Method for Improving Temperature Measurement Accuracy of NTC Thermistors Based on Multisegment Linear Regression
Why this work is in the frame
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Bibliographic record
Abstract
With the continuous advancement of technology and the increasing demand for temperature measurement accuracy in various industries, the design and implementation of a high - performance digital thermometer have become an urgent problem to be solved. In this study, a digital thermometer was successfully designed, which could be widely applied to multiple fields, such as water temperature measurement, temperature monitoring in daily life, and temperature control in industrial production processes. In the hardware architecture of this thermometer, the NTC thermistor and NY8B062D single - chip microcomputer were respectively assigned key functional roles. The former was used to sense the subtle changes in ambient temperature and convert them into electrical signals, while the latter was responsible for the subsequent conversion, analysis, and processing of these electrical signals. The temperature data processed by the single - chip microcomputer would be transmitted to the display module through a specific communication protocol, thus realizing the real - time display of the measured temperature values. In order to effectively overcome the impact of the nonlinear characteristics of the thermistor itself on the measurement accuracy, this study adopted the multisegment linear regression technology. Through the collection and analysis of a large number of experimental data, multiple temperature segmentation intervals were determined, and the optimal linear regression equations were fitted for each interval. At the same time, combined with the 12 - bit ADC technology, the resolution and quantization accuracy of signal sampling were greatly improved. After a series of rigorous experimental tests and optimization adjustments, the measurement accuracy of this thermometer was significantly enhanced, and its measurement error was successfully limited within ±1%, providing reliable technical support for accurate temperature measurement in practical applications.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it