Transfer-Learning-Aided Optimization for a Low-Frequency Wideband MEMS Energy Harvester
Bibliographic record
Abstract
Nowadays high frequency and low bandwidth are two critical challenges for microelectromechanical system (MEMS) energy harvesters. Energy harvesters with high operating frequency are not desirable considering the low-frequency nature of ambient vibrations. The operational frequency range (known as bandwidth) is another important characteristic that should be considered under an unpredictable or uncontrollable condition of ambient vibrations. This paper presents an innovative design of a vibration-based piezoelectric MEMS energy harvester structure, which encloses four masses. To gain premium performance, we have developed a transfer learning method to train a deep neural network model with FEM simulation data. By using this trained model for estimating harvester performance, we optimize the proposed structure with a genetic algorithm. Our optimized harvester not only features four low resonant frequencies (between 70Hz and 161Hz) and high bandwidth, but also reaches a good amount of harvested voltage. Our simulation results confirm its efficacy and superiority over the alternative designs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".