Adaptive Matrix Selection for Compressive Sensing Data Compression
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Solution Overview
Problem
In smart grid systems, data compression using compressive sensing often results in restoration errors when decompressed, necessitating a method to efficiently compress data while minimizing these errors.
Innovation Solution
An electronic device with an adaptive compression unit that employs machine learning to select different representation and measurement matrices based on the type and features of data segments, allowing for adaptive compression and restoration, reducing errors by applying appropriate transforms like DFT, HWT, and DCT.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is compressed using compressive sensing, then data compression efficiency is improved, but restoration error increases
Solution Approach 1:
The patent applies dynamics by adaptively selecting different representation matrices and measurement matrices based on the characteristics of different data segments. Instead of using fixed matrices, the system dynamically adjusts the compression parameters to match the data being compressed, thereby improving both compression efficiency and restoration accuracy simultaneously
Solution Approach 2:
The patent changes parameters by using multiple different representation matrices (e.g., DFT, DCT, wavelet transforms) and measurement matrices rather than relying on a single fixed set. By selecting the appropriate matrix combination based on data characteristics, the system optimizes the balance between compression ratio and restoration fidelity
2Device complexity
If a single representation matrix and measurement matrix are used for compression, then device complexity is reduced, but restoration accuracy deteriorates
Solution Approach 1:
The patent segments the data into different parts or blocks and applies different representation matrices and measurement matrices to each segment. This segmentation allows the system to handle diverse data characteristics without requiring a single complex universal matrix, thus managing complexity while improving restoration accuracy for each segment
3Manufacturing precision
If adaptive compression methods are used to reduce restoration error, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-defining multiple representation matrices and measurement matrices that can be selected based on data characteristics. This preparation allows the adaptive compression system to achieve high restoration accuracy without requiring complex real-time computation, as the appropriate matrices are already available for selection
Data Source
AI summary
A first electronic device according to various embodiments may select one of a plurality of representation matrices and one of a plurality of measurement matrices on the basis of a pattern and/or feature of data received from a sensor. The selection of the representation matrix and the measurement matrix may be performed on the basis of machine learning. Based on the selected representation matrix and measurement matrix, the first electronic device may adaptively compress at least a portion of the data. A second electronic device according to various embodiments may restore compressed data on the basis of the result of selecting the representation matrix and the measurement matrix. By dynamically selecting the representation matrix and the measurement matrix on the basis of machine learning, it is possible to reduce an error in the data restored by the second electronic device (e.g., a restoration error).