Thesis (Ph.D.)--Albert-Ludwigs-Universität Freiburg im Breisgau, 2006. / DatabaseEbrary. EAN: 9781586036744. Includes bibliographical references (p. 201-221) and index.

Neste trabalho, os efeitos das incertezas de estimativa de canal são considerados na otimização da relação sinal-ruído óptica (OSNR) em redes ópticas com multiplexação por divisão de comprimento de onda (WDM). A otimização da OSNR aumenta a vazão da rede e a eficiência energética, permitindo minimizar o número de retransmissões em camadas superiores. Os algorítmos de otimização de OSNR estão relacionados com a estimativa de canal óptico realizada pelas combinações de monitores de desempenho ópticos (OPM), modelos analíticos e simulações numéricas. Além disso, as incertezas de estimativa são introduzidas por vários fatores, como a disponibilidade de informações de monitoramento, defeitos nos monitores, precisão, a imperfeição do modelo da camada física e a dinâmica de alocação de canal. Neste estudo, foi utilizada a otimização da OSNR baseada em técnicas heuríticas por enxame de partículas. Os resultados numéricos demonstraram a relação entre as incertezas na estimativa de erro de canal, média normalizada do erro quadrático (NMSE) e penalidade de potência. A metodologia desenvolvida pode ser utilizada para determinar o nível máximo de incertezas aceitáveis na estimativa de erro de canal de acordo com a penalização de potência permitida na otimização OSNR. / In this work, we investigate the effects of estimation uncertainties in the optimization of the optical signal-to-noise ratio (OSNR) at the wavelength division multiplexing (WDM) optical networks. The OSNR optimization increases the network throughput and energy efficiency and it enables to minimize the number of retransmissions by higher layers. The OSNR optimization algorithms are related to the optical channel estimation performed by the combinations of optical performance monitors (OPM) measurements, analytical models and numerical simulations. Furthermore, the estimation uncertainties are introduced by several factors such as the availability of monitoring information, monitoring accuracy, imperfection of physical layer and dynamic of channel allocation. Our numerical results have demonstrated the relation between the uncertainties in the channel error estimation, normalized mean squared error (NMSE) and power penalty. The developed methodology can be utilized to determinate the level of acceptable uncertainties in the channel error estimation according the power penalty permitted in the OSNR optimization.

Orientadores: Siome Klein Goldenstein, Carlile Campos Lavor / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-22T06:27:38Z (GMT). No. of bitstreams: 1 Sendin_IvandaSilva_D.pdf: 14139008 bytes, checksum: d64497ed260da601f2dde025683df0d0 (MD5) Previous issue date: 2012 / Resumo: A determinação da estrutura de uma proteína usando dados de Ressonância Magnética Nuclear precisa lidar com incertezas provenientes do experimento laboratorial. Neste trabalho, apresentamos um método híbrido utilizando aritmética afim e propagação de incerteza por partículas para o tratamento e o controle de incertezas. Aplicado no cálculo da estrutura de proteínas, o método proposto é capaz de gerar estruturas de proteínas que atendem satisfatoriamente as restrições do problema / Abstract: The protein structure determination using Nuclear Magnetic Resonance data uses imprecise information from laboratorial experiments. In this work we introduce a new hybrid method that combines affine arithmetic and particles to uncertainty propagation and control. Applied to protein structure determination this new method was able to determine protein structures that satisfy most of problem constraints / Doutorado / Ciência da Computação / Doutor em Ciência da Computação

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaves 120-124. / by John Nikolaos Tsitsiklis. / M.S.

Rapid Prototyping (RP) is the process of building three-dimensional objects, in layers, using additive manufacturing. Rapid Manufacturing (RM) is the use of RP technologies to manufacture end-use, or finished, products. At small lot sizes, such as with customized products, traditional manufacturing technologies become infeasible due to the high costs of tooling and setup. RM offers the opportunity to produce these customized products economically. Coupled with the customization opportunities afforded by RM is a certain degree of uncertainty. This uncertainty is mainly attributed to the lack of information known about what the customers specific requirements and preferences are at the time of production. In this thesis, the author presents an overall method for selection of a RM technology, as an investment decision, under the geometric uncertainty inherent to mass customization. Specifically, the author defines the types of uncertainty inherent to RM (epistemic), proposes a method to account for this uncertainty in a selection process (interval analysis), and proposes a method to select a technology under uncertainty (Decision Theory under strict uncertainty). The author illustrates the method with examples on the selection of an RM technology to produce custom caster wheels and custom hearing aid shells. In addition to the selection methodology, the author also develops universal build time and part cost models for the RM technologies. These models are universal in the sense that they depend explicitly on the parameters that characterize each technology and the overall part characteristics.

