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In this paper, we propose an algorithm (SC+PAM) for solving large size linear programming problem with bounded variables where we combined two methods: the selection of constraint method (SC) and the pivot adaptive method (PAM). The step of (SC+PAM), consist in first, selecting the constraints of the starting problem, and a sequence of linear sub-problems of increasing size which will be obtained by adding to the previous a set of constraints unsatisfied by the optimal solution of the current problem, the constraints to be added at each iteration being the most orthogonal to the criterion of the strarting problem. The stop criterion of (SC+PAM) is when all the constraints are satisfied and thus the solution found is opitmal for the strating problem. Thus, this method takes less computing time than (PAM), which is confirmed by numerical examples. MSC code: 90C05, 90C06, 90C51.

Synchronization with other machines is one of those tasks that an intelligent machine should be able to perform. To do so, a general method of synchronization must be defined and that is the ambition of this article. For this purpose, we recall what the main concepts of systemic modelling consist of. Then we define what a Synchronization Problem is and distinguish three types of synchronization problems : predetermined (PSP), stochastic (SSP) and asymptotic (ASP). So, to find the best solution, we define three optimized synchronization problems : Optimized Predetermined Synchronization Problems , Optimized Stochastic Synchronization Problems (OSSP) and Optimized Asymptotic Synchronization Problems (OASP). A general method for solving an optimized synchronization problem and thus synchronize an out of sync system, is then given; it includes four steps : step 1, identify the synchronization functions; step 2, identify the synchronization case : predetermined, stochastic or asymptotic; step 3, select an optimization criterion and solve the optimized synchronization problem corresponding to the chosen synchronization case; step 4, find a control which permits to track the system trajectory, optimal solution of the selected optimized synchronization problem. In the next section, we present a heuristic algorithm that checks the constraints of an ASP for any initial state of the system to be synchronized. The principle of this algorithm is to pursue or track a perfectly synchronized solution. Finally, we present and solve two synchronization problems in the field of Mobile Robot Systems: a synchronized satelliteization problem and the Horse Carousel Problem. We apply to these two problems the general method given above by choosing the asymptotic synchronization case and our tracking algorithm. Simulation results show the efficiency of the method and the relevance of using a tracking algorithm.

In this paper, a concealment method based on multiple-description coding (MDC) is presented, to improve speech quality deterioration caused by packet loss for algebraic code-excited linear prediction (ACELP) based coders. We apply to the ITU-T G.722.2 coder, a packet loss concealment (PLC) technique, which uses packetization schemes based on MDC. This latter is used with two new designed modes, which are modes 5 and 6 (18,25 and 19,85 kbps, respectively). We introduce our new second-order Markov chain model with four states in order to simulate network losses for different loss rates. The performance measures, with objective and subjective tests under various packet loss conditions, show a significant improvement of speech quality for ACELP based coders. The wideband perceptual evaluation of speech quality (WB-PESQ), enhanced modified bark spectral distortion (EMBSD), mean opinion score (MOS) tests and MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) for speech extracted from TIMIT database confirm the efficiency of our proposed approach and show a considerable enhancement in speech quality compared to the embedded algorithm in the standard ITU-T G.722.2.

The concept of Z-equilibrium has been introduced by Zhuk-ovskii (Mathematical Methods in Operations Research. Bulgarian Academy of Sciences, Sofia (1985) 103–195) for games in normal form. This concept is always Pareto optimal and individually rational for the players. Moreover, Pareto optimal Nash equilibria are Z-equilibria. We consider a bi-matrix game whose payoffs are uncertain variables. By appropriate ranking criteria of Liu uncertainty theory, we introduce some concepts of equilibrium based on Z-equilibrium for such games. We provide sufficient conditions for the existence of the introduced concepts. Moreover, using mathematical programming, we present a procedure for their computation. A numerical example is provided for illustration.

