Two-Stage Automated Operational Modal Analysis Based on Power Spectrum Density Transmissibility and Support-Vector Machines

2021 ◽  
pp. 2150068
Author(s):  
Zhi-Wei Chen ◽  
Kui-Ming Liu ◽  
Wang-Ji Yan ◽  
Jian-Lin Zhang ◽  
Wei-Xin Ren
Keyword(s):  
Support Vector Machines ◽  
Power Spectrum ◽  
Modal Analysis ◽  
Power Spectrum Density ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Identification ◽  
Two Stage ◽  
Vector Machines ◽  
Spectrum Density

Power spectrum density transmissibility (PSDT) functions have attracted widespread attention in operational modal analysis (OMA) because of their robustness to excitations. However, the selection of the peaks and stability axes are still subjective and requires further investigation. To this end, this study took advantage of PSDT functions and support-vector machines (SVMs) to propose a two-stage automated modal identification method. In the first stage, the automated identification of peaks is achieved by introducing the peak slope (PS) as a critical index and determining its threshold using the SVM classifier. In the second stage, the automated identification of the stability axis is achieved by introducing the relative difference coefficients (RDCs) of the modal parameters as indicators and determining their thresholds using the SVM classifier. To verify its feasibility and accuracy, the proposed method was applied to an ASCE-benchmark structure in the laboratory and in a high-rise building installed with a structural health monitoring system (SHMS). The results showed that the automated identification method could effectively eliminate spurious modes and accurately identify the closely spaced modes. The proposed method can be automatically applied without manual intervention, and it is robust to noise. It is promising for application to the real-time condition evaluation of civil structures installed with SHMSs.

scholarly journals Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

2003 ◽  
Vol 15 (7) ◽  
pp. 1667-1689 ◽  
Author(s):  
S. Sathiya Keerthi ◽  
Chih-Jen Lin
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Heuristic Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Linear Svm ◽  
Generalization Errors

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and the kernel width σ. This letter analyzes the behavior of the SVM classifier when these hyper parameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.

scholarly journals Correlation Kernels for Support Vector Machines Classification with Applications in Cancer Data

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Hao Jiang ◽  
Wai-Ki Ching
Keyword(s):  
Support Vector Machines ◽  
Positive Semidefinite ◽  
Superior Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Correlation Kernel ◽  
Cancer Data ◽  
Tumor Tissues ◽  
Vector Machines

High dimensional bioinformatics data sets provide an excellent and challenging research problem in machine learning area. In particular, DNA microarrays generated gene expression data are of high dimension with significant level of noise. Supervised kernel learning with an SVM classifier was successfully applied in biomedical diagnosis such as discriminating different kinds of tumor tissues. Correlation Kernel has been recently applied to classification problems with Support Vector Machines (SVMs). In this paper, we develop a novel and parsimonious positive semidefinite kernel. The proposed kernel is shown experimentally to have better performance when compared to the usual correlation kernel. In addition, we propose a new kernel based on the correlation matrix incorporating techniques dealing with indefinite kernel. The resulting kernel is shown to be positive semidefinite and it exhibits superior performance to the two kernels mentioned above. We then apply the proposed method to some cancer data in discriminating different tumor tissues, providing information for diagnosis of diseases. Numerical experiments indicate that our method outperforms the existing methods such as the decision tree method and KNN method.

Classification of High Resolution Sar Imagery Using Local Indicators of Spatial Association

2013 ◽  
Vol 333-335 ◽  
pp. 1080-1084
Author(s):  
Zhang Fei ◽  
Ye Xi
Keyword(s):  
Support Vector Machines ◽  
High Resolution ◽  
Classification Accuracy ◽  
Spatial Association ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Good Classification ◽  
Sar Imagery

In this paper, we will propose a novel classification method of high-resolution SAR using local autocorrelation and Support Vector Machines (SVM) classifier. The commonly applied spatial autocorrelation indexes, called Moran's Index; Geary's Index, Getis's Index, will be used to depict the feature of the land-cover. Then, the SVM based on these indexes will be applied as the high-resolution SAR classifier. A Cosmo-SkyMed scene in ChengDu city, China is used for our experiment. It is shown that the method proposed can lead to good classification accuracy.

