Proper design of castings is essential to avoid casting defects that cannot be eliminated by design of tooling and process parameters. Shrinkage porosity is one of the defects which usually occur at casting junctions ...
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ISBN:
(纸本)9781618390578
Proper design of castings is essential to avoid casting defects that cannot be eliminated by design of tooling and process parameters. Shrinkage porosity is one of the defects which usually occur at casting junctions and cannot be easily eliminated. Currently, designers use rules that were established based on experience and intuition and are not supported by proper experimentation. In this paper an approach to design and optimize design of casting junctions using a database of mathematical models is presented. The mathematical models give relationships between porosity and the junctions' dimensional features. To illustrate this approach, a mathematical model for a V-junction has been developed using Design of Experiments (DoE) and regression analysis techniques. The experimental runs for the study are established using a D-optimality criterion and conducted using MAGMASoft, comprehensive software for casting simulation, for measuring porosity in casting junctions. The simulations carried out in MAGMASoft are validated by comparing its results with experimental results from previous studies. The mathematical model is then developed by applying regression analysis. The results of the model have been validated and found to agree with both the experimental and the validation simulation experiments.
In this paper we extend our path diversity metric to create a composite compensated total graph diversity metric that is representative of a particular topology's survivability with respect to distributed simultan...
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In this paper we extend our path diversity metric to create a composite compensated total graph diversity metric that is representative of a particular topology's survivability with respect to distributed simultaneous link and node failures. We tune the accuracy of this metric using 17 topologies, including 3 real fiber maps, 10 inferred logical maps, and 2 synthetic topologies having simulated their performance under a range of failure severities, and present the results. The topologies used are from national-scale backbone networks, with a variety of characteristics, which we characterize using standard graph-theoretic metrics. The end result is a compensated total graph diversity metric that accurately predicts the survivability of a given network topology.
Large-scale scientific computations are often organized as a composition of many computational tasks linked through data flow. After the completion of a computational scientific experiment, a scientist has to analyze ...
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Effective exploitation of the application-specific parallel patterns and computation operations through their direct implementation in hardware is the base for construction of high-quality application-specific (re-) c...
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This paper presents a deterministic and adaptive spike model derived from radial basis functionsand a leaky integrate-and-fire sampler developed for training spiking neural networks without directweight manipulation. ...
This paper presents a deterministic and adaptive spike model derived from radial basis functions
and a leaky integrate-and-fire sampler developed for training spiking neural networks without direct
weight manipulation. Several algorithms have been proposed for training spiking neural networks
through biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity
and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths,
or weights, directly, through a weight update rule in which the weight increment can be decided
and implemented based on the training equations. However, in several potential applications of
adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale
neuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time
by virtue of the pre- and postsynaptic neural activity. This paper presents an indirect learning method
that induces changes in the synaptic weights by modulating spike-timing-dependent plasticity by means
of controlled input spike trains. In place of the weights, the algorithm manipulates the input spike trains
used to stimulate the input neurons by determining a sequence of spike timings that minimize a desired
objective function and, indirectly, induce the desired synaptic plasticity in the network.
We report on focus group feedback regarding the services provided by existing education-related Digital Libraries (DL). Participants provided insight into how they seek educational resources online, and what they perc...
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The goal of this work is to understand the role of nano-confinement in designing an inexpensive and user friendly 'point- of- care' (POC) protein biosensor. We used printed circuit board based gold chips and i...
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Integrating optical and wireless broadband access networks is an important step in achieving fixed mobile convergence in metropolitan areas. In this paper, a promising network architecture by integrating a passive opt...
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The 19th robotics program at the annual AAAI conference was held in Atlanta, Georgia, in July 2010. In this article we give a summary of three components of the exhibition: the Small-Scale Manipulation Challenge: Robo...
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We show evidence of electrical and thermal conductivity percolation in polymer based carbon nanotube (CNT) composites, which follow power law variations with respect to the CNT concentrations in the matrix. The experi...
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We show evidence of electrical and thermal conductivity percolation in polymer based carbon nanotube (CNT) composites, which follow power law variations with respect to the CNT concentrations in the matrix. The experimentally obtained percolation thresholds, i.e., ~ 0.074 vol % for single walled CNTs and ~ 2.0 vol % for multi-walled CNTs, were found to be aspect ratio dependent and in accordance with those determined theoretically from excluded volume percolation theory. A much greater enhancement, over 10 orders of magnitude, was obtained in the electrical conductivity at the percolation threshold, while a smaller increase of ~ 100 % was obtained in the thermal conductivity values. Such a difference is qualitatively explained on the basis of the respective conductivity contrast between the CNT filler and the polymer matrix.
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