Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired pou er plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.

Six ways to leave unemployment

This paper uses a unique Portuguese dataset to examine the effect of access to unemployment benefits (UBs) and their maximum potential duration on escape rates from unemployment. In examining the time profile of transitions out of unemployment, the principal contributions of the paper are twofold. First, it provides a detailed state space of potential outcomes: open-ended employment, fixed-term contracts, part-time work, government-provided jobs, self employment, and labour force withdrawal. Second, it is able to exploit major exogenous discontinuities in the maximum duration of unemployment benefits to identify disincentive effects. While confirming strong disincentive effects, it is shown that use of an aggregate hazard function regression model compounds very different and even contradictory effects of the determinants of unemployment.

Exploiting spatio-temporal constraints for robust 2D pose tracking

We present a Spatio-temporal 2D Models Framework (STMF) for 2D-Pose tracking. Space and time are discretized and a mixture of probabilistic "local models" is learnt associating 2D Shapes and 2D Stick Figures. Those spatio-temporal models generalize well for a particular viewpoint and state of the tracked action but some spatio-temporal discontinuities can appear along a sequence, as a direct consequence of the discretization. To overcome the problem, we propose to apply a Rao-Blackwellized Particle Filter (RBPF) in the 2D-Pose eigenspace, thus interpolating unseen data between view-based clusters. The fitness to the images of the predicted 2D-Poses is evaluated combining our STMF with spatio-temporal constraints. A robust, fast and smooth human motion tracker is obtained by tracking only the few most important dimensions of the state space and by refining deterministically with our STMF.

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.

Modelação e monitorização do comportamento dinâmico de barragens abóbada: identificação modal automática

Trabalho Final de Mestrado elaborado no Laboratório de Engenharia Civil (LNEC) para obtenção do grau de Mestre em Engenharia Civil pelo Instituto Superior de Engenharia de Lisboa no âmbito do protocolo de cooperação entre o ISEL e o LNEC

Complex dynamics of financial indices

This paper presents a novel method for the analysis of nonlinear financial and economic systems. The modeling approach integrates the classical concepts of state space representation and time series regression. The analytical and numerical scheme leads to a parameter space representation that constitutes a valid alternative to represent the dynamical behavior. The results reveal that business cycles can be clearly revealed, while the noise effects common in financial indices can elegantly be filtered out of the results.

Accessing complexity from genome information

This paper studies the information content of the chromosomes of 24 species. In a first phase, a scheme inspired in dynamical system state space representation is developed. For each chromosome the state space dynamical evolution is shed into a two dimensional chart. The plots are then analyzed and characterized in the perspective of fractal dimension. This information is integrated in two measures of the species’ complexity addressing its average and variability. The results are in close accordance with phylogenetics pointing quantitative aspects of the species’ genomic complexity.

Análise do comportamento sísmico da barragem de Luzzone: desenvolvimento de um programa de EF3D utilizando uma formulação em deslocamentos e pressões

Trabalho Final de Mestrado elaborado no Laboratório Nacional de Engenharia Civil (LNEC) para a obtenção do grau de Mestre em Engenharia Civil pelo Instituto Superior de Engenharia de Lisboa no âmbito do protocolo entre o ISEL e o LNEC

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.

Three essays on the term structure of interest rates

A PhD Dissertation, presented as part of the requirements for the Degree of Doctor of Philosophy from the NOVA - School of Business and Economics

Latent Variable Models for Stochastic Discount Factors.

