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- Biblioteca Digitais de Teses e Dissertações da USP
- AIP Publishing
- World Scientific Publishing Co Pte Ltd
- Kluwer Academic Publishers
- Springer Verlag
- Pergamon-Elsevier Ltd
- Institute of Electrical and Electronics Engineers (IEEE Inc)
- Universidade Cornell
- Universidade Autônoma de Barcelona
- Rochester Instituto de Tecnologia
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## Estimação de estado em sistemas elétricos de potência: composição de erros de medidas; State estimation in power systems: measurement error composition

Fonte: Biblioteca Digitais de Teses e Dissertações da USP
Publicador: Biblioteca Digitais de Teses e Dissertações da USP

Tipo: Tese de Doutorado
Formato: application/pdf

Publicado em 10/08/2011
Português

Relevância na Pesquisa

36.98243%

#Análise de erro grosseiro#Estimação de estado#Gross errors analysis#Orthogonal projections#Power systems#Projeção ortogonal#Recovering errors and innovation index#Recuperação de erros e índice de inovação#Sistemas elétricos de potência#State estimation

Bretas et al. (2009) prova matematicamente, e através da interpretação geométrica, que o erro de medida se divide em componentes detectáveis e não-detectáveis. Demonstra ainda que as metodologias até então utilizadas, para o processamento de Erros Grosseiros (EGs), consideram apenas a componente detectável do erro. Assim, dependendo da amplitude das componentes do erro, essas metodologias podem falhar. Face ao exposto, neste trabalho é proposto uma nova metodologia para processar as medidas portadoras de EGs. Essa proposição será obtida decompondo o erro da medida em duas componentes: a primeira, é ortogonal ao espaço da imagem da matriz jacobiana cuja amplitude é igual ao resíduo da medida, a outra, pertence ao espaço da imagem da matriz jacobiana e que, por conseguinte, não contribui para o resíduo da medida. A relação entre a norma dessas componentes, aqui denominado Índice de Inovação (II), prevê uma nova informação, isto é, informação não contida nas outras medidas. Usando o II, calcula-se um valor limiar (TV) para cada medida, esse limiar será utilizado para inferir se a medida é ou não suspeita de possuir EG. Em seguida, com as medidas suspeitas em mãos, desenvolve-se um índice de filtragem (FI) que será utilizado para identificar qual daquelas medidas tem maior probabilidade de possuir EG. Os sistemas de 14 e 30 barras do IEEE...

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## Controle e filtragem para sistemas lineares discretos incertos sujeitos a saltos Markovianos; Control and filtering for uncertain discrete-time Markovian jump linear systems

Fonte: Biblioteca Digitais de Teses e Dissertações da USP
Publicador: Biblioteca Digitais de Teses e Dissertações da USP

Tipo: Tese de Doutorado
Formato: application/pdf

Publicado em 21/06/2013
Português

Relevância na Pesquisa

37.231367%

#Cadeia de Markov#Control#Controle#Equação de Riccati#Filtering#Filtragem#Função penalidade#Least squares#Linear system#Markov chain#Mínimos quadrados

Esta tese de doutorado aborda os projetos robustos de controle e estimativa de estados para Sistemas Lineares sujeitos a Saltos Markovianos (SLSM) de tempo discreto sob a influência de incertezas paramétricas. Esses projetos são desenvolvidos por meio de extensões dos critérios quadráticos clássicos para SLSM nominais. Os critérios de custo quadrático para os SLSM incertos são formulados na forma de problemas de otimização min-max que permitem encontrar a melhor solução para o pior caso de incerteza (máxima influência de incerteza). Os projetos robustos correspondem às soluções ótimas obtidas por meio da combinação dos métodos de funções penalidade e mínimos quadrados regularizados robustos. Duas situações são investigadas: regular e estimar os estados quando os modos de operações são observados; e estimar os estados sob a hipótese de desconhecimento da cadeia de Markov. Estruturalmente, o regulador e as estimativas de estados assemelham-se às respectivas versões nominais. A recursividade é estabelecida em termos de equações de Riccati sem a necessidade de ajuste de parâmetros auxiliares e dependente apenas das matrizes de parâmetros e ponderações conhecidas.; This thesis deals with recursive robust designs of control and state estimates for discretetime Markovian Jump Linear Systems (MJLS) subject to parametric uncertainties. The designs are developed considering extensions of the standard quadratic cost criteria for MJLS without uncertainties. The quadratic cost criteria for uncertain MJLS are formulated in the form of min-max optimization problems to get the best solution for the worst uncertainty case. The optimal robust schemes correspond to the optimal solution obtained by the combination of penalty function and robust regularized least-squares methods. Two cases are investigated: to control and estimate the states when the operation modes are observed; and...