International audience; This paper deals with the solution of fluid-structure interaction problems using an algorithm consisting in the coupling between two solvers, each one related to the fluid and the structure respectively. Due to the difficult decomposition of the domain into sub-domains, we consider for each environment a parallel multi-splitting algorithm which corresponds to an unified presentation of sub-domain methods with or without overlapping. Such method combines several contractant fixed point mappings and, we show that, under appropriate assumptions, each fixed point mapping are contractive in finite dimensional spaces normed by Hilbertian and non-Hilbertian norms. Moreover, we show in a novel way that such study is valid for the parallel synchronous and more generally asynchronous large scale linear systems arising in the solution of fluid-structure interaction problems and can be extended to the case where the displacement of the structure is submitted to some constraints. We perform parallel simulations for a fluid-structure test case on different clusters, considering blocking and non-blocking communications. The performances of the parallel simulations are presented and analyzed.

An optimal control problem of two Unmanned autonomous Vehicles to follow the trajectory and avoid the collision between them. The aim is to minimize energy, the distance between state and desired, and maximize the distance between the two drones. For this study, we used midpoint such discretization method, the simulation results ar given by Bocop software.

An unmanned aerial vehicle (UAV) become increasingly present in military and commercial applications, the flight path efficiency and integration with current manned aircraft become important research topics to address in the coming years. In this paper, the optimal control problem for the quad-rotor to minimize energy is considered. For this, formulate this problem to an optimal control problem which minimizes energy of quad-rotor in free final time. The Solution for this, used a discretization method, and simulation results are given by Bocop software.

In this work, we have proposed a new approach for solving the linear-quadratic optimal control problem, where the quality criterion is a quadratic function, which can be convex or non-convex. In this approach, we transform the continuous optimal control problem into a quadratic optimization problem using the Cauchy discretization technique, then we solve it with the active-set method. In order to study the efficiency and the accuracy of the proposed approach, we developed an implementation with MATLAB, and we performed numerical experiments on several convex and non-convex linear-quadratic optimal control problems. The obtained simulation results show that our method is more accurate and more efficient than the method using the classical Euler discretization technique. Furthermore, it was shown that our method fastly converges to the optimal control of the continuous problem found analytically using the Pontryagin's maximum principle.

Aeronautics search high performance materials for structural weight reduction and impact energy absorption. Composite offers perspectives related to mass and stiffness ratio. However, the dispersion of their mechanical properties due to environmental conditions or impact behaviour hinders their development. Currently, honeycomb solutions and complex energy absorption mechanisms are used. The sizing of structures is controlled for conventional loads, but not for severe cases such avian impact. In the context of the development of an innovative reinforced aeronautical structure, the objective of this study is to identify and characterize a new concept of absorbent composite material at the coupon scale. In order to improve reliability and optimize its absorption characteristics during high-energy impact, works are realized to develop in parallel an experimental study methodology and a finite element model. The purpose of the latter is to have a predictive tool validated by correlation with experimental to ensure virtual testing. Lack of knowledge of the nonlinear behaviour of the composite material at high deformation, for different speeds, as well as the mechanisms of damage and fracture are locks to its development. Absence of a dedicated experiment matrix presents a difficulty for its characterization and sizing. The approach begins with the analysis and prioritization of the existing to determine the definition criteria of the new concept. After development of an experimental characterization study, the qualification and justification of the new concept is validated by correlation with a dedicated numerical simulation methodology. The results of the study highlight the analysis and development of an interesting concept.

International audience; An unmanned aerial vehicle (UAV) has grown rapidly the last years. They are being engaged in many types of missions, ranging from military to agriculture passing from entertainment and rescue or even delivery. This work consist to ensure the convergence of positions and yaw angle, to their desired trajectories while maintaining stability of roll and pitch angle. For this, formulate this problem to an optimal control problem which minimizes the distance between state and desired state in free final time. Our contribution is to use the Bocop software to solve this problem. And to implement Shooting method with Matlab software.

