École Navale

In this paper, a signal-filtering method based on empirical mode decomposition is proposed. The filtering method is a fully data-driven approach. A noisy signal is adaptively decomposed into intrinsic oscillatory components called intrinsic mode functions (IMFs) by means of an algorithm referred to as a sifting process. The basic principle of the method is to make use of partial reconstructions of the signal, with the relevant IMFs corresponding to the most important structures of the signal (low-frequency components). A criterion is proposed to determine the IMF, after which, the energy distribution of the important structures of the signal overcomes that of the noise and that of the high-frequency components of the signal. The method is illustrated on simulated and real data, and the results are compared to well-known filtering methods. The study is limited to signals that were corrupted by additive white Gaussian noise and is conducted on the basis of extended numerical experiments.

Unsteady partial cavitation can cause damage to hydraulic machinery and understanding it requires knowledge of the basic physics involved. This paper presents the main results of a research program based on wall-pressure measurements aimed at studying unsteadiness in partial cavitation. Several features have been pointed out. For cavity lengths that did not exceed half the foil chord the cavity was stated to be stable. At the cavity closure a peak of pressure fluctuations was recorded originating from local cavity unsteadiness in the closure region at a frequency depending on the cavity length. Conversely, cavities larger than half the foil chord were stated to be unstable. They were characterized by a cavity growth/destabilization cycle settled at a frequency lower than the previous ones. During cavity growth, the closure region fluctuated more and pressure fluctuations traveling in the cavity wake were detected. When the cavity was half the foil chord, cavity growth was slowed down and counterbalanced by large vapor cloud shedding. When the cavity length was maximum (l/c∼0.7–0.8), it was strongly destabilized. The reason for such destabilization is discussed at the end of the paper. It is widely believed that the cavity instability originates from a process involving the shedding of vapor clouds during cavity growth, a re-entrant jet, and a shock wave phenomenon due to the collapse of a large cloud cavitation.

This paper is concerned with hierarchical Markov random field (MRP) models and their application to sonar image segmentation. We present an original hierarchical segmentation procedure devoted to images given by a high-resolution sonar. The sonar image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each object lying on the sea-bed) and sea-bottom reverberation. The proposed unsupervised scheme takes into account the variety of the laws in the distribution mixture of a sonar image, and it estimates both the parameters of noise distributions and the parameters of the Markovian prior. For the estimation step, we use an iterative technique which combines a maximum likelihood approach (for noise model parameters) with a least-squares method (for MRF-based prior). In order to model more precisely the local and global characteristics of image content at different scales, we introduce a hierarchical model involving a pyramidal label field. It combines coarse-to-fine causal interactions with a spatial neighborhood structure. This new method of segmentation, called the scale causal multigrid (SCM) algorithm, has been successfully applied to real sonar images and seems to be well suited to the segmentation of very noisy images. The experiments reported in this paper demonstrate that the discussed method performs better than other hierarchical schemes for sonar image segmentation.

The present work was carried out in the scope of a numerical-experimental collaborative research program, whose main objective is to understand the mechanisms of instabilities in partial cavitating flow. Experiments were conducted in the configuration of a rectangular foil located in a cavitation tunnel. Partial cavitation was investigated by multipoint wall-pressure measurements together with lift and drag measurements and numerical videos. The computations were conducted on two-dimensional hydrofoil section and are based on a single fluid model of cavitation: the liquid/vapor mixture is considered as a homogeneous fluid whose composition is regulated by a barotropic state law. The algorithm of resolution is derived from the SIMPLE approach, modified to take into account the high compressibility of the medium. Several physical features were pointed out by this joint approach. Particularly two distinct cavity self-oscillation dynamics characterized by two different frequencies (dynamics 1 and dynamics 2) were obtained experimentally and numerically at the angles of incidence of 6° and 8°. In both cases, the reentrant jet was found to be mainly responsible for the cavity breakdown. Dynamics 2 corresponds to the “classical” cavity breakdown and resulting cloud cavitation. A more complex flow pattern was evidenced for dynamics 1. In this case the growth/breakdown cycle of the cavity was observed at a lower Strouhal number (∼0.07∕0.09) than dynamics 2 (∼0.3). Moreover, the mechanism is composed of two successive steps: (i) an interaction between the reentrant jet and the cavity interface in the closure region leading to the periodic shedding of secondary cavitation clouds before the main cloud detachment occurs, and (ii) a shock wave induced by the collapse of the main cloud, which influences the growth of the residual cavity.

