High Sensitive Force Sensing Based on the Optical Fiber Coupling Loss
Bibliographic record
Abstract
In the present paper, an innovative miniaturized optical force sensor is introduced for use in medical devices such as minimally invasive robotic-surgery instruments. The sensing principle of the sensor relies on light transmission in optical fibers. Although the sensor is designed for use in surgical systems, it can be used in various other applications due to its novel features. The novelty of the sensor lies in offering four features in a single miniaturized package using a simple optical-based sensing principle. These four features are the high accuracy/resolution, the magnetic resonance compatibility, the electrical passivity, and the absence of drift in the measurement of continuous static force. The proposed sensor was micromachined using microsystems technology and tested. The sensor measures 18 mm, 4 mm, and 1 mm in length, width, and thickness, respectively. The sensor was calibrated and its performance under both static and dynamic loading conditions was investigated. The experimental test results demonstrate a 0.00–2.00 N force range with an rms error of approximately 2% of the force range. Its resolution is 0.02 N. The characteristics of the sensor such as its size, its measurement range, and its sensitivity are also easily tunable.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".