The Experts below are selected from a list of 120 Experts worldwide ranked by ideXlab platform
John C. Macdonald - One of the best experts on this subject based on the ideXlab platform.
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The Australian REEFREP System: a Coastal Vessel Traffic Information Service and Ship Reporting System for the Torres Strait Region and the Inner Route of the Great Barrier Reef
Maritime Studies, 1996Co-Authors: John C. MacdonaldAbstract:AbstractThe new Australian ship reporting System, Identifier ‘REEFREP’, will be the core component of a Vessel Traffic Information Service (VTIS) covering the Torres Strait region and the Great Barrier Reef. It is the first such System to be considered by the International Maritime Organization (IMO) under the terms of the new SOLAS 74 regulation V/8-1, which entered into force on 1 January 1996 and allows for ship reporting Systems adopted by the Organization to be made mandatory for all, or certain categories of vessels.The REEFREP System, planned for implementation on 1 January 1997, extends for some 900 nm or about 1500 km along the Queensland coastline. It will be a VHF radio based System with radars covering three selected focal points in the Torres Strait, off Cairns and in the southern approaches to the inner route. The System will provide a capability for a single Ship Reporting Centre to interact with shipping, enabling the provision of improved information on the presence, movements and pattern...
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The Australian REEFREP System : A coastal Vessel Traffic Information Service and ship reporting System for the Torres Strait region and the inner route of the Great Barrier Reef
Journal of Navigation, 1996Co-Authors: John C. MacdonaldAbstract:The new Australian ship reporting System, Identifier ‘REEFREP’, will be the core component of a Vessel Traffic Information Service (VTIS) covering the Torres Strait region and the Great Barrier Reef (GBR). It is the first such System to be considered by the International Maritime Organization (IMO) under the terms of the new SOLAS 74 regulation v/8-1, which entered into force on 1 January 1996 and allows for ship reporting Systems adopted by the Organization to be made mandatory for all, or certain categories of vessels. The REEFREP System, planned for implementation on 1 January 1997, extends for some 900 n.m. or about 1500 km along the Queensland coastline. It will be a VHF radio-based System with radars covering three selected focal points in the Torres Strait, off Cairns and in the southern approaches to the inner route. The System will provide a capability for a single Ship Reporting Centre to interact with shipping, enabling the provision of improved information on the presence, movements and patterns of shipping in the area and the ability to respond more quickly to an incident or pollution should this occur. An interesting feature and a major factor in the System design is the remoteness of most equipment sites and the limited infrastructure available to support communications and data transmission requiring the application of advanced technology and video transmission, solar power generation and software engineering skills of a high order.
Israel Cohen - One of the best experts on this subject based on the ideXlab platform.
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adaptive System identification in the short time fourier transform domain using cross multiplicative transfer function approximation
IEEE Transactions on Audio Speech and Language Processing, 2008Co-Authors: Y Avargel, Israel CohenAbstract:In this paper, we introduce cross-multiplicative transfer function (CMTF) approximation for modeling linear Systems in the short-time Fourier transform (STFT) domain. We assume that the transfer function can be represented by cross-multiplicative terms between distinct subbands. We investigate the influence of cross-terms on a System Identifier implemented in the STFT domain and derive analytical relations between the noise level, data length, and number of cross-multiplicative terms, which are useful for System identification. As more data becomes available or as the noise level decreases, additional cross-terms should be considered and estimated to attain the minimal mean-square error (mse). A substantial improvement in performance is then achieved over the conventional multiplicative transfer function (MTF) approximation. Furthermore, we derive explicit expressions for the transient and steady-state mse performances obtained by adaptively estimating the cross-terms. As more cross-terms are estimated, a lower steady-state mse is achieved, but the algorithm then suffers from slower convergence. Experimental results validate the theoretical derivations and demonstrate the effectiveness of the proposed approach as applied to acoustic echo cancellation.
