Digital 3D Ecosystem Modeling Oriented to Machine Vision
- 10.2991/eame-17.2017.80How to use a DOI?
- component; product digital ecosystem; assembly relation network; design entropy; digital ecosystem evolution
This paper introduces the concept of ecological system to the design of the product, put forward the concept of product digital ecosystem and established the corresponding model, designed to draw lessons from ecological system, and apply the mechanism of self-organization and adaptive, when design demand change, product system can carry on the corresponding structural changes independently, implement the evolutionary design of the product, to meet the needs of people for the product function, improve efficiency, promote the development of product design. This article first has carried on the modeling of product digital ecosystem, defines the factors of the model such as individual, population, community and ecosystem in detail. At the same time, combining with the infectious disease model, the paper puts forward the evolution model of the ecological system complex network, and introduce products ecosystem evolution with specific instances of process. And, the concept of entropy is introduced into the product digital ecosystem, puts forward digital ecosystem evolutionary algorithm based on assembly relation network, implement product evolutionary design process independently. Finally, apply the theory of this article to engine products, and has obtained the expected effect.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yijie Hao AU - Wenjing Han AU - Chunfeng Shen AU - Shun Zhang PY - 2017/04 DA - 2017/04 TI - Digital 3D Ecosystem Modeling Oriented to Machine Vision BT - Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017) PB - Atlantis Press SP - 338 EP - 341 SN - 2352-5401 UR - https://doi.org/10.2991/eame-17.2017.80 DO - 10.2991/eame-17.2017.80 ID - Hao2017/04 ER -