Bibliographic record
Abstract
These Proceedings present the written contributions of the participants of the 2023 2nd International Conference on Electrical, Control and Information Technology (ECITech 2023) which was held in the form of virtual conference from 24th to 26th February 2023, at the Changsha city, China, supported by Concordia University, Canada. This was the second version of the annual meeting that began in 2022, with about 50 participants. The Proceedings consist the contributions that were presented as speeches or presentations at the Conference. All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Proceedings Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. The topics of papers collected in this volume cover but are not limited to: Electromagnetic Compatibility, High Voltage Insulation Technologies, System Engineering Theory and Method, Intelligent Manufacturing and Industrial Intelligence, Intelligent Robots and Autonomous Agents, Information Technology Management, etc.. The three-day scientific program of the ECITech 2023 consisted of Keynote Speeches, Oral Presentations, Poster Presentations and Academic Investigation with the participation of graduate students, professors, researchers and entrepreneurs from China, Canada, Italy, UK, Kuwait, Spain, Turkey, Iran, Malaysia, India, among others. Moreover, the objective of ECITech was to bring together national and international researchers in order to establish a network of scientific cooperation with a global impact in the area of the electrical, control and information technology; to promote the exchange of creative ideas and the effective transfer of scientific knowledge, from fundamental research to innovation applied to electrical solutions and to advance in the development of new research by means of efficient transference of the knowledge between sectors academia and industry. At the keynote speech part of the Conference, Prof. Marco C. Campi (University of Brescia, Italy) provided a general and easy-to-access overview of the VRFT method in his report Virtual Reference Feedback Tuning (VRFT): Handy Tuning of Industrial Controllers. And Prof. Chun-Yi Su (Concordia University, Canada) discussed a newly proposed modeling and parameters learning of dielectric elastomer enabled soft robots via data-in-loop approach, showing the status of the art of the current research for modeling of the smart-material based soft robots. The speakers’ brilliant speeches made the attendees feel like bathing in a feast of knowledge. We would like to thank the members of reviewers for their kind assistance in reviewing the papers. We would also extend our best gratitude to keynote speakers for their invaluable contribution and worthwhile ideas shared in the Conference. Special acknowledgements go to the Editors and staff of the Journal of Physics: Conference Series for their hard work in making this volume published. We hope that the series conference of ECITech will be even greater than ever before in the future! The Committee of ECITech 2023 List of Committee Member is available in this pdf.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.544 | 0.398 |
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".