Taking cues from outer space : the E&P sector can learn much about asset maintenance from the space and satellite industry

The E&P sector can learn much about asset maintenance from the space and satellite industry. Practitioners from both the upstream oil and gas industry and the space and satellite sector have repeatedly noted several striking similarities between the two industries over the years, which have in turn resulted in many direct comparisons in the media and industry press. The similarities between the two industries have even resulted in a modest amount of cross-pollinating between the respective supply chains. Because the operating conditions of both industries are so extreme, some oil and gas equipment vendors have occasionally sourced motors and other parts from aerospace contractors. Also, satellites are now being used to assess oil fires, detect subsidence in oil fields, measure oil spills, collect and transmit operational data from oil and gas fields, and monitor the movement of icebergs that might potentially collide with offshore oil and gas installations.

Sequential alternating proximal method for scalable sparse structural SVMs

Structural Support Vector Machines (SSVMs) have recently gained wide prominence in classifying structured and complex objects like parse-trees, image segments and Part-of-Speech (POS) tags. Typical learning algorithms used in training SSVMs result in model parameters which are vectors residing in a large-dimensional feature space. Such a high-dimensional model parameter vector contains many non-zero components which often lead to slow prediction and storage issues. Hence there is a need for sparse parameter vectors which contain a very small number of non-zero components. L1-regularizer and elastic net regularizer have been traditionally used to get sparse model parameters. Though L1-regularized structural SVMs have been studied in the past, the use of elastic net regularizer for structural SVMs has not been explored yet. In this work, we formulate the elastic net SSVM and propose a sequential alternating proximal algorithm to solve the dual formulation. We compare the proposed method with existing methods for L1-regularized Structural SVMs. Experiments on large-scale benchmark datasets show that the proposed dual elastic net SSVM trained using the sequential alternating proximal algorithm scales well and results in highly sparse model parameters while achieving a comparable generalization performance. Hence the proposed sequential alternating proximal algorithm is a competitive method to achieve sparse model parameters and a comparable generalization performance when elastic net regularized Structural SVMs are used on very large datasets.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.

Sequential Monte Carlo computation of the score and observed information matrix in state-space models with application to parameter estimation

Sequential Monte Carlo (SMC) methods are popular computational tools for Bayesian inference in non-linear non-Gaussian state-space models. For this class of models, we propose SMC algorithms to compute the score vector and observed information matrix recursively in time. We propose two different SMC implementations, one with computational complexity $\mathcal{O}(N)$ and the other with complexity $\mathcal{O}(N^{2})$ where $N$ is the number of importance sampling draws. Although cheaper, the performance of the $\mathcal{O}(N)$ method degrades quickly in time as it inherently relies on the SMC approximation of a sequence of probability distributions whose dimension is increasing linearly with time. In particular, even under strong \textit{mixing} assumptions, the variance of the estimates computed with the $\mathcal{O}(N)$ method increases at least quadratically in time. The $\mathcal{O}(N^{2})$ is a non-standard SMC implementation that does not suffer from this rapid degrade. We then show how both methods can be used to perform batch and recursive parameter estimation.

An overview of Sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general state-space models. We discuss the advantages and limitations of these methods. © 2009 IFAC.

Investigation of two-proton emission from excited states of the odd-Z nucleus P-28 by complete-kinematics measurements

An experiment to study exotic two-proton emission from excited levels of the odd-Z nucleus P-28 was performed at the National Laboratory of Heavy Ion Research-Radioactive Ion Beam Line (HIRFL-RIBLL) facility. The projectile P-28 at the energy of 46.5 MeV/u was bombarding a Au-197 target to populate the excited states via Coulomb excitation. Complete-kinematics measurements were realized by the array of silicon strip detectors and the CsI + PIN telescope. Two-proton events were selected and the relativistic-kinematics reconstruction was carried out. The spectrum of relative momentum and opening angle between two protons was deduced from Monte Carlo simulations. Experimental results show that two-proton emission from P-28 excited states less than 17.0 MeV is mainly two-body sequential emission or three-body simultaneous decay in phase space. The present simulations cannot distinguish these two decay modes. No obvious diproton emission was found.

Sequential deprotonation of meso-(p-hydroxyphenyl)porphyrins in DMF: From hyperporphyrins to sodium porphyrin complexes

Sequential deprotonations of meso-(p-hydroxyphenyl)porphyrins (p-OHTPPH2) in DMF + H2O (V/V = 1:1) mixture have been verified to result in the appearance of hyperporphyrin spectra. However, when the deprotonations of these p-OHTPPH2 are carried out in DMF, the spectral changes differ considerably from those in the mixture mentioned above. At low [OH-], the optical spectra in the visible region are still considered to have characteristics of hyperporphyrin spectra. Further deprotonation at much higher basicity makes the optical spectra form three-banded spectra similar to those in the acidic solution. To clarify the molecular origins of these changes, UV-vis, resonance Raman (RR), proton nuclear magnetic resonance (H-1 NMR) experiments are carried out. Our data give evidence that p-OHTPPH2 in DMF can be further deprotonated of pyrrolic-H by higher concentrated NaOH, due to an aprotic medium like DMF effectively weakening the basicity of the porphyrin relative to that of the NaOH, and coordinates with two sodium ions (except the sodium ions that interact with the peripherial phenoxide anions) to form the sodium complexes of p-OHTPPH2 (Na2P, to lay a strong emphasis on the sodium ions that coordinate with the central nitrogen atom), which can be regarded as the porphyrin anions being perturbed by the sodium cations due to their highly ionic character.

A Continuous Time Markov Chain Based Sequential Analytical Approach for Composite Power System Reliability Assessment

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.

Spherical space forms - Homotopy self-equivalences and homotopy types, the case of the groups Z/a x (Z/b x TL(2)(F(p)))

Let G = Z/a x(mu) (Z/b x TL(2)(F(p))) and X(n) be an n-dimensional CW-complex with the homotopy type of the n-sphere. We determine the automorphism group Aut(G) and then compute the number of distinct homotopy types of spherical space forms with respect to free and cellular G-actions on all CW-complexes X(2dn - 1), where 2d is a period of G. Next, the group E(X(2dn - 1)/alpha) of homotopy self-equivalences of spherical space forms X(2dn - 1)/alpha, associated with such G-actions alpha on X(2dn - 1) are studied. Similar results for the rest of finite periodic groups have been obtained recently and they are described in the introduction. (C) 2009 Elsevier B.V. All rights reserved.

Combined search for the quarks of a sequential fourth generation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)