189,342 research outputs found
Integrated Neural Based System for State Estimation and Confidence Limit Analysis in Water Networks
In this paper a simple recurrent neural network (NN) is used as
a basis for constructing an integrated system capable of finding
the state estimates with corresponding confidence limits for water
distribution systems. In the first phase of calculations a neural
linear equations solver is combined with a Newton-Raphson
iterations to find a solution to an overdetermined set of nonlinear
equations describing water networks.
The mathematical model of the water system is derived using
measurements and pseudomeasurements consisting certain
amount of uncertainty. This uncertainty has an impact on the
accuracy to which the state estimates can be calculated. The
second phase of calculations, using the same NN, is carried out in
order to quantify the effect of measurement uncertainty on
accuracy of the derived state estimates. Rather than a single
deterministic state estimate, the set of all feasible states
corresponding to a given level of measurement uncertainty is
calculated. The set is presented in the form of upper and lower
bounds for the individual variables, and hence provides limits on
the potential error of each variable.
The simulations have been carried out and results are presented
for a realistic 34-node water distribution network
mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Location
This paper develops mfEGRA, a multifidelity active learning method using
data-driven adaptively refined surrogates for failure boundary location in
reliability analysis. This work addresses the issue of prohibitive cost of
reliability analysis using Monte Carlo sampling for expensive-to-evaluate
high-fidelity models by using cheaper-to-evaluate approximations of the
high-fidelity model. The method builds on the Efficient Global Reliability
Analysis (EGRA) method, which is a surrogate-based method that uses adaptive
sampling for refining Gaussian process surrogates for failure boundary location
using a single-fidelity model. Our method introduces a two-stage adaptive
sampling criterion that uses a multifidelity Gaussian process surrogate to
leverage multiple information sources with different fidelities. The method
combines expected feasibility criterion from EGRA with one-step lookahead
information gain to refine the surrogate around the failure boundary. The
computational savings from mfEGRA depends on the discrepancy between the
different models, and the relative cost of evaluating the different models as
compared to the high-fidelity model. We show that accurate estimation of
reliability using mfEGRA leads to computational savings of 46% for an
analytic multimodal test problem and 24% for a three-dimensional acoustic horn
problem, when compared to single-fidelity EGRA. We also show the effect of
using a priori drawn Monte Carlo samples in the implementation for the acoustic
horn problem, where mfEGRA leads to computational savings of 45% for the
three-dimensional case and 48% for a rarer event four-dimensional case as
compared to single-fidelity EGRA
PF-OLA: A High-Performance Framework for Parallel On-Line Aggregation
Online aggregation provides estimates to the final result of a computation
during the actual processing. The user can stop the computation as soon as the
estimate is accurate enough, typically early in the execution. This allows for
the interactive data exploration of the largest datasets. In this paper we
introduce the first framework for parallel online aggregation in which the
estimation virtually does not incur any overhead on top of the actual
execution. We define a generic interface to express any estimation model that
abstracts completely the execution details. We design a novel estimator
specifically targeted at parallel online aggregation. When executed by the
framework over a massive TPC-H instance, the estimator provides
accurate confidence bounds early in the execution even when the cardinality of
the final result is seven orders of magnitude smaller than the dataset size and
without incurring overhead.Comment: 36 page
Optimal Quantum Measurements of Expectation Values of Observables
Experimental characterizations of a quantum system involve the measurement of
expectation values of observables for a preparable state |psi> of the quantum
system. Such expectation values can be measured by repeatedly preparing |psi>
and coupling the system to an apparatus. For this method, the precision of the
measured value scales as 1/sqrt(N) for N repetitions of the experiment. For the
problem of estimating the parameter phi in an evolution exp(-i phi H), it is
possible to achieve precision 1/N (the quantum metrology limit) provided that
sufficient information about H and its spectrum is available. We consider the
more general problem of estimating expectations of operators A with minimal
prior knowledge of A. We give explicit algorithms that approach precision 1/N
given a bound on the eigenvalues of A or on their tail distribution. These
algorithms are particularly useful for simulating quantum systems on quantum
computers because they enable efficient measurement of observables and
correlation functions. Our algorithms are based on a method for efficiently
measuring the complex overlap of |psi> and U|psi>, where U is an implementable
unitary operator. We explicitly consider the issue of confidence levels in
measuring observables and overlaps and show that, as expected, confidence
levels can be improved exponentially with linear overhead. We further show that
the algorithms given here can typically be parallelized with minimal increase
in resource usage.Comment: 22 page
Gravitational waves: search results, data analysis and parameter estimation
The Amaldi 10 Parallel Session C2 on gravitational wave (GW) search results, data analysis and parameter estimation included three lively sessions of lectures by 13 presenters, and 34 posters. The talks and posters covered a huge range of material, including results and analysis techniques for ground-based GW detectors, targeting anticipated signals from different astrophysical sources: compact binary inspiral, merger and ringdown; GW bursts from intermediate mass binary black hole mergers, cosmic string cusps, core-collapse supernovae, and other unmodeled sources; continuous waves from spinning neutron stars; and a stochastic GW background. There was considerable emphasis on Bayesian techniques for estimating the parameters of coalescing compact binary systems from the gravitational waveforms extracted from the data from the advanced detector network. This included methods to distinguish deviations of the signals from what is expected in the context of General Relativity
Probabilistic Motion Estimation Based on Temporal Coherence
We develop a theory for the temporal integration of visual motion motivated
by psychophysical experiments. The theory proposes that input data are
temporally grouped and used to predict and estimate the motion flows in the
image sequence. This temporal grouping can be considered a generalization of
the data association techniques used by engineers to study motion sequences.
Our temporal-grouping theory is expressed in terms of the Bayesian
generalization of standard Kalman filtering. To implement the theory we derive
a parallel network which shares some properties of cortical networks. Computer
simulations of this network demonstrate that our theory qualitatively accounts
for psychophysical experiments on motion occlusion and motion outliers.Comment: 40 pages, 7 figure
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