This paper introduces a new signal-filtering, which combines the empirical mode decomposition (EMD) and a similarity measure. A noisy signal is adaptively broken down into oscillatory components called intrinsic mode functions by EMD followed by an estimation of the probability density function (pdf) of each extracted mode. The key idea of this paper is to make use of partial reconstruction, the relevant modes being selected on the basis of a striking similarity between the pdf of the input signal and that of each mode. Different similarity measures are investigated and compared. The obtained results, on simulated and real signals, show the effectiveness of the pdf-based filtering strategy for removing both white Gaussian and colored noises and demonstrate its superior performance over partial reconstruction approaches reported in the literature.

This paper deals with the experimental validation of a Matlab-Simulink simulation tool of marine current turbine (MCT) systems. The developed simulator is intended to be used as a sizing and site evaluation tool for MCT installations. For that purpose, the simulator is evaluated within the context of speed control of a permanent magnet synchronous generator-based (PMSG) MCT. To increase the generated power, and therefore the efficiency of an MCT, a nonlinear controller has been proposed. PMSG has been already considered for similar applications, particularly wind turbine systems using mainly PI controllers. However, such kinds of controllers do not adequately handle some of tidal resource characteristics such as turbulence and swell effects. Moreover, PMSG parameter variations should be accounted for. Therefore, a robust nonlinear control strategy, namely second-order sliding mode control, is proposed. The proposed control strategy is inserted in the simulator that accounts for the resource and the marine turbine models. Simulations using tidal current data from Raz de Sein (Brittany, France) and experiments on a 7.5-kW real-time simulator are carried out for the validation of the simulator.

A slow brain potential change, the contingent negative variation (CNV), was recorded in a temporal generalization schedule. The task was to judge the duration of a signal (1.250 to 3.125 s) as being equal or not to that of a 2-s target that had been previously memorized. Two signal modalities, visual and tactile, were contrasted in distinct trial blocks, in order to explore possible localization differences. Significant results were found at CPz, irrespective of signal modality. The CNV that developed during signal presentation peaked around 2 s and then declined when the current signal was longer than the 2-s target, instead of peaking at signal extinction as was the case for shorter signals. Thus, for signals longer than the target, the CNV peak and the following slope change provide a memory trace of the encoded target duration, leading to decision making.

The paper presents a classification of cyber attacks within the context of the state of the art in the maritime industry. A systematic categorization of vessel components has been conducted, complemented by an analysis of key services delivered within ports. The vulnerabilities of the Global Navigation Satellite System (GNSS) have been given particular consideration since it is a critical subcategory of many maritime infrastructures and, consequently, a target for cyber attacks. Recent research confirms that the dramatic proliferation of cyber crimes is fueled by increased levels of integration of new enabling technologies, such as IoT and Big Data. The trend to greater systems integration is, however, compelling, yielding significant business value by facilitating the operation of autonomous vessels, greater exploitation of smart ports, a reduction in the level of manpower and a marked improvement in fuel consumption and efficiency of services. Finally, practical challenges and future research trends have been highlighted.

In this paper a new adaptive audio watermarking algorithm based on Empirical Mode Decomposition (EMD) is introduced. The audio signal is divided into frames and each one is decomposed adaptively, by EMD, into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). The watermark and the synchronization codes are embedded into the extrema of the last IMF, a low frequency mode stable under different attacks and preserving audio perceptual quality of the host signal. The data embedding rate of the proposed algorithm is 46.9-50.3 b/s. Relying on exhaustive simulations, we show the robustness of the hidden watermark for additive noise, MP3 compression, re-quantization, filtering, cropping and resampling. The comparison analysis shows that our method has better performance than watermarking schemes reported recently.

A new similarity measure, called SimilB, for time series analysis, based on the cross- -energy operator (2004), is introduced. is a nonlinear measure which quantifies the interaction between two time series. Compared to Euclidean distance (ED) or the Pearson correlation coefficient (CC), SimilB includes the temporal information and relative changes of the time series using the first and second derivatives of the time series. SimilB is well suited for both nonstationary and stationary time series and particularly those presenting discontinuities. Some new properties of are presented. Particularly, we show that as similarity measure is robust to both scale and time shift. SimilB is illustrated with synthetic time series and an artificial dataset and compared to the CC and the ED measures.

The demand for secure, affordable and clean energy is a priority call to humanity. Challenges associated with conventional energy resources, such as depletion of fossil fuels, high costs and associated greenhouse gas emissions, have stimulated interests in renewable energy resources. For instance, there have been clear gaps and rushed thoughts about replacing fossil-fuel driven engines with electric vehicles without long-term plans for energy security and recycling approaches. This book aims to provide a clear vision to scientists, industrialists and policy makers on renewable energy resources, predicted challenges and emerging applications. It can be used to help produce new technologies for sustainable, connected and harvested energy. A clear response to economic growth and clean environment demands is also illustrated.