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System identification in the short time fourier transform domain with crossband filtering
IEEE Transactions on Audio Speech and Language Processing, 2007Co-Authors: Y Avargel, Israel CohenAbstract:In this paper, we investigate the influence of crossband filters on a System Identifier implemented in the short-time Fourier transform (STFT) domain. We derive analytical relations between the number of crossband filters, which are useful for System identification in the STFT domain, and the power and length of the input signal. We show that increasing the number of crossband filters not necessarily implies a lower steady-state mean-square error (mse) in subbands. The number of useful crossband filters depends on the power ratio between the input signal and the additive noise signal. Furthermore, it depends on the effective length of input signal employed for System identification, which is restricted to enable tracking capability of the algorithm during time variations in the System. As the power of input signal increases or as the time variations in the System become slower, a larger number of crossband filters may be utilized. The proposed subband approach is compared to the conventional fullband approach and to the commonly used subband approach that relies on multiplicative transfer function (MTF) approximation. The comparison is carried out in terms of mse performance and computational complexity. Experimental results verify the theoretical derivations and demonstrate the relations between the number of useful crossband filters and the power and length of the input signal
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on multiplicative transfer function approximation in the short time fourier transform domain
IEEE Signal Processing Letters, 2007Co-Authors: Y Avargel, Israel CohenAbstract:The multiplicative transfer function (MTF) approximation is widely used for modeling a linear time invariant System in the short-time Fourier transform (STFT) domain. It relies on the assumption of a long analysis window compared with the length of the System impulse response. In this paper, we investigate the influence of the analysis window length on the performance of a System Identifier that utilizes the MTF approximation. We derive analytic expressions for the minimum mean-square error (MMSE) in the STFT domain and show that the System identification performance does not necessarily improve by increasing the length of the analysis window. The optimal window length, that achieves the MMSE, depends on the signal-to-noise ratio and the length of the input signal. The theoretical analysis is supported by simulation results
Y Avargel - One of the best experts on this subject based on the ideXlab platform.
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adaptive System identification in the short time fourier transform domain using cross multiplicative transfer function approximation
IEEE Transactions on Audio Speech and Language Processing, 2008Co-Authors: Y Avargel, Israel CohenAbstract:In this paper, we introduce cross-multiplicative transfer function (CMTF) approximation for modeling linear Systems in the short-time Fourier transform (STFT) domain. We assume that the transfer function can be represented by cross-multiplicative terms between distinct subbands. We investigate the influence of cross-terms on a System Identifier implemented in the STFT domain and derive analytical relations between the noise level, data length, and number of cross-multiplicative terms, which are useful for System identification. As more data becomes available or as the noise level decreases, additional cross-terms should be considered and estimated to attain the minimal mean-square error (mse). A substantial improvement in performance is then achieved over the conventional multiplicative transfer function (MTF) approximation. Furthermore, we derive explicit expressions for the transient and steady-state mse performances obtained by adaptively estimating the cross-terms. As more cross-terms are estimated, a lower steady-state mse is achieved, but the algorithm then suffers from slower convergence. Experimental results validate the theoretical derivations and demonstrate the effectiveness of the proposed approach as applied to acoustic echo cancellation.
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System identification in the short time fourier transform domain with crossband filtering
IEEE Transactions on Audio Speech and Language Processing, 2007Co-Authors: Y Avargel, Israel CohenAbstract:In this paper, we investigate the influence of crossband filters on a System Identifier implemented in the short-time Fourier transform (STFT) domain. We derive analytical relations between the number of crossband filters, which are useful for System identification in the STFT domain, and the power and length of the input signal. We show that increasing the number of crossband filters not necessarily implies a lower steady-state mean-square error (mse) in subbands. The number of useful crossband filters depends on the power ratio between the input signal and the additive noise signal. Furthermore, it depends on the effective length of input signal employed for System identification, which is restricted to enable tracking capability of the algorithm during time variations in the System. As the power of input signal increases or as the time variations in the System become slower, a larger number of crossband filters may be utilized. The proposed subband approach is compared to the conventional fullband approach and to the commonly used subband approach that relies on multiplicative transfer function (MTF) approximation. The comparison is carried out in terms of mse performance and computational complexity. Experimental results verify the theoretical derivations and demonstrate the relations between the number of useful crossband filters and the power and length of the input signal
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on multiplicative transfer function approximation in the short time fourier transform domain
IEEE Signal Processing Letters, 2007Co-Authors: Y Avargel, Israel CohenAbstract:The multiplicative transfer function (MTF) approximation is widely used for modeling a linear time invariant System in the short-time Fourier transform (STFT) domain. It relies on the assumption of a long analysis window compared with the length of the System impulse response. In this paper, we investigate the influence of the analysis window length on the performance of a System Identifier that utilizes the MTF approximation. We derive analytic expressions for the minimum mean-square error (MMSE) in the STFT domain and show that the System identification performance does not necessarily improve by increasing the length of the analysis window. The optimal window length, that achieves the MMSE, depends on the signal-to-noise ratio and the length of the input signal. The theoretical analysis is supported by simulation results
Hamidreza Modares - One of the best experts on this subject based on the ideXlab platform.
