Diffraction Separation and Imaging Using an Improved Singular Value Decomposition Method

Jiang, Jinsheng; Yang, Wencai; Ren, Haoran; Chen, Mengtao

doi:10.1109/lgrs.2022.3168016

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Gao et al [23] investigated the use of the SVD algorithm for blind signal separation. In [24], J. Jiang et al used singular value decomposition to identify geological targets. S. Guo and C. Li investigated the advantages of the Funk SVD algorithm for working with sparse data and proposed a new recommendation algorithm that combines Funk-SVD and Kmeans [25].…”

Section: Related Workmentioning

confidence: 99%

Data Optimization for Industrial IoT-Based Recommendation Systems

et al. 2022

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The most common problems that arise when working with big data for intelligent production are analyzed in the article. The work of recommendation systems for finding the most relevant user information was considered. The features of the singular-value decomposition (SVD) and Funk SVD algorithms for reducing the dimensionality of data and providing quick recommendations were determined. An improvement of the Funk SVD algorithm using a smaller required amount of user data for analysis was proposed. According to the results of the experiments, the proposed modification improves the speed of data processing on average by 50–70% depending on the number of users and allows spending fewer computing resources. As follows, recommendations to users are provided in a shorter period and are more relevant. The faster calculation of modified Funk SVD to exchange the optimal parameters between nodes was proposed. It was determined that execution time can be reduced on average by 75% for using ten nodes exchanging the optimal decomposition parameter compared to using one. Using Spark technology for faster calculation on average by 20% compared to Hadoop was proposed. The architecture of the IIoT system was proposed, which uses a modified Funk SVD algorithm to optimize data on edge devices and monitors the effectiveness of providing recommendations using control centers and cloud resources.

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Section: Related Workmentioning

confidence: 99%