1,112 research outputs found
Birnbaum’s measure of component importance for noncoherent systems
Importance analysis of noncoherent systems is limited,
and is generally inaccurate because all measures of importance
that have been developed are strictly for coherent analysis.
This paper considers the probabilistic measure of component
importance developed by Birnbaum (1969). An extension of this
measure is proposed which enables noncoherent importance analysis.
As a result of the proposed extension the average number
of system failures in a given interval for noncoherent systems
can be calculated more efficiently. Furthermore, because Birnbaum’s
measure of component importance is central to many
other measures of importance; its extension should make the
derivation of other measures possible
Importance measures for non-coherent-system analysis
Component importance analysis is a key part of the
system reliability quantification process. It enables the weakest
areas of a system to be identified and indicates modifications,
which will improve the system reliability. Although a wide range of
importance measures have been developed, the majority of these
measures are strictly for coherent system analysis. Non-coherent
systems can occur and accurate importance analysis is essential.
This paper extends four commonly used measures of importance,
using the noncoherent extension of Birnbaum’s measure of
component reliability importance. Since both component failure
and repair can contribute to system failure in a noncoherent
system, both of these influences need to be considered. This paper
highlights that it is crucial to choose appropriate measures to
analyze component importance. First the aims of the analysis
must be outlined and then the roles that component failures and
repairs can play in system state deterioration can be considered.
For example, the failure/repair of components in safety systems
can play only a passive role in system failure, since it is usually
inactive, hence measures that consider initiator importance are
not appropriate to analyze the importance of these components.
Measures of importance must be chosen carefully to ensure
analysis is meaningful and useful conclusions can be drawn
Geometric and dynamic perspectives on phase-coherent and noncoherent chaos
Statistically distinguishing between phase-coherent and noncoherent chaotic
dynamics from time series is a contemporary problem in nonlinear sciences. In
this work, we propose different measures based on recurrence properties of
recorded trajectories, which characterize the underlying systems from both
geometric and dynamic viewpoints. The potentials of the individual measures for
discriminating phase-coherent and noncoherent chaotic oscillations are
discussed. A detailed numerical analysis is performed for the chaotic R\"ossler
system, which displays both types of chaos as one control parameter is varied,
and the Mackey-Glass system as an example of a time-delay system with
noncoherent chaos. Our results demonstrate that especially geometric measures
from recurrence network analysis are well suited for tracing transitions
between spiral- and screw-type chaos, a common route from phase-coherent to
noncoherent chaos also found in other nonlinear oscillators. A detailed
explanation of the observed behavior in terms of attractor geometry is given.Comment: 12 pages, 13 figure
Complex-Valued Random Vectors and Channels: Entropy, Divergence, and Capacity
Recent research has demonstrated significant achievable performance gains by
exploiting circularity/non-circularity or propeness/improperness of
complex-valued signals. In this paper, we investigate the influence of these
properties on important information theoretic quantities such as entropy,
divergence, and capacity. We prove two maximum entropy theorems that strengthen
previously known results. The proof of the former theorem is based on the
so-called circular analog of a given complex-valued random vector. Its
introduction is supported by a characterization theorem that employs a minimum
Kullback-Leibler divergence criterion. In the proof of latter theorem, on the
other hand, results about the second-order structure of complex-valued random
vectors are exploited. Furthermore, we address the capacity of multiple-input
multiple-output (MIMO) channels. Regardless of the specific distribution of the
channel parameters (noise vector and channel matrix, if modeled as random), we
show that the capacity-achieving input vector is circular for a broad range of
MIMO channels (including coherent and noncoherent scenarios). Finally, we
investigate the situation of an improper and Gaussian distributed noise vector.
We compute both capacity and capacity-achieving input vector and show that
improperness increases capacity, provided that the complementary covariance
matrix is exploited. Otherwise, a capacity loss occurs, for which we derive an
explicit expression.Comment: 33 pages, 1 figure, slightly modified version of first paper revision
submitted to IEEE Trans. Inf. Theory on October 31, 201
Recognition and reconstruction of coherent energy with application to deep seismic reflection data
Reflections in deep seismic reflection data tend to be
visible on only a limited number of traces in a common
midpoint gather. To prevent stack degeneration,
any noncoherent reflection energy has to be removed.
In this paper, a standard classification technique in
remote sensing is presented to enhance data quality. It
consists of a recognition technique to detect and extract
coherent energy in both common shot gathers and fi-
nal stacks. This technique uses the statistics of a picked
seismic phase to obtain the likelihood distribution of its
presence. Multiplication of this likelihood distribution
with the original data results in a “cleaned up” section.
Application of the technique to data from a deep seismic
reflection experiment enhanced the visibility of all
reflectors considerably.
Because the recognition technique cannot produce an
estimate of “missing” data, it is extended with a reconstruction
method. Two methods are proposed: application
of semblance weighted local slant stacks after recognition,
and direct recognition in the linear tau-p domain.
In both cases, the power of the stacking process to increase the signal-to-noise ratio is combined with the direct selection of only specific seismic phases. The joint
application of recognition and reconstruction resulted in
data images which showed reflectors more clearly than
application of a single technique
SPS pilot signal design and power transponder analysis, volume 2, phase 3
The problem of pilot signal parameter optimization and the related problem of power transponder performance analysis for the Solar Power Satellite reference phase control system are addressed. Signal and interference models were established to enable specifications of the front end filters including both the notch filter and the antenna frequency response. A simulation program package was developed to be included in SOLARSIM to perform tradeoffs of system parameters based on minimizing the phase error for the pilot phase extraction. An analytical model that characterizes the overall power transponder operation was developed. From this model, the effects of different phase noise disturbance sources that contribute to phase variations at the output of the power transponders were studied and quantified. Results indicate that it is feasible to hold the antenna array phase error to less than one degree per power module for the type of disturbances modeled
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