The current identification algorithm using sonar signal parameters of bandwidth, frequency, duration and pulse waveform which are easy to detect and imitation, to identify the identity of sonar signal, resulting in pa...
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The current identification algorithm using sonar signal parameters of bandwidth, frequency, duration and pulse waveform which are easy to detect and imitation, to identify the identity of sonar signal, resulting in part of the sonar signal identity is not easy to distinguish. Therefore, an algorithm based on signal feature extraction and digital watermarking is proposed to recognize the uncertain sonar signals. The algorithm embeds the digital watermark into the detection signal from the uncertain sonar. The identity of the signal is recognized by detecting whether the received signal contains watermarks. Experimental results showed that the proposed algorithm can effectively improve the recognition performance of sonar signal source.
This work analyzes the performance of several black box nonlinear model identification techniques for input-output models with polynomial nonlinearities on a benchmark identification problem. The case study, proposed ...
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This work analyzes the performance of several black box nonlinear model identification techniques for input-output models with polynomial nonlinearities on a benchmark identification problem. The case study, proposed in Schoukens, Suykens, and Ljung (2008), concerns a nonlinear SISO electronic system with a Wiener-Hammerstein structure, originally documented in Vandersteen (1997). The objective being the obtainment of an accurate simulation model, capable of replicating the dynamic behavior of the system without using past measured output data, various output-error approaches have been tested and compared with more standard equation-error techniques. The provided analysis shows that excellent modeling performance can be obtained with these methods even without explicitly taking into account the block structure of the nonlinear system. (C) 2012 Elsevier Ltd. All rights reserved.
The paper describes an approach to the identification and control of the cross-directional (CD) properties of web-forming processes, and its application to polymer him extrusion. Discrete orthonormal Chebyshev polynom...
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The paper describes an approach to the identification and control of the cross-directional (CD) properties of web-forming processes, and its application to polymer him extrusion. Discrete orthonormal Chebyshev polynomials are used to model the CD behaviour. This allows a Chebyshev domain estimator for CD profile response to be formulated, and constrained Chebyshev domain optimisation of the CD profile to be used. The result is an identification and control procedure for the CD profile problem which is consistent in its use of the Chebyshev framework, and which is also applicable to other web-forming processes. (C) 1998 Elsevier Science Ltd. All rights reserved.
A water-gas lubricated hydrostatic spindle system with big thrust disc, supported by a water-lubricated journal bearing and a gaslubricated thrust bearing, is developed to improve spindle stiffness and to reduce frict...
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A water-gas lubricated hydrostatic spindle system with big thrust disc, supported by a water-lubricated journal bearing and a gaslubricated thrust bearing, is developed to improve spindle stiffness and to reduce friction power of spindle. A new identification algorithm is derivated to identify simultaneously the eighteen dynamic coefficients of the coupled journal and thrust bearings. A comparison with the available experimental results reveals that the bearing coefficients is well estimated by the present theoretical model. The results also indicate that, the estimated bearing parameters by regularization solution are in good agreement with the assumed values;while it has opposite effect by directed solution for discrete ill-posed problems. It is concluded that, the identification accuracy of bearing parameters is govern by the measured errors;contrast to unbalance mass error and unbalance phase error, the displacement error leads the maximal percentage deviations of estimated coefficients.
Many patients with diabetes experience high variability in glucose concentrations that includes prolonged hyperglycemia or hypoglycemia. Models predicting a subject's future glucose concentrations can be used for ...
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Many patients with diabetes experience high variability in glucose concentrations that includes prolonged hyperglycemia or hypoglycemia. Models predicting a subject's future glucose concentrations can be used for preventing such conditions by providing early alarms. This paper presents a time-series model that captures dynamical changes in the glucose metabolism. Adaptive system identification is proposed to estimate model parameters which enable the adaptation of the model to inter-/intra-subject variation and glycemic disturbances. It consists of on-line parameter identification using the weighted recursive least squares method and a change detection strategy that monitors variation in model parameters. Univariate models developed from a subject's continuous glucose measurements are compared to multivariate models that are enhanced with continuous metabolic, physical activity and lifestyle information from a multi-sensor body monitor. A real life application for the proposed algorithm is demonstrated on early (30 min in advance) hypoglycemia detection. (C) 2012 Elsevier Ltd. All rights reserved.
