Data-driven soft sensors have increasingly been applied for the quality measurement of industrial polymerization processes in recent years. However, owing to the costly assay process, the limited labeled data available still pose significant obstacles to the construction of accurate models. In this study, a novel soft sensor named the selective Wasserstein generative adversarial network, with gradient penalty-based support vector regression (SWGAN-SVR), is proposed to enhance quality prediction with limited training samples. Specifically, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is employed to capture the distribution of the available limited labeled data and to generate virtual candidates. Subsequently, an effective data-selection strategy is developed to alleviate the problem of varied-quality samples caused by the unstable training of the WGAN-GP. The selection strategy includes two parts: the centroid metric criterion and the statistical characteristic criterion. An SVR model is constructed based on the qualified augmented training data to evaluate the prediction performance. The superiority of SWGAN-SVR is demonstrated, using a numerical example and an industrial polyethylene process.
In the Internet of Things, image acquisition equipment is very important, which will generate lots of invalid data when monitoring in real time. Analyzing the data collected from the terminal directly by edge calculation can remove invalid frames and improve the accuracy of system detection. SSD algorithm model is relatively light and fast detection speed. However, SSD algorithms don't take full advantage of both shallow and deep information, a multiscale feature fusion attention mechanism structure based on SSD algorithm was proposed in this paper, which combines the idea of multiscale feature fusion and attention mechanism. Improve the feature information expression ability by fusing adjacent feature layers for each detection layer. Then, add the attention mechanism to to improve the algorithm's attention to the feature map channels. The results of the experiment show that the optimized model detection accuracy is improved, which will greatly improve the reliability of edge calculation.
In the Internet of Things, image acquisition equipment is very important, which will generate lots of invalid data when monitoring in real time. Analyzing the data collected from the terminal directly by edge calculation can remove invalid frames and improve the accuracy of system detection. SSD algorithm model is relatively light and fast detection speed. However, SSD algorithms don't take full advantage of both shallow and deep information, a multiscale feature fusion attention mechanism structure based on SSD algorithm was proposed in this paper, which combines the idea of multiscale feature fusion and attention mechanism. Improve the feature information expression ability by fusing adjacent feature layers for each detection layer. Then, add the attention mechanism to to improve the algorithm's attention to the feature map channels. The results of the experiment show that the optimized model detection accuracy is improved, which will greatly improve the reliability of edge calculation.
In the Internet of Things, image acquisition equipment is very important, which will generate lots of invalid data when monitoring in real time. Analyzing the data collected from the terminal directly by edge calculation can remove invalid frames and improve the accuracy of system detection. SSD algorithm model is relatively light and fast detection speed. However, SSD algorithms don't take full advantage of both shallow and deep information, a multiscale feature fusion attention mechanism structure based on SSD algorithm was proposed in this paper, which combines the idea of multiscale feature fusion and attention mechanism. Improve the feature information expression ability by fusing adjacent feature layers for each detection layer. Then, add the attention mechanism to to improve the algorithm's attention to the feature map channels. The results of the experiment show that the optimized model detection accuracy is improved, which will greatly improve the reliability of edge calculation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.