Bibliographic record
Abstract
The main focus of this thesis is the reduction of the size of the switch mode power supplies (SMPS), in low power applications. The SMPS (dominantly made of switching converters) size is dominated by their passive components, where the output capacitor is the largest contributor to the area consumed by the SMPS. Output capacitor size reduction leads to higher power density, and subsequently increase in the power processing efficiency, while maintaining a low cost of the system. These factors are relevant in design of low-power electronic devices. Therefore, the work in this thesis presents one of the possible solutions that will reduce the size of the output capacitor of a converter. These goals are achieved by implementing a fast transient controller for the SMPS. The introduced controller is applicable to multiple conventional switching stages (e.g., buck, boost, buck-boost, etc.) that are most dominantly used in the low- power, low-voltage applications. Additionally, it is shown that the controller is applicable to emerging topologies (e.g. Multi-Level Flying Capacitor (ML-FC) converters), that allow further miniaturization of the SMPS, thus presenting an interesting alternative to conventional converters. This controller utilizes same hardware for all the switching stages, and has simple tuning procedure. It is developed as a combination of analog components and synthesizable Verilog code, thus making the controller suitable for integrated circuit (IC) implementation. The effectiveness of the control method has been experimentally verified with a low-power conventional and a ML-FC converter, processing power up to 100W.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".