This thesis summarizes investigations performed within design, analysis and experimental evaluation of flexible aircraft structures. Not only the problems, but rather the opportunities related to aeroelasticity are discussed. In the first part of the thesis, different concepts for using active aeroelastic configurations to increase aircraft performance are considered. In particular, one study deals with the minimization of the induced drag of a highly flexible wing by using multiple control surfaces. Another study deals with a possible implementation of a high-bandwidth piezo electric actuator for control applications using aeroelastic amplification. The second part of the thesis deals with the development of an approach for modeling and analysis of flexible structures considering uncertainties in analysis models. Especially in cases of large structural variations, such as fuel level variations, a fixed-base modal formulation in robust flutter analysis may lead to incorrect results. Besides a discussion about this issue, possible means of treating this problem are presented. / QC 20101130

Hydro power producers are faced with the task of releasing water from the reservoirs in the right time. To do this there are tools using stochastic optimization that aims at maximizing the income of that producer. The existing methods have a high computing time and grow exponentially with the size of the problem. A new approach that uses linear decision rules is investigated in this thesis to see if it is possible to maintain the same quality of the solutions and in the same time decrease run times. With this method the hydro power producer receives policies as an affine function of the realization of the uncertainty variables in inflow and price. This thesis presents a deterministic model and then converts it into an linear decision rules, LDR, model. It also presents a way to model the uncertainty in both inflow to the reservoir and the spot price. The result is that the LDR approach generates reasonable policies with low run times but loses a lot of optimality compared to solutions that are used today. Therefore it is concluded that this approach needs further development before commercial use. The work described in this thesis has been done in cooperation with three master students at NTNU. The approach of using linear decision rules are the same in the two projects but the differences are the models evaluated.

In this dissertation the problem of recognising vehicle licence plates of which the sym¬bols can not be segmented by standard image processing techniques is addressed. Most licence plate recognition systems proposed in the literature do not compensate for dis¬torted, obscured and damaged licence plates. We implemented a novel system which uses a neural network/ hidden Markov model hybrid for licence plate recognition. We implemented a region growing algorithm, which was shown to work well when used to extract the licence plate from a vehicle image. Our vertical edges algorithm was not as successful. We also used the region growing algorithm to separate the symbols in the licence plate. Where the region growing algorithm failed, possible symbol borders were identified by calculating local minima of a vertical projection of the region. A multilayer perceptron neural network was used to estimate symbol probabilities of all the possible symbols in the region. The licence plate symbols were the inputs of the neural network, and were scaled to a constant size. We found that 7 x 12 gave the best character recognition rate. Out of 2117 licence plate symbols we achieved a symbol recognition rate of 99.53%. By using the vertical projection of a licence plate image, we were able to separate the licence plate symbols out of images for which the region growing algorithm failed. Legal licence plate sequences were used to construct a hidden Markov model contain¬ing all allowed symbol orderings. By adapting the Viterbi algorithm with sequencing constraints, the most likely licence plate symbol sequences were calculated, along with a confidence measure. The confidence measure enabled us to use more than one licence plate and symbol segmentation technique. Our recognition rate increased dramatically when we com¬bined the different techniques. The results obtained showed that the system developed worked well, and achieved a licence plate recognition rate of 93.7%. / Dissertation (MEng (Computer Engineering))--University of Pretoria, 2002. / Electrical, Electronic and Computer Engineering / unrestricted

Thesis advisor: Uzi Segal / The dissertation studies the property of transitivity in the social choice theory. I explain why we should care about transitivity in decision theory. I propose two social decision theories: redistribution regret and ranking regret, study their properties of transitivity, and discuss the possibility to find a best choice for the social planner. Additionally, in the joint work, we propose a general method to construct a consistent estimator given two parametric models, one of which could be incorrectly specified. In “Why Transitivity”, to explain behaviors violating transitivity, e.g., preference reversals, some models, like regret theory, salience theory were developed. However, these models naturally violate transitivity, which may not lead to a best choice for the decision maker. This paper discusses the consequences and the possible extensions to deal with it. In “Redistribution Regret and Transitivity”, a social planner wants to allocate resources, e.g., the government allocates fiscal revenue or parents distribute toys to children. The social planner cares about individuals' feelings, which depend both on their assigned resources, and on the alternatives they might have been assigned. As a result, there could be intransitive cycles. This paper shows that the preference orders are generally non-transitive but there are two exceptions: fixed total resource and one extremely sensitive individual, or only two individuals with the same non-linear individual regret function. In “Ranking Regret”, a social planner wants to rank people, e.g., assign airline passengers a boarding order. A natural ranking is to order people from most to least sensitive to their rank. But people's feelings can depend both on their assigned rank, and on the alternatives they might have been assigned. As a result, there may be no best ranking, due to intransitive cycles. This paper shows how to tell when a best ranking exists, and that when it exists, it is indeed the natural ranking. When this best does not exist, an alternative second-best group ranking strategy is proposed, which resembles actual airline boarding policies. In “Over-Identified Doubly Robust Identification and Estimation”, joint with Arthur Lewbel and Jinyoung Choi, we consider two parametric models. At least one is correctly specified, but we don't know which. Both models include a common vector of parameters. An estimator for this common parameter vector is called Doubly Robust (DR) if it's consistent no matter which model is correct. We provide a general technique for constructing DR estimators (assuming the models are over identified). Our Over-identified Doubly Robust (ODR) technique is a simple extension of the Generalized Method of Moments. We illustrate our ODR with a variety of models. Our empirical application is instrumental variables estimation, where either one of two instrument vectors might be invalid. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

