized ultrasound scanning system and more generally in any system which interacts with a human, the used control laws have to satisfy a safety of operating and functioning. Moreover these control laws have to guarantee a high precision in the medical gesture tracking, and especially within the framework of the ultrasound scanning, they assure the preservation of the ultrasound plane. The mechanical architecture of the designed robot takes into account the specifications of the medical application of the remote ultrasound scan. This robot presents singular configurations on its work space. These configurations perturb the follow-up of the medical remote gesture and lead to a loss of precision which induce errors in the follow-up of trajectory as well as in the ultrasonic plan. To solve this problem we set up specific control laws which take into account these singular configurations and allow to reproduce the movements generated by the medical expert with a great accuracy. The first part of this paper presents the geometric and kinematic modelling of the Protech robot used for the remote ultrasound and an analysis of its work space and its singular configurations. The second part describes the various approaches used for the management of these singular configurations. The third and last part presents the simulations of these approaches and the obtained results.

International audience; In this paper, the problem of eco-driving for a conventional vehicle equipped with an internal combustion engine is studied. The associated optimal control problem is formulated and solved using Dynamic Programming (DP). The impact of the mesh choice on the optimality of the DP solution is investigated in order to find a trade-off between the optimality of the DP solution and its computation time. The eco-driving speed trajectories obtained were tested on a high-frequency HIL (Hardware-In-the-Loop) engine test bench to quantify the real reductions in fuel consumption. Simulations and experiments are compared.

International audience; In this paper, a first practical extension of the Equivalent Consumption Minimization Strategy (ECMS) is proposed to include thermal dynamics (engine and catalyst temperatures) in the optimal design of an Energy Management System (EMS) for a parallel Hybrid-Electric light-duty Vehicle (HEV). The task of this novel multi-state ECMS is to achieve a sufficient level of performance with respect to pollutant emissions while keeping fuel consumption within acceptable limits. The extension suggested here is based on correlations between the thermal state and their corresponding adjoint states, observed along extremal calculated from extensive offline solutions of optimal control problems. Simulation results stress that the obtained performance is sufficient to satisfy the environmental norms while keeping fuel consumption sub-optimality relatively marginal.

ASME Turbo Expo 2017: Turbomachinery Technical Conference and ExpositionVolume 4A: Combustion, Fuels and EmissionsCharlotte, North Carolina, USA, June 26–30, 2017Conference Sponsors: International Gas Turbine InstituteISBN: 978-0-7918-5084-8; International audience; A solution for CCS (Carbon Dioxide Capture and Sequestration of CO2) is oxycombustion. Due to the high cost of pure O2 production, however, other approaches recently emerged such as post-combustion coupled with Oxygen Enhanced Air (OEA). This is the solution studied in this paper, which presents an innovative gas turbine cycle, the Oxygen Enriched Air Steam Injection Gas Turbine Cycle (OEASTIG). The OEASTIG cycle is composed of Methane combustion with OEA (Oxygen Enhanced Air), EGR (Exhaust Gas Recirculation) and H2O coming from a STIG (Steam Injection Gas Turbine). CO2 capture is achieved by a membrane separator. The final aim of this work is to predict NO and CO emissions in the gas turbine by experimental and numerical approaches. Before carrying out this study, the validation of a reaction mechanism is mandatory. Moreover, this new gas turbine cycle impacts on the combustion zone and it is therefore necessary to understand the consequences of H2O and CO2 dilution on combustion parameters. While a large number of papers deal with CO2 dilution, only a few papers have investigated the impact of water dilution on methane combustion. A study of the influence of H2O dilution on the combustion parameters by experimental and numerical approaches was therefore carried out and is reported in the present paper. The paper is divided in three parts: i) description of the innovative gas turbine (OEASTIG) cycle and determination of the reactive mixtures compatible with its operation; ii) validation of the reaction mechanism by comparing laminar methane flame velocity measurements performed in a stainless steel spherical combustion chamber with calculations carried out in a freely propagating flame using the Chemical Workbench v.4.1. Package in conjunction with the GRIMech3.0 reaction mechanism; iii) Extrapolation to gas turbine conditions by prediction of flame velocities and determination of the feasible conditions from a gas turbine point of view (flame stability). In particular, mixtures (composed of CH4/O2/N2/H2O or CO2) leading to the same adiabatic temperature were investigated. Lastly, the influence of oxygen enrichment and H2O dilution (compared to CO2 dilution) were investigated.

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Cette étude est consacrée à la modélisation du comportement au fluage des géo-matériaux en utilisant une méthode d’homogénéisation numérique basée sur une microstructure périodique. Le matériau étudié dans cette investigation est prélevé du gisement de Hammam Boughrara situé à l’ouest Algérien. Une caractérisation physicochimique avec une observation microstructurale a été réalisée sur le matériau dit bentonite tout venant afin de choisir le volume élémentaire représentatif utilisé dans la modélisation numérique. Le comportement différé du matériau est étudié dans un premier temps au moyen d’essai de fluage de courte durée en utilisant un essai oedométrique haute pression. Le matériau a été modélisé en utilisant une cellule de base (VER) composée d’une matrice argileuse et un pore sphérique avec des conditions de périodicité. Les paramètres du modèle de fluage utilisé ont été déterminés à partir d’une analyse inverse en utilisant les résultats expérimentaux. La confrontation des résultats numériques et expérimentaux montre la capacité prédictive de la modélisation micro-macro numérique proposée., Academic Journal of Civil Engineering, Vol 35 No 1 (2017): Special Issue - RUGC 2017 Nantes

International audience; The paper addresses the 2D observer-based control of a magnetic microrobot navigating in a cylindrical blood vessel along a reference trajectory. In particular, this robot faces the nonlinear drag force induced by the pulsatile blood flow, which can hardly be measured. Consequently, a mean value theorem (MVT) based observer to estimate the blood velocity from the sole measurement of the robot position is proposed. Also, the stability of the observer-based backstepping controller is proved. The resulting estimation and tracking are then illustrated through simulations, as well as robustness to parametric uncertainty, measurement noise, and dynamical errors when the pulsatile blood flow is incorrectly modeled.

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