2nd International Conference on Design and Manufacturing Engineering (ICDME2017)
Bibliographic record
Abstract
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".