Modeling and recognition of steel-plate surface defects based on a new backward boosting algorithm

Tao F, Cheng J, Cheng Y, Gu S, Zheng T, Yang H (2017) SDMSim: a manufacturing service supply-demand matching simulator under cloud environment. Robot Comput Integr Manuf 45:34–46

Xiang F, Jiang G, Xu L, Wang N (2016) The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system. Int J Adv Manuf Technol 84(1–4):59–70

Sharifzadeh M, Alirezaee S, Amirfattahi R, Sadri S (2008) Detection of steel defect using the image processing algorithms. In: Multitopic Conference, 2008. INMIC 2008. IEEE International, IEEE, pp 125–127

Xiang F, Hu Y, Yu Y, Wu H (2014) QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system. CEJOR 22(4):663–685

Neogi N, Mohanta DK, Dutta PK (2014) Review of vision-based steel surface inspection systems. EURASIP J Image Video Process 2014(1):50

Suresh BR, Fundakowski RA, Levitt TS, Overland JE (1983) A real-time automated visual inspection system for hot steel slabs. IEEE Trans Pattern Anal Mach Intell 6:563–572

Choi K, Koo K, Lee JS (2006) Development of defect classification algorithm for POSCO rolling strip surface inspection system. In: SICE-ICASE, 2006. International Joint Conference, IEEE, pp 2499–2502

Wu G, Zhang H, Sun X, Xu J, Xu K (2007) A bran-new feature extraction method and its application to surface defect recognition of hot rolled strips. In: Automation and Logistics, 2007 I.E. International Conference on, IEEE, pp 2069–2074

Dupont F, Odet C, Cartont M (1997) Optimization of the recognition of defects in flat steel products with the cost matrices theory. NDT E Int 30(1):3–10

Jia H, Murphey YL, Shi J, Chang TS (2004) An intelligent real-time vision system for surface defect detection. In: Pattern recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on IEEE, vol 3, pp 239–242

Ghorai S, Mukherjee A, Gangadaran M, Dutta PK (2013) Automatic defect detection on hot-rolled flat steel products. IEEE Trans Instrum Meas 62(3):612–621

Du W, Zhan Z (2002) Building decision tree classifier on private data. In: proceedings of the IEEE international conference on privacy, security and data mining-vol 14. Australian Computer Society, Inc, pp 1–8

Sahami M, Dumais S, Heckerman D, Horvitz E (1998) A Bayesian approach to filtering junk e-mail. In Learning for text categorization: papers from the 1998 workshop, vol 62, pp 98–105

Liu W, Liu H, Tao D, Wang Y, Lu K (2016) Manifold regularized kernel logistic regression for web image annotation. Neurocomputing 172:3–8

Yazdchi MR, Mahyari AG, Nazeri A (2008) Detection and classification of surface defects of cold rolling mill steel using morphology and neural network. In: Computational intelligence for modelling control & automation, 2008 international conference on, IEEE, pp 1071–1076

Peng K, Zhang X (2009) Classification technology for automatic surface defects detection of steel strip based on improved BP algorithm. In: Natural computation, 2009. ICNC'09. Fifth international conference on, IEEE, vol 1, pp 110–114

Liu YC, Hsu YL, Sun YN, Tsai SJ, Ho, CY, Chen CM (2010) A computer vision system for automatic steel surface inspection. In: Industrial electronics and applications (ICIEA), 2010 the 5th IEEE conference on, IEEE, pp 1667–1670

Valiant LG (1984) A theory of the learnable. Commun ACM 27(11):1134–1142

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

Freund Y, Schapire RE (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In: European conference on computational learning theory, Springer Berlin Heidelberg, pp 23–37

Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, Zhou ZH (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37

Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: icml, vol. 96, pp 148–156

Schapire RE, Singer Y (1998) Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the eleventh annual conference on Computational learning theory, ACM, pp 80–91

Zou H, Rosset S, Hastie T (2009) Multi-class AdaBoost. Statistics and its Interface, 2(3):349–360

Zhou M, Kong L, Xie L, Fu T, Jiang G, Feng Q (2017) Design and optimization of non-circular mortar nozzles using finite volume method and Taguchi method. Int J Adv Manuf Technol 90(9–12):3543–3553

Tao F, Guo H, Zhang L, Cheng Y (2012) Modelling of combinable relationship-based composition service network and the theoretical proof of its scale-free characteristics. Enterp Inf Syst 6(4):373–404

Tao F, Zhao D, Hu Y, Zhou Z (2008) Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Trans Ind Inf 4(4):315–327

Zhang Q, Sun S (2009) Weighted data normalization based on eigenvalues for artificial neural network classification. In: Neural information processing, Springer Berlin/Heidelberg, pp 349–356

Li N, Cheng X, Zhang S, Wu Z (2013) Recognizing human actions by BP-AdaBoost algorithm under a hierarchical recognition framework. In: Acoustics, speech and signal processing (ICASSP), 2013 I.E. international conference on, IEEE, pp 3407–3411

Uhrig RE (1995) Introduction to artificial neural networks. In: Industrial electronics, control, and instrumentation, proceedings of the 1995 I.E. IECON 21st International Conference on, IEEE, vol. 1, pp 33–37