The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Chokri Ben Amar - One of the best experts on this subject based on the ideXlab platform.
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Performance of Genetic Algorithm and Levenberg Marquardt Method on Multi-Mother Wavelet Neural Network Training for 3D Huge Meshes Deformation: A Comparative Study
Neural Processing Letters, 2021Co-Authors: Naziha Dhibi, Chokri Ben AmarAbstract:We propose, in this paper, a novel technique for large Laplacian boundary deformations using estimated rotations. The introduced method is used in the domain of Region of Interest (ROI) to align features of mesh based on Multi Mother Wavelet Neural Network (MMWNN) structure found in several Mother Wavelet families. The Wavelet network allows the alignment of the characteristic points of the original mesh towards the target mesh. The key component of our correspondence scheme is a deformation energy that penalizes geometric distortion, encourages structure preservation and simultaneously allows mesh topology changes. To ensure the design of Wavelet neural network architecture, an optimization algorithm should be applied to estimate and optimize the network parameters. Therefore, we compare our approach of 3d mesh deformation using MMWNN architecture based on genetic algorithm and our approach relying on Levenberg-Marquardt Method. We also discuss the existing comparison metrics for static and deformed triangle meshes employing the two mentioned approaches. Besides, we enumerate their strengths, weaknesses and relative performance.
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multi Mother Wavelet neural network based on genetic algorithm and multiresolution analysis for fast 3d mesh deformation
Iet Image Processing, 2019Co-Authors: Naziha Dhibi, Chokri Ben AmarAbstract:The current study presents a new 3D mesh deformation process using multi-Mother Wavelet neural network architecture, which relies on genetic algorithm and multiresolution analysis. Classic forming algorithms begin with a predetermined network architecture that may be insufficient or too complicated. In addition, the solving of Wavelet neural network training problems is described by their perceived inability to escape local optima. The main objective of the authors' proposed approach is that it prevents both the insufficiency and local minima by integrating the genetic algorithm; their Wavelet network is used as an approximation tool to align the features of mesh to have efficient deformation processes. Also, such meshes are especially expensive to transmit and are awkward to deform. For this reason, they propose to use multiresolution analysis to decompose a surface geometry into several levels of detail in order to work only with the approximation coefficient at a chosen decomposition level. Hierarchical triangle mesh representations provide access to a triangle mesh at the desired resolution without omitting any information. The experimental results showed the validity of the generalisation ability and the efficiency of their suggested multi-Mother Wavelet network architecture based on genetic algorithm and multiresolution analysis for 3D mesh modelling and deformation.
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multi Mother Wavelet neural network training using genetic algorithm based approach to optimize and improves the robustness of gradient descent algorithms 3d mesh deformation application
International Work-Conference on Artificial and Natural Neural Networks, 2019Co-Authors: Naziha Dhibi, Chokri Ben AmarAbstract:This paper presents the implementation of genetic algorithm which aims at searching for an optimal or near optimal solution to the deformation 3D objects problem based on multi-Mother Wavelet neural network training. First, we introduce the problem of 3D high mesh deformation using Multi-Mother Wavelet Neural Network architecture (MMWNN). Furthermore, gradient training limits of Wavelet networks are characterized by their inability to evade local optima. The idea is to integrate genetic algorithms into the Wavelet network to avoid both insufficiency and local minima in the 3D mesh deformation technique. Simulation results validate the generalization ability and efficiency of the proposed network based on genetic algorithms (MMWNN-GA). Thus the significant improvement of the performances in terms of quality of 3D meshes deformation.
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a high dimension 3d object representation using multi Mother Wavelet network
International Symposium on Visual Computing, 2010Co-Authors: Mohamed Othmani, Chokri Ben AmarAbstract:This paper presents an approach for 3D object representation. Our major contribution is proves that Wavelet network are capable for reconstruction and representing irregular 3D objects used in computer graphics. It consists to transform an input surface vertices into signals and to provide instantaneously an estimation of the output values for input values. To prove this, we will use a new structure of Wavelet network founded on several Mother Wavelet families. This structure uses several Mother Wavelet, in order to maximize best Wavelet selection probability. An algorithm to construct this structure is presented. First, data is taken from 3D object. The vertices and their corresponding normal values of a 3D object are used to create a training set. To this stage, the training set can be expressed according to three functions, which interpolates all their vertices. Second we approximate each function using Wavelet network. To achieve a better approximation, the network is trained several iterations to optimize Wavelet selection for every Mother. To guarantee a small error criterion, we adjust Wavelet network parameters (weight, translation and dilation) by using an improved Orthogonal Least Squares method version. We consider our proposed approach on two 3D examples to prove that the new approach is able to approximate 3D objects with a good approximation ability.
