Signal denoising in engineering problems through the minimum gradient method

Vieira, D.A.G.; Travassos, Xisto Lucas; Saldanha, R.R.; Palade, Vasile

doi:10.1016/j.neucom.2009.01.004

Cited by 9 publications

(7 citation statements)

References 20 publications

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“…The noise needs to be considered in the training process to reduce its impact on the output results. The NN is a flexible denoising framework in signal processing [ 28 ] as long as the noise is considered in the training dataset. It can reduce the noise by merely replacing the noisy signal to the training dataset.…”

Section: Real Scene Experimentsmentioning

confidence: 99%

Radar Cross Section Near-Field to Far-Field Prediction for Isotropic-Point Scattering Target Based on Regression Estimation

Yang

Zhang

et al. 2020

Sensors

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Radar cross section near-field to far-field transformation (NFFFT) is a well-established methodology. Due to the testing range constraints, the measured data are mostly near-field. Existing methods employ electromagnetic theory to transform near-field data into the far-field radar cross section, which is time-consuming in data processing. This paper proposes a flexible framework, named Neural Networks Near-Field to Far-Filed Transformation (NN-NFFFT). Unlike the conventional fixed-parameter model, the near-field RCS to far-field RCS transformation process is viewed as a nonlinear regression problem that can be solved by our fast and flexible neural network. The framework includes three stages: Near-Field and Far-field dataset generation, regression estimator training, and far-field data prediction. In our framework, the Radar cross section prior information is incorporated in the Near-Field and Far-field dataset generated by a group of point-scattering targets. A lightweight neural network is then used as a regression estimator to predict the far-field RCS from the near-field RCS observation. For the target with a small RCS, the proposed method also has less data acquisition time. Numerical examples and extensive experiments demonstrate that the proposed method can take less processing time to achieve comparable accuracy. Besides, the proposed framework can employ prior information about the real scenario to improve performance further.

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Section: Real Scene Experimentsmentioning

confidence: 99%

Radar Cross Section Near-Field to Far-Field Prediction for Isotropic-Point Scattering Target Based on Regression Estimation

Yang

Zhang

et al. 2020

Sensors

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“…To achieve the desired filtering properties, the NN learning is modelled as a bi-objective optimization problem which considers simultaneously the minimization of the empirical error and network output complexity [10]:…”

Section: The Parallel Layer Perceptron With Complexity Controlmentioning

confidence: 99%

“…Moreover, the PLP model had a better performance during all simulations, giving the better correlation and smaller prediction mean square error in comparison with MLP model results. It is probably due to the noise filtering property of the bi-objective training applied to the PLP, as described in [10].…”

Section: The Parallel Layer Perceptron With Complexity Controlmentioning

confidence: 99%

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Estimating the Convection Heat Transfer Coefficient of a Run-Out Cooling Table in a Steel-Making Process by Neural Networks

Barcelos

Vieira²,

Saldanha

et al. 2012

2012 11th International Conference on Machine Learning and Applications

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This paper presents a real-world application of neural networks. This application considers the estimation of the convection heat transfer coefficient of a run-out cooling table in a steel-making process. Firstly, data of several runs were collected considering the cooling table variables and the reached temperatures. Afterwards, using numerical models and optimization, the equivalent heat transfer coefficient is evaluated for each run. Finally, a neural network is applied to define the relationships between the process variables (thickness, water flow, among others) and the estimated heat transfer coefficient. The results are compared with some models derived from the process physics.

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“…3. The proposed configuration is tested to filter the noise of the scattered wave from a cylindrical air inclusion buried in a non-homogenous host medium, Vieira et al (2009). Tables 3 and 4 considers white and colored Gaussian noise respectively.…”

Section: Denoising Ground Penetrating Radar Datamentioning

confidence: 99%

Recent Advances in Neural Networks Structural Risk Minimization Based on Multiobjective Complexity Control Algorithms

Vieira¹,

Vasconcelos²,

Saldanha³

2010

Machine Learning

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Signal denoising in engineering problems through the minimum gradient method

Cited by 9 publications

References 20 publications

Radar Cross Section Near-Field to Far-Field Prediction for Isotropic-Point Scattering Target Based on Regression Estimation

Radar Cross Section Near-Field to Far-Field Prediction for Isotropic-Point Scattering Target Based on Regression Estimation

Estimating the Convection Heat Transfer Coefficient of a Run-Out Cooling Table in a Steel-Making Process by Neural Networks

Recent Advances in Neural Networks Structural Risk Minimization Based on Multiobjective Complexity Control Algorithms

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