Bi-Mode Electromagnetic Energy Harvester and Energy Management Strategy for Long-Time Monitoring Sensor
Bibliographic record
Abstract
This paper introduces a bi-mode electromagnetic energy harvester (BEEH) designed to harvest energy from micro-vibrations in both translational and rotational modes. The vibration amplitude can be amplified by the vibrator with a suspending structure, and two groups of magnet sets are designed to supply magnetic fields for the coil. The finite element simulations are employed to obtain the optimal magnetic field configuration. An analytical model of the magnetic field and system output of the BEEH is developed, and its accuracy is validated through comparative analysis. In order to improve the output efficiency and stability, an energy management strategy and the corresponding circuit modules are designed. To validate the design and modeling, a prototype of the BEEH is fabricated, and experimental results confirm that the two operational frequencies (100[Formula: see text]Hz and 110[Formula: see text]Hz) align well with the theoretical predictions. The optimum load resistance for the harvester is approximately 10[Formula: see text]k[Formula: see text], and the maximum output power is up to 28[Formula: see text]mW at 100[Formula: see text]Hz under vibration excitation with an amplitude of 10[Formula: see text][Formula: see text]m. Furthermore, the BEEH demonstrates the capability to power multiple loads, thereby validating its efficiency in harvesting energy from micro-vibrations for long-time monitoring applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".