The Experts below are selected from a list of 132771 Experts worldwide ranked by ideXlab platform
Israel Cohen - One of the best experts on this subject based on the ideXlab platform.
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relative transfer function Identification using speech signals
IEEE Transactions on Speech and Audio Processing, 2004Co-Authors: Israel CohenAbstract:An important component of a multichannel hands-free communication System is the Identification of the relative transfer function between sensors in response to a desired source signal. In this paper, a robust System Identification Approach adapted to speech signals is proposed. A weighted least-squares optimization criterion is introduced, which considers the uncertainty of the desired signal presence in the observed signals. An asymptotically unbiased estimate for the System's transfer function is derived, and a corresponding recursive online implementation is presented. We show that compared to a competing nonstationarity-based method, a smaller error variance is achieved and generally shorter observation intervals are required. Furthermore, in the case of a time-varying System, faster convergence and higher reliability of the System Identification are obtained by using the proposed method than by using the nonstationarity-based method. Evaluation of the proposed System Identification Approach is performed under various noise conditions, including simulated stationary and nonstationary white Gaussian noise, and car interior noise in real pseudo-stationary and nonstationary environments. The experimental results confirm the advantages of proposed Approach.
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relative transfer function Identification using speech signals
IEEE Transactions on Speech and Audio Processing, 2004Co-Authors: Israel CohenAbstract:An important component of a multichannel hands-free communication System is the Identification of the relative transfer function between sensors in response to a desired source signal. In this paper, a robust System Identification Approach adapted to speech signals is proposed. A weighted least-squares optimization criterion is introduced, which considers the uncertainty of the desired signal presence in the observed signals. An asymptotically unbiased estimate for the System's transfer function is derived, and a corresponding recursive online implementation is presented. We show that compared to a competing nonstationarity-based method, a smaller error variance is achieved and generally shorter observation intervals are required. Furthermore, in the case of a time-varying System, faster convergence and higher reliability of the System Identification are obtained by using the proposed method than by using the nonstationarity-based method. Evaluation of the proposed System Identification Approach is performed under various noise conditions, including simulated stationary and nonstationary white Gaussian noise, and car interior noise in real pseudo-stationary and nonstationary environments. The experimental results confirm the advantages of proposed Approach.
D Zangani - One of the best experts on this subject based on the ideXlab platform.
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combining genetic algorithms with a meso scale Approach for System Identification of a smart polymeric textile
Computer-aided Civil and Infrastructure Engineering, 2013Co-Authors: C Fuggini, Eleni Chatzi, D ZanganiAbstract:: This article describes a structural System Identification Approach for the characterization of a novel retrofitting textile, the “Composite Seismic Wallpaper.” This polymeric textile was developed within the EU co-funded project Polytect as a full coverage method for increasing the seismic resistance of masonry structures. Recently, the wallpaper has been full-scale tested, on a two storey building, at the Eucentre (Pavia) as part of the Seismic Engineering Research Infrastructures for European Synergies (SERIES) program. In this article, an advanced multistage Identification methodology is proposed for the successful simulation of this novel material based on the results of the extensive experimental campaign. The Identification is essentially formulated as an inverse problem that combines a Genetic Algorithm (GA) as the optimizer and a finite element (FE) model as the physical model of the structure. The aim is material characterization and modeling of the dynamic response of the structure; an issue which is nontrivial due to the intrinsic complexities associated with both masonry and polymers. The process outlined herein is successful in yielding a calibrated model that can more accurately capture the experimentally observed behavior of this three-dimensional full-scale test case.
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combining genetic algorithms with a meso scale Approach for System Identification of a smart polymeric textile
Computer-aided Civil and Infrastructure Engineering, 2013Co-Authors: C Fuggini, Eleni Chatzi, D ZanganiAbstract:: This article describes a structural System Identification Approach for the characterization of a novel retrofitting textile, the “Composite Seismic Wallpaper.” This polymeric textile was developed within the EU co-funded project Polytect as a full coverage method for increasing the seismic resistance of masonry structures. Recently, the wallpaper has been full-scale tested, on a two storey building, at the Eucentre (Pavia) as part of the Seismic Engineering Research Infrastructures for European Synergies (SERIES) program. In this article, an advanced multistage Identification methodology is proposed for the successful simulation of this novel material based on the results of the extensive experimental campaign. The Identification is essentially formulated as an inverse problem that combines a Genetic Algorithm (GA) as the optimizer and a finite element (FE) model as the physical model of the structure. The aim is material characterization and modeling of the dynamic response of the structure; an issue which is nontrivial due to the intrinsic complexities associated with both masonry and polymers. The process outlined herein is successful in yielding a calibrated model that can more accurately capture the experimentally observed behavior of this three-dimensional full-scale test case.
C Fuggini - One of the best experts on this subject based on the ideXlab platform.
