Lookup Table Gradient Constraints for Non-Uniform Data Calibration
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Solution Overview
Problem
Conventional calibration techniques for lookup tables, especially those dealing with non-uniformly spaced data, struggle to accurately calibrate systems, leading to inefficient system performance and excessive fuel consumption in applications like automobile engines.
Innovation Solution
The technique involves mapping quantities onto cells and applying a table gradient constraint to maintain variation within a bound, allowing for optimization and calibration of lookup tables even when data points do not coincide with cell locations, using methods like radial basis functions and interpolation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration techniques are used with non-uniformly spaced data, then the calibration process is simpler, but accurate lookup table calibration becomes difficult or impossible
Solution Approach 1:
The patent introduces an intermediary mapping process that transforms non-uniformly spaced data points into a uniform grid structure. This intermediary step enables conventional calibration techniques to work effectively with non-uniform data by first mapping the data onto a regular lattice, applying calibration constraints, then mapping back to the original non-uniform points.
Solution Approach 2:
The patent changes the parameter representation by transforming the calibration problem from operating directly on non-uniform data points to operating on uniformly spaced grid points. This parameter transformation allows standard calibration algorithms to be applied while still achieving accurate calibration for the original non-uniform data structure.
2Loss of energy
If lookup tables are improperly configured, then system performance may be simpler to implement, but fuel consumption increases and efficiency decreases
Solution Approach 1:
The patent implements feedback mechanisms through gradient constraints that monitor and control the variation between adjacent lookup table cells. By constraining the gradient (rate of change) between neighboring cells, the system ensures smooth transitions that prevent abrupt changes in controlled parameters, thereby optimizing fuel consumption and system efficiency.
Solution Approach 2:
The patent applies local quality control by imposing gradient constraints specifically on adjacent cells in the lookup table. This ensures that local variations between neighboring points are bounded, creating smooth local behavior that prevents abrupt changes while allowing global optimization of the entire lookup table configuration.
3Adaptability or versatility
If data points do not coincide with cell locations, then more flexible data representation is achieved, but conventional calibration cannot access or manipulate the data
Solution Approach 1:
The patent performs preliminary action by mapping non-uniform data points onto a uniform grid structure before applying calibration operations. This pre-mapping step prepares the data in a format that is compatible with conventional calibration techniques, enabling easy access and manipulation during the calibration process while preserving the original flexible data representation.
Solution Approach 2:
The uniform grid serves as an intermediary representation that bridges the gap between flexible non-uniform data points and the requirements of conventional calibration algorithms. This intermediary structure allows calibration operations to be performed easily on grid-aligned data while the mapping transformations maintain compatibility with the original non-uniform data format.
Data Source
AI summary
A technique for operating on points having quantities associated therewith using a table gradient constraint is provided. The technique may include mapping the quantities onto cells, where at least one of the quantities is not on a cell prior to the mapping. The technique may further include applying a table gradient constraint to the mapped quantities, where the applying constrains quantities to maintain variation among the quantities within a bound.