Study of Soft Force and Displacement Sensor Based on Dielectric Elastomer
Bibliographic record
Abstract
With the desirable physical performances of high strain and low stiffness, a novel soft sensor based on the dielectric elastomer (DE) material shows great potential in the wearable devices and soft robots. Since the DE material has the complex nonlinear characteristics and the electromechanical coupling, most early works only devoted to the sensor structure design. However, the studies on the mathematical modeling of the DE sensor (DES) are insufficient. In this paper, a mathematical model of the soft sensor based on the DE material is built to describe its sensing property. Because the DES is used to measure the displacement and force simultaneously, it can serve as a soft force and displacement sensor (SFDS). Based on the experimental data, the differential evolution algorithm is employed to identify the undetermined parameters of the developed mathematical model of the SFDS. The result of model validation demonstrates the effectiveness of the model.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".