Improved Capacitive Proximity Detection for Conductive Objects through Target Profile Estimation
Why this work is in the frame
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Bibliographic record
Abstract
The accuracy of a capacitive proximity sensor is affected by various factors, including the geometry and composition of the nearby object. The quantitative regression models that are used to seek out the relationship between the measured capacitances and distances to objects are highly dependent on the geometrical properties of the objects. Consequently, the application of capacitive proximity sensors has been mainly limited to detection of objects rather than estimation of distances to them. This paper presents a capacitive proximity sensing system for the detection of metallic objects with improved accuracy based on target profile estimation. The presented approach alleviates large errors in distance estimation by implementing a classifier to recognize the surface profiles before using a suitable regression model to estimate the distance. The sensing system features an electrode matrix that is configured to sweep a series of inner-connection patterns and produce features for profile classification. The performance of the sensing modalities is experimentally assessed with an industrial robot. Two-term exponential regression models provide a high degree of fittings for an object whose shape is known. Recognizing the shape of the object improved the regression models and reduced the close-distance measurement error by a factor of five compared to methods that did not take the geometry into account. The breakthroughs made through this work will make capacitive sensing a viable low-cost alternative to existing technologies for proximity detection in robotics and other fields.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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