Power density improvement based on investigation of initial relative position in an electromagnetic energy harvester with self-powered applications
Bibliographic record
Abstract
Abstract In this paper, we originally report a breakthrough in the power density of a novel electromagnetic energy harvester to scavenge ambient low-frequency vibration energy. The harvester adopted a configuration of alternating south- and north-pole magnet array, which causes a step-change in magnetic flux density, contributing to high electromotive force output. Through analysis of the coil configuration and the initial relative position between coils and magnets, the harvester can take full advantage of the abrupt flux density change, which enhances its output power significantly. Experimental results adequately validated the simulation analysis regarding the correlation between the initial relative position and output power, and exhibited a high output performance. Namely, the maximum average power and power density the harvester yields are 44.8 mW and 1.6 mW c m − 3 , respectively, with the optimum resistance of 30 Ω at resonance under the excitation of 1 g. It took the harvester around 5 min to charge a button lithium battery up to 21%. Meanwhile, a LED array composed of 180 diodes was successfully lighted up, and a calculator was powered for around 630 s within a 20 s of charging period. This research shows great potential in the development of self-powered systems.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".