The Influence of the Load Type on the Center Frequency Tuning of the Variable-Flux Biaxial Energy Harvester
Bibliographic record
Abstract
The variable-flux biaxial energy harvester <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="(" close=")" separators="|"> <mrow> <mi mathvariant="normal">V</mi> <mi mathvariant="normal">B</mi> <mi mathvariant="normal">H</mi> </mrow> </mfenced> </math> is a novel vibration energy harvester <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mfenced open="(" close=")" separators="|"> <mrow> <mi mathvariant="normal">V</mi> <mi mathvariant="normal">E</mi> <mi mathvariant="normal">H</mi> </mrow> </mfenced> </math> architecture in which a ferromagnetic mass moves against an assembly of a coil and a stationary permanent magnet. The varying flux lines induce potential differences across the coil terminals. Unlike conventional VEHs, the center frequency of the VBH can be tuned by mechanical, electronic, or electromagnetic mechanisms to maximize the generated power. In this research, we demonstrate the impact of two types of loads, purely resistive and complex RC loads, on the harvester center frequency. We also demonstrate a mechanism that utilizes RC load to tune the center frequency of the VBH. In comparison to conventional purely resistive loads, the introduced capacitive load allows control of the total VBH stiffness and, accordingly, modulates the center frequency and eliminates the need for mechanical pretuning of the VBH center frequency.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".