The Future of Power Electronics Circuits: New Technologies and Managed Complexity Will Drive the Future
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The field of power electronics advances through a number of different innovations, ranging from new and better semiconductors (e.g., power MOSFET, insulated-gate bipolar transistor, gallium nitride, silicon carbide), to improved passive components enabled through material science breakthroughs. Moreover, through improved integration and packaging, higher performance and more complex circuits can be implemented. Thanks to digital control and improved simulation tools, new circuit topologies that better utilize the active and passive devices can be implemented in practical designs. At the 10th IEEE Future of Electronic Power Processing and Conversion (FEPPCON X), several invited speakers and participants presented viewpoints and discussed ideas in the session "Future of Power Electronics Circuits." The two invited speakers were Prof. Johann Kolar of Power Electronic Systems Laboratory at ETH Zurich, Switzerland, and Prof. David Perreault of the Power Electronics Research Group at the Massachusetts Institute of Technology, Cambridge. The session also included two invited panelists, Dr. Isik Kizilyalli of the Advanced Research Project Agency for Energy (ARPA-E), Washington, D.C., and Prof. Cian O'Mathuna of Tyndall Institute, University College Cork, Ireland. In addition, Prof. Yan-Fei Liu of Queen's University, Canada, served as note-taker and panelist. Finally, serving as session organizer and panelist was Prof. Robert Pilawa-Podgurski of the University of California, Berkeley.
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it