Title: Study on automatic detection of doped proportions of polyimide matrix inorganic nanocomposite films based on wavelet energy distribution proportion features and extreme learning machine
Authors: Hai Guo
Addresses: College of Computer Science and Engineering, Dalian Nationalities University, 18 Liaohe West Road, Dalian Development Zone, Dalian, 116600, China
Abstract: In order to test the component of nanocomposite films, this article presents a model for automatically detecting the doping ratio of polyimide matrix inorganic nanocomposite film based on wavelet energy distribution proportion features and extreme learning machine. In-situ polymerisation was used to prepare nanocomposite films doped with 15 wt%, 20 wt% and 25 wt% BaTiO3, respectively, while SEM was used to obtain the surface texture image. Experimental results show that the average TP rate obtained by this model is 0.922; average FP rate is 0.039 and average precision 0.924, indicating that the model is efficient in detecting and recognising films that have different doped proportions. Compared with the traditional model, predictive ability of this model is more predictive performance.
Keywords: nanocomposite film; automatic detection; pattern recognition; wavelet features; extreme learning machines; ELM; doping ratio; polyimide matrix inorganic nanocomposites; energy distribution; nanotechnology; barium titanate; BaTiO3; modelling; prediction.
International Journal of Materials and Product Technology, 2016 Vol.52 No.3/4, pp.298 - 311
Received: 19 Jan 2015
Accepted: 20 Aug 2015
Published online: 11 Mar 2016 *