NASA Workshop on Distributed Parameter Modeling and Control of Flexible Aerospace Systems

Although significant advances have been made in modeling and controlling flexible systems, there remains a need for improvements in model accuracy and in control performance. The finite element models of flexible systems are unduly complex and are almost intractable to optimum parameter estimation for refinement using experimental data. Distributed parameter or continuum modeling offers some advantages and some challenges in both modeling and control. Continuum models often result in a significantly reduced number of model parameters, thereby enabling optimum parameter estimation. The dynamic equations of motion of continuum models provide the advantage of allowing the embedding of the control system dynamics, thus forming a complete set of system dynamics. There is also increased insight provided by the continuum model approach

Supervised and Ensemble Classification of Multivariate Functional Data: Applications to Lupus Diagnosis

abstract: This dissertation investigates the classification of systemic lupus erythematosus (SLE) in the presence of non-SLE alternatives, while developing novel curve classification methodologies with wide ranging applications. Functional data representations of plasma thermogram measurements and the corresponding derivative curves provide predictors yet to be investigated for SLE identification. Functional nonparametric classifiers form a methodological basis, which is used herein to develop a) the family of ESFuNC segment-wise curve classification algorithms and b) per-pixel ensembles based on logistic regression and fused-LASSO. The proposed methods achieve test set accuracy rates as high as 94.3%, while returning information about regions of the temperature domain that are critical for population discrimination. The undertaken analyses suggest that derivate-based information contributes significantly in improved classification performance relative to recently published studies on SLE plasma thermograms.Dissertation/ThesisDoctoral Dissertation Applied Mathematics 201

Fourth NASA Workshop on Computational Control of Flexible Aerospace Systems, part 2

A collection of papers presented at the Fourth NASA Workshop on Computational Control of Flexible Aerospace Systems is given. The papers address modeling, systems identification, and control of flexible aircraft, spacecraft and robotic systems

Methods of analysis and empirical evidence of farm structural change

The dissertation aims to develop and apply new empirical methods to analyze and model farm structural change. Changes of the farm structure are not only important for the sector itself but may have broader economic, social and environmental consequences for a region. Understanding this process is important for assessing the impact of (agricultural-) policies. A common approach to analyze farm structural change are Markov chains. The dissertation provides a Bayesian estimation framework that allows to more consistently and transparently combine individual and aggregated data in the estimation of non-stationary Markov models compared to existing methods. It is shown that the data combination improves precision and numerical stability of the estimation. Building on this, a Bayesian prediction framework for EU farm structural change is developed exploiting the available information more fully. Secondly, farm interdependences and their importance for farm structural change are analyzed. It is argued that the assumption of independence between farm behavior as implied by the Markov approach may become problematic in specific applications. Empirical evidence is provided that these interactions are indeed important to consider for a consistent aggregation of farm level results when assessing policy effects at regional level. Specifically, it is shown for the case of Norway that it is important to consider neighboring farm characteristics when analyzing the influence of direct payments on farm survival. To the knowledge of the author, the study is the first to show empirically that spatial interdependence at farm level is important for farm structural change. With respect to policy assessment, the results indicate that direct payments a farm receives itself have a positive influence on farm survival while neighboring direct payments have a negative one. For an overall assessment of the policy effects it is thus necessary to consider the interdependencies between farms. Ignoring these interdependencies might lead to an overestimation of the effects of direct payments.Analysemethoden und empirische Erkenntnisse zum landwirtschaftlichen Strukturwandel Ziel der Arbeit ist die Entwicklung und Anwendung von Methoden zur empirischen Analyse und Modellierung des Agrarstrukturwandels. Veränderungen der Agrarstruktur sind nicht allein für den Sektor bedeutend, sondern können weitreichende ökonomische, soziale und ökologische Konsequenzen für eine Region haben. Ein Verständnis des Strukturwandels ist somit wichtig für die Folgenabschätzung (agrar-)politischer Maßnahmen, sowie deren Gestaltung im Hinblick auf konkrete (agrar-)politische Ziele. Ein häufig verwendeter methodischer Ansatz zur Untersuchung des Agrarstrukturwandels ist die Markowketten-Analyse. In dieser Arbeit wird ein Bayes‘scher Schätzansatz entwickelt, der eine Kombination von einzelbetrieblichen und aggregierten Daten in der Schätzung von nicht-stationären Markowketten erlaubt. Die Datenkombination erfolgt auf eine, im Vergleich zu existierenden Ansätzen, konsistentere und transparentere Weise und es wird gezeigt, dass sie die Präzision sowie die numerische Stabilität des Schätzers erhöht. Darauf aufbauend wird ein Bayes‘scher Ansatz zur Vorhersage des EU Strukturwandels entwickelt, der es erlaubt die verfügbaren Daten besser zu nutzen. Darüber hinaus befasst sich die Arbeit mit Interdependenzen auf Betriebsebene und deren Bedeutung für den Strukturwandel. Es wird argumentiert, dass sich das Verhalten von Betrieben gegenseitig bedingt und die Annahme einer unabhängigen Entwicklung, wie sie der Markowketten-Analyse zugrundeliegt, zu Problemen führen kann. Es wird empirisch gezeigt, dass die Berücksichtigung von Interdependenzen zwischen Betrieben wichtig für eine konsistente Aggregation der Ergebnisse der Betriebsebene zur Politikfolgenabschätzung auf regionaler Ebene ist. Am Beispiel Norwegens wird gezeigt, dass zur Abschätzung der Effekte von Direktzahlungen die Charakteristika benachbarter Betriebe berücksichtigt werden müssen. Nach Wissen des Autors ist die Arbeit die erste, die empirisch die Bedeutung von Interdependenzen auf Betriebsebene für den Strukturwandel belegt. Mit Blick auf eine Politikfolgenabschätzung zeigen die Ergebnisse, dass Direktzahlungen, die ein Betrieb selbst erhält, einen positiven Einfluss auf das Überleben des Betriebs haben, während Direktzahlungen an benachbarte Betriebe einen negativen Einfluss haben. Zur Abschätzung des generellen Effekts von Direktzahlungen ist es somit notwendig, die Interdependenzen zwischen Betrieben zu berücksichtigen. Werden diese vernachlässigt, kann der Effekt von Direktzahlungen überschätzt werden

