270,741 research outputs found
Statistical analysis of SSME system data
A statistical methodology to enhance the Space Shuttle Main Engine (SSME) performance prediction accuracy is proposed. This methodology was to be used in conjunction with existing SSME performance prediction computer codes to improve parameter prediction accuracy and to quantify that accuracy. However, after a review of related literature, researchers concluded that the proposed problem required a coverage of areas such as linear and nonlinear system theory, measurement theory, statistics, and stochastic estimation. Since state space theory is the foundation for a more complete study of each of the before mentioned areas, these researchers chose to refocus emphasis to cover the more specialized topic of state vector estimation procedures. State vector estimation was also selected because of current and future concerns by NASA for SSME performance evaluation; i.e., there is a current interest in an improved evaluation procedure for actual SSME post flight performance as well as for post static test performance of a single SSME. A current investigation of analytical methods may be used to improve test stand failure detection. This paper considers the issue of post flight/test state variable reconstruction through the application of observations made on the output of the Space Shuttle propulsion system. Rogers used the Kalman filtering procedure to reconstruct the state variables of the Space Shuttle propulsion system. An objective of this paper is to give the general setup of the Kalman filter and its connection to linear regression. A second objective is to examine the reconstruction methodology for application to the reconstruction of the state vector of a single Space Shuttle Main Engine (SSME) by using static test firing data
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance
Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of
exploration such as the additive action noise often used in continuous control
domains. Typically, the scaling factor of this action noise is chosen as a
hyper-parameter and is kept constant during training. In this paper, we focus
on action noise in off-policy deep reinforcement learning for continuous
control. We analyze how the learned policy is impacted by the noise type, noise
scale, and impact scaling factor reduction schedule. We consider the two most
prominent types of action noise, Gaussian and Ornstein-Uhlenbeck noise, and
perform a vast experimental campaign by systematically varying the noise type
and scale parameter, and by measuring variables of interest like the expected
return of the policy and the state-space coverage during exploration. For the
latter, we propose a novel state-space coverage measure
that is more robust to estimation
artifacts caused by points close to the state-space boundary than
previously-proposed measures. Larger noise scales generally increase
state-space coverage. However, we found that increasing the space coverage
using a larger noise scale is often not beneficial. On the contrary, reducing
the noise scale over the training process reduces the variance and generally
improves the learning performance. We conclude that the best noise type and
scale are environment dependent, and based on our observations derive heuristic
rules for guiding the choice of the action noise as a starting point for
further optimization.Comment: Published in Transactions on Machine Learning Research (11/2022)
https://openreview.net/forum?id=NljBlZ6hm
EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries
The paper deals with the estimation of monthly indicators of economic activity for the Euro area and
its largest member countries that possess the following attributes: relevance, representativeness and
timeliness. Relevance is determined by comparing our monthly indicators to the gross domestic product
at chained volumes, as the most important measure of the level of economic activity. Representativeness
is achieved by considering a very large number of (timely) time series of monthly indicators relating to
the level of economic activity, providing a more or less complete coverage. The indicators are modelled
using a large-scale parametric factor model. We discuss its specification and provide details of the
statistical treatment. Computational efficiency is crucial for the estimation of large-scale parametric
factor models of the dimension used in our application (considering about 170 series). To achieve it,
we apply state-of-the-art state space methods that can handle temporal aggregation, and any pattern of
missing values
Coverage and Field Estimation on Bounded Domains by Diffusive Swarms
In this paper, we consider stochastic coverage of bounded domains by a
diffusing swarm of robots that take local measurements of an underlying scalar
field. We introduce three control methodologies with diffusion, advection, and
reaction as independent control inputs. We analyze the diffusion-based control
strategy using standard operator semigroup-theoretic arguments. We show that
the diffusion coefficient can be chosen to be dependent only on the robots'
local measurements to ensure that the swarm density converges to a function
proportional to the scalar field. The boundedness of the domain precludes the
need to impose assumptions on decaying properties of the scalar field at
infinity. Moreover, exponential convergence of the swarm density to the
equilibrium follows from properties of the spectrum of the semigroup generator.
In addition, we use the proposed coverage method to construct a
time-inhomogenous diffusion process and apply the observability of the heat
equation to reconstruct the scalar field over the entire domain from
observations of the robots' random motion over a small subset of the domain. We
verify our results through simulations of the coverage scenario on a 2D domain
and the field estimation scenario on a 1D domain.Comment: To appear in the proceedings of the 55th IEEE Conference on Decision
and Control (CDC 2016
Adaptive Density Estimation for Generative Models
Unsupervised learning of generative models has seen tremendous progress over
recent years, in particular due to generative adversarial networks (GANs),
variational autoencoders, and flow-based models. GANs have dramatically
improved sample quality, but suffer from two drawbacks: (i) they mode-drop,
i.e., do not cover the full support of the train data, and (ii) they do not
allow for likelihood evaluations on held-out data. In contrast,
likelihood-based training encourages models to cover the full support of the
train data, but yields poorer samples. These mutual shortcomings can in
principle be addressed by training generative latent variable models in a
hybrid adversarial-likelihood manner. However, we show that commonly made
parametric assumptions create a conflict between them, making successful hybrid
models non trivial. As a solution, we propose to use deep invertible
transformations in the latent variable decoder. This approach allows for
likelihood computations in image space, is more efficient than fully invertible
models, and can take full advantage of adversarial training. We show that our
model significantly improves over existing hybrid models: offering GAN-like
samples, IS and FID scores that are competitive with fully adversarial models,
and improved likelihood scores
An Estimation of County-Level Vaccination Coverage for Human Papillomavirus Vaccine among Adolescents Aged 13-17 Years in South Eastern United States of America Using Bayesian and Spatial Effects Models
This dissertation applies Bayesian Hierarchical (BH) methods and Spatial effects at both the state and county levels to estimate Human papillomavirus (HPV) vaccination initiation coverage at the county level in the ten Southeastern U.S. states (925 counties) using 2016 National Immunization Survey-Teen (NIS-Teen) adequate provider data. Small sample sizes yield inadequate precision for direct domain estimators. Bayesian methods allows indirect estimation with small sample size, missing values and covariates via the Markov Chain Monte Carlo (MCMC) method. The BH method, which allows the parameters of a prior distribution or a population distribution themselves to be estimated from data, is one of the appropriate ways in handling small areas with sparse data because posterior inference is exact which does not rely on asymptotic arguments. We use the conditional autoregressive (CAR) model to capture the spatial correlation and study its role in modeling the HPV vaccination initiation coverage. Additionally, we applied Bayesian modeling of temporal trends of HPV vaccination initiation coverage over time (quarter of survey year) and space (in the 10 southeastern states in US) using NIS-Teen survey years 2011 to 2016 adequate provider data. These methods can be used in further analysis for the temporal trend of HPV vaccination initiation coverage at the county level
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