High Accuracy, Low-Invasive Displacement Sensor (HALIDS)
Why this work is in the frame
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Bibliographic record
Abstract
Sub-micron accuracy and precision in measuring unconstrained, spatial motion is pivotal in science and engineering. It imposes stringent requirements on the accuracy, reliability, and invasiveness of sensing devices (including lasers, lidar sensors, or optical scales). While the capabilities of these devices have seen dramatic improvements in the last decades, the needs for sub-micron accuracy, low-invasive sensors greatly outpace the available solutions. The root cause of measurement difficulties is a conflict between the very nature of motion (simultaneous translations and rotations relative to a chosen reference base) and the fundamental requirement of measurement accuracy known as the Abbe principle. Small and accurate Microsystems Technology based inertial sensors (accelerometer and gyroscopes) can alleviate, or at least significantly mitigate, many of the current difficulties. If contained in small Inertial Measurement Units (IMU) and equipped with a wireless signal transmission, they can be placed on or very close to the objects whose motion is to be measured. Furthermore, as long as the IMU, its fixture, and some region of this object around the fixture can be considered as rigid, coordinate transformation rules facilitate converting signals measured by IMU into translations and rotations of any point in this rigid region. Consequently, a virtual 6-DOF sensor can be created. Its dimensions are infinitesimally small, and it can be “placed” anywhere within the above rigid region. In particular, it can be placed such that it is collinear with the displacements of the cutting tool or robot’s end effector, and satisfies the Abbe principle. We present a High Accuracy, Low-Invasive Displacement Sensor (HALIDS) for application in manufacturing and in engineering design. The sensor is capable of measuring simultaneously 6-degrees-of-freedom displacements of objects. Its short term resolution is down to 0.1 nanometer and accuracy better than 1 micron. The sensor can be built small, light and wireless. Results from experimental evaluation of two prototype versions are presented.
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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) | 0.001 | 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 it