Support Vector Regression Model for Measuring the Permittivity of Asphalt Concrete

Meng, Jicheng; Gao, Yuanci; Shi, Yue

doi:10.1109/lmwc.2007.910462

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2008

2024

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Cited by 10 publications

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“…Compared to traditional ANN, SVM (Support Vector Machine) exhibits the major advantage of global optimization, higher generalization capability and input-dimension independence. Alternatively, SVM unfold a promising means to estimate nonlinear system models accurately [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Faulty Cable Network Using Time-Domain Reflectometry

Zhang¹,

Zhang²,

Liu³

2013

PIER

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Abstract-Based on Time-Domain Reflectometry (TDR) technique, a novel method which could locate faults on the coaxial cable distribution network by using Support Vector Machine (SVM) is proposed in this paper. This approach allows the faulty network to be reconstructed by estimating the lengths of branches. A State-transition Matrix model is employed to simulate the TDR response at any port and evaluate the transfer function between two points. SVM is used to solve the inversion problem through training datasets created by the State-transition matrix model. Compared to the existing reflectometry methods, our proposed method can tackle multiple faults in the complex cable networks. Numerical and experimental results pointing out the performance of the SVM model in locating faults are reported.

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