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3d object modeling using multi Mother Wavelet network
ACS IEEE International Conference on Computer Systems and Applications, 2010Co-Authors: Mohamed Othmani, Wajdi Bellil, Chokri Ben Amar, Adel M AlimiAbstract:This paper deals with an experiment which proves that Wavelet networks are capable for 3D objects modeling. To prove this, we will propose a new structure of Wavelet network founded on several Mother Wavelets families. This new structure is in some ways similar to the classic Wavelet networks but it admits some originality. Actually, Wavelet network basically uses dilations and translations versions of only one Mother Wavelet to construct the network. The proposed structure uses several Mother Wavelets, in order to maximize best Wavelets selection probability. An algorithm to construct this structure is presented. First, 3D object model vertices and their corresponding normal values are used to create a training set. Then, an improved Orthogonal Least Squares method version is applied to optimize Wavelet selection for every Mother Wavelet. Some simulation results will describe the proposed Wavelet network performance employing several types of Polywogs as Mother Wavelets.
Naziha Dhibi - One of the best experts on this subject based on the ideXlab platform.
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Performance of Genetic Algorithm and Levenberg Marquardt Method on Multi-Mother Wavelet Neural Network Training for 3D Huge Meshes Deformation: A Comparative Study
Neural Processing Letters, 2021Co-Authors: Naziha Dhibi, Chokri Ben AmarAbstract:We propose, in this paper, a novel technique for large Laplacian boundary deformations using estimated rotations. The introduced method is used in the domain of Region of Interest (ROI) to align features of mesh based on Multi Mother Wavelet Neural Network (MMWNN) structure found in several Mother Wavelet families. The Wavelet network allows the alignment of the characteristic points of the original mesh towards the target mesh. The key component of our correspondence scheme is a deformation energy that penalizes geometric distortion, encourages structure preservation and simultaneously allows mesh topology changes. To ensure the design of Wavelet neural network architecture, an optimization algorithm should be applied to estimate and optimize the network parameters. Therefore, we compare our approach of 3d mesh deformation using MMWNN architecture based on genetic algorithm and our approach relying on Levenberg-Marquardt Method. We also discuss the existing comparison metrics for static and deformed triangle meshes employing the two mentioned approaches. Besides, we enumerate their strengths, weaknesses and relative performance.
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Multiresolution analysis relying on Beta Wavelet transform and multi-Mother Wavelet network for a novel 3D mesh alignment and deformation technique
International Journal of Machine Learning and Cybernetics, 2020Co-Authors: Naziha Dhibi, Chokri Ben AmarAbstract:In this paper, we propose a new 3D high mesh deformation technique to extract intuitive and interpretable deformation and alignment components. Our framework is based on a fast Beta Wavelet transform for a multi-resolution analysis relying on multi-library Wavelet neural network architecture. The main drawback of 3D high mesh deformation is the large number of triangles necessary to characterize a smooth surface; the majority of these techniques impose a very high computational cost. Our approach is based on the idea of combining the decomposition technique of multi-resolution analysis by Beta Wavelet transform for each level of deformation process and a multi-Mother Wavelet network structure to construct an effective 3D alignment algorithm. We use, in our experiment, only the approximation coefficients at a chosen decomposition level to reduce the complexity of the mesh and to facilitate the alignment until reaching the target mesh, for the purpose of improving various executions and obtaining an optimal solution while reducing the error between the original and the reconstructed object to create a well-formed object. Then, to enhance the performance of Wavelet networks, a novel learning algorithm based on multi-Mother Wavelet neural network architecture using trust region spherical is employed as an approximation tool for feature alignment between the source and the target models. This network architecture ensures the use of several Mother Wavelets to solve the problem of high mesh deformation utilizing the best Wavelet Mother that well models the object. Extensive experimental results demonstrate that the progressive deformation processes aim at avoiding the weaknesses of traditional approaches such as the slowness and the difficulty of finding an exact reconstruction.
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multi Mother Wavelet neural network based on genetic algorithm and multiresolution analysis for fast 3d mesh deformation
Iet Image Processing, 2019Co-Authors: Naziha Dhibi, Chokri Ben AmarAbstract:The current study presents a new 3D mesh deformation process using multi-Mother Wavelet neural network architecture, which relies on genetic algorithm and multiresolution analysis. Classic forming algorithms begin with a predetermined network architecture that may be insufficient or too complicated. In addition, the solving of Wavelet neural network training problems is described by their perceived inability to escape local optima. The main objective of the authors' proposed approach is that it prevents both the insufficiency and local minima by integrating the genetic algorithm; their Wavelet network is used as an approximation tool to align the features of mesh to have efficient deformation processes. Also, such meshes are especially expensive to transmit and are awkward to deform. For this reason, they propose to use multiresolution analysis to decompose a surface geometry into several levels of detail in order to work only with the approximation coefficient at a chosen decomposition level. Hierarchical triangle mesh representations provide access to a triangle mesh at the desired resolution without omitting any information. The experimental results showed the validity of the generalisation ability and the efficiency of their suggested multi-Mother Wavelet network architecture based on genetic algorithm and multiresolution analysis for 3D mesh modelling and deformation.