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## A comparison between single site modeling and multiple site modeling approaches using Kalman filtering

Fonte: AIP Publishing
Publicador: AIP Publishing

Tipo: Conferência ou Objeto de Conferência

Português

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This work presents a comparative study between two approaches to calibrate radar rainfall in real time. The weather radar provides continuous measurements in real-time which have errors of either meteorological or instrumental nature. Locally, gauge measurements have a greater performance than radar measurements that can be used to improve radar estimates. One way of doing that is via a state space representation associated to the Kalman filter algorithm. In the single- site modeling approach we use the linear calibration model applied in [1] and [3] while the multivariate state-space model proposed in [6] is used in the multiple site approach. This work aims to discuss and compare these two different state space formulations based on the same data set.

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## Spectral Rate Theory for Two-State Kinetics

Fonte: PubMed
Publicador: PubMed

Tipo: Artigo de Revista Científica

Publicado em /02/2014
Português

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Classical rate theories often fail in cases where the observable(s) or order parameter(s) used is a poor reaction coordinate or the observed signal is deteriorated by noise, such that no clear separation between reactants and products is possible. Here, we present a general spectral two-state rate theory for ergodic dynamical systems in thermal equilibrium that explicitly takes into account how the system is observed. The theory allows the systematic estimation errors made by standard rate theories to be understood and quantified. We also elucidate the connection of spectral rate theory with the popular Markov state modeling approach for molecular simulation studies. An optimal rate estimator is formulated that gives robust and unbiased results even for poor reaction coordinates and can be applied to both computer simulations and single-molecule experiments. No definition of a dividing surface is required. Another result of the theory is a model-free definition of the reaction coordinate quality. The reaction coordinate quality can be bounded from below by the directly computable observation quality, thus providing a measure allowing the reaction coordinate quality to be optimized by tuning the experimental setup. Additionally, the respective partial probability distributions can be obtained for the reactant and product states along the observed order parameter...

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## A hidden Markov approach to the forward premium puzzle

Fonte: World Scientific Publishing Co Pte Ltd
Publicador: World Scientific Publishing Co Pte Ltd

Tipo: Artigo de Revista Científica

Publicado em //2006
Português

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A Hidden Markov Chain (HMC) is applied to study the forward premium puzzle. The weekly quotient of the interest rate differential divided by the log exchange rate change is modeled as a Hidden Markov process. Compared with existing standard approaches, the Hidden Markov approach allows a detailed analysis of the puzzle on a day-to-day basis while taking into full account the presence of noise in the observations. Two and three state models are investigated. A three-state HMC model performs better than two-state models. Application of the three-state model reveals that the above quotient is mostly zero, and hence leads to the rejection of the uncovered interest rate parity hypothesis.; Robert J. Elliott; Bing Han

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## Multiple Priors and Asset Pricing

Fonte: Kluwer Academic Publishers
Publicador: Kluwer Academic Publishers

Tipo: Artigo de Revista Científica

Publicado em //2009
Português

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The asset pricing implications of a statistical model consistent with multiple priors, or beliefs about return distributions, are developed. It is shown that quite generally equilibrium differences in mean returns across priors are to be explained in terms of perceived risk differences between these priors. Advances in filtering theory are employed on time series data to filter all the multiple state conditional components of risks and rewards. It is then observed that excess return differentials across priors are broadly consistent with required risk compensations under these priors, though the sharp hypothesis of zero intercept and unit slope is rejected. The filtered results also deliver numerous other interesting statistics. Here we focus on the construction of long horizon return distributions from data on daily returns using a Markov chain approach to incorporate stochasticity in elementary risk characterizations like volatility, skewness and kurtosis.; Dilip B. Madan and Robert J. Elliott

