achine ensemble approach for simultaneous detection of transient and radual abnormalities in end milling using multisensor fusion
暂无分享,去创建一个
Sohyung Cho | Shihab S Asfour | Arzu Onar | Sultan Binsaeid | S. Asfour | Sohyung Cho | A. Onar | S. Binsaeid | ultan Binsaeida | Shihab Asfoura | Sohyung Chob | Arzu Onarc
[1] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[2] Remco R. Bouckaert,et al. Choosing Between Two Learning Algorithms Based on Calibrated Tests , 2003, ICML.
[3] Jiri Matas,et al. On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[4] Matti Rantatalo,et al. Study of a sensor platform for monitoring machining of aluminium and steel , 2007 .
[5] Amiya R Mohanty,et al. Estimation of tool wear during CNC milling using neural network-based sensor fusion , 2007 .
[6] David H. Wolpert,et al. Stacked generalization , 1992, Neural Networks.
[7] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[8] Usama M. Fayyad,et al. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.
[9] Adam G. Rehorn,et al. State-of-the-art methods and results in tool condition monitoring: a review , 2005 .
[10] Bernhard Sick,et al. ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH , 2002 .
[11] Richard L. Kegg,et al. One-Line Machine and Process Diagnostics , 1984 .
[12] Thomas G. Dietterich. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.
[13] Yang Shuzi,et al. Tool Wear Length Estimation with a Self-Learning Fuzzy Inference Algorithm in Finish Milling , 1999 .
[14] Colin Bradley,et al. A review of machine vision sensors for tool condition monitoring , 1997 .
[15] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[16] Mark A. Hall,et al. Correlation-based Feature Selection for Machine Learning , 2003 .
[17] Ibrahim N. Tansel,et al. Detection of tool breakage in milling operations—II. The neural network approach , 1993 .
[19] J. Kasac,et al. Tool wear monitoring using radial basis function neural network , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[20] Alexander K. Seewald,et al. How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness , 2002, International Conference on Machine Learning.
[21] Vladimir Vapnik,et al. An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.
[22] Li Dan,et al. Tool wear and failure monitoring techniques for turning—A review , 1990 .
[23] Sohyung Cho,et al. Tool breakage detection using support vector machine learning in a milling process , 2005 .
[24] D. E. Rumelhart,et al. chapter Parallel Distributed Processing, Exploration in the Microstructure of Cognition , 1986 .
[25] Peter Norman,et al. A sophisticated platform for characterization, monitoring and control of machining , 2006 .
[26] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .