Axis-Dependent Data Encoding Using Linear Combination Approximation
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Solution Overview
Problem
Conventional data encoding techniques struggle to compress axis-dependent data for industrial machines due to its uniform appearance frequency, making it difficult to improve error compensation accuracy beyond a certain data size limit.
Innovation Solution
A data encoding device that utilizes a model approximation encoder to encode axis-dependent data as a linear combination of each axis error, allowing for compression and approximation of data that was previously difficult to compress using conventional entropy encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of input points of error amount is increased to improve error compensation accuracy, then error compensation accuracy is improved, but the data size exceeds the upper limit of inputtable data size
Solution Approach 1:
The patent segments the error compensation data by dividing it into multiple regions based on coordinate values. Instead of treating all data points uniformly, the system divides the data space into regions and selects representative points within each region, thereby reducing the total number of data points while preserving the essential error characteristics across the entire workspace.
Solution Approach 2:
The patent transforms the error compensation data from a dense set of coordinate-error pairs into a reduced set by changing the selection criterion from including all points to including only representative points. This parameter change in data selection strategy reduces data size while maintaining the ability to represent the error distribution accurately.
2Quantity of substance
If conventional entropy encoding is applied to compress axis-dependent data, then data compression is achieved for data with non-uniform appearance frequency, but axis-dependent data with uniform appearance frequency cannot be compressed
Solution Approach 1:
The patent segments axis-dependent data into multiple regions based on coordinate values, transforming the data structure from a uniform distribution across the entire space to localized regions. This segmentation creates non-uniform appearance frequency within each region, making the data suitable for entropy encoding compression.
Solution Approach 2:
The patent applies different encoding strategies to different regions of the data space. By dividing the data into regions and selecting representative points locally, the system creates local non-uniformity in appearance frequency, enabling entropy encoding to be applied effectively to each region while maintaining overall data accuracy.
Data Source
AI summary
Provided is an encoding technology with which it is possible to encode and compress shaft-dependent data that is dependent on coordinate values of each shaft of an industrial machine. A data encoding device 1 comprising a model approximation encoding unit 11 that, on the basis of some shaft-dependent data that is dependent on coordinate values of each shaft of an industrial machine and a linear combination model that approximates the shaft-dependent data as a linear combination of shaft data pertaining to the industrial machine, generates encoded shaft-dependent data in which the shaft-dependent data is encoded.