Minimax D-optimal designs for regression models with heteroscedastic errors are studied and constructed. These designs are robust against possible misspecification of the error variance in the model. We propose a flexible assumption for the error variance and use a minimax approach to define robust designs. As usual it is hard to find robust designs analytically, since the associated design problem is not a convex optimization problem. However, the minimax D-optimal design problem has an objective function as a difference of two convex functions. An effective algorithm is developed to compute minimax D-optimal designs under the least squares estimator and generalized least squares estimator. The algorithm can be applied to construct minimax D-optimal designs for any linear or nonlinear regression model with heteroscedastic errors. In addition, several theoretical results are obtained for the minimax D-optimal designs. / Graduate

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This dissertation demonstrates novel holistic approaches on how to link policy, clean energy innovations, and robust energy modeling techniques to help build more resilient and cost-effective energy systems for the Canadian Arctic region and remote communities in general. In spite of the diversity among Arctic jurisdictions, various energy issues and challenges are shared pan-territorially in the North. For instance, 53 out of 80 remote communities in the Northern territories rely exclusively on diesel-based infrastructures to generate electricity, with heating oil as their primary source of heat. This critical dependence on fossil fuels exposes the Indigenous peoples and other Canadians living in the North to high energy costs and environmental vulnerabilities which is exacerbated by the local and global catastrophic effects of climate change in the Arctic. Aside from being strong point sources of greenhouse gases and other airborne pollutants, this reliance on carbon-intensive sources of energy elevates risk of oils spills during fuel transport and storage. Further, conventional transportation mode via ice roads is now increasingly unreliable because of the rising Arctic temperatures which is twice the global average rate. As a result, most fuels are being transported by small planes which contribute to high energy costs and fuel poverty rates, or via boats which also increases the risk of oil spills in the Arctic waters. Methodologically, this thesis presents a multi-domain perspective on how to accelerate energy transitions among Northern remote communities. In particular, a multi-objective optimization energy model was developed in order to capture complex trade-offs in designing integrated electrical and thermal energy systems. In comparison with traditional single-objective optimization approach, this technique offers diversity of solutions to represent multiple energy solution philosophies from various stakeholders and practitioners in the North. A case study in the Northernmost community of the Northwest Territories demonstrates the applicability of this framework - from modeling a range of energy solutions (supply and demand side aspects) to exploring insights and recommendations while taking into account uncertainties. Overall, this dissertation makes a set of contributions, including: (i) Development of a robust energy modeling framework that integrates complex trade-offs and multiple overlapping uncertainties in designing energy systems for the Arctic and remote communities in general; (ii) Extension of previous Arctic studies - where focused has solely been on the electricity sector - by integrating heating technology options in the proposed modeling framework in conjunction with methods on obtaining `high performance' buildings in the North; (iii) Overall energy system performance evaluation when integrating heat and electricity sectors, as well as the role of battery storage systems and diesel generator on facilitating variable renewable energy generation among isolated communities; (iv) Formulation of a community-scale energy trilemma index model which helps design policies that are accelerating (or hindering) energy transitions among remote communities by assessing quantitatively challenges relating to energy security, affordability, and environmental sustainability; (v) Synthesized holistic insights and recommendations on how to create opportunities for Indigenous peoples-led energy projects while discussing interwoven links between energy system operations, relationship building and stakeholders engagement, policy design, and research (energy modeling and analysis). Collectively, the new methods and recommendations demonstrated herein offer evidence-based decision making and innovative solutions for policy makers, utility companies, Indigenous peoples, and other stakeholders in the Arctic and beyond. / Graduate