The purpose of this model-based study was to determine the accuracy of placing dental implants using a new dynamic navigation system. This investigation focuses on measurements of overall accuracy for implant placement relative to the virtual plan in both dentate and edentulous models, and provides a comparison with a meta-analysis of values reported in the literature for comparable static guidance, dynamic guidance, and freehand placement studies. This study involves 1 surgeon experienced with dynamic navigation placing implants in models under clinical simulation using a dynamic navigation system (X-Guide, X-Nav Technologies, LLC, Lansdale, Pa) based on optical triangulation tracking. Virtual implants were placed into planned sites using the navigation system computer. Post-implant placement cone-beam scans were taken. These scans were mesh overlaid with the virtual plan and used to determine deviations from the virtual plan. The primary outcome variables were platform and angular deviations comparing the actual placement to the virtual plan. The angular accuracy of implants delivered using the tested device was 0.89° ± 0.35° for dentate case types and 1.26° ± 0.66° for edentulous case types, measured relative to the preoperative implant plan. Three-dimensional positional accuracy was 0.38 ± 0.21 mm for dentate and 0.56 ± 0.17 mm for edentulous, measured from the implant apex.

Brain insults are a major cause of acute mortality and chronic morbidity. Given the largely ineffective current therapeutic strategies, the development of new and efficient therapeutic interventions is clearly needed. A series of previous investigations has shown that the noble and anesthetic gas xenon, which has low-affinity antagonistic properties at the N-methyl-D-aspartate (NMDA) receptor, also exhibits potentially neuroprotective properties with no proven adverse side effects. Surprisingly and in contrast with most drugs that are being developed as therapeutic agents, the dose-response neuroprotective effect of xenon has been poorly studied, although this effect could be of major critical importance for its clinical development as a neuroprotectant. Here we show, using ex vivo and in vivo models of excitotoxic insults and transient brain ischemia, that xenon, administered at subanesthetic doses, offers global neuroprotection from reduction of neurotransmitter release induced by ischemia, a critical event known to be involved in excitotoxicity, to reduction of subsequent cell injury and neuronal death. Maximal neuroprotection was obtained with xenon at 50 vol%, a concentration at which xenon further exhibited significant neuroprotective effects in vivo even when administered up to 4 h after intrastriatal NMDA injection and up to at least 2 h after induction of transient brain ischemia.

Automotive embedded electronic systems have been increasing in power and complexity and, therefore, more advanced power electronic converters are necessary in these vehicles. Several dual-voltage (42 V/14 V) bidirectional converter architectures have been proposed for automotive systems in recent years. However, most of them have low efficiency or are based in series and parallel configurations with large number of semiconductors and magnetics devices. Therefore, in this paper, we propose a bidirectional high-efficiency converter with lower number of components. This converter was created by merging a switched-capacitor converter and a conventional bidirectional converter, resulting in a hybrid topology. The voltage across the semiconductors of the proposed converter is equal to half of the highest voltage source value. Furthermore, the topology is composed of only one inductor to control the power flow between the two voltage sources. To verify all the mentioned features, a prototype was implemented experimentally, reaching a maximum efficiency of 97.5%.

Emerging technologies for marine current turbines are mainly related to works that have been carried out on wind turbines and ship propellers. It is then obvious that many electric generator topologies could be used for marine current turbines. As in the wind turbine context, doubly-fed induction generators and permanent magnet generators seem to be attractive solutions for harnessing the tidal current energy. In this paper, a comparative study between these two generator types is presented and fully analyzed in terms of generated power, maintenance, and operation constraints. This comparison is done for the Raz de Sein site (Brittany, France) using a multiphysics modeling simulation tool. This tool integrates, in a modular environment, the resource model, the turbine hydrodynamic model, and generator models. Experiments have also been carried out to confirm the simulation results.

