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Real-Time Identification of Media in a Laboratory-Scaled Penetrating Process
Abstract:In this paper, a neural network technique is applied to real-time classifying media while a projectile is penetrating through them. A laboratory-scaled penetrating setup was built for the experiment. Features used as the network inputs were extracted from the acceleration of penetrator. 6000 set of features from a single penetration with known media and status were used to train the neural network. The trained system was tested on 30 different penetration experiments. The system produced an accuracy of 100% on the training data set. And, their precision could be 99% for the test data from 30 tests.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332518Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 946
 Min, Kwang S. and Min, Hisook L., "Real-time identification of a medium for a high-speed penetrator", United States Patent 5,255,608 , 1993.
 B.Widrow, D. E. Rumelhard, and M. A. Lehr, "Neural networks: Applications in industry, business and science," Commun. ACM, vol. 37, pp. 93-105, 1994.
 Guoqiang Peter Zhang, "Neural Networks for Classification: A Survey", IEEE transactions on systems, man, and cyberneticsÔÇöpart c: applications and reviews, vol. 30, no. 4, November 2000.
 Zukas, J. A., "Impact Dynamics", John Wiley & Sons. , 1982.
 Smith, S. W., "The Scientist and Engineer's Guide to Digital Signal Processing", San Diego: California Technical Publishing. , 1997.
 Kenney, J. F. and Keeping, E. S., "Mathematics of Statistics", Princeton, NJ: Van Nostrand, pp. 221-223 , 1962.
 Oppenheim, Alan V., Schafer, R. W., and Buck, J. R., "Discrete-time signal processing", Upper Saddle River, N.J., Prentice Hall, 1999.