This paper tackles the issue of model consistency in identification procedures for SISO LTI compartmental systems of given order. In fact, in this case, the identified model must have a compartmental realization. In t...
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This paper tackles the issue of model consistency in identification procedures for SISO LTI compartmental systems of given order. In fact, in this case, the identified model must have a compartmental realization. In this paper, a finite set of constraints ensuring the existence of such a realization of a given order is proposed, in the case of real distinct eigenvalues. As a byproduct, the same set of conditions also ensure nonnegativity of the estimated impulse response. (C) 2002 Elsevier Science Ltd. All rights reserved.
Today's model-based dynamic positioning (DP) systems require that the ship and thruster dynamics are known with some accuracy in order to use linear quadratic optimal control theory. However, it is difficult to id...
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Today's model-based dynamic positioning (DP) systems require that the ship and thruster dynamics are known with some accuracy in order to use linear quadratic optimal control theory. However, it is difficult to identify the mathematical model of a dynamically positioned (DP) ship, since the ship is not persist entry excited under DP. In add it ion, the ship parameter-estimation problem is nonlinear and multivariable, with only position and thruster state measurements available for parameter estimation. The process and measurement noise must also be modeled in order to avoid parameter drift due to environmental disturbances and sensor failure. This article discusses an off-line parallel extended Kalman filter (EKF) algorithm utilizing two measurement series in parallel to estimate the parameters in the DP ship model. Full-scale experiments with a supply vessel are used to demonstrate the convergence and robustness of the proposed parameter estimator.
The problem of identifying essentially nonstationary parameters of linear dynamic systems is considered. algorithms that ensure tracking of the change in the parameters with errors proportional to their highest deriva...
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The problem of identifying essentially nonstationary parameters of linear dynamic systems is considered. algorithms that ensure tracking of the change in the parameters with errors proportional to their highest derivatives with respect to time are proposed using methods of local approximation. A recursion realization is proposed for one of the versions of the algorithms. Theoretical results which give an estimate of the accuracy of the proposed algorithms are presented.
In the context of bounded-error estimation, it is customary to assume that the error between the model output and output data should lie between some known prior bounds. In this paper, it is also assumed that the fact...
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In the context of bounded-error estimation, it is customary to assume that the error between the model output and output data should lie between some known prior bounds. In this paper, it is also assumed that the factors characterizing the experiments that have been carried out (e.g., measurement times) are uncertain, with known prior bounds. An algorithm based on interval analysis is used to characterize the set of all values of the parameter vector to be estimated that are consistent with these hypotheses. This is performed in a guaranteed way, even when the model output is a nonlinear function of the parameters and factors characterizing the experiments. (C) 1999 Elsevier Science Ltd. All rights reserved.
The need to identify and discern identities gradually rises in social and economic interactions along with following the rules and other fields. Accordingly, biometric data as basic information of an individual's ...
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ISBN:
(纸本)9781467365079
The need to identify and discern identities gradually rises in social and economic interactions along with following the rules and other fields. Accordingly, biometric data as basic information of an individual's physical and behavioral features play an important role. This article reviews the duties of face recognition, thence to make a comparison among the function of five face-recognition algorithm, called PCA, ICA, FLDA, Eigen features, and Eigen face, in which the basis for comparing these algorithms was the rate of face identification accuracy. The mentioned algorithms have been analyzed in the databases of ORL, AR, FERET, and YALE. This article shows that ICA Algorithm give better results than the other algorithms in the introduced databases.
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