References
- Akay, M.F. and Ipek, A. (2010), "Predicting the performance measures of an optical distributed shared memory multiprocessor by using support vector regression", Expert Syst. Appl., 37(9), 6293-6301. https://doi.org/10.1016/j.eswa.2010.02.092
- Andres, A.T., Alberto, A.S. and Sen, R.J. (1998), "Galvanic interaction between carbon fiber reinforced plastic (CFRP) composites and steel in chloride contaminated concrete", Proceedings of the 1998 NACE International Conference, Houston, Aug.
- Ankit, B.G., JoshiJ, B., Valadi, K.J. and Bhaskar, D.K. (2007), "Development of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas-liquid systems", Chem. Eng. Sci., 62(7), 7078-7089. https://doi.org/10.1016/j.ces.2007.07.071
- Chan, T.H.T., Yu, L., Tam, H.Y., Ni, Y.Q., Liu, S.Y., Chung, W.H. and Cheng, L.K. (2006), "Fiber Bragg grating sensors for structural health monitoring of Tsing Ma bridge: Background and experimental observation", Eng. Struct., 28(5), 648-659. https://doi.org/10.1016/j.engstruct.2005.09.018
- Dai, Y.B., Liu, Y.J., Leng, J.S., Deng, G., and Asundi, A. (2009), "A novel time-division multiplexing fiber Bragg grating sensor interrogator for structural health monitoring", Opt. Laser. Eng., 47(10), 1028-1033. https://doi.org/10.1016/j.optlaseng.2009.05.012
- Guo, Z.W. and Bai G.C. (2009), "Application of least squares support vector machine for regression to reliability analysis", Chinese J. Aeronautics, 22(2), 160-166. https://doi.org/10.1016/S1000-9361(08)60082-5
- Hoschke, N., Lewis, C.J., Price, D.C., Scott, D.A., Gerasimov, V. and Wang, P. (2008), A self-organizing sensing system for structural health monitoring of aerospace vehicles, Advanced Information and Knowledge Processing, Part II: 51-76.
- Herszberg, I., Li, H.C.H., Dharmawan, F., Mouritz, A.P., Nguyen, M. and Bayandor, J. (2005), "Damage assessment and monitoring of composite ship joints", Compos. Struct., 67(2), 205-216. https://doi.org/10.1016/j.compstruct.2004.09.017
- Hu, X.L., Liang, D.K., Zeng, J. and Lu, G. (2010), "A long period grating for simultaneous measurement of temperature and strain based on support vector regression", J. Intel. Mat. Syst. Str., 21(10), 955-959. https://doi.org/10.1177/1045389X10374163
- KapilS, B. (2010), "On Lagrangian support vector regression", Expert Syst. Appl., 37(12), 8784-8792. https://doi.org/10.1016/j.eswa.2010.06.028
- Kerrouche, A., Boyle, W.J.O., Sun, T. and Grattan, K.T.V. (2009), "Design and in-the-field performance evaluation of compact FBG sensor system for structural health monitoring applications", Sensor. Actuat. A - Phys., 151(2), 107-112. https://doi.org/10.1016/j.sna.2009.01.021
- Maaskant, R., Alavie, T., Measures, R.M., Tadros, G., Rizkalla, S.H. and Guha-Thakurta, A. (1997), "Fiber-optic Bragg grating sensors for bridge monitoring", Cement Concrete Comp., 19(1), 21-33. https://doi.org/10.1016/S0958-9465(96)00040-6
- Mieloszyk, M., Krawczuk, M., Zak, A. and Ostachowicz, W. (2010), "An adaptive wing for a small-aircraft application with a configuration of fiber Bragg grating sensors", Smart Mater. Struct., 19(8), 085009(12pp). https://doi.org/10.1088/0964-1726/19/8/085009
- Moyo, P., Brownjohn, J.M.W., Suresh, R. and Tjin, S.C. (2005), "Development of fiber Bragg grating sensors for monitoring civil infrastructure", Eng. Struct., 27(7), 1828-1834. https://doi.org/10.1016/j.engstruct.2005.04.023
- Peng, C.P., Wang, J.B. and Huang, K.Y. (2010), "Reliable fiber sensor system with star-ring-bus architecture", Sensors, 10(8), 4194-4205. https://doi.org/10.3390/s100504194
- Prokopenko, M., Wang, P., Foreman, M., Valencia, P., Price, D. and Poulton, G. (2005), "On connectivity of reconfigurable impact networks in ageless aerospace vehicles", Robot. Auton. Syst., 53(1), 36-58. https://doi.org/10.1016/j.robot.2005.06.003
- Scott, D.A., Price, D.C., Hoschke, N. and Richards, W.L. (2009), "Structural health monitoring of thermal protection systems", Mater. Forum., 33, 457-464.
- Vapnik, V.(1999), The nature of statistical learning theory, Springer, USA.
- Yang, Z., Gu, X.S., Liang, X.Y. and Ling, L.C. (2010), "Genetic algorithm-least squares support vector regression based predicting and optimizing model on carbon fiber composite integrated conductivity", Mater. Design, 31(3), 1042-1049. https://doi.org/10.1016/j.matdes.2009.09.057
- Yeh, C.H., Chow, C.W., Wang, C.H., Shih, F.Y., Wu, Y.F. and Chi, S. (2009), "A simple self-restored fiber Bragg grating (FBG)-based passive sensing ring network", Meas. Sci. Technol., 20(4), 043001 (5pp). https://doi.org/10.1088/0957-0233/20/4/043001
- Yeo, T.L., Sun, T., Grattan, K.T.V., Parry, D., Lade, R. and Powell, B.D. (2005), "Characterization of polymer-coated fiber Bragg grating sensor for relative humidity sensing", Sensor. Actuat. B - Chem., 110(1), 148-155. https://doi.org/10.1016/j.snb.2005.01.033
- Zhang, J., Sato, T. and Susumu, I. (2006), "Support vector regression for on-line health monitoring of large-scale structures", Struct. Saf., 28(4), 392-406. https://doi.org/10.1016/j.strusafe.2005.12.001
Cited by
- A soft self-repairing for FBG sensor network in SHM system based on PSO–SVR model reconstruction vol.343, 2015, https://doi.org/10.1016/j.optcom.2014.12.079
- Wheel tread defect detection for high-speed trains using FBG-based online monitoring techniques vol.21, pp.5, 2014, https://doi.org/10.12989/sss.2018.21.5.687
- Impact force localization for civil infrastructure using augmented Kalman Filter optimization vol.23, pp.2, 2014, https://doi.org/10.12989/sss.2019.23.2.123
- Real-time seismic structural response prediction system based on support vector machine vol.18, pp.2, 2014, https://doi.org/10.12989/eas.2020.18.2.163
- Prediction of Smooth Hysteretic Model Parameters Using Support Vector Regression vol.3, pp.2, 2021, https://doi.org/10.1007/s42493-021-00065-6