scholarly journals A new three-dimensional magnetopause model with a support vector regression machine and a large database of multiple spacecraft observations

2013 ◽  
Vol 118 (5) ◽  
pp. 2173-2184 ◽  
Author(s):  
Y. Wang ◽  
D. G. Sibeck ◽  
J. Merka ◽  
S. A. Boardsen ◽  
H. Karimabadi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Three Dimensional ◽  
Support Vector ◽  
Large Database ◽  
Multiple Spacecraft

Damaged cable identification in cable-stayed bridge from bridge deck strain measurements using support vector machine

2022 ◽  
pp. 136943322110499
Author(s):  
Jianying Ren ◽  
Bing Zhang ◽  
Xinqun Zhu ◽  
Shaohua Li
Keyword(s):  
Support Vector Machine ◽  
Support Vector Regression ◽  
Three Dimensional ◽  
Element Model ◽  
Support Vector ◽  
Generalization Capability ◽  
Damage Scenarios ◽  
Cable Stayed Bridge ◽  
Bending Strain ◽  
Strain Responses

A new two-step approach is developed for damaged cable identification in a cable-stayed bridge from deck bending strain responses using Support Vector Machine. A Damaged Cable Identification Machine (DCIM) based on support vector classification is constructed to determine the damaged cable and a Damage Severity Identification Machine (DSIM) based on support vector regression is built to estimate the damage severity. A field cable-stayed bridge with a long-term monitoring system is used to verify the proposed method. The three-dimensional Finite Element Model (FEM) of the cable-stayed bridge is established using ANSYS, and the model is validated using the field testing results, such as the mode shape, natural frequencies and its bending strain responses of the bridge under a moving vehicle. Then the validated FEM is used to simulate the bending strain responses of the longitude deck near the cable anchors when the vehicle is passing over the bridge. Different damage scenarios are simulated for each cable with various severities. Based on damage indexes vector, the training datasets and testing datasets are acquired, including single damaged cable scenarios and double damaged cable scenarios. Eventually, DCIM is trained using Support Vector Classification Machine and DSIM is trained using Support Vector Regression Machine. The testing datasets are input in DCIM and DSIM to check their accuracy and generalization capability. Different noise levels including 5%, 10%, and 20% are considered to study their anti-noise capability. The results show that DCIM and DSIM both have good generalization capability and anti-noise capability.

scholarly journals Continuous Monitoring of the Ambient Factors via ε-Smooth Support Vector Regression

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 63
Author(s):  
Yan-Ru Jhuo ◽  
Chi-Yu Chen ◽  
Yu-Hsuan Yang ◽  
Hsing-Chuan Hsieh ◽  
Yuh-Jye Lee
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Continuous Monitoring ◽  
Three Dimensional ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Heat Map ◽  
Proposed Model ◽  
Whole Space ◽  
The Internet Of Things

Thanks to the advances of the Internet of Things (IoTs), more and more wireless sensor networks applications have been realized. One of the fundamental but crucial applications is the continuous monitoring of environmental factors including temperature, humidity, illumination, etc. We develop a nonlinear regression model which takes spatial and temporal information into account to construct a globally three-dimensional heat map for a closed space based on very sparse sensor deployment. However, fitting the whole-space heat map with a very limited number of sensor observations gives a very poor estimation when we use a nonlinear model. We call it the coverage hole problem. We utilize the uniform experimental design which is well known in industrial statistics to allocate the synthetic sensors. We estimate those synthetic sensor readings on the basis of linear model locally. We then apply ε -SSVR, a nonlinear support vector regression model to fit the globally three-dimensional heat map by combining real sensor and synthetic sensor readings. The numerical results demonstrate our proposed model can enhance the accuracy significantly.

