Magnetic Field Control for Haptic Display: System Design and Simulation
Bibliographic record
Abstract
We present magnetic rendering, a new haptic display method applying an electromagnet array to produce magnetic field in mid-air where the force field can be felt as magnetic repulsive force exerted on the hand through the attached magnet disks. The magnetic field is generated by a specifically designed electromagnet array driven by direct current. By attaching small magnet disks on the hand, the tactile sensation can be perceived by the user. This method can provide a strong tactile force and avoid cumbersome attachments with wires, and thus, it is suitable for a colocated visual and haptic display. In this paper, we introduce the detailed design of the electromagnet array for haptic rendering purposes, which is modeled and tested using finite-element method simulations. We characterize the model mathematically, and apply recursive least squares adaptive control algorithm for controlling the magnetic field. We evaluate the performance of our simulated model in terms of force strength, operation distance, and force stiffness. We then implement and test the control algorithm, which results in fast and accurate convergence. We conclude with simulations on a 15-by-15 model to generate a haptic human face, which results in a smooth force field and accurate force exertion on the control points.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".