Resampling and bootstrap algorithms to assess the relevance of variables: applications to cross-section entrepreneurship data

In this paper, we propose an algorithmic approach based on resampling and bootstrap techniques to measure the importance of a variable, or a set of variables, in econometric models. This algorithmic approach allows us to check the real weight of a variable in a model, avoiding the biases of classical tests, and to select the more relevant variables, or models, in terms of predictability, by reducing dimensions. We apply this methodology to the Global Entrepreneurship Monitor data for the year 2014, to analyze the individual- and national-level determinants of entrepreneurial activity, and compare the results with a forward selection approach, also based on resampling predictability, and a standard forward stepwise selection process. We find that our proposed techniques offer more accurate results, which show that innovation and new technologies, peer effects, the sociocultural environment, entrepreneurial education at University, R&D transfers, and the availability of government subsidies are among the most important predictors of entrepreneurial behavior

New Directions for Contact Integrators

Contact integrators are a family of geometric numerical schemes which guarantee the conservation of the contact structure. In this work we review the construction of both the variational and Hamiltonian versions of these methods. We illustrate some of the advantages of geometric integration in the dissipative setting by focusing on models inspired by recent studies in celestial mechanics and cosmology.Comment: To appear as Chapter 24 in GSI 2021, Springer LNCS 1282

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Algoritmos de aprendizagem adaptativos para classificadores de redes Bayesianas