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## Two-phase layered learning recommendation via category structure

Fonte: Springer Verlag
Publicador: Springer Verlag

Tipo: Conference paper

Publicado em //2014
Português

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Context and social network information have been introduced to improve recommendation systems. However, most existing work still models users’ rating for every item directly. This approach has two disadvantages: high cost for handling large amount of items and unable to handle the dynamic update of items. Generally, items are classified into many categories. Items in the same category have similar/relevant content, and hence may attract users of the same interest. These characteristics determine that we can utilize the item’s content similarity to overcome the difficultiess of large amount and dynamic update of items. In this paper, aiming at fusing the category structure, we propose a novel two-phase layered learning recommendation framework, which is matrix factorization approach and can be seen as a greedy layer-wise training: first learn user’s average rating to every category, and then, based on this, learn more accurate estimates of user’s rating for individual item with content and social relation ensembled. Based on two kinds of classifications, we design two layered gradient algorithms in our framework. Systematic experiments on real data demonstrate that our algorithms outperform other state-of-the-art methods, especially for recommending new items.; Ke Ji...

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## Filtragem robusta recursiva para sistemas lineares a tempo discreto com parâmetros sujeitos a saltos Markovianos; Recursive robust filtering for discrete-time Markovian jump linear systems

Fonte: Biblioteca Digitais de Teses e Dissertações da USP
Publicador: Biblioteca Digitais de Teses e Dissertações da USP

Tipo: Tese de Doutorado
Formato: application/pdf

Publicado em 26/08/2011
Português

Relevância na Pesquisa

37.063252%

#Algoritmos array#Array algorithms#Discrete-time filters#Estimativa robusta#Filtragem H 'INFINITO'#Filtros de informação#Filtros discretos no tempo#H 'INFINTO' filtering#Information filter#Linear systems#Markovian systems

Este trabalho trata de filtragem robusta para sistemas lineares sujeitos a saltos Markovianos discretos no tempo. Serão desenvolvidas estimativas preditoras e filtradas baseadas em algoritmos recursivos que são úteis para aplicações em tempo real. Serão desenvolvidas duas classes de filtros robustos, uma baseada em uma estratégia do tipo H \'INFINITO\' e a outra baseada no método dos mínimos quadrados regularizados robustos. Além disso, serão desenvolvidos filtros na forma de informação e seus respectivos algoritmos array para estimar esse tipo de sistema. Neste trabalho assume-se que os parâmetros de saltos do sistema Markoviano não são acessíveis.; This work deals with the problem of robust state estimation for discrete-time uncertain linear systems subject to Markovian jumps. Predicted and filtered estimates are developed based on recursive algorithms which are useful in on-line applications. We develop two classes of filters, the first one is based on a H \'INFINITO\' approach and the second one is based on a robust regularized leastsquare method. Moreover, we develop information filter and their respective array algorithms to estimate this kind of system. We assume that the jump parameters of the Markovian system are not acessible.

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## Stability of Kalman filtering with Markovian packet losses

Fonte: Pergamon-Elsevier Ltd
Publicador: Pergamon-Elsevier Ltd

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

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#Keywords: Error analysis#Markov processes#Signal filtering and prediction#System stability#Covariance matrix#Networked systems#Packet losses#Peak covariance#Kalman filters Kalman filtering#Networked systems#Packet losses

We consider Kalman filtering in a network with packet losses, and use a two state Markov chain to describe the normal operating condition of packet delivery and transmission failure. Based on the sojourn time of each visit to the failure or successful pac

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## Kalman Filtering with Markovian Packet Losses and Stability Criteria

Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc)
Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)

