A new generalized computational framework for finding object orientation using perspective trihedral angle constraint

1994 ◽  
Vol 16 (10) ◽  
pp. 961-975 ◽  
Author(s):  
Yuyan Wu ◽  
S.S. Iyengar ◽  
R. Jain ◽  
S. Bose
Keyword(s):  
Object Orientation ◽  
Computational Framework ◽  
Trihedral Angle

Animal Recognition and Eye Movements

2010 ◽  
Vol 57 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Toby J. Lloyd-Jones ◽  
Juergen Gehrke ◽  
Jason Lauder
Keyword(s):  
Eye Movements ◽  
Response Times ◽  
Real Life ◽  
Object Orientation ◽  
Decision Task ◽  
Line Drawings ◽  
Object Decision ◽  
Individual Features ◽  
And Performance

We assessed the importance of outline contour and individual features in mediating the recognition of animals by examining response times and eye movements in an animal-object decision task (i.e., deciding whether or not an object was an animal that may be encountered in real life). There were shorter latencies for animals as compared with nonanimals and performance was similar for shaded line drawings and silhouettes, suggesting that important information for recognition lies in the outline contour. The most salient information in the outline contour was around the head, followed by the lower torso and leg regions. We also observed effects of object orientation and argue that the usefulness of the head and lower torso/leg regions is consistent with a role for the object axis in recognition.

A Multiphysics Computational Framework for Cylindrical Battery Behavior upon Mechanical Loading Based on LS-DYNA

2019 ◽  
Vol 166 (6) ◽  
pp. A1160-A1169 ◽  
Author(s):  
Chunhao Yuan ◽  
Xiang Gao ◽  
Hin Kwan Wong ◽  
Bill Feng ◽  
Jun Xu
Keyword(s):  
Mechanical Loading ◽  
Computational Framework

Qualitative simulation and related approaches for the analysis of dynamic systems

2004 ◽  
Vol 19 (2) ◽  
pp. 93-132 ◽  
Author(s):  
HIDDE DE JONG
Keyword(s):  
Qualitative Analysis ◽  
Dynamic Systems ◽  
Quantitative Information ◽  
Simulation Methods ◽  
Science And Engineering ◽  
Computational Framework ◽  
Combinatorial Explosion ◽  
Qualitative Simulation ◽  
Qualitative Phase ◽  
Made In

Methods for qualitative simulation allow predictions on the dynamics of a system to be made in the absence of quantitative information, by inferring the range of possible qualitative behaviors compatible with the structure of the system. This article reviews QSIM and other qualitative simulation methods. It discusses two problems that have seriously compromised the application of these methods to realistic problems in science and engineering: the occurrence of spurious behavior predictions and the combinatorial explosion of the number of behavior predictions. In response to these problems, related approaches for the qualitative analysis of dynamic systems have emerged: qualitative phase-space analysis and semi-quantitative simulation. The article argues for a synthesis of these approaches in order to obtain a computational framework for the qualitative analysis of dynamic systems. This should provide a solid basis for further upscaling and for the development of model-based reasoning applications of a wider scope.

scholarly journals Computational framework for monolithic coupling for thin fluid flow in contact interfaces

2021 ◽  
Vol 379 ◽  
pp. 113738
Author(s):  
Andrei G. Shvarts ◽  
Julien Vignollet ◽  
Vladislav A. Yastrebov
Keyword(s):  
Fluid Flow ◽  
Computational Framework ◽  
Thin Fluid

scholarly journals A computational framework for modeling cell–matrix interactions in soft biological tissues

2021 ◽  
Author(s):  
Jonas F. Eichinger ◽  
Maximilian J. Grill ◽  
Iman Davoodi Kermani ◽  
Roland C. Aydin ◽  
Wolfgang A. Wall ◽  
...  
Keyword(s):  
Soft Tissues ◽  
Three Dimensional ◽  
Biological Tissues ◽  
Computational Framework ◽  
Cell Matrix ◽  
Physiological Processes ◽  
Matrix Interactions ◽  
Mechanical Homeostasis ◽  
Cell Matrix Interactions ◽  
Short Time

AbstractLiving soft tissues appear to promote the development and maintenance of a preferred mechanical state within a defined tolerance around a so-called set point. This phenomenon is often referred to as mechanical homeostasis. In contradiction to the prominent role of mechanical homeostasis in various (patho)physiological processes, its underlying micromechanical mechanisms acting on the level of individual cells and fibers remain poorly understood, especially how these mechanisms on the microscale lead to what we macroscopically call mechanical homeostasis. Here, we present a novel computational framework based on the finite element method that is constructed bottom up, that is, it models key mechanobiological mechanisms such as actin cytoskeleton contraction and molecular clutch behavior of individual cells interacting with a reconstructed three-dimensional extracellular fiber matrix. The framework reproduces many experimental observations regarding mechanical homeostasis on short time scales (hours), in which the deposition and degradation of extracellular matrix can largely be neglected. This model can serve as a systematic tool for future in silico studies of the origin of the numerous still unexplained experimental observations about mechanical homeostasis.

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