Paper Info
Title
Comparative Research On Som With Torus And Sphere Topologies For Peculiarity Classification Of Flat Finishing Skill Training
Abstract
The paper compares classification performances on Self-Organizing Maps (SOMs) by torus and spherical topologies in the case of peculiarities classification of flat finishing motion with an iron file measured by a 3D stylus. In case of manufacturing skill training, peculiarities of tool motion are useful information for learners. Classified peculiarities are also useful especially for trainers to grasp effectively the tendency of the learners' peculiarities in their class. In the authors' former studies, a torus SOM are considered to be powerful tools to classify and visualize peculiarities with its borderless topological feature map structure. In this paper, the authors compare the classification performance of two kind of borderless topological SOMs: torus SOM and spherical SOM by quality measurements.
Year
DOI
Venue
2019
10.1007/978-3-030-30508-6_41
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: IMAGE PROCESSING, PT III
Keywords
DocType
Volume
Motion analysis, Skill training, Self Organizing Maps, Clustering
Conference
11729
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Masaru Teranishi1179.91
Shimpei Matsumoto2109.77
Hidetoshi Takeno385.99