2nd International Conference on Design and Manufacturing Engineering (ICDME2017)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,006 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,147 | 0,086 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».