Research on Flow Object Knowledge Extraction Method

2008 ◽  
Author(s):  
Shouning Qu ◽  
Guangqiang Cui ◽  
Zhaolian Liu ◽  
Jinbin Wu
Keyword(s):  
Extraction Method ◽  
Knowledge Extraction ◽  
Object Knowledge

scholarly journals Study on the Extraction Method of Deformation Influence Factors of Flexible Material Processing Based on Information Entropy

2014 ◽  
Vol 6 ◽  
pp. 547947 ◽  
Author(s):  
Yaohua Deng ◽  
Qiwen Lu ◽  
Jiayuan Chen ◽  
Sicheng Chen ◽  
Liming Wu ◽  
...  
Keyword(s):  
Material Processing ◽  
Information Entropy ◽  
Extraction Method ◽  
Knowledge Extraction ◽  
Attribute Importance ◽  
Compensation Control ◽  
Flexible Material ◽  
Extraction Algorithm ◽  
Control Knowledge ◽  
Deformation Compensation

Through analyzing the flexible material processing (FMP) deformation factors, it is pointed out that without a choice of deformation influence quantity would increase the compensation control predict model system input. In order to reduce the count of spatial dimensions of knowledge, we proposed the method by taking the use of FMP deformation compensation control knowledge extraction, which is based on decision table (DT) attribute reduction, deriving the algorithm that is based on information entropy attribute importance, to find the dependencies between attributes through attribute significance (AS) and to extract the intrinsic attributes which is the most close to deformation compensation control decision making. Finally, through an example presented in this paper to verify the efficiency of RS control knowledge extraction method. Compared with the Pawlak method and genetic extraction algorithm, the prediction accuracy of after reduction data is 0.55% less than Pawlak method and 3.64% higher than the genetic extraction algorithm; however, the time consumption of forecast calculation is 30.3% and 11.53% less than Pawlak method and genetic extraction algorithm, respectively. Knowledge extraction entropy methods presented in this paper have the advantages of fast calculating speed and high accuracy and are suitable for FMP deformation compensation of online control.

scholarly journals Virtual Reality-Based Fuzzy Spatial Relation Knowledge Extraction Method for Observer-Centered Vague Location Descriptions

2021 ◽  
Vol 10 (12) ◽  
pp. 833
Author(s):  
Jun Xu ◽  
Xin Pan ◽  
Jian Zhao ◽  
Haohai Fu
Keyword(s):  
Virtual Reality ◽  
Extraction Method ◽  
Spatial Relation ◽  
Spatial Relations ◽  
Knowledge Extraction ◽  
Transformation Process ◽  
Spatial Knowledge ◽  
Fuzzy Function ◽  
Observer Perspective ◽  
Fuzzy Regions

Many documents contain vague location descriptions of observed objects. To represent location information in geographic information systems (GISs), these vague location descriptions need to be transformed into representable fuzzy spatial regions, and knowledge about the location descriptions of observer-to-object spatial relations must serve as the basis for this transformation process. However, a location description from the observer perspective is not a specific fuzzy function, but comes from a subjective viewpoint, which will be different for different individuals, making the corresponding knowledge difficult to represent or obtain. To extract spatial knowledge from such subjective descriptions, this research proposes a virtual reality (VR)-based fuzzy spatial relation knowledge extraction method for observer-centered vague location descriptions (VR-FSRKE). In VR-FSRKE, a VR scene is constructed, and users can interactively determine the fuzzy region corresponding to a location description under the simulated VR observer perspective. Then, a spatial region clustering mechanism is established to summarize the fuzzy regions identified by various individuals into fuzzy spatial relation knowledge. Experiments show that, on the basis of interactive scenes provided through VR, VR-FSRKE can efficiently extract spatial relation knowledge from many individuals and is not restricted by requirements of a certain place or time; furthermore, the knowledge obtained by VR-FSRKE is close to the knowledge obtained from a real scene.

scholarly journals Knowledge Extraction Method for Power Grid Fault Text Based on Ontology

2020 ◽  
Vol 1626 ◽  
pp. 012059
Author(s):  
Bo Peng ◽  
Ji Lai ◽  
Yanru Hao ◽  
Mengke Yuwen ◽  
Ding Xiao
Keyword(s):  
Extraction Method ◽  
Power Grid ◽  
Knowledge Extraction
Sign in / Sign up

Export Citation Format

Share Document