Diss. (sammanfattning) Umeå : Sveriges lantbruksuniv. / Härtill 4 uppsatser.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniversitet, 2004. / Härtill 4 uppsatser.

Diss. Linköping : Linköpings universitet, 2007.

Submitted by Bruna Alves Ferraz (bruna.alves.ferraz@gmail.com) on 2018-01-12T12:56:17Z No. of bitstreams: 1 Dissertacao_BrunaAF.pdf: 1135818 bytes, checksum: 02e01c1ac05f20f13657b40ed7d959fe (MD5) / Rejected by Adriana Aparecida Puerta null (dripuerta@rc.unesp.br), reason: Prezada Bruna Alves Ferraz, Solicitamos que realize uma nova submissão seguindo as orientações abaixo: - Capa - Faltou a capa no documento enviado. Este item é elemento obrigatório de acordo com as normas de trabalhos do seu Programa de Pós Graduação e deve vir antes da Página de rosto. Agradecemos a compreensão e aguardamos o envio do novo arquivo. Atenciosamente, Biblioteca Campus Rio Claro Repositório Institucional UNESP on 2018-01-12T16:18:28Z (GMT) / Submitted by Bruna Alves Ferraz (bruna.alves.ferraz@gmail.com) on 2018-01-15T18:07:31Z No. of bitstreams: 1 Bruna Alves Ferraz.pdf: 1341591 bytes, checksum: 6e9837abd2a00d05c9ba70d843b7c4c2 (MD5) / Approved for entry into archive by Adriana Aparecida Puerta null (dripuerta@rc.unesp.br) on 2018-01-15T18:18:13Z (GMT) No. of bitstreams: 1 ferraz_ba_me_rcla.pdf: 1265910 bytes, checksum: 9b0bcbf17772b4ac87b8058427405948 (MD5) / Made available in DSpace on 2018-01-15T18:18:13Z (GMT). No. of bitstreams: 1 ferraz_ba_me_rcla.pdf: 1265910 bytes, checksum: 9b0bcbf17772b4ac87b8058427405948 (MD5) Previous issue date: 2017-12-19 / Neste trabalho discutiremos alguns métodos clássicos para otimização irrestrita, a saber o Método de Cauchy e o Método de Newton, e analisaremos a convergência desses métodos. Veremos que o Método de Cauchy, que faz a cada iteração uma busca unidirecional na direção de máxima descida, ou seja, na direção oposta ao gradiente, tem convergência linear. O método de Newton, por outro lado, minimiza, em cada iteração, a aproximação quadrática da função objetivo. Nos métodos de busca unidirecional é preciso minimizar uma função a partir de um certo ponto, segundo uma direção dada, que é a direção de busca. Por essa razão, estudaremos o Método da Seção Áurea, que fornece uma minimização exata de uma função real de uma variável real. / In this work we will discuss some classic methods for unrestricted optimization, namely the Cauchy Method and Newton’s Method, and we will analyze the convergence of those methods. We will see that the Cauchy Method, that realizes on each iteration a unidirectional search in the direction of maximum descent, that is, in the direction opposite to the gradient, has linear convergence. The Newton Method, on the other hand, minimizes, in each iteration, the quadratic approximation of the objective function. In unidirectional search methods, one must minimize a function from a certain point in a given direction, which is the search direction. For that reason, we will study the Golden Section Method, which provides the exact minimization of a real function of a real variable.

