Design and Optimization of Wideband MEMS Energy Harvester Using Graph Neural Network
Bibliographic record
Abstract
Piezoelectric microelectromechanical system (MEMS) vibration energy harvesters commonly experience frequency incompetence in real-world applications. Operating at higher frequencies is often insufficient due to the low-frequency nature of ambient vibrations. The operational frequency range, so-called bandwidth, is a crucial characteristic especially under the unpredictable or uncontrollable ambient vibration conditions. In this study, we propose an innovative design methodology for piezoelectric MEMS energy harvesters that incorporate multiple cantilevers and masses to convert vibration to electricity as a power source. To achieve optimal performance, we employ a machine learning approach by using a graph neural network (GNN) model trained with finite element method (FEM) simulation data. Along with the trained model, we focus on optimizing the geometry sizes of the proposed structure to reach the largest harvested voltages and the frequency metrics by using a genetic algorithm. The optimized harvester exhibits six low resonant frequencies from 91.17 Hz to 102.54 Hz, featuring advantageous wideband nature, in addition to significant voltage outputs.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".