A low power electronics converter with input resistance control for piezoelectric energy harvesting
Bibliographic record
Abstract
This work describes the modeling and analysis of a low power electronics converter for piezoelectric energy harvesting. The proposed converter consists of a diode bridge rectifier, a MOSFET switch driven by a PWM signal, and a rechargeable battery as the storage device. The circuit is used to convert mechanical vibration energy into electric charge stored in a battery using piezoelectric transducers mounted on a flexible structure. By utilizing an averaging scheme, it is shown that the converter exhibits a pseudo-resistive behavior across its input terminals. An analytical expression for the input resistance of the converter is derived and further evaluated by simulations and experiments. A self-powered converter is utilized to convert vibration energy into electric charge whose input resistance can be set to a prescribed value depending on the input source resistance. It is also shown that by applying a feedback controller to the circuit, the input resistance of the converter can be regulated to a desired value. This feature may be utilized to achieve impedance matching in applications requiring maximum power transfer. Experimental studies are conducted to verify the performance of the self-powered circuit and the feedback control scheme.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".