Adaptive Sampling for Measurement Uncertainty Quantification
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Solution Overview
Problem
Measurement uncertainty in sensors and measurement devices is challenging due to factors like sensor drift and environmental noise, leading to inaccurate data analysis and potentially safety-critical decisions, especially in systems like driverless cars or automated aircraft piloting.
Innovation Solution
A method to quantify aleatoric uncertainty by adaptively sampling data at a rate higher than the Nyquist frequency, using a sliding window approach to identify intervals where short-term characteristics of the signal change, allowing for the estimation of measurement uncertainty with a smaller number of samples, and representing it as a probability distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measurements are taken at arbitrary time points when the measurand changes over time, then the measurement process is simple, but the distribution of measurements does not represent measurement uncertainty accurately
Solution Approach 1:
The patent implements dynamic sampling by continuously monitoring the measurand and adjusting the sampling rate and timing based on real-time changes. The system transitions from static arbitrary sampling to dynamic adaptive sampling that responds to the actual behavior of the measurand, ensuring accurate uncertainty representation without requiring complex manual intervention
Solution Approach 2:
The patent employs feedback mechanisms where the system monitors the measurand values and uses this information to determine optimal sampling moments. The feedback loop ensures that samples are collected when the measurand is stable, creating a self-regulating sampling process that automatically adapts to changing conditions while maintaining measurement uncertainty accuracy
2Measurement precision
If a large number of samples are collected to represent measurement uncertainty, then the probability distribution estimation is more accurate, but the time required and data processing load increase
Solution Approach 1:
The patent applies preliminary action by identifying and sampling during periods when the measurand is known to be stable or unchanging. By proactively selecting optimal sampling moments before significant changes occur, the system achieves accurate uncertainty representation with fewer samples than would be required if sampling occurred continuously or arbitrarily throughout all time periods
Solution Approach 2:
The patent uses partial action by collecting samples only during specific intervals when the measurand is stable, rather than continuously sampling. This selective sampling approach provides sufficient accuracy for probability distribution estimation while significantly reducing the total number of samples needed and the associated time and processing requirements
3Measurement precision
If the sampling rate is increased to capture rapid changes in the measurand, then the representation of measurement uncertainty improves, but the number of samples and processing complexity increase
Solution Approach 1:
The patent implements dynamic sampling rates that automatically adjust based on the observed behavior of the measurand. When the measurand is stable, the sampling rate is reduced; when changes are detected, the system increases sampling frequency accordingly. This dynamic adaptation captures rapid changes when needed while avoiding unnecessary sampling during stable periods, reducing overall system complexity
4Quantity of substance
If measurements are taken during periods when the measurand is changing, then more data is collected, but the short-term characteristics of the signal change and compromise uncertainty representation
Solution Approach 1:
The patent applies preliminary action by monitoring the measurand in advance and identifying periods of stability before collecting samples. This allows the system to proactively select sampling intervals when the signal characteristics are known to be stable, ensuring that the collected data represents true measurement uncertainty rather than being contaminated by transient changes during collection
Data Source
AI summary
A computer-implemented method for sampling data from a measurement device for representing uncertainty in measurements made by the measurement device, the method comprising: obtaining a data set comprising time-sequential data elements generated by the measurement device; and: (a) calculating a statistic of a sub-set of data elements consecutive within the data set; (b) comparing the value of the statistic to a reference value; and, (c) if the value of the statistic differs from the reference value by less than a threshold amount, then: modifying the sub-set by appending to the sub-set at least one additional data element which is subsequent to the sub-set; and, repeating steps (a) to (c) for the modified sub-set of data elements; (d) if the value of the statistic differs from the reference value by more than said threshold amount, then: outputting the sub-set collectively as a sample set of data elements generated by the measurement device for representing uncertainty in measurements made by the measurement device; repeating steps (a) to (d) in respect of a subsequent sub-set of data elements consecutive within the data set.