2,941 research outputs found
Direct tensor expression by Eulerian approach for constitutive relations based on strain invariants in transversely isotropic green elasticity - finite extension and torsion
It has been proven by J.C.Criscione that constitutive relations(mixed approach) based
on a set of five strain invariants (Beta-1, Beta-2, Beta-3, Beta-4, Beta-5) are useful and stable for experimentally
determining response terms for transversely isotropic material. On the other
hand, Rivlin’s classical model is an unsuitable choice for determining response terms
due to the co-alignment of the five invariants (I1, I2, I3, I4, I5). Despite this, however,
a mixed (Lagrangian and Eulerian) approach causes unnecessary computational time
and requires intricate calculation in the constitutive relation. Through changing the
way to approach the derivation of a constitutive relation, we have verified that using
an Eulerian approach causes shorter computational time and simpler calculation than
using a mixed approach does. We applied this approach to a boundary value problem
under specific deformation, i.e. finite extension and torsion to a fiber reinforced circular
cylinder. The results under this deformation show that the computational time
by Eulerian is less than half of the time by mixed. The main reason for the difference
is that we have to determine two unit vectors on the cross fiber direction from the
right Cauchy Green deformation tensor at every radius of the cylinder when we use a
mixed approach. On the contrary, we directly use the left Cauchy Green deformation
tensor in the constitutive relation by the Eulerian approach without defining the two
cross fiber vectors. Moreover, the computational time by the Eulerian approach is not influenced by the degree of deformation even in the case of computational time
by the Eulerian approach, possibly becoming the same as the computational time by
the mixed approach. This is from the theoretical thought that the mixed approach
is almost the same as the Eulerian approach under small deformation. This new
constitutive relation by Eulerian approach will have more advantages with regard
to saving computational time as the deformation gets more complicated. Therefore,
since the Eulerain approach effectively shortens computational time, this may enhance
the computational tools required to approach the problems with greater degrees of
anisotropy and viscoelasticity
Andro-Simnet: Android Malware Family Classification Using Social Network Analysis
While the rapid adaptation of mobile devices changes our daily life more
conveniently, the threat derived from malware is also increased. There are lots
of research to detect malware to protect mobile devices, but most of them adopt
only signature-based malware detection method that can be easily bypassed by
polymorphic and metamorphic malware. To detect malware and its variants, it is
essential to adopt behavior-based detection for efficient malware
classification. This paper presents a system that classifies malware by using
common behavioral characteristics along with malware families. We measure the
similarity between malware families with carefully chosen features commonly
appeared in the same family. With the proposed similarity measure, we can
classify malware by malware's attack behavior pattern and tactical
characteristics. Also, we apply a community detection algorithm to increase the
modularity within each malware family network aggregation. To maintain high
classification accuracy, we propose a process to derive the optimal weights of
the selected features in the proposed similarity measure. During this process,
we find out which features are significant for representing the similarity
between malware samples. Finally, we provide an intuitive graph visualization
of malware samples which is helpful to understand the distribution and likeness
of the malware networks. In the experiment, the proposed system achieved 97%
accuracy for malware classification and 95% accuracy for prediction by K-fold
cross-validation using the real malware dataset.Comment: 13 pages, 11 figures, dataset link:
http://ocslab.hksecurity.net/Datasets/andro-simnet , demo video:
https://youtu.be/JmfS-ZtCbg4 , In Proceedings of the 16th Annual Conference
on Privacy, Security and Trust (PST), 201
Self-Supervised Motion Retargeting with Safety Guarantee
In this paper, we present self-supervised shared latent embedding (S3LE), a
data-driven motion retargeting method that enables the generation of natural
motions in humanoid robots from motion capture data or RGB videos. While it
requires paired data consisting of human poses and their corresponding robot
configurations, it significantly alleviates the necessity of time-consuming
data-collection via novel paired data generating processes. Our self-supervised
learning procedure consists of two steps: automatically generating paired data
to bootstrap the motion retargeting, and learning a projection-invariant
mapping to handle the different expressivity of humans and humanoid robots.
Furthermore, our method guarantees that the generated robot pose is
collision-free and satisfies position limits by utilizing nonparametric
regression in the shared latent space. We demonstrate that our method can
generate expressive robotic motions from both the CMU motion capture database
and YouTube videos
- …