2nd International Conference on Design and Manufacturing Engineering (ICDME2017)
Bibliographic record
Abstract
Preface The 2017 International Conference on Design and Manufacturing Engineering (ICDME 2017) was successfully held at Guangdong University of Technology, Guangzhou, a famous megacity for remarkable history and fine cuisine, China from August 1-3, 2017. Guangzhou also has the most dynamic and pioneering economy with fast growing manufacturing sector spearheaded by automotive and shipbuilding industries, among many others, demanding the latest knowledge and technology for creative products and efficient manufacturing process in meeting challenges of future manufacturing technology. The ICDME 2017 reflected the technology needs of Guangzhou and beyond with its global participation and diverse but focused topics concerning design and manufacturing engineering presented in the truly international conference. This proceeding contains the reviewed papers presented at the ICDME 2017 and covered most hot and important issues related to the design and manufacturing engineering of many industries with a focus on the automotive technology. The ICDME 2017 conference program consisted of presentations in forms of keynote, oral, and poster from researchers, engineers, and graduate students working in fields of materials science, mechanical engineering, measurement technology, materials processing, and design methodology to report novel methods, findings, and designs. Although in its second since the inception, the ICDME has positioned itself as a platform for technical exchanges, academic promotion, and collaboration enabling for participants with diverse interests, background, objectives, and expertise. With the fast globalization of technology and manufacturing, technical issues must be discussed and disseminated in a global forum participated by researchers and engineers with international experiences and activities. This goal of the ICDEM is well achieved by the international participants from countries with extensive technology development such as Egypt, Malaysia, Turkey, in addition to more active countries like Germany, China, Canada, India, and Australia. As its tradition, in this year's ICDME, there were keynote talks presented by internationally renowned scholars from top institutions with broad topics on design and manufacturing. Then there were sessions focused on materials, signal analysis, vehicular engineering, machine structures, mechatronics, and robotics with oral presentations and posters. Papers in this volume are selections based on reviews from the conference technical committee and they reflected the high quality and broad technical interests of the conference. We want to express our gratitude to all members of conference committees and reviewers who spent their valuable time for the successful conference with carefully organized programs, well-planned sessions, high quality conference papers, and pleasant social hours and events. We would also like to thank all authors who have contributed to this conference and committee members, reviewers, speakers, session chairs, sponsors, and support staff for the great success of ICDME 2017.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.147 | 0.086 |
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".