Using Artificial Neural Network for Error Reduction in a Nondispersive Thermopile Device
Bibliographic record
Abstract
The outputs of electronic devices are sensitive to the noise (thermal noise, background noise, etc.) and the change of operating conditions such as temperature or power supply voltage. As a result, the output data may have errors. Theoretically, if these changes are monitored, they can be used to support to correct the errors. The device using three nondispersive thermopiles to detect Fusarium encounters the same problem. The three outputs come from broadband, λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , and λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> thermopiles. The broadband thermopile works in 1μm to 20μm range; λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -thermopiles work in 6.09μm±0.02μm and 9.49μm±0.22μm respectively. One temperature sensor and two voltage-monitoring-modules were installed to monitor the operating conditions of the device. The information from the changes and the background noise of the device are used to train an artificial neural network. The training data are collected under unstable operating conditions. After the training, the trained neural network is used to fix errors in the output data. From the experiments results, the best error ratios of the raw and corrected data, E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">raw</sub> /E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">corrected</sub> , are 23.6, 13.8, and 18.5, respectively. The results were achieved by applying the external training and forcing method. Through the promising results, the technique of using support inputs and artificial neural network to correct data can be applied in any device which encounters similar problem. This method can help to improve accuracy and reliability of the sensor systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".