Noise-Robust Self-Adaptive Support Vector Machine for Residual Oxygen Concentration Measurement

Abstract

In this article, a noise-robust self-adaptive support vector machine (namely, NSSVM) is developed for the multiclass classification problem in the residual oxygen concentration measurement of pharmaceutical vials. Two key elements are emphasized by the NSSVM framework: First, targeting the fast time-varying noises, two mutually compensated signal enhancement measures of synchrosqueezed wavelet transform (SWT)-based filtering and adaptive iterative reweighted penalty least squares (AIRPLS)-based baseline corrector are contributed to obtain stable features being fed to classifier. Second, most typical slow time-varying interferences are dynamically avoided by the self-adaptive thresholding scheme learned from the intrinsic priori of production line. The experimental results indicate that our NSSVM-based measurement method achieves a considerable performance, with an average accuracy of 97.24% when applied on an automated visual inspection (AVI) machine of Truking Tech., which indicates that the proposed method is promising to the in situ instrumentation for the residual oxygen concentration measurement of pharmaceutical glass vials.