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combining genetic algorithms with a meso scale Approach for System Identification of a smart polymeric textile
Computer-aided Civil and Infrastructure Engineering, 2013Co-Authors: C Fuggini, Eleni Chatzi, D ZanganiAbstract:: This article describes a structural System Identification Approach for the characterization of a novel retrofitting textile, the “Composite Seismic Wallpaper.” This polymeric textile was developed within the EU co-funded project Polytect as a full coverage method for increasing the seismic resistance of masonry structures. Recently, the wallpaper has been full-scale tested, on a two storey building, at the Eucentre (Pavia) as part of the Seismic Engineering Research Infrastructures for European Synergies (SERIES) program. In this article, an advanced multistage Identification methodology is proposed for the successful simulation of this novel material based on the results of the extensive experimental campaign. The Identification is essentially formulated as an inverse problem that combines a Genetic Algorithm (GA) as the optimizer and a finite element (FE) model as the physical model of the structure. The aim is material characterization and modeling of the dynamic response of the structure; an issue which is nontrivial due to the intrinsic complexities associated with both masonry and polymers. The process outlined herein is successful in yielding a calibrated model that can more accurately capture the experimentally observed behavior of this three-dimensional full-scale test case.
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combining genetic algorithms with a meso scale Approach for System Identification of a smart polymeric textile
Computer-aided Civil and Infrastructure Engineering, 2013Co-Authors: C Fuggini, Eleni Chatzi, D ZanganiAbstract:: This article describes a structural System Identification Approach for the characterization of a novel retrofitting textile, the “Composite Seismic Wallpaper.” This polymeric textile was developed within the EU co-funded project Polytect as a full coverage method for increasing the seismic resistance of masonry structures. Recently, the wallpaper has been full-scale tested, on a two storey building, at the Eucentre (Pavia) as part of the Seismic Engineering Research Infrastructures for European Synergies (SERIES) program. In this article, an advanced multistage Identification methodology is proposed for the successful simulation of this novel material based on the results of the extensive experimental campaign. The Identification is essentially formulated as an inverse problem that combines a Genetic Algorithm (GA) as the optimizer and a finite element (FE) model as the physical model of the structure. The aim is material characterization and modeling of the dynamic response of the structure; an issue which is nontrivial due to the intrinsic complexities associated with both masonry and polymers. The process outlined herein is successful in yielding a calibrated model that can more accurately capture the experimentally observed behavior of this three-dimensional full-scale test case.
S P Ghoshal - One of the best experts on this subject based on the ideXlab platform.
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a new design method based on firefly algorithm for iir System Identification problem
Journal of King Saud University: Engineering Sciences, 2016Co-Authors: Prashant Upadhyay, Durbadal Mandal, S P GhoshalAbstract:Abstract In this paper a population based evolutionary optimization methodology called firefly algorithm (FFA) is applied for the optimization of System coefficients of the infinite impulse response (IIR) System Identification problem. FFA is inspired by the flash pattern and characteristics of fireflies. In FFA technique, behaviour of flashing firefly towards its competent mate is structured. In this algorithm attractiveness depends on brightness of light and a bright firefly feels more attraction for the brighter one. For this optimization problem, brightness varies inversely proportional to the error fitness value, so the position of the brightest firefly gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic movement equation results in balancing of exploration and exploitation of search space. The proposed FFA based System Identification Approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed System Identification Approach using FFA over GA, PSO and DE in terms of convergence speed, identifying plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters.
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craziness based particle swarm optimization algorithm for iir System Identification problem
Aeu-international Journal of Electronics and Communications, 2014Co-Authors: Prashant Upadhyay, Durbadal Mandal, S P GhoshalAbstract:Abstract In this paper a variant of particle swarm optimization (PSO), called craziness based particle swarm optimization (CRPSO) technique is applied to the infinite impulse response (IIR) System Identification problem. A modified version of PSO, called CRPSO adopts a number of random variables for having better and faster exploration and exploitation in multidimensional search space. Incorporation of craziness factor in the basic velocity expression of PSO not only brings diversity in particles but also ensures convergence to optimal solution. The proposed CRPSO based System Identification Approach has alleviated from the inherent drawbacks of premature convergence and stagnation, unlike real coded genetic algorithm (RGA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed System Identification Approach using CRPSO over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters.
A M Abdelghaffar - One of the best experts on this subject based on the ideXlab platform.
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application of a web enabled real time structural health monitoring System for civil infrastructure Systems
Smart Materials and Structures, 2004Co-Authors: Sami F Masri, L H Sheng, John P Caffrey, Robert L Nigbor, M Wahbeh, A M AbdelghaffarAbstract:The System architecture of a novel structural health monitoring System that is optimized for the continuous real-time monitoring of dispersed civil infrastructures is presented. The monitoring System is based on a highly efficient multithreaded software design that allows the System to acquire data from a large number of channels, monitor and condition this data, and distribute it, in real time, over the Internet to multiple remote locations. Bandwidth and latency issues that impact the operation of monitoring Systems are discussed. The application of the monitoring System under discussion to a long span, flexible bridge in the metropolitan Los Angeles region is described. The bridge had previously been instrumented with 26 strong motion accelerometers. Sample 'quick analysis' results continuously provided by the monitoring System are presented and interpreted. System Identification results, obtained through off-line batch processing, are presented for a data set from a recent earthquake that automatically triggered the recording capability of the System. It is shown that, using a time domain System Identification Approach, the bridge stiffness and damping matrices can be identified from the earthquake data set and subsequently used to determine the bridge modal properties, such as frequencies and damping ratios. In this Approach the bridge is modeled as a multi-input/multi-output System with order compatible with the number of available sensors. Implementation issues requiring further investigation are presented and discussed.