International audience; The main objective of an energy regulation of a building is to maintain internal thermal comfort, as well as minimize energy consumption, or reduce the peak of electrical consumption. The dynamic programming has been used to minimize a cost function, accounting for a high peak electricity tariff, under constraints related to comfort (minimal temperature, maximal temperature variation) and the maximum heating power. The proposed energy management consists in over-heating the building during the hours before the peak knowing in advance the weather, occupation and internal gains for the day. The method has been tested in a case study corresponding to a house of a four-person family with performance levels: high construction and poorly insulated old house.

International audience; A new variant of the Adaptive Method (AM) of Gabasov is presented, to minimize the computation time. Unlike the original method and its some variants, we need not to compute the inverse of the basic matrix at each iteration, or to solve the linear systems with the basic matrix. In fact, to compute the new support feasible solution, the simplex pivoting rule is used by introducing a matrix that we will define. This variant is called “the Pivot Adaptive Method” (PAM); it allows presenting the resolution of a given problem under the shape of successive tables as we will see in example. The proofs that are not given by Gabasov will also be presented here, namely the proofs for the theorem of the optimality criterion and for the theorem of existence of an optimal support, and at the end, a brief comparison between our method and the Simplex Method will be given.

International audience; no abstract

In this paper we present an algorithm for solving a DC problem nonconvex on an interval (a, b) of R. We use the DCA (Dierence of Convex Algorithm) and the minimum of the average of two approximations of the function from a and b. This strategy has the advantage of giving in general a minimum to be situated in the attraction zone of the global minimum searched. After applying the DCA from this minimum we certainly arrive at the global minimum searched.

International audience; Path planning presents a key question for an autonomous robot to evolve in its environment. Hence, it has been largely dealt in recent years. Actually, finding feasible paths and optimizing them for different objectives is computationally difficult. In this context, this paper introduces a new mobile robot path planning algorithm by introducing an optimized NURBS (Non Uniform Rational B-Spline) curve modelling using Genetic Algorithm to represent the generated path from the specified start location to the desired goal. Thus, given an a priori knowledge of the environment, an accurate fitness function is used to compute a curvature-constrained and obstacles-avoiding smooth path, with minimum length and low variations of curvature. The performance of the proposed algorithm is demonstrated through extensive MATLAB simulation studies.

International audience; In this paper we present an algorithm for solving a DC problem non convex on an interval [a, b] of R. We use the DCA (Difference of Convex Algorithm) and the minimum of the average of two approximations of the function from a and b.This strategy has the advantage of giving in general a minimum to be situated in the attraction zone of the global minimum searched. After applying the DCA from this minimum we certainly arrive at the global minimum searched.

The authors develop optimal criteria for detection and localization of step edges in single look complex (SLC) synthetic aperture radar (SAR) images. By working on complex data rather than intensity images, they can easily take the speckle autocorrelation into account, obtain more accurate estimates of local mean reflectivities, and thus achieve better edge detection and edge localization than with operators known from the literature. Algorithms for the two-dimensional (2D) implementation of the methods are proposed, and some segmentation results are shown.

In a paper on morphological image segmentation, Najman and Schmitt (1996) introduce the powerful concept of edge dynamics. In this communication, we show that the method that they propose to compute the edge dynamics gives erroneous results for certain spatial configurations, and we propose a new algorithm which always yields correct edge dynamics. The reply presents in detail the algorithm of the watershed, which have been sketched in the original article and criticized in the comment. First, the formal definition of the flooding list, the key data structure of the algorithm, is given. Then, the construction of this flooding list and of the watershed are described and proved.

