An Efficient Web Visualization Method of Large Factory Data based on Partial Macro Parametric for Building Digital Twins

2021 ◽  
Vol 45 (5) ◽  
pp. 435-442
Author(s):  
SangSu Choi ◽  
Inho Song
2018 ◽  
Author(s):  
Menghua Duan ◽  
Lin Chen ◽  
Yongchang Feng ◽  
Junnosuke Okajima ◽  
Atsuki Komiya

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
M.A. Boyarchuk ◽  
I.G. Zhurkin ◽  
V.B. Nepoklonov

Author(s):  
V. A. Minaev ◽  
A. V. Mazin ◽  
K. B. Zdiruk ◽  
L. S. Kulikov

The article presents the scientific and methodological issues of formation of digital twins collections based on the use of the multi-aspect recursive decomposition algorithm of the subject area. The general approaches to the solution of topical issues of the modern stage of artificial intelligence are considered. The terminology is concretized in the interrelated areas of knowledge – information – data and its relation with the term of «digital twins» as information containers of knowledge is discussed. The structure, power estimation and metrizability of the information space presented as a recursively defined ordered set of elements – a collection of digital twins (DT-collections) are considered. It is shown that the practical implementation of this approach and its application as part of automated control systems involves maintaining the life cycle of the creation and operation of digital twins in the Integrated information storage, implementing a two-circuit scheme (model) of management. A new cognitive approach to assess the completeness of the knowledge measure in the information space is proposed. The model of the integrated information storage realizing accumulation of knowledge in data banks of primary and secondary information is considered. As an example, a recursive decomposition of a subset of engineering systems of an educational institution is performed.


Sensors ◽  
2015 ◽  
Vol 15 (10) ◽  
pp. 26675-26693 ◽  
Author(s):  
Yiqing Li ◽  
Yu Wang ◽  
Yanyang Zi ◽  
Mingquan Zhang

2010 ◽  
Vol 2010 (1) ◽  
pp. 325-325
Author(s):  
NORIYUKI KOBAYASHI ◽  
UGAI TADANORI ◽  
KOJI AOYAMA ◽  
AKIHIKO OBATA

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