scholarly journals Use of graphs in the statistical modeling process

2014 ◽  
Vol 27 (1) ◽  
pp. 142-157
Author(s):  
Hasmeek Wartan
Actuators ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 68 ◽  
Author(s):  
Takuya Taniguchi ◽  
Loïc Blanc ◽  
Toru Asahi ◽  
Hideko Koshima ◽  
Pierre Lambert

Mechanically responsive materials are promising as next-generation actuators for soft robotics, but have scarce reports on the statistical modeling of the actuation behavior. This research reports on the development and modeling of the photomechanical bending behavior of hybrid silicones mixed with azobenzene powder. The photo-responsive hybrid silicone bends away from the light source upon light irradiation when a thin paper is attached on the hybrid silicone. The time courses of bending behaviors were fitted well with exponential models with a time variable, affording fitting constants at each experimental condition. These fitted parameters were further modeled using the analysis of variance (ANOVA). Cubic models were proposed for both the photo-bending and unbending processes, which were parameterized by the powder ratio and the light intensity. This modeling process allows such photo-responsive materials to be controlled as actuators, and will possibly be effective for engineering mechanically responsive materials.


Author(s):  
Susanna Makela ◽  
Yajuan Si ◽  
Andrew Gelman

This chapter argues that it is wasteful to do a large, expensive poll and then just report a few percentages. Statistical modeling allows researchers to make the most effective use of available data, and graphs make it possible to convey more information more directly, both to general audiences and to specialists. Graphs are an invaluable tool at each step of the modeling process: exploring raw data, building and refining the model, and understanding and communicating the results are all made easier with graphs. In addition, graphical methods can be useful to survey researchers to understand weighting and other aspects of survey construction and analysis. The chapter includes several examples.


Author(s):  
V.I. Kucheryavy ◽  
◽  
A.M. Sharygin ◽  
V.L. Savich ◽  
S.N. Milkov ◽  
...  

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.


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