Comparative Analysis of Machine Learning Techniques for Temperature Compensation in Microwave Sensors
Bibliographic record
Abstract
The planar nature of microwave sensors leaves them vulnerable to ambient temperature changes with potential impact on the perception of the material under test. A temperature compensation technique is required to consider its direct effect on the dielectric property of materials. In this article, machine learning algorithms are employed in two configurations of classifier and regressor on frequency response of a split-ring resonator operating at 1.19 GHz. A wide range of dielectric constant is covered with concentrations of [0:20%:100%]-methanol/acetone in water with a temperature cycle of 25 °C–50 °C. This broad variety of cases captures the complicacy of entangled trends that are recognized using classifiers regardless of the measurement temperature. In the next step, the ambient temperature is extracted from the same measured data. This is accomplished by cascading the classifier with a regressor pool that contains trained parameters for individual classes. Highly accurate classification of material types followed by their corresponding temperature (using linear regression with$R^{2}=97$% averaged over tenfold cross validation and a mean absolute error of 0.58 °C) leads to investigating limit of detection in the proposed scheme. This step, through testing of [0:1%:5%] methanol in water, identified multilayer perceptron (MLP) and support vector machine (SVM) as the best performing algorithms. Final hyperparameter optimization yields parameters for these two models that provide accuracy of 0.97 and 0.99, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".