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multi Mother Wavelet neural network training using genetic algorithm based approach to optimize and improves the robustness of gradient descent algorithms 3d mesh deformation application
International Work-Conference on Artificial and Natural Neural Networks, 2019Co-Authors: Naziha Dhibi, Chokri Ben AmarAbstract:This paper presents the implementation of genetic algorithm which aims at searching for an optimal or near optimal solution to the deformation 3D objects problem based on multi-Mother Wavelet neural network training. First, we introduce the problem of 3D high mesh deformation using Multi-Mother Wavelet Neural Network architecture (MMWNN). Furthermore, gradient training limits of Wavelet networks are characterized by their inability to evade local optima. The idea is to integrate genetic algorithms into the Wavelet network to avoid both insufficiency and local minima in the 3D mesh deformation technique. Simulation results validate the generalization ability and efficiency of the proposed network based on genetic algorithms (MMWNN-GA). Thus the significant improvement of the performances in terms of quality of 3D meshes deformation.
Chokri Ben Amar - One of the best experts on this subject based on the ideXlab platform.
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Multiresolution analysis relying on Beta Wavelet transform and multi-Mother Wavelet network for a novel 3D mesh alignment and deformation technique
International Journal of Machine Learning and Cybernetics, 2020Co-Authors: Naziha Dhibi, Chokri Ben AmarAbstract:In this paper, we propose a new 3D high mesh deformation technique to extract intuitive and interpretable deformation and alignment components. Our framework is based on a fast Beta Wavelet transform for a multi-resolution analysis relying on multi-library Wavelet neural network architecture. The main drawback of 3D high mesh deformation is the large number of triangles necessary to characterize a smooth surface; the majority of these techniques impose a very high computational cost. Our approach is based on the idea of combining the decomposition technique of multi-resolution analysis by Beta Wavelet transform for each level of deformation process and a multi-Mother Wavelet network structure to construct an effective 3D alignment algorithm. We use, in our experiment, only the approximation coefficients at a chosen decomposition level to reduce the complexity of the mesh and to facilitate the alignment until reaching the target mesh, for the purpose of improving various executions and obtaining an optimal solution while reducing the error between the original and the reconstructed object to create a well-formed object. Then, to enhance the performance of Wavelet networks, a novel learning algorithm based on multi-Mother Wavelet neural network architecture using trust region spherical is employed as an approximation tool for feature alignment between the source and the target models. This network architecture ensures the use of several Mother Wavelets to solve the problem of high mesh deformation utilizing the best Wavelet Mother that well models the object. Extensive experimental results demonstrate that the progressive deformation processes aim at avoiding the weaknesses of traditional approaches such as the slowness and the difficulty of finding an exact reconstruction.
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A novel approach for high dimension 3D object representation using Multi-Mother Wavelet Network
Multimedia Tools and Applications, 2012Co-Authors: Mohamed Othmani, Wajdi Bellil, Chokri Ben Amar, Adel M AlimiAbstract:In this paper, we present a novel approach for 3D objects representation. Our idea is to prove that Wavelet networks are capable for reconstruction and representing irregular 3D objects used in computer graphics. The major contribution consist to transform an input surface vertices into signals and to provide instantaneously an estimation of the output values for input values. To prove this, we will use a new structure of Wavelet network founded on several Mother Wavelet families. This structure uses several Mother Wavelet, in order to maximize best Wavelet selection probability. An algorithm to construct this structure is presented. First, Data is taken from 3D object. The vertices and their corresponding normal values of a 3D object are used to create a training set. To this stage, the training set can be expressed according to three functions, which interpolates all their vertices. Second we approximate each function using Wavelet network. To achieve a better approximation, the network is trained several iterations to optimize Wavelet selection for every Mother. To guarantee a small error criterion, we adjust Wavelet network parameters (weight, translation and dilation) by using an improved Orthogonal Least Squares method version. We consider our proposed approach on some 3D examples to prove that the new approach is able to approximate 3D objects with a good approximation ability.
J Rafiee - One of the best experts on this subject based on the ideXlab platform.