Tipo: Conference paper

Português

Relevância na Pesquisa

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#Keywords: Error analysis#Markov processes#Numerical methods#Packet loss#Stability criteria#System stability#Vectors#Error covariance#Peak covariance#Suboptimal estimator#Kalman filters

We consider Kalman filtering in a network with packet losses, and use a two state Markov chain to describe the normal operating condition of packet delivery and transmission failure. We analyze the behavior of the estimation error covariance matrix and in

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## Two-Time-Scale Approximation for Wonham Filters

Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc)
Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

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#Keywords: Approximation theory#Computational complexity#Hidden Markov models#State space methods#Time scale separation#Wonham filter#Signal filtering and prediction Hidden Markov chain#Two-time-scale Markov process#Wonham filter

This paper is concerned with approximation of Wonham filters. A focal point is that the underlying hidden Markov chain has a large state space. To reduce computational complexity, a two-time-scale approach is developed. Under time scale separation, the st

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## Solving single molecules: filtering noisy discrete data made of photons and other type of observables

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 11/06/2013
Português

Relevância na Pesquisa

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In numerous systems in biophysics and related fields, scientists measure
(with very smart methods) individual molecules (e.g. biopolymers (proteins,
DNA, RNA, etc), nano - crystals, ion channels), aiming at finding a model from
the data. But the noise is not solved accurately in not so few cases and this
may lead to misleading models. Here, we solve the noise. We consider two state
photon trajectories from any on off kinetic scheme (KS): the process emitting
photons with a rate {\gamma}on when it is in the on state, and emitting with a
rate {\gamma}off when it is in the off state. We develop a filter that removes
the noise resulting in clean data also in cases where binning fails. The filter
is a numerical algorithm with various new statistical treatments. It is based
on a new general likelihood function developed here, with observable dependent
form. The filter can solve the noise, in the detectable region of the rate
space: that is, we also find a region where the data is "too" noisy.
Consistency tests will find the region's type from the data. If the data is
ruled "too noisy", binning obviously fails, and one should apply simpler
methods on the raw data and realizing that the extracted information is
partial. We show that not applying the filter while cleaning results in
erroneous rates. This filter (with minor adjustments) can solve the noise in
any discrete state trajectories...

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## Spectral rate theory for projected two-state kinetics

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.182754%

#Physics - Data Analysis, Statistics and Probability#Condensed Matter - Soft Condensed Matter#Condensed Matter - Statistical Mechanics#Mathematical Physics#Physics - Biological Physics#Physics - Chemical Physics#Physics - Computational Physics

Classical rate theories often fail in cases where the observable(s) or order
parameter(s) used are poor reaction coordinates or the observed signal is
deteriorated by noise, such that no clear separation between reactants and
products is possible. Here, we present a general spectral two-state rate theory
for ergodic dynamical systems in thermal equilibrium that explicitly takes into
account how the system is observed. The theory allows the systematic estimation
errors made by standard rate theories to be understood and quantified. We also
elucidate the connection of spectral rate theory with the popular Markov state
modeling (MSM) approach for molecular simulation studies. An optimal rate
estimator is formulated that gives robust and unbiased results even for poor
reaction coordinates and can be applied to both computer simulations and
single-molecule experiments. No definition of a dividing surface is required.
Another result of the theory is a model-free definition of the reaction
coordinate quality (RCQ). The RCQ can be bounded from below by the directly
computable observation quality (OQ), thus providing a measure allowing the RCQ
to be optimized by tuning the experimental setup. Additionally, the respective
partial probability distributions can be obtained for the reactant and product
states along the observed order parameter...

