Comparison of harvested energy in AC and DC standard interface circuits using Metglas2605SC
Bibliographic record
Abstract
One of the solutions for power generation in wireless technology is energy harvesting technique. The energy harvesting means the conversion of ambient energy such as vibration, heat, light, RF energy, etc. to electrical energy using smart materials and their structures. Harvesting vibration energy from magnetostrictive material attracts more attention in recent years and it can be used in applications of intelligent microgenerator. In this paper, a simple interface circuits have been introduced for magnetostrictive materials. 15 layers Metglas2605SC are used for magneto-elements. Theoretical modeling of the system done to deliver results. First, the standard AC circuit and then standard DC circuit was investigated by adding an electrical bridge and their harvested energy was compared. Followed by mentioned energy harvesting analyses the effect of four important parameters, equivalent load resistance, frequency, displacement amplitude and external force amplitude are examined. Finally, considering the obtained equations and effective parameters that was tested in this paper to compare the Metglas energy harvesting in AC and DC standard circuits, indicated that the AC Standard circuit harvested more energy. Keywords: Energy harvesting, Smart materials, Standard AC circuit, Standard DC circuit, Magnetostrictive, Microgenerators, MEMS, Metglas2605SC
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".