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K-SVD-dictionary-learning-based data reconstruction method of seawater temperature field

A technology of dictionary learning and data reconstruction, which is applied in character and pattern recognition, pattern recognition in signals, instruments, etc., can solve problems such as sparse representation, and achieve the effect of more targeted sparse representation and good sparse representation effect

Inactive Publication Date: 2018-11-16
HARBIN ENG UNIV
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Problems solved by technology

However, the seawater temperature field data has unique signal characteristics, and the traditional fixed transformation is not enough to represent it very effectively.

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  • K-SVD-dictionary-learning-based data reconstruction method of seawater temperature field
  • K-SVD-dictionary-learning-based data reconstruction method of seawater temperature field
  • K-SVD-dictionary-learning-based data reconstruction method of seawater temperature field

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Embodiment Construction

[0042] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0043] combine figure 1 , figure 2 , the present invention comprises the following steps:

[0044] Step 1. Preprocessing of historical data of seawater temperature field

[0045] According to the size of the seawater temperature field, assuming that the size of the seawater temperature field is p×q, and N=p×q, then more than N groups of historical data of the seawater temperature field are required as training samples. The seawater temperature field data of H (H>N) groups at different times in the near future can be selected, and all historical data can be processed into N×1 dimensional signals, and combined into an H×N data matrix, in which each column is a time seawater temperature field data temperature field data.

[0046] Step 2. Use the K-SVD dictionary learning algorithm to obtain a sparse basis suitable for the seawater t...

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Abstract

The invention provides a K-SVD-dictionary-learning-based data reconstruction method of a seawater temperature field. The method comprises: preprocessing historical data of a seawater temperature fieldto obtain a training sample set; calculating a sparse matrix that is suitable for the seawater temperature field by using a K-SVD dictionary learning algorithm; selecting an observation matrix and carrying out random sampling on the seawater temperature field; and reconstructing seawater temperature field distribution by using a reconstruction algorithm. Compared with the traditional orthogonal-type sparse matrix, the sparse matrix obtained by using the K-SVD-dictionary-learning-based data reconstruction method is oriented to the seawater temperature characteristics well and thus the reconstruction effect of the seawater temperature field is improved.

Description

technical field [0001] The invention relates to a seawater temperature field data reconstruction method based on K-SVD dictionary learning, belonging to the field of data reconstruction. Background technique [0002] In the relevant research on seawater parameter recovery using compressed sensing reconstruction technology, most of the reconstruction process uses traditional orthogonal sparse bases, such as DCT bases. However, the seawater temperature field data has unique signal characteristics, and the traditional fixed transformation is not enough to represent it very effectively. If the sparse basis for the seawater temperature field data can be adaptively constructed according to the characteristics of the seawater temperature field data itself, the characteristics of the temperature field data can be processed and analyzed in a more targeted manner, and the obtained sparse representation has a smaller degree of sparsity , the seawater temperature field data with high p...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06F2218/02
Inventor 刘厂雷宇宁周学文高峰赵玉新何忠杰成巍
Owner HARBIN ENG UNIV
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