REFERENCES
- SambathS, NagarajP, SelvakumarN. Automatic defect classification in ultrasonic NDT using artificial intelligence. J Nondestr Eval. 2011;30(1):20–28.
- MartínÓ, LópezM, MartínF. Artificial neural networks for quality control by ultrasonic testing in resistance spot welding. J Mater Process Technol. 2007;183:226–233.
- ZhangG-M, HarveyDM. Contemporary ultrasonic signal processing approaches for nondestructive evaluation of multilayered structures. Nondestr Test Eval. 2012;27:1–27.
- MasnataA, SunseriM. Neural network classification of flaws detected by ultrasonic means. NDT&E Int. 1996;29(2):87–93.
- CaseT, WaagR. Flaw identification from time and frequency features of ultrasonic waveforms. IEEE Trans Ultrason Ferroelectr Freq Control. 1996;43:592–600.
- PolikarR, UdpaL, UdpaSS, TaylorT. Frequency invariant classification of ultrasonic weld inspection signals. IEEE Trans Ultrason Ferroelectr Freq Control. 1998;45:614–625.
- DraiR, KhelilM, BenchaalaA. Time frequency and wavelet transform applied to selected problems in ultrasonics NDE. NDT&E Int. 2002;35(8):567–572.
- LeeK, Estivill-CastroV. Feature extraction and gating techniques for ultrasonic shaft signal classification. Appl Soft Comput. 2007;7:156–165.
- VieiraAP, de MouraEP, GonçalvesLL, RebelloJMA. Characterization of welding defects by fractal analysis of ultrasonic signals. Chaos Solitons Fractals. 2008;38(3):748–754.
- ZapatabJ, VilaraR, RuizbR. An adaptive-network-based fuzzy inference system for classification of welding defects. NDT&E Int. 2010;43(3):191–199.
- BettayeF, RachediT, BenbartaouiH. An improved automated ultrasonic NDE system by wavelet and neuron networks. Ultrasonics. 2004;42(1–9):853–858.
- ChangC-C, LinC-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2011;2(3):1–39.
- AthimethphatM, LerteerawongB. Binary classification tree with tuned observation-based clustering. Int J Comput Inf Eng. 2012;6:141–146.
- MadzarovG, GjorgjevikjD, ChorbevI. A multi-class SVM classifier utilizing binary decision tree. Informatica. 2009;33:233–241.
- MuWL, GaoJM, JiangHQ, WangZ, ChenFM, DangCY. Automatic classification approach of weld defects based on PCA and SVM. INSIGHT: Non-Destr Test Cond Monit. 2013;55(10):535–539.
- Pham DT, Ghanbarzadeh A, Koç E, Otri S, Rahim S, Zaidi M. The bees algorithm – a novel tool for complex optimization problems, Proceedings of Innovative Production Machines and Systems Virtual conference, Oxford, UK2006 p. 454–461.
- PittnerS, KamarthiSV. Feature extraction from wavelet coefficients for pattern recognition tasks. IEEE Trans Pattern Anal Mach Intell. 1999;21(1):83–88.
- RobiniMC, MagninIE, Benoit-CattinH, BaskurtA. Two-dimensional ultrasonic flaw detection based on the wavelet packet transform. IEEE Trans Ultrason Ferroelectr Freq Control. 1997;44(6):1382–1394.
- CrammerK, SingerY. On the algorithmic implementation of multiclass kernel-based vector machines. J Mach Learn Res. 2001;2:265–292.
- VapnikV. Statistical learning theory. New York, NY: Wiley; 1998.
- HastieT, TibshiraniR. Classification by pairwise coupling. Ann Stat. 1998;26:451–471.
- LinSW, YingKC, ChenSC, LeeZJ. Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst App. 2008;35:1817–1824.
- DonohoDL. De-noising by soft thresholding. IEEE Trans Inf Theory. 1995;41:613–627.