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adaptive optimal control of unknown constrained input Systems using policy iteration and neural networks
IEEE Transactions on Neural Networks, 2013Co-Authors: Hamidreza Modares, Frank L Lewis, Mohammadbagher NaghibisistaniAbstract:This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input Systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the System dynamics is obviated by employing a novel NN Identifier in conjunction with the actor and critic NNs. It is shown how the Identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the Identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the Identifier weights. Stability of the whole System consisting of the actor, critic, System state, and System Identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.
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Memory-Augmented System Identification With Finite-Time Convergence
IEEE Control Systems Letters, 1Co-Authors: Amin Vahidi-moghaddam, Majid Mazouchi, Hamidreza ModaresAbstract:This letter presents a memory-augmented System Identifier with finite-time convergence for continuous-time uncertain nonlinear Systems. A memory of events with significant effect on the performance of the Identifier is formed, and reuse of historic data is leveraged in the Identifier’s update law to guarantee that the Identifier’s error converges to zero in finite time. An easy-to-check and verifiable metric defined on samples collected along the System’s trajectories is provided to certify the finite-time convergence. The robustness of the proposed Identifier to mismatched modeling error is analyzed. Finally, a simulation example verifies the efficiency of the proposed Identifier.
Dirk Söffker - One of the best experts on this subject based on the ideXlab platform.
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Data-driven stabilization of unknown nonlinear dynamical Systems using a cognition-based framework
Nonlinear Dynamics, 2016Co-Authors: Xi Nowak, Dirk SöffkerAbstract:In this paper, a cognitive stabilizer concept is introduced. The framework acts as an adaptive discrete control approach. The aim of the cognitive stabilizer is to stabilize a specific class of unknown nonlinear MIMO Systems. The cognitive stabilizer is able to gain useful local knowledge of the System assumed as unknown. The approach is able to define autonomously suitable control inputs to stabilize the System. The System class to be considered is described by the following assumptions: unknown input/output behavior, fully controllable, stable zero dynamics, and measured state vector. The cognitive stabilizer is realized by its four main modules: (1) “perception and interpretation” using System Identifier for the System local dynamic online identification and multi-step-ahead prediction; (2) “expert knowledge” relating to the quadratic stability criterion to guarantee the stability of the considered motion of the controlled System; (3) “planning” to generate a suitable control input sequence according to a certain cost function; (4) “execution” to generate the optimal control input in a corresponding feedback form. Each module can be realized using different methods. Two realizations will be stated in this paper. Using the cognitive stabilizer, the control goal can be achieved efficiently without an individual control design process for different kinds of unknown Systems. Numerical examples (e.g., a chaotic nonlinear MIMO System–Lorenz System) demonstrate the successful application of the proposed methods.
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Data-driven stabilization of unknown nonlinear dynamical Systems using a cognition-based framework
Nonlinear Dynamics, 2016Co-Authors: Xi Nowak, Dirk SöffkerAbstract:In this paper, a cognitive stabilizer concept is introduced. The framework acts as an adaptive discrete control approach. The aim of the cognitive stabilizer is to stabilize a specific class of unknown nonlinear MIMO Systems. The cognitive stabilizer is able to gain useful local knowledge of the System assumed as unknown. The approach is able to define autonomously suitable control inputs to stabilize the System. The System class to be considered is described by the following assumptions: unknown input/output behavior, fully controllable, stable zero dynamics, and measured state vector. The cognitive stabilizer is realized by its four main modules: (1) “perception and interpretation” using System Identifier for the System local dynamic online identification and multi-step-ahead prediction; (2) “expert knowledge” relating to the quadratic stability criterion to guarantee the stability of the considered motion of the controlled System; (3) “planning” to generate a suitable control input sequence according to a certain cost function; (4) “execution” to generate the optimal control input in a corresponding feedback form. Each module can be realized using different methods. Two realizations will be stated in this paper. Using the cognitive stabilizer, the control goal can be achieved efficiently without an individual control design process for different kinds of unknown Systems. Numerical examples (e.g., a chaotic nonlinear MIMO System–Lorenz System) demonstrate the successful application of the proposed methods.
