Application of signal processing and support vector machine to transverse cracking detection in asphalt pavement

2021 ◽  
Vol 28 (8) ◽  
pp. 2451-2462
Author(s):  
Qun Yang ◽  
Shi-shi Zhou ◽  
Ping Wang ◽  
Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Signal Processing ◽  
Asphalt Pavement ◽  
Support Vector ◽  
Transverse Cracking

scholarly journals A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuancang Wang ◽  
Jing Zhao ◽  
Qiqi Li ◽  
Naren Fang ◽  
Peicheng Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Performance Prediction ◽  
Asphalt Pavement ◽  
Pavement Performance ◽  
Support Vector ◽  
Grey Relation Analysis ◽  
Relation Analysis ◽  
Pavement Performance Prediction ◽  
Grey Relation

Pavement performance prediction is a crucial issue in big data maintenance. This paper develops a hybrid grey relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. The prediction model can solve the shortcomings of the traditional model including a single consideration factor, a short prediction period, and easy overfitting. GAR is employed in selecting the main factors affecting the performance of asphalt pavement. The SVR is performed to predict the performance. Finally, the data collected from the weather station installed on Guangyun Expressway were adopted to verify the validity of the GRA-SVR model. Meanwhile, the contrast with the grey model (GM (1, 1)), genetic algorithm optimization BP[[parms resize(1),pos(50,50),size(200,200),bgcol(156)]]081%, −0.823%, 1.270%, and −4.569%, respectively. The study concluded that the nonlinear and multivariate prediction model established by GRA-SVR has higher precision and operability, which can be used in long-period pavement performance prediction.

Indexing and classifying snore characteristics using Support Vector Machine and integrated signal processing algorithm

2018 ◽  
Author(s):  
Jessie R. Balbin ◽  
Ernesto M. Vergara ◽  
Ross Junior S. Calma ◽  
Nicole Marie Antonette A. Cuevas ◽  
James Erwin V. Paningbatan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Signal Processing ◽  
Processing Algorithm ◽  
Support Vector ◽  
Signal Processing Algorithm

The Research of Signal Processing in Ultrasonic Nondestructive Testing and Evaluation

2013 ◽  
Vol 712-715 ◽  
pp. 2069-2075
Author(s):  
Chun An Ai ◽  
Qiao Wang ◽  
Zhi Gao Xu
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Signal Processing ◽  
Nondestructive Testing ◽  
Ultrasonic Testing ◽  
Processing Technology ◽  
Support Vector ◽  
Ultrasonic Nondestructive Testing ◽  
Qualitative And Quantitative ◽  
Testing And Evaluation

The development of signal processing technology not only improves the reliability of qualitative and quantitative ultrasound detection, but also promotes the sensitivity and precision. This paper introduces the new progress of signal processing technology in application of Ultrasonic Nondestructive Testing, including the basic principle, characteristic and localization of Wavelet Transform, Adaptive Filter Technique, Artificial Neural Network and Support Vector Machine application in Ultrasonic Testing, and the trend of development in the future.

scholarly journals Support Vector Machine-Based EMG Signal Classification Techniques: A Review

2019 ◽  
Vol 9 (20) ◽  
pp. 4402 ◽  
Author(s):  
Diana C. Toledo-Pérez ◽  
Juvenal Rodríguez-Reséndiz ◽  
Roberto A. Gómez-Loenzo ◽  
J. C. Jauregui-Correa
Keyword(s):  
Support Vector Machine ◽  
Signal Processing ◽  
Support Vector Machines ◽  
Feature Vector ◽  
Signal Classification ◽  
Support Vector ◽  
Classification Techniques ◽  
Emg Signal ◽  
Vector Machines ◽  
Electromyographic Signals

This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

POWER DISTURBANCES PATTERN RECOGNITION USING SUPPORT VECTOR MACHINE

2011 ◽  
Vol 08 (01) ◽  
pp. 53-64
Author(s):  
K. MANIMALA ◽  
K. SELVI ◽  
R. AHILA
Keyword(s):  
Support Vector Machine ◽  
Signal Processing ◽  
Fourier Transform ◽  
Power Quality ◽  
Hilbert Transform ◽  
Noise Analysis ◽  
Support Vector ◽  
Classification Problems ◽  
Processing Techniques ◽  
Signal Processing Techniques

Recently, many signal processing techniques, such as fast Fourier transform, short-time Fourier transform, wavelet transform (WT), and wavelet packet transform (WPT), have been applied to detect, identify, and classify power quality (PQ) disturbances. For research on PQ analysis, it is critical to apply the appropriate signal processing techniques and classifier to solve PQ problems. The aim of this paper is to develop a classification method based on the combination of Hilbert transform (HT) and support vector machine (SVM) for the assessment of power quality events. Recent data mining literature has shown that support vector machine methods generally outperform traditional statistical and neural methods in classification problems involving power disturbance signals. The features obtained from the Hilbert transform are distinct, understandable and immune to noise. Analysis is presented to verify that the merits of HT and SVM combination make it adequate for PQ analysis when compared with the existing techniques in the literature.

scholarly journals An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter-Based Feature Extraction

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Nhat-Duc Hoang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Least Squares ◽  
Classification Accuracy ◽  
Asphalt Pavement ◽  
Support Vector ◽  
Accuracy Rate ◽  
Pothole Detection ◽  
Steerable Filter

This study establishes an artificial intelligence (AI) model for detecting pothole on asphalt pavement surface. Image processing methods including Gaussian filter, steerable filter, and integral projection are utilized for extracting features from digital images. A data set consisting of 200 image samples has been collected to train and validate the predictive performance of two machine learning algorithms including the least squares support vector machine (LS-SVM) and the artificial neural network (ANN). Experimental results obtained from a repeated subsampling process with 20 runs show that both LS-SVM and ANN are capable methods for pothole detection with classification accuracy rate larger than 85%. In addition, the LS-SVM has achieved the highest classification accuracy rate (roughly 89%) and the area under the curve (0.96). Accordingly, the proposed AI approach used with LS-SVM can be very potential to assist transportation agencies and road inspectors in the task of pavement pothole detection.

Sign in / Sign up

Export Citation Format

Share Document