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## Two-state filtering for joint state-parameter estimation

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 24/03/2014
Português

Relevância na Pesquisa

57.82999%

This paper presents an approach for simultaneous estimation of the state and
unknown parameters in a sequential data assimilation framework. The state
augmentation technique, in which the state vector is augmented by the model
parameters, has been investigated in many previous studies and some success
with this technique has been reported in the case where model parameters are
additive. However, many geophysical or climate models contains non-additive
parameters such as those arising from physical parametrization of sub-grid
scale processes, in which case the state augmentation technique may become
ineffective since its inference about parameters from partially observed states
based on the cross covariance between states and parameters is inadequate if
states and parameters are not linearly correlated. In this paper, we propose a
two-stages filtering technique that runs particle filtering (PF) to estimate
parameters while updating the state estimate using Ensemble Kalman filter
(ENKF; these two "sub-filters" interact. The applicability of the proposed
method is demonstrated using the Lorenz-96 system, where the forcing is
parameterized and the amplitude and phase of the forcing are to be estimated
jointly with the states. The proposed method is shown to be capable of
estimating these model parameters with a high accuracy as well as reducing
uncertainty while the state augmentation technique fails.

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## Kalman Filtering over Gilbert-Elliott Channels: Stability Conditions and the Critical Curve

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 05/11/2014
Português

Relevância na Pesquisa

37.40031%

This paper investigates the stability of Kalman filtering over
Gilbert-Elliott channels where random packet drop follows a time-homogeneous
two-state Markov chain whose state transition is determined by a pair of
failure and recovery rates. First of all, we establish a relaxed condition
guaranteeing peak-covariance stability described by an inequality in terms of
the spectral radius of the system matrix and transition probabilities of the
Markov chain. We further show that that condition can be interpreted using a
linear matrix inequality feasibility problem. Next, we prove that the
peak-covariance stability implies mean-square stability, if the system matrix
has no defective eigenvalues on the unit circle. This connection between the
two stability notions holds for any random packet drop process. We prove that
there exists a critical curve in the failure-recovery rate plane, below which
the Kalman filter is mean-square stable and no longer mean-square stable above,
via a coupling method in stochastic processes. Finally, a lower bound for this
critical failure rate is obtained making use of the relationship we establish
between the two stability criteria, based on an approximate relaxation of the
system matrix.

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## Optimal unambiguous filtering of a quantum state: An instance in mixed state discrimination

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 18/04/2005
Português

Relevância na Pesquisa

37.45689%

Deterministic discrimination of nonorthogonal states is forbidden by quantum
measurement theory. However, if we do not want to succeed all the time, i.e.
allow for inconclusive outcomes to occur, then unambiguous discrimination
becomes possible with a certain probability of success. A variant of the
problem is set discrimination: the states are grouped in sets and we want to
determine to which particular set a given pure input state belongs. We consider
here the simplest case, termed quantum state filtering, when the $N$ given
non-orthogonal states, $\{|\psi_{1} >,..., |\psi_{N} > \}$, are divided into
two sets and the first set consists of one state only while the second consists
of all of the remaining states. We present the derivation of the optimal
measurement strategy, in terms of a generalized measurement (POVM), to
distinguish $|\psi_1>$ from the set $\{|\psi_2 >,...,|\psi_N > \}$ and the
corresponding optimal success and failure probabilities. The results, but not
the complete derivation, were presented previously [\prl {\bf 90}, 257901
(2003)] as the emphasis there was on appplication of the results to novel
probabilistic quantum algorithms. We also show that the problem is equivalent
to the discrimination of a pure state and an arbitrary mixed state.; Comment: 8 pages

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## Quantum-state filtering applied to the discrimination of Boolean functions

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 10/07/2005
Português

Relevância na Pesquisa

47.73618%

Quantum state filtering is a variant of the unambiguous state discrimination
problem: the states are grouped in sets and we want to determine to which
particular set a given input state belongs.The simplest case, when the N given
states are divided into two subsets and the first set consists of one state
only while the second consists of all of the remaining states, is termed
quantum state filtering. We derived previously the optimal strategy for the
case of N non-orthogonal states, {|\psi_{1} >, ..., |\psi_{N} >}, for
distinguishing |\psi_1 > from the set {|\psi_2 >, ..., |\psi_N >} and the
corresponding optimal success and failure probabilities. In a previous paper
[PRL 90, 257901 (2003)], we sketched an appplication of the results to
probabilistic quantum algorithms. Here we fill in the gaps and give the
complete derivation of the probabilstic quantum algorithm that can optimally
distinguish between two classes of Boolean functions, that of the balanced
functions and that of the biased functions. The algorithm is probabilistic, it
fails sometimes but when it does it lets us know that it did. Our approach can
be considered as a generalization of the Deutsch-Jozsa algorithm that was
developed for the discrimination of balanced and constant Boolean functions.; Comment: 8 pages

