6,990 research outputs found
System and method for cognitive processing for data fusion
A system and method for cognitive processing of sensor data. A processor array receiving analog sensor data and having programmable interconnects, multiplication weights, and filters provides for adaptive learning in real-time. A static random access memory contains the programmable data for the processor array and the stored data is modified to provide for adaptive learning
Conservative-dissipative approximation schemes for a generalized Kramers equation
We propose three new discrete variational schemes that capture the
conservative-dissipative structure of a generalized Kramers equation. The first
two schemes are single-step minimization schemes while the third one combines a
streaming and a minimization step. The cost functionals in the schemes are
inspired by the rate functional in the Freidlin-Wentzell theory of large
deviations for the underlying stochastic system. We prove that all three
schemes converge to the solution of the generalized Kramers equation
A new shell formulation for graphene structures based on existing ab-initio data
An existing hyperelastic membrane model for graphene calibrated from
ab-initio data (Kumar and Parks, 2014) is adapted to curvilinear coordinates
and extended to a rotation-free shell formulation based on isogeometric finite
elements. Therefore, the membrane model is extended by a hyperelastic bending
model that reflects the ab-inito data of Kudin et al. (2001). The proposed
formulation can be implemented straight-forwardly into an existing finite
element package, since it does not require the description of molecular
interactions. It thus circumvents the use of interatomic potentials that tend
to be less accurate than ab-initio data. The proposed shell formulation is
verified and analyzed by a set of simple test cases. The results are in
agreement to analytical solutions and satisfy the FE patch test. The
performance of the shell formulation for graphene structures is illustrated by
several numerical examples. The considered examples are indentation and peeling
of graphene and torsion, bending and axial stretch of carbon nanotubes.
Adhesive substrates are modeled by the Lennard-Jones potential and a coarse
grained contact model. In principle, the proposed formulation can be extended
to other 2D materials.Comment: New examples are added and some typos are removed. The previous
results are unchanged, International Journal of Solids and Structures (2017
Real-Time Cognitive Computing Architecture for Data Fusion in a Dynamic Environment
A novel cognitive computing architecture is conceptualized for processing multiple channels of multi-modal sensory data streams simultaneously, and fusing the information in real time to generate intelligent reaction sequences. This unique architecture is capable of assimilating parallel data streams that could be analog, digital, synchronous/asynchronous, and could be programmed to act as a knowledge synthesizer and/or an "intelligent perception" processor. In this architecture, the bio-inspired models of visual pathway and olfactory receptor processing are combined as processing components, to achieve the composite function of "searching for a source of food while avoiding the predator." The architecture is particularly suited for scene analysis from visual data and odorant
Quantification of coarse-graining error in Langevin and overdamped Langevin dynamics
In molecular dynamics and sampling of high dimensional Gibbs measures
coarse-graining is an important technique to reduce the dimensionality of the
problem. We will study and quantify the coarse-graining error between the
coarse-grained dynamics and an effective dynamics. The effective dynamics is a
Markov process on the coarse-grained state space obtained by a closure
procedure from the coarse-grained coefficients. We obtain error estimates both
in relative entropy and Wasserstein distance, for both Langevin and overdamped
Langevin dynamics. The approach allows for vectorial coarse-graining maps.
Hereby, the quality of the chosen coarse-graining is measured by certain
functional inequalities encoding the scale separation of the Gibbs measure. The
method is based on error estimates between solutions of (kinetic) Fokker-Planck
equations in terms of large-deviation rate functionals
A segmentation-free isogeometric extended mortar contact method
This paper presents a new isogeometric mortar contact formulation based on an
extended finite element interpolation to capture physical pressure
discontinuities at the contact boundary. The so called two-half-pass algorithm
is employed, which leads to an unbiased formulation and, when applied to the
mortar setting, has the additional advantage that the mortar coupling term is
no longer present in the contact forces. As a result, the computationally
expensive segmentation at overlapping master-slave element boundaries, usually
required in mortar methods (although often simplified with loss of accuracy),
is not needed from the outset. For the numerical integration of general contact
problems, the so-called refined boundary quadrature is employed, which is based
on adaptive partitioning of contact elements along the contact boundary. The
contact patch test shows that the proposed formulation passes the test without
using either segmentation or refined boundary quadrature. Several numerical
examples are presented to demonstrate the robustness and accuracy of the
proposed formulation.Comment: In this version, we have removed the patch test comparison with the
classical mortar method and removed corresponding statements. They will be
studied in further detail in future work, so that the focus is now entirely
on the new IGA mortar formulatio
Method and System for Object Recognition Search
A method for object recognition using shape and color features of the object to be recognized. An adaptive architecture is used to recognize and adapt the shape and color features for moving objects to enable object recognition
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