Bilateral Magnetic Micromanipulation Using Off-Board Force Sensor
Bibliographic record
Abstract
This paper introduces a novel haptic-enabled magnetic micromanipulation platform with promising potential for extensive biological and biomedical applications. This platform consists of two separated basic sites: 1) the slave site that uses a controlled magnetic field for manipulating a ferromagnetic microdevice and 2) the master site that uses a haptic-enabled device for the position and the force communication between the human operator and the microdevice. Due to the size restriction of the microdevice, attaching force sensors to the microdevice is impractical. Thus, to preserve a high feeling of a microdomain environment for the human operator, the applied force/torque from the environment to the microdevice is estimated with a novel off-board force sensing mechanism. This force sensing mechanism uses the produced magnetic flux information and the real position of the microdevice to estimate the environmental force applied to the microdevice. A scaled force-position teleoperation scheme is employed for this haptic application to scale down the macrodomain position for microdomain application and scale up the microdomain force for macrodomain sensing of the human operator. Conducting several experiments in different conditions, precise motion tracking with high accurate force transfer to human operator has been reported, RMS of position tracking errors of 0.2 mm with 1.27- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>$\mu$</tex-math></inline-formula> N accuracy force sensing for single-axis motion.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".