High Accuracy, Low-Invasive Displacement Sensor (HALIDS)
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".