GPR Data Regression and Clustering by the Fuzzy Support Vector Machine and Regression

Hosseinzadeh, Shahram; Shaghaghi, M.

doi:10.2528/pierm20050805

Cited by 8 publications

(8 citation statements)

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“…In this section, the proposed surrogate modelling approach is compared to the state-of-the-art techniques utilized for buried object characterization. The benchmark methods include CNN 14 , 15 , 31 , 39 , MLP 3 , 11 , 13 , and SVRM 44 . Also, two of the benchmark cases are analyzed using the M2LP framework operating on different data sets generated by PCA.…”

Section: Methodsmentioning

confidence: 99%

Buried object characterization by data-driven surrogates and regression-enabled hyperbolic signature extraction

Yurt

Torpi

Kızılay

et al. 2023

Sci Rep

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This work addresses artificial-intelligence-based buried object characterization using FDTD-based electromagnetic simulation toolbox of a Ground Penetrating Radar (GPR) to generate B-scan data. In data collection, FDTD-based simulation tool, gprMax is used. The task is to estimate geophysical parameters of a cylindrical shape object of various radii, buried at different positions in the dry soil medium simultaneously and independently of each other. The proposed methodology capitalizes on a fast and accurate data-driven surrogate model developed for object characterization in terms of its vertical and lateral position, and the size. The surrogate is constructed in a computationally efficient manner as compared to methodologies using 2D B-scan image. This is achieved by operating at the level of hyperbolic signatures extracted from the B-scan data through linear regression, which effectively reduces the dimensionality and the size of data. The proposed methodology relies on reducing of 2D B-scan image to 1D data including variation of reflected electric fields’ amplitudes with respect to the scanning aperture. The input of the surrogate model is the extracted hyperbolic signature obtained through linear regression executed on the background subtracted B-scan profiles. The hyperbolic signatures encode information about the geophysical parameters of the buried object, including depth, lateral position, and radius, all of which can be extracted using proposed methodology. Parametric estimation of the object radius and the estimation of the location parameters simultaneously is a challenging problem. Applying the application of processing steps on B-scan profiles incurs high computational costs, which is a limitation of the current methodologies. The metamodel itself is rendered using a novel deep-learning-based modified multilayer perceptron (M2LP) framework. The presented object characterization technique is favourably benchmarked against the state-of-the-art regression techniques, including Multilayer Perceptron (MLP), Support Vector Regression Machine (SVRM), and Convolutional Neural Network (CNN). The verification results demonstrate the average mean absolute error of 10 mm, and the average relative error of 8 percent, both corroborating the relevance of the proposed M2LP framework. In addition, the presented methodology provides a well-structured relation between the geophysical parameters of object and the extracted hyperbolic signatures. For the sake of supplementary verification under realistic scenarios, it is also applied for scenarios involving noisy data. The environmental and internal noise of the GPR system and their effect is analyzed as well. Furthermore, the proposed surrogate modeling approach is validated using measurement data, which is indicative of suitability of the approach to handle physical measurements as data sources.

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Section: Methodsmentioning

confidence: 99%

Buried object characterization by data-driven surrogates and regression-enabled hyperbolic signature extraction

Yurt

Torpi

Kızılay

et al. 2023

Sci Rep

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“…Surrogate-based characterization of buried objects has been a subject of extensive research over the last years. Some of popular methods utilized in this context include SVRM [32], MLP [3,9,11], GP regression [17] and CNN [12,13,30,33]. These methods are briefly characterized below, and will be used as benchmark methods compared to the modelling approach introduced in Section III.B.…”

Section: A State-of-the-art Of Surrogate-based Characterization Of Bu...mentioning

confidence: 99%

“…Another surrogate modeling technique in the machine learning class is support vector regression machine (SVRM), which has been applied not only to object and material type detection through classification [7,11,16] by using support vector machine (SVM), but also to prediction of soil permittivity and depth [32] in regression approach. It belongs to the group of supervised statistical learning methods [7,11,16,32]. MLP mimics biological neural systems in the form of interconnected neurons.…”

Section: A State-of-the-art Of Surrogate-based Characterization Of Bu...mentioning

confidence: 99%

“…The learning rate has been set to 10 -3 until maximum epoch number reached to 300. Other userdefined parameters, such as the size and the number of the convolution filter (32,64,128) and pooling layer are set up as recommended in the literature in the context of buried object detection and characterization [13]. At the same time, it should be mentioned that-in the cited works-the input data is twodimensional, therefore, the filters of the CNN layers are of the corresponding dimensionality, whereas the filters of the CNN model used in this work are one-dimensional.…”

Section: A State-of-the-art Of Surrogate-based Characterization Of Bu...mentioning

confidence: 99%

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Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models

et al. 2023

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This work addresses artificial-intelligence-based buried object characterization using 3-D fullwave electromagnetic simulations of a ground penetrating radar (GPR). The task is to characterize cylindrical shape, perfectly electric conductor (PEC) object buried in various dispersive soil media, and in different positions. The main contributions of this work are (i) development of a fast and accurate data driven surrogate modeling approach for buried objects characterization, (ii) construction of the surrogate model in a computationally efficient manner using small training datasets, (iii) development of a novel deep learning method, time-frequency regression model (TFRM), that employes raw signal (with no pre-processing) to achieve competitive estimation performance. The presented approach is favourably benchmarked against the state-of-the-art regression techniques, including multilayer perceptron (MLP), Gaussian process (GP) regression, support vector regression machine (SVRM), and convolutional neural network (CNN).INDEX TERMS Buried object characterization, ground penetrating radar (GPR), surrogate modeling; microwave modeling; artificial intelligence, A-scan data analysis.

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“…Artificial intelligence (AI ) and machine learning (ML) are promising candidates to effectively address the PV forecasting problem [3]. More specifically, the learning-by-examples (LBE ) paradigm encompasses different supervised learning methodologies [e.g., support vector machines (SVM s), artificial neural networks (ANN s), Gaussian processes (GP s), and deep neural networks (DNN s)] enabling the creation of accurate predictors starting from the information embedded within an off-line generated database of training examples/observations [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In such a framework, different strategies have been proposed in the state-of-the-art to predict the output power of PV plants [3].…”

Section: Introductionmentioning

confidence: 99%

A Two-Step Learning-by-Examples Method for Photovoltaic Power Forecasting

Polo¹

2022

PIER C

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In this paper, an innovative machine learning (ML) approach for the prediction of the output power generated by photovoltaic (PV ) plants is presented. Toward this end, a two-step learning-byexamples (LBE ) strategy based on support vector regression (SVR) is proposed to learn the complex relation among the heterogeneous parameters affecting the energy production of the power plant. More specifically, the first step is aimed at down-scaling the weather forecasts from the standard air temperature and the solar irradiance to the local module temperature and the plane-of-array (POA) irradiance. Then, the second step predicts the output power profile given the down-scaled forecasts estimated at the previous step. The advantages and limitations of the proposed two-step approach have been experimentally analyzed exploiting a set of measurements acquired in a real PV plant. The obtained results are presented and discussed to point out the capabilities of the proposed LBE method to provide robust and reliable power predictions starting from simple weather forecasts.

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GPR Data Regression and Clustering by the Fuzzy Support Vector Machine and Regression

Cited by 8 publications

References 21 publications

Buried object characterization by data-driven surrogates and regression-enabled hyperbolic signature extraction

Buried object characterization by data-driven surrogates and regression-enabled hyperbolic signature extraction

Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models

A Two-Step Learning-by-Examples Method for Photovoltaic Power Forecasting

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