360 research outputs found
An Integrated Design and Simulation Environment for Rapid Prototyping of Laminate Robotic Mechanisms
Laminate mechanisms are a reliable concept in producing lowcost robots for
educational and commercial purposes. These mechanisms are produced using
low-cost manufacturing techniques which have improved significantly during
recent years and are more accessible to novices and hobbyists. However,
iterating through the design space to come up with the best design for a robot
is still a time consuming and rather expensive task and therefore, there is
still a need for model-based analysis before manufacturing. Until now, there
has been no integrated design and analysis software for laminate robots. This
paper addresses some of the issues surrounding laminate analysis by introducing
a companion to an existing laminate design tool that automates the generation
of dynamic equations and produces simulation results via rendered plots and
videos. We have validated the accuracy of the software by comparing the
position, velocity and acceleration of the simulated mechanisms with the
measurements taken from physical laminate prototypes using a motion capture
system
Leavitt path algebras: Graded direct-finiteness and graded -injective simple modules
In this paper, we give a complete characterization of Leavitt path algebras
which are graded - rings, that is, rings over which a direct sum of
arbitrary copies of any graded simple module is graded injective. Specifically,
we show that a Leavitt path algebra over an arbitrary graph is a graded
- ring if and only if it is a subdirect product of matrix rings of
arbitrary size but with finitely many non-zero entries over or
with appropriate matrix gradings. We also obtain a graphical
characterization of such a graded - ring % . When the graph
is finite, we show that is a graded - ring is graded directly-finite has bounded index of
nilpotence is graded semi-simple. Examples show that
the equivalence of these properties in the preceding statement no longer holds
when the graph is infinite. Following this, we also characterize Leavitt
path algebras which are non-graded - rings. Graded rings which
are graded directly-finite are explored and it is shown that if a Leavitt path
algebra is a graded - ring, then is always graded
directly-finite. Examples show the subtle differences between graded and
non-graded directly-finite rings. Leavitt path algebras which are graded
directly-finite are shown to be directed unions of graded semisimple rings.
Using this, we give an alternative proof of a theorem of Va\v{s} \cite{V} on
directly-finite Leavitt path algebras.Comment: 21 page
EXPERIMENTAL MEASUREMENTS OF FLAME TRANSFER FUNCTION
poster abstractIn order to conform to pollutant-related legislations and minimize NOx emissions, modern household boilers and central heating systems are mov-ing towards premixed combustors. These combustors have been very suc-cessful with regards to emissions along with thermal efficiency. However, there implementation has been associated with acoustical instability prob-lems that are best solved through precise design optimization rather than trial and error experimentation.
This poster introduces an experimental setup which is designed to inves-tigate and study, acoustic instability at the flame level. The methodology is an experimental determination of the Flame Transfer Function and compari-son of the experimental data with a theoretical model of the flame-burner. A procedure is designed to diagnose the origins of the combustion instabilities by measurement of the Flame Transfer Function experimentally. The exper-imental setup provides an improved assessment of the acoustic instability problem for industrial applications
Ridge regression and its applications in genetic studies
With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling. When multicollinearity exists in the data set with outliers, we consider a robust ridge estimator, namely the rank ridge regression estimator, for parameter estimation and prediction. On the other hand, the efficiency of the rank ridge regression estimator is highly dependent on the ridge parameter. In general, it is difficult to provide a satisfactory answer about the selection for the ridge parameter. Because of the good properties of generalized cross validation (GCV) and its simplicity, we use it to choose the optimum value of the ridge parameter. The GCV function creates a balance between the precision of the estimators and the bias caused by the ridge estimation. It behaves like an improved estimator of risk and can be used when the number of explanatory variables is larger than the sample size in high-dimensional problems. Finally, some numerical illustrations are given to support our findings
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