TS-LSSVM: Triple sparse least squares support vector machine for residual oxygen concentration detection of encapsulated pharmaceutical vials. (15th June 2023)

• Record Type: Journal Article
• Authors: Luo, Qiwu; Zhou, Bingxing; Geng, Jingxuan; Liu, Zihuai; Su, Jiaojiao; Yang, Chunhua
• Is Part Of: Measurement. Volume 214(2023)
• Publication Date: 2023-06-15
• Subjects: Automated optical inspection (AOI) -- Residual oxygen -- Oxygen concentration detection -- Least squares support vector machine (LSSVM) -- Sparse learning; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2023.112717
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: We feed SSWT-decomposed components into the LSSVM without signal reconstruction to form an integral model. We propose TS-LSSVM by introducing the structural sparse learning so as to adaptively select support vectors. We collect a practical dataset and design TS-LSSVM algorithm to dynamically predict with a minimal training set. Abstract: Accurate measurement of residual oxygen concentration in encapsulated pharmaceutical vials is adequate to ensure the quality of inner sterile preparations. However, the critical characteristic signal is feeble and covered by enormous environmental interference in the actual production. Inspired by structural sparse learning, we propose a novel prediction model in this paper, triple sparse least squares support vector machine (TS-LSSVM), in which the production priors are deeply excavated, and the feature, sample, and structure sparsity are realized simultaneously by redefining the objective function. In addition, the selection of support vectors can be adjusted adaptively according to the time-varying environmental noise, so as to ensure the reliability of long-term operation. First, the time–frequency components containing the prior knowledge are extracted based on the synchro squeezing wavelet transform (SSWT). Then, a triple sparse learning strategy is designed, which can accurately eliminate redundant wavelet coefficients and adaptively select training samples. Finally, a strict and fast optimization solution is proposed under the alternating direction method of multipliers (ADMM) framework. Experimental results on public and practical datasets prove the superiority of TS-LSSVM.