9,532 research outputs found
On adaptive decision rules and decision parameter adaptation for automatic speech recognition
Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing.published_or_final_versio
Neural Networks for Modeling and Control of Particle Accelerators
We describe some of the challenges of particle accelerator control, highlight
recent advances in neural network techniques, discuss some promising avenues
for incorporating neural networks into particle accelerator control systems,
and describe a neural network-based control system that is being developed for
resonance control of an RF electron gun at the Fermilab Accelerator Science and
Technology (FAST) facility, including initial experimental results from a
benchmark controller.Comment: 21 p
Model Predictive Control for Offset-Free Reference Tracking
The paper deals with the offset-free reference tracking problem of the Model Predictive Control (MPC). That problem is considered for a class of the constant or occasionally changed constant reference signals. Proposed solution arises from a simple subtraction of the ARX model of two consecutive time steps. The solution is adapted to a state-space form and it corresponds to usual predictive control design without increase of the design complexity. The construction of the prediction equations and predictive controller structure is explained in the paper
Spatio-angular Minimum-variance Tomographic Controller for Multi-Object Adaptive Optics systems
Multi-object astronomical adaptive-optics (MOAO) is now a mature wide-field
observation mode to enlarge the adaptive-optics-corrected field in a few
specific locations over tens of arc-minutes.
The work-scope provided by open-loop tomography and pupil conjugation is
amenable to a spatio-angular Linear-Quadratic Gaussian (SA-LQG) formulation
aiming to provide enhanced correction across the field with improved
performance over static reconstruction methods and less stringent computational
complexity scaling laws.
Starting from our previous work [1], we use stochastic time-progression
models coupled to approximate sparse measurement operators to outline a
suitable SA-LQG formulation capable of delivering near optimal correction.
Under the spatio-angular framework the wave-fronts are never explicitly
estimated in the volume,providing considerable computational savings on
10m-class telescopes and beyond.
We find that for Raven, a 10m-class MOAO system with two science channels,
the SA-LQG improves the limiting magnitude by two stellar magnitudes when both
Strehl-ratio and Ensquared-energy are used as figures of merit. The
sky-coverage is therefore improved by a factor of 5.Comment: 30 pages, 7 figures, submitted to Applied Optic
PMU-Based ROCOF Measurements: Uncertainty Limits and Metrological Significance in Power System Applications
In modern power systems, the Rate-of-Change-of-Frequency (ROCOF) may be
largely employed in Wide Area Monitoring, Protection and Control (WAMPAC)
applications. However, a standard approach towards ROCOF measurements is still
missing. In this paper, we investigate the feasibility of Phasor Measurement
Units (PMUs) deployment in ROCOF-based applications, with a specific focus on
Under-Frequency Load-Shedding (UFLS). For this analysis, we select three
state-of-the-art window-based synchrophasor estimation algorithms and compare
different signal models, ROCOF estimation techniques and window lengths in
datasets inspired by real-world acquisitions. In this sense, we are able to
carry out a sensitivity analysis of the behavior of a PMU-based UFLS control
scheme. Based on the proposed results, PMUs prove to be accurate ROCOF meters,
as long as the harmonic and inter-harmonic distortion within the measurement
pass-bandwidth is scarce. In the presence of transient events, the
synchrophasor model looses its appropriateness as the signal energy spreads
over the entire spectrum and cannot be approximated as a sequence of
narrow-band components. Finally, we validate the actual feasibility of
PMU-based UFLS in a real-time simulated scenario where we compare two different
ROCOF estimation techniques with a frequency-based control scheme and we show
their impact on the successful grid restoration.Comment: Manuscript IM-18-20133R. Accepted for publication on IEEE
Transactions on Instrumentation and Measurement (acceptance date: 9 March
2019
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