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application of Mother Wavelet functions for automatic gear and bearing fault diagnosis
Expert Systems With Applications, 2010Co-Authors: J Rafiee, M A RafieeAbstract:This paper introduces an automatic feature extraction system for gear and bearing fault diagnosis using Wavelet-based signal processing. Vibration signals recorded from two experimental set-ups were processed for gears and bearing conditions. Four statistical features were selected: standard deviation, variance, kurtosis, and fourth central moment of continuous Wavelet coefficients of synchronized vibration signals (CWC-SVS). In this research, the Mother Wavelet selection is broadly discussed. 324 Mother Wavelet candidates were studied, and results show that Daubechies 44 (db44) has the most similar shape across both gear and bearing vibration signals. Next, an automatic feature extraction algorithm is introduced for gear and bearing defects. It also shows that the fourth central moment of CWC-SVS is a proper feature for both bearing and gear failure diagnosis. Standard deviation and variance of CWC-SVS demonstrated more appropriate outcome for bearings than gears. Kurtosis of CWC-SVS illustrated the acceptable performance for gears only. Results also show that although db44 is the most similar Mother Wavelet function across the vibration signals, it is not the proper function for all Wavelet-based processing.
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biorobotics optimized biosignal classification using Mother Wavelet matrix
Northeast Bioengineering Conference, 2009Co-Authors: J Rafiee, Mohammad A Rafiee, Nicole Prause, Marco P. SchoenAbstract:This paper presents a new technique to optimally classify forearm electromyographic (EMG) measures using proposed Mother Wavelet matrix (MWM) for biorobots. Among 324 Mother Wavelet candidates, a MWM including 15 potential Mother Wavelet functions were selected to optimally classify surface and intramuscular EMG signals collected from multiple locations on the upper forearm of six subjects for nine classes of hand motions plus a rest state.
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a novel technique for selecting Mother Wavelet function using an intelli gent fault diagnosis system
Expert Systems With Applications, 2009Co-Authors: J Rafiee, A Harifi, Mostafa SadeghiAbstract:This paper presents an optimized gear fault identification system using genetic algorithm (GA) to investigate the type of gear failures of a complex gearbox system using artificial neural networks (ANNs) with a well-designed structure suited for practical implementations due to its short training duration and high accuracy. For this purpose, slight-worn, medium-worn, and broken-tooth of a spur gear of the gearbox system were selected as the faults. In fault simulating, two very similar models of worn gear have been considered with partial difference for evaluating the preciseness of the proposed algorithm. Moreover, the processing of vibration signals has become much more difficult because a full-of-oil complex gearbox system has been considered to record raw vibration signals. Raw vibration signals were segmented into the signals recorded during one complete revolution of the input shaft using tachometer information and then synchronized using piecewise cubic hermite interpolation to construct the sample signals with the same length. Next, standard deviation of Wavelet packet coefficients of the vibration signals considered as the feature vector for training purposes of the ANN. To ameliorate the algorithm, GA was exploited to optimize the algorithm so as to determine the best values for ''Mother Wavelet function'', ''decomposition level of the signals by means of Wavelet analysis'', and ''number of neurons in hidden layer'' resulted in a high-speed, meticulous two-layer ANN with a small-sized structure. This technique has been eliminated the drawbacks of the type of Mother function for fault classification purpose not only in machine condition monitoring, but also in other related areas. The small-sized proposed network has improved the stability and reliability of the system for practical purposes.
Mostafa Sadeghi - One of the best experts on this subject based on the ideXlab platform.
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a novel technique for selecting Mother Wavelet function using an intelli gent fault diagnosis system
Expert Systems With Applications, 2009Co-Authors: J Rafiee, A Harifi, Mostafa SadeghiAbstract:This paper presents an optimized gear fault identification system using genetic algorithm (GA) to investigate the type of gear failures of a complex gearbox system using artificial neural networks (ANNs) with a well-designed structure suited for practical implementations due to its short training duration and high accuracy. For this purpose, slight-worn, medium-worn, and broken-tooth of a spur gear of the gearbox system were selected as the faults. In fault simulating, two very similar models of worn gear have been considered with partial difference for evaluating the preciseness of the proposed algorithm. Moreover, the processing of vibration signals has become much more difficult because a full-of-oil complex gearbox system has been considered to record raw vibration signals. Raw vibration signals were segmented into the signals recorded during one complete revolution of the input shaft using tachometer information and then synchronized using piecewise cubic hermite interpolation to construct the sample signals with the same length. Next, standard deviation of Wavelet packet coefficients of the vibration signals considered as the feature vector for training purposes of the ANN. To ameliorate the algorithm, GA was exploited to optimize the algorithm so as to determine the best values for ''Mother Wavelet function'', ''decomposition level of the signals by means of Wavelet analysis'', and ''number of neurons in hidden layer'' resulted in a high-speed, meticulous two-layer ANN with a small-sized structure. This technique has been eliminated the drawbacks of the type of Mother function for fault classification purpose not only in machine condition monitoring, but also in other related areas. The small-sized proposed network has improved the stability and reliability of the system for practical purposes.