Latent variable models in finance originate both from asset pricing theory and time series analysis. These two strands of literature appeal to two different concepts of latent structures, which are both useful to reduce the dimension of a statistical model specified for a multivariate time series of asset prices. In the CAPM or APT beta pricing models, the dimension reduction is cross-sectional in nature, while in time-series state-space models, dimension is reduced longitudinally by assuming conditional independence between consecutive returns, given a small number of state variables. In this paper, we use the concept of Stochastic Discount Factor (SDF) or pricing kernel as a unifying principle to integrate these two concepts of latent variables. Beta pricing relations amount to characterize the factors as a basis of a vectorial space for the SDF. The coefficients of the SDF with respect to the factors are specified as deterministic functions of some state variables which summarize their dynamics. In beta pricing models, it is often said that only the factorial risk is compensated since the remaining idiosyncratic risk is diversifiable. Implicitly, this argument can be interpreted as a conditional cross-sectional factor structure, that is, a conditional independence between contemporaneous returns of a large number of assets, given a small number of factors, like in standard Factor Analysis. We provide this unifying analysis in the context of conditional equilibrium beta pricing as well as asset pricing with stochastic volatility, stochastic interest rates and other state variables. We address the general issue of econometric specifications of dynamic asset pricing models, which cover the modern literature on conditionally heteroskedastic factor models as well as equilibrium-based asset pricing models with an intertemporal specification of preferences and market fundamentals. We interpret various instantaneous causality relationships between state variables and market fundamentals as leverage effects and discuss their central role relative to the validity of standard CAPM-like stock pricing and preference-free option pricing.

Queues with inter- ruption in Random/ Markovian Environment

In many situations probability models are more realistic than deterministic models. Several phenomena occurring in physics are studied as random phenomena changing with time and space. Stochastic processes originated from the needs of physicists.Let X(t) be a random variable where t is a parameter assuming values from the set T. Then the collection of random variables {X(t), t ∈ T} is called a stochastic process. We denote the state of the process at time t by X(t) and the collection of all possible values X(t) can assume, is called state space