Decision making is necessary to provide a synthesis scheme to design activities and identify the most preferred design alternative. There exist several methods that address modeling designer preferences in a graphical manner to aid the decision making process. For instance, the Conjoint Analysis has been proven effective for various multi-attribute design problems by utilizing a ranking- or rating-based approach along with the graphical representation of the designer preference. However, the ranking or rating of design alternatives can be inconsistent from different users and it is often difficult to get customer responses in a timely fashion. The high number of alternative comparisons required for complex engineering problems can be exhausting for the decision maker. In addition, many design objectives can have interdependencies that can increase complexity and uncertainty throughout the decision making process. The uncertainties apparent in the attainment of subjective data as well as with system models can reduce the reliability of decision analysis results. To address these issues, the use of a new technique, the Improved Conjoint Analysis, is proposed to enable the modeling of designer preferences and trade-offs under the consideration of uncertainty. Specifically, a simulation-based ranking scheme is implemented and incorporated into the traditional process of the Conjoint Analysis. The proposed ranking scheme can reduce user fatigue and provide a better schematic decision support process. In addition, the incorporation of uncertainty in the design process provides the capability of producing robust or reliable products. The efficacy and applicability of the proposed framework are demonstrated with the design of a cantilever beam, a power-generating shock absorber, and a mesostructured hydrogen storage tank.

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The past few decades have witnessed a growing interest in the engineering communities to approach the handling of imperfect information from a quantitatively justifiable angle. In the aerospace engineering domain, the movement to develop creative avenues to nondeterministically solving engineering problems has emerged in the field of aerospace systems design. Inspired by statistical data modeling and numerical analysis techniques that used to be relatively foreign to the designers of aerospace systems, a variety of strategies leveraging upon the probabilistic treatment of uncertainty has been, and continue to be, reported. Although each method differs in the sequence in which probabilistic analysis and numerical optimization are performed, a common motif in all of them is the lack of any built-in provisions to compensate for infeasibilities that occur during optimization. Constraint violations are either strictly prohibited or striven to be held to an acceptable probability threshold, implying that most hitherto developed probabilistic design methods promote an avoid-failure approach to developing aerospace systems under uncertainty. It is the premise of this dissertation that such a dichotomous structure of addressing imperfections is hardly a realistic model of how product development unfolds in practice. From a time-phased view of engineering design, it is often observed that previously unknown parameters become known with the passing of each design milestone, and their effects on the system are realized. Should these impacts happen to be detrimental to critical system-level metrics, then a compensatory action is taken to remedy any unwanted deviations from the target or required bounds, rather than starting the process completely anew. Anecdotal accounts of numerous real-world design projects confirm that such remedial actions are commonly practiced means to ensure the successful fielding of aerospace systems. Therefore, formalizing the remedial aspect of engineering design into a new methodological capability would be the next logical step towards making uncertainty handling more pragmatic for this generation of engineers. In order to formulate a nondeterministic solution approach that capitalizes on the practice of compensatory design, this research introduces the notion of recourse. Within the context of engineering an aerospace system, recourse is defined as a set of corrective actions that can be implemented in stages later than the current design phase to keep critical system-level figures of merit within the desired target ranges, albeit at some penalty. The terminology is inspired by the concept of the same name in the field of statistical decision analysis, where it refers to an action taken by a decision maker to mitigate the unfavorable consequences caused by uncertainty realizations. Recourse programs also introduce the concept of stages to optimization formulations, and allow each stage to encompass as many sequences or events as determined necessary to solve the problem at hand. Together, these two major premises of classical stochastic programming provide a natural way to embody not only the remedial, but also the temporal and nondeterministic aspects of aerospace systems design. A two-part strategy, which partitions the design activities into stages, is proposed to model the bi-phasal nature of recourse. The first stage is defined as the time period in which an a priori design is identified before the exact values of the uncertain parameters are known. In contrast, the second stage is a period occurring some time after the first stage, when an a posteriori correction can be made to the first-stage design, should the realization of uncertainties impart infeasibilities. Penalizing costs are attached to the second-stage corrections to reflect the reality that getting it done right the first time is almost always less costly than fixing it after the fact. Consequently, the goal of the second stage becomes identifying an optimal solution with respect to the second-stage penalty, given the first-stage design, as well as a particular realization of the random parameters. This two-stage model is intended as an analogue of the traditional practice of monitoring and managing key Technical Performance Measures (TPMs) in aerospace systems development settings. Whenever an alarmingly significant discrepancy between the demonstrated and target TPM values is noted, it is generally the case that the most cost-effective recourse option is selected, given the available resources at the time, as well as scheduling and budget constraints. One obvious weakness of the two-stage strategy as presented above is its limited applicability as a forecasting tool. Not only cannot the second stage be invoked without a first-stage starting point, but also the second-stage solution differs from one specific outcome of uncertainties to another. On the contrary, what would be more valuable given the time-phased nature of engineering design is the capability to perform an anticipatory identification of an optimum that is also expected to incur the least costly recourse option in the future. It is argued that such a solution is in fact a more balanced alternative than robust, probabilistically maximized, or chance-constrained solutions, because it represents trading the design optimality in the present with the potential costs of future recourse. Therefore, it is further proposed that the original two-stage model be embedded inside a larger design loop, so that the realization of numerous recourse scenarios can be simulated for a given first-stage design. The repetitive procedure at the second stage is necessary for computing the expected cost of recourse, which is equivalent to its mathematical expectation as per the strong law of large numbers. The feedback loop then communicates this information to the aggregate-level optimizer, whose objective is to minimize the sum total of the first-stage metric and the expected cost of future corrective actions. The resulting stochastic solution is a design that is well-hedged against the uncertain consequences of later design phases, while at the same time being less conservative than a solution designed to more traditional deterministic standards. As a proof-of-concept demonstration, the recourse-based solution approach is presented as applied to a contemporary aerospace engineering problem of interest - the integration of fuel cell technology into uninhabited aerial systems. The creation of a simulation environment capable of designing three system alternatives based on Proton Exchange Membrane Fuel Cell (PEMFC) technology and another three systems leveraging upon Solid Oxide Fuel Cell (SOFC) technology is presented as the means to notionally emulate the development process of this revolutionary aeropropulsion method. Notable findings from the deterministic trade studies and algorithmic investigation include the incompatibility of the SOFC based architectures with the conceived maritime border patrol mission, as well as the thermodynamic scalability of the PEMFC based alternatives. It is the latter finding which justifies the usage of the more practical specific-parameter based approach in synthesizing the design results at the propulsion level into the overall aircraft sizing framework. The ensuing presentation on the stochastic portion of the implementation outlines how the selective applications of certain Design of Experiments, constrained optimization, Surrogate Modeling, and Monte Carlo sampling techniques enable the visualization of the objective function space. The particular formulations of the design stages, recourse, and uncertainties proposed in this research are shown to result in solutions that are well compromised between unfounded optimism and unwarranted conservatism. In all stochastic optimization cases, the Value of Stochastic Solution (VSS) proves to be an intuitively appealing measure of accounting for recourse-causing uncertainties in an aerospace systems design environment.