The growing need for ocean surveillance and exploration has pushed the development of novel autonomous underwater vehicle (AUV) technology. A current trend is to make use of bio-inspired propulsor to increase the overall system efficiency and performance, an improvement that has deep implications in the dynamics of the system. The goal of this dissertation is to propose a generic robust control framework specific for bio-inspired autonomous underwater vehicles (BIAUV). These vehicles utilize periodic oscillation of a flexible structural component to generate thrust, a propulsion mechanism that can be tuned to operate under resonance and consequently improve the overall system efficiency. The control parameter should then be selected to keep the system operating in such a condition. Another important aspect is to have a controller design technique that can address the time-varying behaviors, structured uncertainties and system nonlinearities. To address these needs a robust, model-based, nonlinear controller design technique is presented, called digital sliding mode controller (DSMC), which also takes into account the discrete implementation of these laws using microcontrollers. The control law is implemented in the control of a jellyfish-inspired autonomous underwater vehicle. / Ph. D.

In this thesis we study some storage loading problems motivated from several practical contexts, under different types of uncertainty on the items’ data. To have robust stacking solutions against the data uncertainty, we apply the concepts of strict and adjustable robustness. We first give complexity results for various storage loading problems with stacking constraints, and point out some interesting settings in which the adjustable robust problems can be solved more efficiently than the strict ones. Then we propose different solution algorithms for the robust storage loading problems, and figure out which algorithm performs best for which data setting. We also propose a robust optimization framework dealing with storage loading problems under stochastic uncertainty. In this framework, we offer several rule-based ways of scenario generation to derive different uncertainty sets, and analyze the trade-off between cost and robustness of the robust stacking solutions. Additionally, we introduce a novel approach in dealing with stability issues of stacking configurations. Our key idea is to impose a limited payload on each item depending on its weight. We then study a storage loading problem with the interaction of stacking and payload constraints, as well as uncertainty on the weights of items, and propose different solution approaches for the robust problems.

En radioastronomie, les signaux d'intérêt mesurés par les interféromètres sont perturbés par de nombreux effets environnementaux et instrumentaux, nécessitant la mise en œuvre de techniques algorithmiques pour les traiter et pouvoir ainsi reconstruire in fine des images parfaitement nettes de l'espace. Cette étape de correction des perturbations se nomme la calibration et repose généralement sur une modélisation gaussienne du bruit, pour une seule fréquence considérée. Cependant, en pratique, cette l'hypothèse n'est pas toujours valide car de multiples sources inconnues à faible intensité sont visibles dans le champ de vision et des interférences radioélectriques perturbent les données. En outre, réaliser une calibration indépendante, fréquence par fréquence, n'est pas la manière la plus optimale de procéder. Le but de ce travail est donc de développer des algorithmes de correction dans le traitement des signaux radio qui soient robustes à la présence d'éventuelles valeurs aberrantes ou sources d'interférences, et qui soient adaptés au contexte multi-fréquentiel. Par conséquent, nous nous appuyons sur une modélisation plus générale que la loi gaussienne, appelé processus Gaussien composé, et proposons un algorithme itératif basé sur l'estimation au sens du maximum de vraisemblance. En accord avec le scénario multi-fréquentiel sous étude, nous exploitons la variation spectrale des perturbations en utilisant des méthodologies telles que l'optimisation distribuée sous contraintes et le traitement parallèle des données. / Accurate calibration is of critical importance for new advanced interferometric systems in radio astronomy in order to recover high resolution images with no distortions. This process consists in correcting for all environmental and instrumental effects which corrupt the observations. Most state-of-the-art calibration approaches assume a Gaussian noise model and operate mostly in an iterative manner for a mono-frequency scenario. However, in practice, the Gaussian classical noise assumption is not valid as radio frequency interference affects the measurements and multiple unknown weak sources appear within the wide field-of-view. Furthermore, considering one frequency bin at a time with a single centralized agent processing all data leads to suboptimality and computational limitations. The goal of this thesis is to explore robustness of calibration algorithms w.r.t. the presence of outliers in a multi-frequency scenario. To this end, we propose the use of an appropriate noise model, namely, the so-called coumpound-Gaussian which encompasses a broad range of different heavy-tailed distributions. To combine limited computational complexity and quality of calibration, we designed an iterative calibration algorithm based on the maximum likelihood estimator under the compound-Gaussian modeling. In addition, a computationally efficient way to handle multiple sub-frequency bands is to apply distributed and decentralized strategies. Thus, the global operational load is distributed over a network of computational agents and calibration amounts to solve a global constrained problem thanks to available variation models or by assuming smoothness across frequency.