A determinação de propriedades físicas dos óleos essenciais é fundamental para sua aplicação na indústria de alimentos e também em projetos de equipamentos. A vasta quantidade de variáveis envolvidas no processo de desterpenação, tais como temperatura, pressão e composição, tornam a utilização de modelos preditivos de viscosidade necessária. Este trabalho teve como objetivo a obtenção de parâmetros para o modelo preditivo de viscosidade UNIFAC-VISCO com aplicação do método de otimização do gradiente descendente, a partir de dados de viscosidade de sistemas modelo que representam as fases que podem ser formadas em processos de desterpenação por extração líquido-líquido dos óleos essenciais de bergamota, limão e hortelã, utilizando como solvente uma mistura de etanol e água, em diferentes composições, a 25ºC. O experimento foi dividido em duas configurações; na primeira os parâmetros de interação previamente reportados na literatura foram mantidos fixos; na segunda todos os parâmetros de interação foram ajustados. O modelo e o método de otimização foram implementados em linguagem MATLAB®. O algoritmo de otimização foi executado 10 vezes para cada configuração, partindo de matrizes de parâmetros de interação iniciais diferentes obtidos pelo método de Monte Carlo. Os resultados foram comparados com o estudo realizado por Florido et al. (2014), no qual foi utilizado algoritmo genético como método de otimização. A primeira configuração obteve desvio médio relativo (DMR) de 1,366 e a segunda configuração resultou um DMR de 1,042. O método do gradiente descendente apresentou melhor desempenho para a primeira configuração em comparação com o método do algoritmo genético (DMR 1,70). Para a segunda configuração o método do algoritmo genético obteve melhor resultado (DMR 0,68). A capacidade preditiva do modelo UNIFAC-VISCO foi avaliada para o sistema de óleo essencial de eucalipto com os parâmetros determinados, obtendo-se DMR iguais a 17,191 e 3,711, para primeira e segunda configuração, respectivamente. Esses valores de DMR foram maiores do que os encontrados por Florido et al. (2014) (3,56 e 1,83 para primeira e segunda configuração, respectivamente). Os parâmetros de maior contribuição para o cálculo do DMR são CH-CH3 e OH-H2O para a primeira e segunda configuração, respectivamente. Os parâmetros que envolvem o grupo C não influenciam no valor do DMR, podendo ser excluído de análises futuras. / The determination of physical properties of essential oils is critical to their application in the food industry and also in equipment design. The large number of variables involved in deterpenation process, such as temperature, pressure and composition, to make use of viscosity predictive models required. This study aimed obtain parameters for the viscosity predictive model UNIFAC-VISCO using gradient descent as optimization method to model systems viscosity data representing the phases that can be formed in deterpenation processes for extraction liquid-liquid of bergamot, lemon and mint essential oils, using aqueous ethanol as solvente in different compositions at 25 º C. The work was divided in two configurations; in the first one the interaction parameters previously reported in the literature were kept fixed; in the second one all interaction parameters were adjusted. The model and the gradient descent method were implemented in MATLAB language. The optimization algorithm was runned 10 times for each configuration, starting from different arrays of initial interaction parameters obtained by the Monte Carlo method. The results were compared with the study carried out by Florido et al. (2014), which used genetic algorithm as optimization method. The first configuration provided an average deviation (DMR) of 1,366 and the second configuration resulted in a DMR 1,042. The gradient descent method showed better results for the first configuration comparing with the genetic algorithm method (DMR 1.70). On the other hand, for the second configuration the genetic algorithm method had a better result (DMR 0.68). The UNIFAC-VISCO model predictive ability was evaluated for eucalyptus essential oil system using the obtained parameters, providing DMR equal to 17.191 and 3.711, for the first and second configuration, respectively. The parameters determined by genetic algorithm presented lower DMR for the two settings (3.56 and 1.83 to the first and second configuration, respectively). The major parameters for calculating the DMR are CH-CH3 and OH-H2O to the first and second configuration, respectively. The parameters involving the C group did not influence the DMR and may be excluded from further analysis.

Dans cette thèse, nous nous intéressons à la microscopie DIC (Differential interference contrast) en couleur. L’imagerie DIC est reconnue pour produire des images à haut contraste et à haute résolution latérale. L'un de ses inconvénients est que les images observées ne peuvent pas être utilisées directement pour l'interprétation topographique et morphologique, car les changements de phase de la lumière, produits par les variations de l'indice de réfraction de l'objet, sont cachés dans l'image d'intensité. Il s’agit donc d’un problème de reconstruction de phase. Nous présentons un modèle de formation d'image pour la lumière polychromatique, et décrivons de manière détaillée la réponse impulsionnelle du système. Le problème de la reconstruction de phase est abordé sous l’angle d’un problème inverse par minimisation d’un terme d’erreur des moindres carrés (LS) non linéaire avec un terme de régularisation préservant les discontinuités, soit par le potentiel hypersurface (HS), soit par la variation totale (TV). Nous étudions les propriétés des fonctions objectives non convexes résultantes, prouvons l'existence de minimisateurs et proposons une formulation compacte du gradient permettant un calcul rapide. Ensuite, nous proposons des outils d'optimisation efficaces récents permettant d'obtenir à la fois des reconstructions précises pour les deux régularisations lisse (HS) et non lisse (TV) et des temps de calculs réduits. / In this dissertation we address the problem of estimating the phase from colorimages acquired with differential–interference–contrast (DIC) microscopy. This technique has been widely recognized for producing high contrast images at high lateral resolution. One of its disadvant ages is that the observed images cannot be easily used for topographical and morphological interpretation, because the changes in phase of the light, produced by variations in the refractive index of the object, are hidden in the intensity image. We present an image formation model for polychromatic light, along with a detailed description of the point spread function (PSF). As for the phase recovery problem, we followed the inverse problem approach by means of minimizing a non-linear least–squares (LS)–like discrepancy term with an edge–preserving regularizing term, given by either the hypersurface (HS) potential or the total variation (TV) one. We investigate the analytical properties of the resulting objective non-convex functions, prove the existence of minimizers and propose a compact formulation of the gradient allowing fast computations. Then we use recent effective optimization tools able to obtain in both the smooth and the non-smooth cases accurate reconstructions with a reduced computational demand. We performed different numerical tests on synthetic realistic images and we compared the proposed methods with both the original conjugate gradient method proposed in the literature, exploiting a gradient–free linesearch for the computation of the steplength parameter, and other standard conjugate gradient approaches.