Edge detection is a fundamental issue in image analysis. Due to the presence of speckle, which can be modeled as a strong, multiplicative noise, edge detection in synthetic aperture radar (SAR) images is extremely difficult, and edge detectors developed for optical images are inefficient. Several robust operators have been developed for the detection of isolated step edges in speckled images. The authors propose a new step-edge detector for SAR images, which is optimal in the minimum mean square error (MSSE) sense under a stochastic multiedge model. It computes a normalized ratio of exponentially weighted averages (ROEWA) on opposite sides of the central pixel. This is done in the horizontal and vertical direction, and the magnitude of the two components yields an edge strength map. Thresholding of the edge strength map by a modified version of the watershed algorithm and region merging to eliminate false edges complete an efficient segmentation scheme. Experimental results obtained from simulated SAR images as well as ERS-1 data are presented.

International audience; The process of finding an optimum, smooth and feasible global path for mobile robot navigation usually involves determining the shortest polyline path, which will be subsequently smoothed to satisfy the requirements. Within this context, this paper deals with a novel roadmap algorithm for generating an optimal path in terms of Non-Uniform Rational B-Splines (NURBS) curves. The generated path is well constrained within the curvature limit by exploiting the influence of the weight parameter of NURBS and/or the control points' locations. The novelty of this paper lies in the fact that NURBS curves are not used only as a means of smoothing, but they are also involved in meeting the system's constraints via a suitable parameterization of the weights and locations of control points. The accurate parameterization of weights allows for a greater benefit to be derived from the influence and geometrical effect of this factor, which has not been well investigated in previous works. The effectiveness of the proposed algorithm is demonstrated through extensive MATLAB computer simulations.

As acquisition technology progresses, remote sensing data contains an ever increasing amount of information: optical and radar images, low, high and very high-resolution, multitemporal hyperspectral images, derived images, and physical or ancillary data (databases, Digital Elevation Model (D.E.M), Geographical Information System (G.I.S.)). Future projects in remote sensing will give high repeatability of acquisition like Venμs (CNES) which may provide data every 2 days with a resolution of 5.3 meters on 12 bands (420nm-900nm) and Sentinel-2 (ESA) 13 bands, 10-60m resolution and 5 days. With such data, supervised classification gives excellent results in term of accuracy indices (like Overall Accuracy, Kappa coefficient). In this paper, we present advantages and disadvantages of existing indices and propose a new index to evaluate supervised classification using all the information available from the confusion matrix. In addition to accuracy, a new feature is introduced in this index: fidelity. For example, a class could have a high accuracy (low omission error) but could be over-represented with other classes (high commission error). The new index reflects accuracy and correct representation of classes (fidelity) using commission and omission errors. Environment applications are in land cover and land use and the goal is to have the best classification for all classes, whether the biggest (corn, trees) or the lightest (rivers, hedges). The tests are performed on Formosat-2 images (every 2 days, 8 meters resolution on 4 bands) in the area of Toulouse (France). Tests used to validate the new index by demonstrating benefits of its use through various thematical studies.

As acquisition technology progresses, remote sensing data contains an ever increasing amount of information. Future projects in remote sensing will give high repeatability of acquisition like Venμs (CNES1) which may provide data every 2 days with a resolution of 5.3 meters on 12 bands (420nm–900nm) and Sentinel−2 (ESA) 13 bands, 10–60m resolution and 5 days. With such data, process automation appears crucial. For that purpose, we develop several algorithms to automate image processing (classification, segmentation, interpretation, etc.). In this paper, we present an algorithm of automatic analysis which selects the best dataset of dates maximizing classification quality indices. We create two indices to evaluate jointly accuracy and precision. We present tests performed on Formosat-2 images which are similar to Venμs and Sentinel−2 for temporal repetitiveness. These tests allow validating the presented process for temporal discrimination improvement.

This paper addresses the issue of cartographic database creation or updating using high resolution SAR and optical images. It proposes a processing chain to create or update road databases in urban environment. The approach is composed of two steps. First, if a database is available, the presence of each database object is checked in the images. Then, we verify if road hypotheses extracted from images should be included in the database. These two steps are conducted by extracting relevant features from the images in the neighborhood of the considered object. The object removal/inclusion in the database is based on a score obtained by the fusion of features in the framework of Dempster-Shafer evidence theory.