Improved SVD-based data compression method for synchronous phasor measurement in distribution networks

Zhao, Jianli; Ye, Yuzhuan; Yu, Hao; Li, Peng; Wu, Jianzhong; Wang, Chengshan

doi:10.1016/j.ijepes.2021.106877

Cited by 6 publications

(11 citation statements)

References 32 publications

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“…erefore, this paper discusses the data storage optimization method in the database of online educational resources and finds that data processing not only includes read-only but can be stored through digital logs, i.e., through text, which in turn achieves storage optimization [14]. is approach can not only reduce the delay in downloading online educational resources, but also reduce the learning cost of users [15].…”

Section: Design Of Data Compression Strategymentioning

confidence: 99%

Construction of Teaching Management Platform for Universities Based on Big Data

2022

Advances in Multimedia

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The construction of the teaching management platform for colleges and universities needs to actively embrace information technology, change the problems existing in traditional offline teaching through technical innovation, make full use of massive educational resources, and improve teaching efficiency. However, no research has yet discussed how to handle the increasing scale of online teaching resources, leading to the hindrance of building teaching management platforms in universities, which requires the use of big data technologies and thinking to build new platforms. We propose a dynamic compression method to compress the data file backup in the database based on the dynamic adjustment of the system workload, and optimize the data file backup model of the database to reduce the total cost of ownership, which provides a basis for managing massive online education resources and building a teaching management platform for universities.

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Section: Design Of Data Compression Strategymentioning

confidence: 99%

Construction of Teaching Management Platform for Universities Based on Big Data

2022

Advances in Multimedia

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“…As to the traditional SVD data compression, only U p×p 11 , U are needed to reconstruct approximate matrix A [7]. It is clear to see that the total number of elements needed to be stored is (M + N + 1) × p, which is much less than original scale M × N .…”

Section: 22mentioning

confidence: 99%

“…CR is defined as the ratio of raw data size over compressed data size. As illustrated before, the CR of traditional SVD can be described by (7)while the CR of improved SVD described by (8). In (9), n represents the number of blocks and p i means the number of retained singular values of each block.…”

Section: Compression Ratio(cr)mentioning

confidence: 99%

“…However, lossless compression sacrifices compression performance for accuracy, which is not very well for big data [6]. Within the accuracy requirement, lossy compression can achieve a better result compared with lossless compression [7]. Furthermore, lossy and lossless compression are not opposite, lossless compression is at binary level whereas lossy is at data element level so they can be combined [8].…”

Section: Introductionmentioning

confidence: 99%

“…Singular Value Decomposition (SVD) is a commonly used lossy compression method whose compression performance can be further enhanced by a lossless process called improved SVD [7]. Moreover, as to large data matrices, it is possible to achieve better results by appling block SVD [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ocean data compression based on block SVD

Wang

Zhou

Zhang

2023

J. Phys.: Conf. Ser.

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Fast development of ocean observations and numerical modeling increases the need for data transmission, storage and extraction. This paper presented a new data compression method based on Singular Value Decomposition (SVD) with data matrix divided into different sub-matrices with consideration of odevity and remnant. An automatic matrix-dividing method is applied to divide smartly the data matrix into sub-matrices. These sub-matrices are then compressed based on an improved SVD, which enhances the compression performance by utilizing the orthogonal property of vectors generated by SVD. A dynamic optimization method which is capable of determining the proper scale of retained data under the accuracy requirement of ocean data is also established. Two indices are derived mathematically to search the best block pattern quickly. The performance and reliability of the block-based SVD compression is verified with the successful compression and recovery of the Hybrid Coordinate Ocean Model data.

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