Two-Stage Frequency Selection for Microwave Moisture Sensors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current microwave frequency sweep methods lack a comprehensive approach to select optimal frequencies, leading to noise and redundant data in moisture content measurements, which affects measurement accuracy.
Innovation Solution
A two-stage frequency selection method using a random forest-recursive feature elimination algorithm to generate candidate frequency subsets, evaluate their performance, and select an optimal frequency subset through majority voting, optimizing the selection process by varying the hyper-parameter PreNum and combining attenuation and phase shift data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microwave frequency sweep data is collected across a wide frequency range, then measurement coverage and data completeness are improved, but noise and redundant data increase, reducing measurement accuracy
Solution Approach 1:
The patent extracts and removes inferior frequencies from the frequency sweep data that contribute noise and redundancy. By identifying and eliminating these problematic frequency components, the method retains only the useful frequency ranges that provide meaningful measurement information, thereby improving moisture content measurement accuracy while reducing data noise.
Solution Approach 2:
The patent applies different quality assessments to different frequency components within the sweep data. Rather than treating all frequencies uniformly, the method identifies specific frequency ranges with superior measurement characteristics and weights or prioritizes them, while downweighting or removing frequencies with poor measurement quality, thus optimizing the overall measurement precision.
2Quantity of substance
If all measured frequency data is retained for analysis, then data completeness is maintained, but processing complexity and computational load increase
Solution Approach 1:
The patent extracts only the essential frequency components that contribute meaningfully to moisture content measurement. By removing inferior frequencies that add computational burden without providing proportional measurement value, the method reduces data volume and processing complexity while maintaining measurement accuracy.
Solution Approach 2:
The patent segments the frequency sweep data into superior and inferior frequency components, processing only the beneficial segments for final measurement calculation. This segmentation approach allows the system to handle reduced data volumes efficiently while focusing computational resources on the most informative frequency ranges.
3Ease of operation
If a fixed frequency selection method is used, then implementation simplicity is maintained, but adaptability to different materials is reduced
Solution Approach 1:
The patent implements a dynamic frequency selection approach where the system automatically identifies and adapts to the optimal frequency characteristics for each specific material being measured. Rather than using a static, pre-defined frequency set, the method dynamically adjusts which frequencies are considered superior based on the material's specific electromagnetic properties, thereby achieving both ease of operation and material-specific adaptability.
Solution Approach 2:
The patent enables the measurement system to automatically identify and select its own optimal frequency range for each material without requiring manual configuration or expert intervention. The system self-adapts by analyzing the frequency sweep data characteristics and autonomously determining which frequencies provide the best measurement signal for the specific material being tested.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method effectively removes inferior frequencies, reducing noise and redundant data, thereby improving the accuracy of moisture content measurements by selecting the most relevant frequencies for microwave frequency sweep signals.
Implementation Method 1
each component in a material has different effects on microwave signals
Implementation Method 2
microwave characteristics (such as attenuation and phase shift) measured at each frequency
Data Source
AI summary
Disclosed is a two-stage frequency selection method and device for microwave frequency sweep data. The method includes: acquiring microwave frequency sweep data; performing frequency selection on the microwave frequency sweep data by using a random forest-recursive feature elimination algorithm, taking a preset parameter in the random forest-recursive feature elimination algorithm as a hyper-parameter, changing the value of the hyper-parameter, and generating a series of candidate frequency subsets within different frequencies; building prediction models on the basis of the frequency sweep data corresponding to the candidate frequency subsets of different frequencies; evaluating the performance of each prediction model by means of 10 fold cross validation, and calculating evaluation index values of model performance; and taking the evaluation indexes as a voting basis, and selecting an optimal frequency subset by using a majority voting method.