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## Filtering of matter wave vibrational states via spatial adiabatic passage

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 28/07/2011
Português

Relevância na Pesquisa

47.81332%

We discuss the filtering of the vibrational states of a cold atom in an
optical trap, by chaining this trap with two empty ones and controlling
adiabatically the tunneling. Matter wave filtering is performed by selectively
transferring the population of the highest populated vibrational state to the
most distant trap while the population of the rest of the states remains in the
initial trap. Analytical conditions for two-state filtering are derived and
then applied to an arbitrary number of populated bound states. Realistic
numerical simulations close to state-of-the-art experimental arrangements are
performed by modeling the triple well with time dependent P\"oschl-Teller
potentials. In addition to filtering of vibrational states, we discuss
applications for quantum tomography of the initial population distribution and
engineering of atomic Fock states that, eventually, could be used for tunneling
assisted evaporative cooling.; Comment: 7 pages, 6 figures

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## Filtering of matter-wave vibrational states via spatial adiabatic passage

Fonte: Universidade Autônoma de Barcelona
Publicador: Universidade Autônoma de Barcelona

Tipo: Artigo de Revista Científica
Formato: application/pdf

Publicado em //2011
Português

Relevância na Pesquisa

47.81332%

We discuss the filtering of the vibrational states of a cold atom in an optical trap by chaining this trap with two empty ones and adiabatically controlling the tunneling. Matter-wave filtering is performed by selectively transferring the population of the highest populated vibrational state to the most distant trap while the population of the rest of the states remains in the initial trap. Analytical conditions for two-state filtering are derived and then applied to an arbitrary number of populated bound states. Realistic numerical simulations close to state-of-the-art experimental arrangements are performed by modeling the triple well with time-dependent Pöschl-Teller potentials. In addition to filtering of vibrational states, we discuss applications for quantum tomography of the initial population distribution and engineering of atomic Fock states that, eventually, could be used for tunneling-assisted evaporative cooling.

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## Development of a ground truth simulator and application of a generalized multiple-model adaptive estimation approach to tune a state estimation filter

Fonte: Rochester Instituto de Tecnologia
Publicador: Rochester Instituto de Tecnologia

Tipo: Tese de Doutorado

Português

Relevância na Pesquisa

37.319429%

#Kalman filter#Maritime simulation#MMAE#PDAF#State estimation#V169 .W94 2007#Naval tactics--Computer simulation#Multisensor data fusion#Kalman filtering#Decision making--Data processing

In this thesis, a maritime scenario simulator is developed and a data processing/filtering
algorithm is applied to estimate the ground truth of the simulated scenario from noisy
measurements and system model for the Hierarchical High Level Information Fusion
Technologies (H2LIFT) project. H2LIFT is an adaptable information fusion frame-
work which takes as input Levels 0/1 (local) data and performs fusion at Levels two and
three (distributed, and network centric) hierarchically, in different stages, to provide real-
time situational/impact assessment efficiently while avoiding the overload of information
to the human decision maker. First, a simulator is developed that imitates a naval threat
from an incoming vessel (such as a cargo ship containing a weapon of mass destruction),
included in a group of non-threatening vessels. The developed simulations are used as
evaluation metrics and performance platforms providing an operational utility assessment
tool for the H2LIFT algortithm. Next, a Generalized Multiple-Model Adaptive Estima-
tion (GMMAE) technique is used to estimate the unknown parameters involved with a
Probability Data Association Filter (PDAF) which includes a Kalman Filter (KF). The
properly tuned state estimator is used to provide estimates of the ground truth data from
the noisy sensor measurements and incomplete system model. These estimates are used
as inputs to the H2LIFT algorithm and can be tested against the known ground truth
to gauge filter performance. A demonstration of the process is provided in the simulation
section.

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