Doutoramento em MatemáticaNesta tese consideramos o desenvolvimento de algoritmos adaptativos para classificadores de redes Bayesianas (BNCs) num cenário on-line. Neste cenário os dados são apresentados sequencialmente. O modelo de decisão primeiro faz uma predição e logo este é actualizado com os novos dados. Um cenário on-line de aprendizagem corresponde ao cenário “prequencial” proposto por Dawid. Um algoritmo de aprendizagem num cenário prequencial é eficiente se este melhorar o seu desempenho dedutivo e, ao mesmo tempo, reduzir o custo da adaptação. Por outro lado, em muitas aplicações pode ser difícil melhorar o desempenho e adaptar-se a fluxos de dados que apresentam mudança de conceito. Neste caso, os algoritmos de aprendizagem devem ser dotados com estratégias de controlo e adaptação que garantem o ajuste rápido a estas mudanças. Todos os algoritmos adaptativos foram integrados num modelo conceptual de aprendizagem adaptativo e prequencial para classificação supervisada designado AdPreqFr4SL, o qual tem como objectivo primordial atingir um equilíbrio óptimo entre custo-qualidade e controlar a mudança de conceito. O equilíbrio entre custo-qualidade é abordado através do controlo do viés (bias) e da adaptação do modelo. Em vez de escolher uma única classe de BNCs durante todo o processo, propomo-nos utilizar a classe de classificadores Bayesianos k-dependentes (k-DBCs) e começar com o seu modelo mais simples: o classificador Naïve Bayes (NB) (quando o número máximo de dependências permissíveis entre os atributos, k, é 0). Podemos melhorar o desempenho do NB se reduzirmos o bias produto das restrições de independência. Com este fim, propomo-nos incrementar k gradualmente de forma a que em cada etapa de aprendizagem sejam seleccionados modelos de k-DBCs com uma complexidade crescente que melhor se vai ajustando ao actual montante de dados. Assim podemos evitar os problemas causados por demasiado viés (underfitting) ou demasiada variância (overfiting). Por outro lado, a adaptação da estrutura de um BNC com novos dados implica um custo computacional elevado. Propomo-nos reduzir nos custos da adaptação se, sempre que possível, usarmos os novos dados para adaptar os parâmetros. A estrutura é adaptada só em momentos esporádicos, quando é detectado que a sua adaptação é vital para atingir uma melhoria no desempenho. Para controlar a mudança de conceito, incluímos um método baseado no Controlo de Qualidade Estatístico que tem mostrado ser efectivo na detecção destas mudanças. Avaliamos os algoritmos adaptativos usando a classe de classificadores k-DBC em diferentes problemas artificiais e reais e mostramos as vantagens da sua implementação quando comparado com as versões no adaptativas.This thesis mainly addresses the development of adaptive learning algorithms for Bayesian network classifiers (BNCs) in an on-line leaning scenario. In this scenario data arrives at the learning system sequentially. The actual predictive model must first make a prediction and then update the current model with new data. This scenario corresponds to the Dawid’s prequential approach for statistical validation of models. An efficient adaptive algorithm in a prequential learning framework must be able, above all, to improve its predictive accuracy over time while reducing the cost of adaptation. However, in many real-world situations it may be difficult to improve and adapt to existing changing environments, a problem known as concept drift. In changing environments, learning algorithms should be provided with some control and adaptive mechanisms that effort to adjust quickly to these changes. We have integrated all the adaptive algorithms into an adaptive prequential framework for supervised learning called AdPreqFr4SL, which attempts to handle the cost-performance trade-off and also to cope with concept drift. The cost-quality trade-off is approached through bias management and adaptation control. The rationale is as follows. Instead of selecting a particular class of BNCs and using it during all the learning process, we use the class of k-Dependence Bayesian classifiers and start with the simple Naïve Bayes (by setting the maximum number of allowable attribute dependence k to 0). We can then improve the performance of Naïve Bayes over time if we trade-off the bias reduction which leads to the addition of new attribute dependencies with the variance reduction by accurately estimating the parameters. However, as the learning process advances we should place more focus on bias management. We reduce the bias resulting from the independence assumption by gradually adding dependencies between the attributes over time. To this end, we gradually increase k so that at each learning step we can use a class-model of k-DBCs that better suits the available data. Thus, we can avoid the problems caused by either too much bias (underfitting) or too much variance (overfitting). On the other hand, updating the structure of BNCs with new data is a very costly task. Hence some adaptation control is desirable to decide whether it is inevitable to adapt the structure. We reduce the cost of updating by using new data to primarily adapt the parameters. Only when it is detected that the use of the current structure no longer guarantees the desirable improvement in the performance, do we adapt the structure. To handle concept drift, our framework includes a method based on Statistical Quality Control, which has been demonstrated to be efficient for recognizing concept changes. We experimentally evaluated the AdPreqFr4SL on artificial domains and benchmark problems and show its advantages in comparison against its nonadaptive versions