Three-dimensional acoustic emission source localisation in concrete based on sparse least-squares support vector regression

2020 ◽  
Vol 62 (8) ◽  
pp. 471-477
Author(s):  
Yan Wang ◽  
Lijun Chen ◽  
Na Wang ◽  
Jie Gu ◽  
Zhaozhu Wang
Keyword(s):  
Acoustic Emission ◽  
Least Squares ◽  
Support Vector Regression ◽  
Three Dimensional ◽  
The Other ◽  
Support Vector ◽  
Source Localisation ◽  
Concrete Damage ◽  
Signal Combination ◽  
Actual Damage

In order to improve the accuracy of concrete damage localisation based on acoustic emission (AE) monitoring, a multi-output sparse least-squares support vector regression (S-LS-SVR) method is attempted for AE source localisation in concrete. The AE events are produced by pencil lead breaks and the response wave is received by piezoelectric sensors. A Newton iterative method, an improved exhaustive method and two S-LS-SVR approaches (S-LS-SVR(A) and S-LS-SVR(B)) are used to locate the AE source, then the positioning accuracies of the methods in the three coordinate directions are compared and analysed. The results show that the accuracy of AE source localisation using the S-LS-SVR(B) model is higher than that of the other methods. The accuracy of the S-LS-SVR model using the time difference of arrival (TDOA) and the sequential number of sensors that arrive successively as input parameters is higher than that of the other AE signal combination trialled as the input. This shows that the S-LS-SVR(B) model is better than the S-LS-SVR(A) model. The intelligent S-LS-SVR(B)-based localisation method provides a basis for application in actual damage detection.

3D Machined Surface Topography Forecasting With Space-Time Multioutput Support Vector Regression Using High Definition Metrology

2017 ◽  
Author(s):  
Yiping Shao ◽  
Shichang Du ◽  
Lifeng Xi
Keyword(s):  
Support Vector Regression ◽  
Surface Topography ◽  
Three Dimensional ◽  
Milling Process ◽  
Space Time ◽  
Cylinder Block ◽  
Support Vector ◽  
Real Surface ◽  
High Definition ◽  
Machined Surface

Satisfied surface topography is important to achieve the function of a part, thereby machined surface prediction is essential. A surface forecasting model called space-time multioutput support vector regression (STMSVR) is developed in this paper. With machined surfaces pervading in manufacturing, high definition metrology (HDM) is adopted to measure the three dimensional machined surface. Millions of data points are generated to represent the entire surface. The STMSVR model captures the spatial-temporal characteristics of the successively machined surface and predicts the future surface. To verify the prediction accuracy of STMSVR, a case study on the engine cylinder block face milling process is applied. The results indicate that the developed model achieves a good agreement between the predicted surface and the real surface using four important indexes.

scholarly journals A Decoupled Calibration Method Based on the Multi-Output Support Vector Regression Algorithm for Three-Dimensional Electric-Field Sensors

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8196
Author(s):  
Wei Zhao ◽  
Zhizhong Li ◽  
Haitao Zhang ◽  
Yuan Yuan ◽  
Ziwei Zhao
Keyword(s):  
Electric Field ◽  
Support Vector Regression ◽  
Field Intensity ◽  
Electric Field Intensity ◽  
Three Dimensional ◽  
Calibration Method ◽  
Field Vector ◽  
Support Vector ◽  
Calculation Error ◽  
Calibration Matrix

Aiming at the problem that the measured accuracy of the electric field intensity which is affected by the coupling interference by sensor output signal from the component of a three dimensional electric field, the causes of the coupling error was analyzed, and a decoupled calibration method based on support vector regression algorithm for three-dimensional electric field sensor is proposed. The solution of the decoupled calibration matrix was regarded as a multi-objective optimization process, and the optimal decoupling calibration matrix was obtained by the ν-SVR algorithm. The complex inverse calculation of the matrix was avoided, and the calculation error was reduced. A rotary calibration device was designed to accurately measure the angle between the induction electrode of the sensor and the electric-field vector, and an accurate calculation model of the theoretical electric field was established. The experimental results showed that the error between the calculated and theoretical values of the electric-field components obtained by the proposed method were smaller than those obtained by the traditional inverse matrix calibration method, the accuracy of the calibration was improved, the rationality of the calibration method was proven, and the accuracy of the three-dimensional electric-field intensity measurements was further improved.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection
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