Optoelectronic instrumentation enhancement using data mining feedback for a 3D measurement system

Abstract3D measurement by a cyber-physical system based on optoelectronic scanning instrumentation has been enhanced by outliers and regression data mining feedback. The prototype has applications in (1) industrial manufacturing systems that include: robotic machinery, embedded vision, and motion control, (2) health care systems for measurement scanning, and (3) infrastructure by providing structural health monitoring. This paper presents new research performed in data processing of a 3D measurement vision sensing database. Outliers from multivariate data have been detected and removal to improve artificial intelligence regression algorithm results. Physical measurement error regression data has been used for 3D measurements error correction. Concluding, that the joint of physical phenomena, measurement and computation is an effectiveness action for feedback loops in the control of industrial, medical and civil tasks.

[1]  Wenjia Li,et al.  Poster abstract: Finding abnormal data in vehicular cyber physical systems , 2013, 2013 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).

[2]  P. Mahalanobis On the generalized distance in statistics , 1936 .

[3]  Larry A. Wasserman,et al.  A Comparison of the Lasso and Marginal Regression , 2012, J. Mach. Learn. Res..

[4]  A. Dobson An introduction to generalized linear models , 1990 .

[5]  Daniel Hernandez-Balbuena,et al.  A Method and Electronic Device to Detect the Optoelectronic Scanning Signal Energy Centre , 2013 .

[6]  M. Rivas,et al.  Spatial data acquisition by laser scanning for robot or SHM task , 2008, 2008 IEEE International Symposium on Industrial Electronics.

[7]  Oleg Sergiyenko,et al.  Continuous 3D scanning mode using servomotors instead of stepping motors in dynamic laser triangulation , 2015, 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE).

[8]  Oleg Starostenko,et al.  Optical 3D laser measurement system for navigation of autonomous mobile robot , 2014 .

[9]  Sancho Salcedo-Sanz,et al.  Multi-parametric Gaussian Kernel Function Optimization for ε-SVMr Using a Genetic Algorithm , 2011, IWANN.

[10]  A. Asadpour,et al.  Design and application of industrial machine vision systems , 2007 .

[11]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[12]  Oleg Sergiyenko,et al.  Machine Vision: Approaches and Limitations , 2008 .

[13]  P. Filzmoser A MULTIVARIATE OUTLIER DETECTION METHOD , 2004 .

[14]  Blake Hannaford,et al.  Mapping surgical fields by moving a laser-scanning multimodal scope attached to a robot arm , 2014, Medical Imaging.

[15]  Jeffrey E. Thatcher,et al.  Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging , 2015, Journal of biomedical optics.

[16]  Mark M Derriso,et al.  Industrial Age non-destructive evaluation to Information Age structural health monitoring , 2014 .

[17]  Wendy Flores-Fuentes,et al.  Combined application of Power Spectrum Centroid and Support Vector Machines for measurement improvement in Optical Scanning Systems , 2014, Signal Process..

[18]  Cheng Sheng,et al.  Fusing range and 2-D images in multi-sensor for robot vision , 2002, 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM '02. Proceedings..

[19]  P. Konar,et al.  Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) , 2011, Appl. Soft Comput..

[20]  Jean-Luc Aider,et al.  Closed-loop separation control using machine learning , 2014, Journal of Fluid Mechanics.

[21]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[22]  Oleg Sergiyenko,et al.  Surface recognition improvement in 3D medical laser scanner using Levenberg-Marquardt method , 2013, Signal Process..

[23]  Clemens Reimann,et al.  Multivariate outlier detection in exploration geochemistry , 2005, Comput. Geosci..

[24]  Patrik Kamencay,et al.  2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm , 2014 .

[25]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[26]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..