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Proceedings of the 3rd annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2022). Volume 1, Advanced materials and manufacturing technologies / Banh Tien Long, Kozo Ishizaki, Hyung Sun Kim, Yun-Hea Kim, Nguyen Duc Toan, Nguyen Thi Hong Minh, Pham Duc An, editors.

9783031318245 (electronic bk.)
3031318242 (electronic bk.)
9783031318238 (print)

Cham : Springer, 2023.

English

1 online resource (xxix, 552 pages) : illustrations (some color).

10.1007/978-3-031-31824-5 doi

TA5

620

This book presents selected, peer-reviewed proceedings of the 3rd International Conference on Material, Machines and Methods for Sustainable Development (MMMS2022), held in the city of Can Tho, Vietnam, from 10 to 13 November 2022. The purpose of the conference is to explore and ensure an understanding of the critical aspects contributing to sustainable development with a focus on advanced mechanical engineering, automation, materials, machines and methods. The contributions published in this book come from authors representing universities, research institutes and industrial companies and reflect the results of a very broad spectrum of research, from micro- and nanoscale materials design and processing, to mechanical engineering technology in industry. Many of the contributions selected for these proceedings focus on materials modeling, eco-material processes and mechanical manufacturing. Volume 1 of this book focuses on topics dedicated to advanced materials and manufacturing technologies, ranging from synthesis of new materials to sustainable development manufacturing technology.

References -- Improving the Accuracy of ACI 318-08 Design Standard for Predicting Strength of CFST Columns Using Machine Learning -- 1 Introduction -- 2 ACI 318-08 Standard -- 3 Establish a Prediction Model -- 3.1 Create and Review Data -- 3.2 Prediction Model -- 4 Results and Discussions -- 5 Conclusion -- References -- The Recognition Accuracy in the SSD Model -- 1 Introduction -- 2 Research Deployment -- 2.1 Data Collection and Flower Labeling -- 2.2 Operating Model Environment -- 2.3 Model of Training for Learning -- 2.4 The Real Model Operation -- 2.5 The Performance of the Algorithm

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Online resource; title from PDF title page (SpringerLink, viewed September 7, 2023).

Lecture notes in mechanical engineering, 2195-4364

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