A New SVM Kernel for Keyword Spotting Using Confidence Measures

2015 ◽  
Vol 24 (03) ◽  
pp. 1550010 ◽  
Author(s):  
Yassine Ben Ayed
Keyword(s):  
Support Vector Machines ◽  
Hidden Markov ◽  
Support Vector ◽  
Svm Classifier ◽  
Keyword Spotting ◽  
Confidence Measure ◽  
Confidence Measures ◽  
Vector Machines ◽  
Harmonic Means ◽  
Speech Recognizer

In this paper, we propose an alternative keyword spotting method relying on confidence measures and support vector machines. Confidence measures are computed from phone information provided by a Hidden Markov Model based speech recognizer. We use three kinds of techniques, i.e., arithmetic, geometric and harmonic means to compute a confidence measure for each word. The acceptance/rejection decision of a word is based on the confidence vector processed by the SVM classifier for which we propose a new Beta kernel. The performance of the proposed SVM classifier is compared with spotting methods based on some confidence means. Experimental results presented in this paper show that the proposed SVM classifier method improves the performances of the keyword spotting system.

Combined application of Power Spectrum Centroid and Support Vector Machines for measurement improvement in Optical Scanning Systems

2014 ◽  
Vol 98 ◽  
pp. 37-51 ◽  
Author(s):  
Wendy Flores-Fuentes ◽  
Moises Rivas-Lopez ◽  
Oleg Sergiyenko ◽  
Felix F. Gonzalez-Navarro ◽  
Javier Rivera-Castillo ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Power Spectrum ◽  
Support Vector ◽  
Optical Scanning ◽  
Combined Application ◽  
Vector Machines ◽  
Scanning Systems

Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy

2021 ◽  
Vol 12 (3) ◽  
pp. 96-115
Author(s):  
Manal Tantawi ◽  
Aya Naser ◽  
Howida Shedeed ◽  
Mohammed Fahmy Tolba
Keyword(s):  
Support Vector Machines ◽  
Epileptic Seizures ◽  
Bat Algorithm ◽  
Line Length ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Vector Machines ◽  
Electroencephalogram Eeg ◽  
Source Of Information

Electroencephalogram (EEG) signals are a valuable source of information for detecting epileptic seizures. However, monitoring EEG for long periods of time is very exhausting and time consuming. Thus, detecting epilepsy in EEG signals automatically is highly appreciated. In this study, three classes, namely normal, interictal (out of seizure time), and ictal (during seizure), are considered. Moreover, a comparative study is provided for the efficient features in literature resulting in a suggested combination of only three discriminative features, namely R'enyi entropy, line length, and energy. These features are calculated from each of the EEG sub-bands. Finally, support vector machines (SVM) classifier optimized using BAT algorithm (BAT-SVM) is introduced by this study for discriminating between the three classes. Experiments were conducted using Andrzejak database. The accomplished experiments and comparisons in this study emphasize the superiority of the proposed BAT-SVM along with the suggested feature set in achieving the best results.

scholarly journals Understanding Power Spectrum Density Transmissibility

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Yu Zhang ◽  
Tong Zhu ◽  
Jing Zhou
Keyword(s):  
Experimental Data ◽  
Power Spectrum ◽  
Modal Analysis ◽  
Load Condition ◽  
Power Spectrum Density ◽  
Primary Data ◽  
Modal Parameters ◽  
Complex Frequency ◽  
Spectrum Density ◽  
Local Reference

Power spectrum density transmissibility (PSDT) is a type of complex frequency domain function proposed recently. It describes the relation between cross-spectra of system outputs. Since PSDTs with same local-reference degree of freedom (DOF) combination but with different transferring output DOFs cross each other at the system’s poles under certain load condition, the functions have been used as the primary data in operational modal analysis (OMA) to extract modal parameters, and such technique is named as PSDT-based OMA (PSDTOMA). Because PSDT is a concept that appears recently, researches on which, especially, in-depth discussions aim at the essence and properties of PSDT are very rare. For appropriate application of PSDTOMA, it is necessary to perform further study on such problem obviously. In this paper, two paths to get PSDT, which, respectively, are referred to as PSDT estimator and PSDT syntheticism, are given firstly; some properties about PSDT are explored based on the PSDT syntheticism, and the relation between PSDT and single reference transmissibility function (STF) is analyzed. Finally, the above conclusions are verified with numerical values and experimental data.

Sign in / Sign up

Export Citation Format

Share Document