LOCALLY WEIGHTED REGRESSION IN SENSOR CALIBRATION TECHNIQUES
Bibliographic record
Abstract
Recently, the technology of sensor arrays combined with a neural network system applied to multi-component analysis has been used in many industrial areas. For some difficult or inaccessible environments, such as the agricultural environment, nuclear reactors, underwater, and space environments, it is desirable that sensors are calibrated online for more reliable measurements.\n\nIn this thesis, research into a new technique of determining the individual component concentration in a mixture is described. Locally Weighted Regression (LWR), a memory-based statistical learning algorithm, combined with sensor fusion and a simulated, partially selective sensor array were used to identify individual concentrations in a mixture. The performance of the LWR technique was compared with the performance of the neural network technique. This thesis also presents an online sensor calibration scheme for an un-calibrated replacement sensor using the LWR technique.\n\nThe validity of the proposed scheme was evaluated through computer simulations and compared with artificial neural network results. The results show that the LWR technique is able to accurately determine the individual component concentrations in a mixture. Compared with the neural network technique, the LWR algorithm is more flexible and time saving for real-time measurements. This research has also shown that the calibration of a replacement sensor could be accomplished\nonline using the outputs of the other sensors in the sensor array as a reference for the calibration process.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".