Model development decisions are an important feature of engineering design. The quality of simulation models often dictates the quality of design decisions, seeing as models guide decision makers (DM) in choosing design decisions. A quality model accurately represents the modeled system and is helpful for exploring what-if scenarios, optimizing design parameters, estimating design performance, and predicting the effect of design changes. However, obtaining a quality model comes at a cost in terms of model development--in experimentation, labor, model development time, and simulation time. Thus, DMs must make appropriate trade-offs when considering model development decisions. The primary challenge in model development is making decisions under significant uncertainty. This thesis addresses model development in the conceptual design phase where uncertainty levels are high. In the conceptual design phase, there are many information constraints which may include an incomplete requirements list, unclear design goals, and/or undefined resource constrains. During the embodiment design phase, the overall objective of the design is more clearly defined, and model development decisions can be made with respect to an overall objective function. For example, the objective may be to maximize profit, where the profit is a known function of the model output. In the conceptual design phase, this level of clarity is not always present, so the DM must make decisions under significant model uncertainty and objective uncertainty. In this thesis, conjoint analysis is employed to solicit the preferences of the decision maker for various model attributes, and the preferences are used to formulate a quasi-objective function during the conceptual design phase--where the overall design goals are vague. Epistemic uncertainty (i.e., imprecision) in model attributes is represented as intervals and propagated through the proposed model development framework. The model development framework is used to evaluate the best course of action (i.e., model development decision) for a real-world packaging design problem. The optimization of medical product packaging is assessed via mass spring damper models which predict contact forces experienced during shipping and handling. Novel testing techniques are employed to gather information from drop tests, and preliminary models are developed based on limited information. Imprecision in preliminary test results are quantified, and multiple model options are considered. Ultimately, this thesis presents a model development framework in which decision makers have systematic guidance for choosing optimal model development decisions.