[1]GE Biaobiao,YANG Chunyan,TANG Long.Extension knowledge representation of geometric effects[J].CAAI Transactions on Intelligent Systems,2022,17(6):1235-1243.[doi:10.11992/tis.202112016]
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Extension knowledge representation of geometric effects

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 6 Page number: 1235-1243 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2022-11-05
Title:
Extension knowledge representation of geometric effects
Author(s):
GE Biaobiao1; 2;  YANG Chunyan1; 2;  TANG Long3
1. Institute of Extenics and Innovation Methods, Guangdong University of Technology, Guangzhou 510006, China;
2. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China;
3. School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China
Keywords:
scientific effect; geometric effect; extension knowledge representation; extension rule; conductive transformation rule; correlation rule; correlation relationship; extension transformation
CLC:
TP18
DOI:
10.11992/tis.202112016
Abstract:
Scientific effect contains huge scientific knowledge, which is an essential basis for solving functional contradiction problems. To establish an extension knowledge base of scientific effect and realize extension intelligent design for function problems, we propose an extension knowledge representation approach for practical application based on geometric effect. With the help of this technique, extension knowledge found in the input-output relationship of geometric effects in real-world issues can be formally represented. First, the basic-element model is developed according to the geometric effect. Domain knowledge and correlation rules in Extenics are used to establish the relationship between basic-elements. According to the knowledge of active transformation, conductive transformation, and the divergence rules of basic-elements, active transformation is performed on basic-elements in the correlation network, and then a series of conductive transformations are obtained based on the extension transformation implication system and conductive transformation rules, and then the extension knowledge expression is established to realize the expression of transformation relationship between input and output. Finally, the hyperbolic mixer design is taken as an example to further expand corresponding extension knowledge to address the field problems. This study can help designers better understand how geometric effects are realized, enabling them to select them more precisely to solve real-world problems. It can also serve as a solid foundation for the extension knowledge representation to include other types of scientific effects.
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