A dependable infrastructure of the electric network for e-textiles
Bibliographic record
Abstract
Electronic textiles, known as computational fabrics, offer an emerging method for constructing wearable and large area applications. Because e-textiles are battery-driven and fault-prone systems, there is a need for developing a dependable infrastructure of the electric networks for e-textiles. In this paper, a new infrastructure of the power networks for e-textiles, flexible power network (FPN), is presented. Instead of drawing power from a fixed battery as in the conventional electric networks, the power consuming nodes in a FPN can obtain power energy from one of the choices of batteries available with the help of the battery selectors. We also introduce the over current protectors into the battery nodes (BN) to protect the batteries from wasting the charge when short-circuit faults occur. The electric features of battery selectors and over current protectors, the two types of important electric devices used in FPNs, are illustrated in the paper. We have performed simulation experiments and the results show that our FPNs are more dependable than some common electric networks published before in the cases of short- and open-circuit faults.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".