Institutionalism Theories and Hegemonic Practices in Global Polity Formation

Institutionalistische Theorien und hegemoniale Praktiken Globaler Politikgestaltung. Eine neue Beleuchtung der Prämissen Liberaler Demokratischer National-Staatlicher Ordnungen. Deutsche Zusammenfassung: Moderne Sozialwissenschaften, seien es Metatheorien der Internationalen Beziehungen, die Geschichte politischer Ökonomie oder Institutionentheorien, zeigen eine klare Dreiteilung von Weltanschauungen bzw. Paradigmen auf, die sich in allen „großen Debatten“ nachvollziehen lassen: Realismus, Liberalismus und Historischer Materialismus. Diese Grund legend unterschiedlichen Paradigmen lassen sich auch in aktuellen Ansätzen des Institutionalismus aufzeigen, liegen aber quer zu den von anderen Wissenschaftlern (Meyer, Rittberger, Hasenclever, Peters, Zangl) vorgenommenen Kategorisierungen der Institutionalismusschulen, die systemkritische Perspektiven in der Regel ignorieren oder vergleichsweise rudimentär diskutieren. Deshalb entwickelt diese Arbeit einen Vergleich von Institutionalismusschulen entlang der oben skizzierten Weltanschauungen. Das Ziel ist es, fundamentale Unterschiede zwischen den drei Paradigmen zu verdeutlichen und zu zeigen, wie ihre jeweiligen ontologischen und epistemologischen Prämissen die Forschungsdesigns und Methodologien der Institutionalismusschulen beeinflussen. In Teil I arbeite ich deshalb die Grund legenden Prämissen der jeweiligen Paradigmen heraus und entwickle in Teil II und III diesen Prämissen entsprechende Institutionalismus-Schulen, die Kooperation primär als Organisation von unüberwindbarer Rivalität, als Ergebnis zunehmender Konvergenz, oder als Ergebnis und Weiterentwicklung von Prozeduren der Interaktion versteht. Hier greife ich auf zeitgenössische Arbeiten anderer Autoren zurück und liefere damit einen Vergleich des aktuellen Forschungsstandes in allen drei Denktraditionen. Teil II diskutiert die zwei dominanten Institutionalismusschulen und Teil III entwickelt einen eigenen Gramscianischen Ansatz zur Erklärung von internationaler Kooperation und Institutionalisierung. Die übergeordnete These dieser Arbeit lautet, dass die Methodologien der dominanten Institutionalismusschulen teleologische Effekte haben, die aus dem Anspruch auf universell anwendbare, abstrahiert Konzepte resultieren und die Interpretation von Beobachtungen limitieren. Prämissen eines rational handelnden Individuums - entweder Konsequenzen kalkulierend oder Angemessenheit reflektierend – führen dazu, dass Kooperation und Institutionalisierung notwendiger Weise als die beste Lösung für alle Beteiligten in dieser Situation gelten müssen: Institutionen würden nicht bestehen, wenn sie nicht in der Summe allen Mitgliedern (egoistisch oder kooperativ motiviert) nützten. Durch diese interpretative „Brille“ finden wichtige strukturelle Gründe für die Verabschiedung internationaler Abkommen und Teile ihrer Effekte keine Berücksichtigung. Folglich können auch Abweichungen von erwarteten Ergebnissen nicht hinreichend erklärt werden. Meine entsprechende Hypothese lautet, dass systemkritische Kooperation konsistenter erklären können, da sie Akteure, Strukturen und die sie umgebenden Weltanschauungen selbst als analytische Kriterien berücksichtigen. Institutionalisierung wird dann als ein gradueller Prozess politischer Entscheidungsfindung, –umsetzung und –verankerung verstanden, der durch die vorherrschenden Institutionen und Interpretationen von „Realität“ beeinflusst wird. Jede politische Organisation wird als zeitlich-geographisch markierter Staatsraum (state space) verstanden, dessen Mandat die Festlegung von Prozeduren der Interaktion für gesellschaftliche Entwicklung ist. Politische Akteure handeln in Referenz auf diese offiziellen Prozeduren und reproduzieren und/oder verändern sie damit kontinuierlich. Institutionen werden damit als integraler Bestandteil gesellschaftlicher Entwicklungsprozesse verstanden und die Wirkungsmacht von Weltanschauungen – inklusive theoretischer Konzepte - berücksichtigt. Letztere leiten die Wahrnehmung und Interpretation von festgeschriebenen Regeln an und beeinflussen damit ihre empfundene Legitimation und Akzeptanz. Dieser Effekt wurde als „Staatsgeist“ („State Spirit“) von Montesquieu und Hegel diskutiert und von Antonio Gramsci in seiner Hegemonialtheorie aufgegriffen. Seine Berücksichtigung erlaubt eine konsistente Erklärung scheinbar irrationalen oder unangemessenen individuellen Entscheidens, sowie negativer Effekte konsensualer Abkommen. Zur Veranschaulichung der neu entwickelten Konzepte werden in Teil II existierende Fallstudien zur Welthandelsorganisation analysiert und herausgearbeitet, wie Weltanschauungen oder Paradigmen zu unterschiedlichen Erklärungen der Praxis führen. Während Teil II besonderes Augenmerk auf die nicht erklärten und innerhalb der dominanten Paradigmen nicht erklärbaren Beobachtungen legt, wendet Teil III die Gramscianischen Konzepte auf eben diese blinden Stellen an und liefert neue Einsichten. Im Ausblick wird problematisiert, dass scheinbar „neutrale“ wissenschaftliche Studien politische Positionen und Forderungen legitimieren und verdeutlicht im Sinne der gramscianischen Theorie, dass Wissenschaft selbst Teil politischer Auseinandersetzungen ist.

Taming Chaotic Circuits

Control algorithms that exploit chaotic behavior can vastly improve the performance of many practical and useful systems. The program Perfect Moment is built around a collection of such techniques. It autonomously explores a dynamical system's behavior, using rules embodying theorems and definitions from nonlinear dynamics to zero in on interesting and useful parameter ranges and state-space regions. It then constructs a reference trajectory based on that information and causes the system to follow it. This program and its results are illustrated with several examples, among them the phase-locked loop, where sections of chaotic attractors are used to increase the capture range of the circuit.