Institut de Recherche de l’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.

The authors propose a topological analysis of large urban street networks based on a computational and functional graph representation. This representation gives a functional view in which vertices represent named streets and edges represent street intersections. A range of graph measures, including street connectivity, average path length, and clustering coefficient, are computed for structural analysis. In order to characterise different clustering degrees of streets in a street network they generalise the clustering coefficient to a k-clustering coefficient that takes into account k neighbours. Based on validations applied to three cities, the authors show that large urban street networks form small-world networks but exhibit no scale-free property.

This research introduces a path planning method based on the geometric A-star algorithm. The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross paths produced by the traditional A-star algorithm. First, a grid method models a port environment. Second, the nodes in the close-list are filtered by the functions P(x,y ) and W(x,y ) and the nodes that do not meet the requirements are removed to avoid the generation of irregular paths. Simultaneously, to enhance the stability of the AGV regarding turning paths, the polyline at the turning path is replaced by a cubic B-spline curve. The path planning experimental results applied to different scenarios and different specifications showed that compared with other seven different algorithms, the geometric A-star algorithm reduces the number of nodes by 10% ~ 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5%. Overall, the simulation results of the path planning confirmed the effectiveness of the geometric A-star algorithm.

The research field of transportation demand forecasting has started to focus on disaggregate travel behavior and micro‐simulation models. To create data infrastructure, disaggregate trip surveys are conducted and large numbers of observations are collected. To efficiently exploit these surveys, the transfer of the individual trip data to a GIS must start with the development of a solid conceptual data model that fully captures the semantic richness of the application domain and emphasizes its spatio‐temporal properties. This paper presents a data modeling process that is based on a combination of complex system theory and the object‐oriented paradigm and produced an object‐oriented spatio‐temporal data model. Main domain entities are modeled as highly structured classes. They encapsulate a memory of their time bound connections and states. Observation data sets are sampled from the origin‐destination survey conducted in the Québec region in 1991. This survey incorporated street networks and activity places. The model was smoothly implemented into a proof‐of‐concept database prototype hosted by an object‐oriented GIS shell. The prototype offers a means to navigate through a nested hierarchy of objects, providing a description of an individual’s travel behavior over space and time. The objects have a solid conceptual basis and can meet the needs of scientific research such as hypothesis formulation, simulation, forecasting and induction.

The potential of electric power generation from marine tidal currents is enormous. Tidal currents are being recognized as a resource to be exploited for the sustainable generation of electrical power. The high load factors resulting from the fluid properties and the predictable resource characteristics make marine currents particularly attractive for power generation and advantageous when compared to other renewable energies. Moreover, international treaties related to climate control have triggered resurgence in development of renewable ocean energy technology. Therefore, several demonstration projects in tidal power are scheduled to capture the tidal generated coastal currents. Regarding this emerging and promising area of research, this paper reviews marine tidal power fundamental concepts and main projects around the world. It also report issues regarding electrical generator topologies associated to tidal turbines. Moreover, attempts are made to highlight future issues so as to index some emerging technologies mainly according to relevant works that have been carried out on wind turbines and on ship propellers.

As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade's Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks. The manuscript highlights that current IDS research covers only 33.3% of our threat taxonomy. Current datasets demonstrate a clear lack of real-network threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets.

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.

All-electric ships are now a standard offering for energy/propulsion systems in boats. In this context, integrating fuel cells (FCs) as power sources in hybrid energy systems can be an interesting solution because of their high efficiency and low emission. The energy management strategy for different power sources has a great influence on the fuel consumption, dynamic performance and service life of these power sources. This paper presents a hybrid FC/battery power system for a low power boat. The hybrid system consists of the association of a proton exchange membrane fuel cell (PEMFC) and battery bank. The mathematical models for the components of the hybrid system are presented. These models are implemented in Matlab/Simulink environment. Simulations allow analyzing the dynamic performance and power allocation according to a typical driving cycle. In this system, an efficient energy management system (EMS) based on operation states is proposed. This EMS strategy determines the operating point of each component of the system in order to maximize the system efficiency. Simulation results validate the adequacy of the hybrid power system and the proposed EMS for real ship driving cycles.

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.

Ship trajectory prediction is a key requisite for maritime navigation early warning and safety, but accuracy and computation efficiency are major issues still to be resolved. The research presented in this paper introduces a deep learning framework and a Gate Recurrent Unit (GRU) model to predict vessel trajectories. First, series of trajectories are extracted from Automatic Identification System (AIS) ship data (i.e., longitude, latitude, speed, and course). Secondly, main trajectories are derived by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Next, a trajectory information correction algorithm is applied based on a symmetric segmented-path distance to eliminate the influence of a large number of redundant data and to optimize incoming trajectories. A recurrent neural network is applied to predict real-time ship trajectories and is successively trained. Ground truth data from AIS raw data in the port of Zhangzhou, China were used to train and verify the validity of the proposed model. Further comparison was made with the Long Short-Term Memory (LSTM) network. The experiments showed that the ship's trajectory prediction method can improve computational time efficiency even though the prediction accuracy is similar to that of LSTM.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper deals with the development of a Matlab–Simulink model of a marine current turbine system through the modeling of the resource and the rotor. The simulation model has two purposes: performances and dynamic loads evaluation in different operating conditions and control system development for turbine operation based on pitch and speed control. In this case, it is necessary to find a compromise between the simulation model accuracy and the control-loop computational speed. The blade element momentum (BEM) approach is then used for the turbine modeling. As the developed simulation model is intended to be used as a sizing and site evaluation tool for current turbine installations, it has been applied to evaluate the extractable power from the Raz de Sein (Brittany, France). Indeed, tidal current data from the Raz de Sein are used to run the simulation model over various flow regimes and yield the power capture with time. </para>

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.

