9,951 research outputs found
Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm
For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process
Long gravitational-wave transients and associated detection strategies for a network of terrestrial interferometers
Searches for gravitational waves (GWs) traditionally focus on persistent sources (e.g., pulsars or the stochastic background) or on transients sources (e.g., compact binary inspirals or core-collapse supernovae), which last for time scales of milliseconds to seconds. We explore the possibility of long GW transients with unknown waveforms lasting from many seconds to weeks. We propose a novel analysis technique to bridge the gap between short O(s) “burst” analyses and persistent stochastic analyses. Our technique utilizes frequency-time maps of GW strain cross power between two spatially separated terrestrial GW detectors. The application of our cross power statistic to searches for GW transients is framed as a pattern recognition problem, and we discuss several pattern-recognition techniques. We demonstrate these techniques by recovering simulated GW signals in simulated detector noise. We also recover environmental noise artifacts, thereby demonstrating a novel technique for the identification of such artifacts in GW interferometers. We compare the efficiency of this framework to other techniques such as matched filtering
Optimal generalization of power filters for gravitational wave bursts, from single to multiple detectors
Searches for gravitational wave signals which do not have a precise model
describing the shape of their waveforms are often performed using power
detectors based on a quadratic form of the data. A new, optimal method of
generalizing these power detectors so that they operate coherently over a
network of interferometers is presented. Such a mode of operation is useful in
obtaining better detection efficiencies, and better estimates of the position
of the source of the gravitational wave signal. Numerical simulations based on
a realistic, computationally efficient hierarchical implementation of the
method are used to characterize its efficiency, for detection and for position
estimation. The method is shown to be more efficient at detecting signals than
an incoherent approach based on coincidences between lists of events. It is
also shown to be capable of locating the position of the source.Comment: 16 pages, 5 figure
A burst search for gravitational waves from binary black holes
Compact binary coalescence (CBC) is one of the most promising sources of
gravitational waves. These sources are usually searched for with matched
filters which require accurate calculation of the GW waveforms and generation
of large template banks. We present a complementary search technique based on
algorithms used in un-modeled searches. Initially designed for detection of
un-modeled bursts, which can span a very large set of waveform morphologies,
the search algorithm presented here is constrained for targeted detection of
the smaller subset of CBC signals. The constraint is based on the assumption of
elliptical polarisation for signals received at the detector. We expect that
the algorithm is sensitive to CBC signals in a wide range of masses, mass
ratios, and spin parameters. In preparation for the analysis of data from the
fifth LIGO-Virgo science run (S5), we performed preliminary studies of the
algorithm on test data. We present the sensitivity of the search to different
types of simulated CBC waveforms. Also, we discuss how to extend the results of
the test run into a search over all of the current LIGO-Virgo data set.Comment: 12 pages, 4 figures, 2 tables, submitted for publication in CQG in
the special issue for the conference proceedings of GWDAW13; corrected some
typos, addressed some minor reviewer comments one section restructured and
references updated and correcte
Searching for continuous gravitational wave signals: the hierarchical Hough transform algorithm
It is well known that matched filtering techniques cannot be applied for
searching extensive parameter space volumes for continuous gravitational wave
signals. This is the reason why alternative strategies are being pursued.
Hierarchical strategies are best at investigating a large parameter space when
there exist computational power constraints. Algorithms of this kind are being
implemented by all the groups that are developing software for analyzing the
data of the gravitational wave detectors that will come online in the next
years. In this talk we will report about the hierarchical Hough transform
method that the GEO 600 data analysis team at the Albert Einstein Institute is
developing. The three step hierarchical algorithm has been described elsewhere.
In this talk we will focus on some of the implementational aspects we are
currently concerned with.Comment: 9 pages, 1 figure. To appear in the proceedings of the conference
``Gravitational waves: a challenge to theoretical astrophysics'', (June 5-9
2000, Trieste), ICTP Lecture Notes Serie
A Three-Stage Search for Supermassive Black Hole Binaries in LISA Data
Gravitational waves from the inspiral and coalescence of supermassive
black-hole (SMBH) binaries with masses ~10^6 Msun are likely to be among the
strongest sources for the Laser Interferometer Space Antenna (LISA). We
describe a three-stage data-analysis pipeline designed to search for and
measure the parameters of SMBH binaries in LISA data. The first stage uses a
time-frequency track-search method to search for inspiral signals and provide a
coarse estimate of the black-hole masses m_1, m_2 and of the coalescence time
of the binary t_c. The second stage uses a sequence of matched-filter template
banks, seeded by the first stage, to improve the measurement accuracy of the
masses and coalescence time. Finally, a Markov Chain Monte Carlo search is used
to estimate all nine physical parameters of the binary. Using results from the
second stage substantially shortens the Markov Chain burn-in time and allows us
to determine the number of SMBH-binary signals in the data before starting
parameter estimation. We demonstrate our analysis pipeline using simulated data
from the first LISA Mock Data Challenge. We discuss our plan for improving this
pipeline and the challenges that will be faced in real LISA data analysis.Comment: 12 pages, 3 figures, submitted to Proceedings of GWDAW-11 (Berlin,
Dec. '06
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