As two fluid particles separate in time, the entire spectrum of eddy motions is being sampled from the smallest to the largest scales. In large-scale geophysical systems for which the Earth rotation is important, it has been conjectured that the relative diffusivity should vary respectively as $D^{2}$ and $D^{4/3}$ for distances respectively smaller and larger than a well-defined forcing scale of the order of the internal Rossby radius (with $D$ the r.m.s. separation distance). Particle paths data from a mid-latitude float experiment in the central part of the North Atlantic appear to support these statements partly: two particles initially separated by a few km within two distinct clusters west and east of the mid-Atlantic ridge, statistically dispersed following a Richardson regime ( $D^{2}\,{\sim}\,t^{3}$ asymptotically) for r.m.s. separation distances between 40 and 300 km, in agreement with a $D^{4/3}$ law. At early times, and for smaller separation distances, an exponential growth, in agreement with a $D^{2}$ law, was briefly observed but only for the eastern cluster (with an e-folding time around 6 days). After a few months or separation distances greater than 300 km, the relative dispersion slowed down naturally to the Taylor absolute dispersion regime.

The startup of rocket engine turbopumps is generally performed only in a few seconds. It implies that these pumps reach their nominal operating conditions after only a few rotations. During these first rotations of the blades, the flow evolution in the pump is governed by transient phenomena, based mainly on the flow rate and rotation speed evolution. These phenomena progressively become negligible when the steady behavior is reached. The pump transient behavior induces significant pressure fluctuations, which may result in partial flow vaporization, i.e., cavitation. An existing experimental test rig has been updated in the LML Laboratory (Lille, France) for the startups of a centrifugal pump. The study focuses on the cavitation induced during the pump startup. Instantaneous measurement of torque, flow rate, inlet and outlet unsteady pressures, and pump rotation velocity enable to characterize the pump behavior during rapid starting periods. Three different types of fast startup behaviors have been identified. According to the final operating point, the startup is characterized either by a single drop of the delivery static pressure, by several low-frequency drops, or by a water hammer phenomenon that can be observed in both the inlet and outlet of the pump. A physical analysis is proposed to explain these three different types of transient flow behavior.

We present an original statistical classification method using a deformable template model to separate natural objects from man-made objects in an image provided by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is captured by a prototype template, along with a set of admissible linear transformations, to take into account the shape variability. Then, the classification problem is defined as a two-step process: 1) the detection problem of a region of interest in the input image is stated as the minimization of a cost function; and 2) the value of this function at convergence allows one to determine whether the desired object is present or not in the sonar image. The energy minimization problem is tackled using relaxation techniques. In this context, we compare the results obtained with a deterministic relaxation technique and two stochastic relaxation methods: simulated annealing and a hybrid genetic algorithm. This latter method has been successfully tested on real and synthetic sonar images, yielding very promising results.

For numerical simulations of cavitating flows, many physical models are currently used. One approach is the void fraction transport equation‐based model including source terms for vaporization and condensation processes. Various source terms have been proposed by different researchers. However, they have been tested only in different flow configurations, which make direct comparisons between the results difficult. A comparative study, based on the expression of the source terms as a function of the pressure, is presented in the present paper. This analytical approach demonstrates a large resemblance between the models, and it also clarifies the influence of the model parameters on the vaporization and condensation terms and, therefore, on the cavity shape and behavior. Some of the models were also tested using a 2D CFD code in configurations of cavitation on two‐dimensional foil sections. Void fraction distributions and frequency of the cavity oscillations were compared to existing experimental measurements. These numerical results confirm the analytical study.

Segmented attenuation correction is now a widely accepted technique to reduce noise propagation from transmission scanning in positron emission tomography (PET). In this paper, we present a new method for segmenting transmission images in whole-body scanning. This reduces the noise in the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the fuzzy C-means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are, therefore, segmented into populations of uniform attenuation based on knowledge of the human anatomy. The clustering procedure starts with an overspecified number of clusters followed by a merging process to group clusters with similar properties (redundant clusters) and removal of some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and allows the classification of both pre- or post-injection transmission images obtained using either coincident 68Ge or single-photon 137Cs sources into main tissue components in terms of attenuation coefficients. A high-quality transmission image of the scanner bed is obtained from a high statistics scan and added to the transmission image. The segmented transmission images are then forward projected to generate attenuation correction factors to be used for the reconstruction of the corresponding emission scan. The technique has been tested on a chest phantom simulating the lungs, heart cavity and the spine, the Rando-Alderson phantom, and whole-body clinical PET studies showing a remarkable improvement in image quality, a clear reduction of noise propagation from transmission into emission data allowing for reduction of transmission scan duration. There was very good correlation (R2 = 0.96) between maximum standardized uptake values (SUVs) in lung nodules measured on images reconstructed with measured and segmented attenuation correction with a statistically significant decrease in SUV (17.03% +/- 8.4%, P < 0.01) on the latter images, whereas no proof of statistically significant differences on the average SUVs was observed. Finally, the potential of the FCM algorithm as a segmentation method and its limitations as well as other prospective applications of the technique are discussed.