This paper surveys indoor spatial models developed for research fields ranging from mobile robot mapping, to indoor location-based services (LBS), and most recently to context-aware navigation services applied to indoor environments. Over the past few years, several studies have evaluated the potential of spatial models for robot navigation and ubiquitous computing. In this paper we take a slightly different perspective, considering not only the underlying properties of those spatial models, but also to which degree the notion of context can be taken into account when delivering services in indoor environments. Some preliminary recommendations for the development of indoor spatial models are introduced from a context-aware perspective. A taxonomy of models is then presented and assessed with the aim of providing a flexible spatial data model for navigation purposes, and by taking into account the context dimensions.

Having read Dekker's ‘Field Guide to Understanding Human Error’ a few years ago, I was interested to learn what more he had to say about safety.Quite a lot, it seems.‘Safety Differently’ is written...

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.

Variations of marine current speed may lead to strong fluctuations in the power extracted by a marine current turbine (MCT). During a short-time period, swell effect is the main cause for the current speed variations. The conventional tip speed ratio maximum power point tracking (MPPT) algorithm will require the MCT to accelerate or to decelerate frequently under swell effect, which can cause severe fluctuations in the generator power. This paper focuses on power smoothing control of the grid-connected MCT system. In the first step, a modified MPPT algorithm with filter strategy is proposed in generator-side control to mitigate the fluctuation of generator power. In the second step, a supercapacitor (SC) energy storage system (ESS) is added to compensate the residual power fluctuations. Simulations of a 1.5-MW direct-driven grid-connected MCT system are carried out. The swell effect is calculated based on typical system location and sea state. Detailed control strategies and SC sizing are described. The results demonstrate that the association of the generator-side filter strategy with the SC ESS system achieves a smoothed power injected to the grid in case of swell disturbances.

The objective of this paper is to investigate cavitating flows around a pitching hydrofoil via combined physical and numerical studies. The aims are to (1) improve the understanding of the interplay between unsteady cavitating flow, hydrofoil motion, and hydrodynamic performance, (2) quantify the influence of pitching rate on subcavitating and cavitating responses, and (3) quantify the influence of cavitation on the hydrodynamic load coefficients and surrounding flow structures. Results are presented for a NACA66 hydrofoil undergoing controlled, slow $(\dot \alpha = 6^\circ /{\rm s})$(α̇=6∘/s) and fast $(\dot \alpha = 63^\circ /{\rm s})$(α̇=63∘/s) pitching motions from α = 0° to α = 15° and back to α = 0° for both subcavitating and cavitating conditions at a moderate Reynolds number of Re = 750 000. The experimental studies were conducted in a cavitation tunnel at the French Naval Academy, France. The numerical simulations are performed by solving the incompressible, multiphase Unsteady Reynolds-Averaged Navier-Stokes Equations via the commercial code CFX using a transport equation-based cavitation model; a modified k-ω SST turbulence model is used to account for the effect of local compressibility on the turbulent eddy viscosity. The results showed that increases in the pitching rate suppressed laminar to turbulent transition, delayed stall, and significantly modified post-stall behavior. Cavitation inception at the leading edge modified the pressure distribution, which in turn significantly changed the interaction between leading edge and trailing edge vortices, and hence the magnitude as well as the frequency of the load fluctuations. For a fixed cavitation number, increases in pitching rate lead to increase in cavitation volume, which in turn changed the cavity shedding frequencies and significantly modified the hydrodynamic loads. Inversely, the leading edge cavitation observed for the low pitching velocity case tends to stabilize the stall because of the decrease of the pressure gradient due to the formation of the cavity. The results showed strong correlation between the cavity and vorticity structures, which suggest that the inception, growth, collapse and shedding of sheet/cloud cavities are important mechanisms for vorticity production and modification.

Hydrothermal circulation at ultramafic‐hosted sites supports a large variety of high‐ and low‐temperature hydrothermal vents and associated ecosystems. The discovery of abundant fossil vesicomyid and thyasirid shell accumulations at the ridge crest, approximately 2.5 km east of the active Rainbow vent field on the Mid‐Atlantic Ridge (MAR, 36°13′N), increased our knowledge regarding the diversity of vent communities at slow spreading ridges. Bivalve molluscs of the family Vesicomyidae were represented by the genus Phreagena . Here we present the first record of this genus in the Atlantic Ocean. This second vesicomyid species known from the MAR, Phreagena sp., was found to be associated with a Thyasira species that is affiliated with T. southwardae (at the Logatchev vent field on the MAR) and with T. vulcolutre (in the Gulf of Cadiz). These two clams have close relationships with seep taxa along the continental margin, and were likely associated with sedimented vent fields. δ 18 O and δ 13 C analyses of the shells suggested that the burrowing bivalve Thyasira could incorporate isotopically light carbon, derived from the oxidation of methane in the sediment, while the signature of Phreagena sp. shells denoted a different carbonate source. 14 C dating of the shells denoted that the hydrothermal activity in the Rainbow area began at least ∼25.5 kyr BP, which is similar to the model of the hydrothermal vent field distribution that was proposed for the Logatchev hydrothermal site. The results provide new insight regarding the diversity of chemosynthetic fauna on the MAR over geologic time. Ultramafic‐hosted, on‐axis sedimented vent fields extend the range of habitats for chemosynthetic communities, underlying the need to further explore the geology of these types of environments on slow‐spreading ridges and to determine their role in the ecology of deep‐sea vent communities.

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.