In this contemporary world, when the globalization is on the first place, is possible to satisfy one's needs immediately. Road haulage becomes in last few years a significant market's part, which is necessary to the right function in all branches in the Czech Republic. The aim of this thesis is to analyse level of principle operation's research's support in TASW solution for the society, which is directed at road transport. Simultaneously I project a study, which supports a solution for small shippers, who aren't able or willing to acquire an expensive software for haulage entrepreneurship. I characterize issues of transportation's company to achieve my aim and I think about the principle's uses and method of operation's research in process of haulage entrepreneurship. I analyse a situation of operation's research's support with TASW, which is oriented on haulage entrepreneurship. After that I describe a study, where I project a solution for small shippers, which takes in the consideration the components of operation's research in their processes and TASW and this contributes to make the decision of small shippers easier to buy the suitable software for haulage. This thesis is divided into few parts -- at the begging I theoretically follow up haulage entrepreneurship, its division and kind of current freight. I describe the price creation to make really obvious, which knowledge the shipper have to know. I explain particular disciplines of research's operation, which are connected to make more effective the workings of haulage entrepreneurship. I use these results of the theoretical parts to create my own model of haulage entrepreneurship. I analyse the market's system in the Czech market, which is fixed to support process of haulage entrepreneurship, which applies the principles of operation's research. In conclusion I compare the variants of using different types of IS for transport. The biggest benefit of my thesis I see in that also the laymen can understand it. It makes easier to choose a specialized software for haulage entrepreneurship and the process analyse binding on possibly company's analyse and on field operation's research, which enable more effective delivery.

No presente trabalho, estudamos alguns conceitos relacionados ao desenvolvimento de programas paralelos, algumas formas de aplicar computação paralela em métodos de otimização contínua e dois métodos que envolvem o uso de otimização. O primeiro método que apresentamos, chamado PUMA (Pointwise Unconstrained Minimization Approach), recupera constantes óticas e espessuras de filmes finos a partir de valores de transmitância. O problema de recuperação é modelado como um problema inverso e resolvido com auxílio de um método de otimização. Através da paralelização do PUMA viabilizamos a recuperação empírica de constantes e espessuras de sistemas compostos por até dois filmes sobrepostos. Relatamos aqui os resultados obtidos e discutimos o desempenho da versão paralela e a qualidade dos resultados obtidos. O segundo método estudado tem o objetivo de obter configurações iniciais de moléculas para simulações de dinâmica molecular e é chamado PACKMOL. O problema de obter uma configuração inicial de moléculas é modelado como um problema de empacotamento e resolvido com o auxílio de um método de otimização. Construímos uma versão paralela do PACKMOL e mostramos os ganhos de desempenho obtidos com a paralelização. / In this work we studied some concepts of parallel programming, some ways of using parallel computing in continuous optimization methods and two optimization methods. The first method we present is called PUMA (Pointwise Unconstrained Minimization Approach), and it retrieves optical constants and thicknesses of thin films from transmitance data. The problem of retrieve thickness and optical constants is modeled as an inverse problem and solved with aid of an optimization method. Through the paralelization of PUMA we managed to retrieve optical constants and thicknesses of thin films in structures with one and two superposed films. We describe some results and discuss the performance of the parallel PUMA and the quality of the retrievals. The second studied method is used to build an initial configuration of molecules for molecular dynamics simulations and it is called PACKMOL. The problem of create an initial configuration of molecules is modeled as a packing problem and solved with aid of an optimization method. We developed a parallel version of PACKMOL and we show the obtained performance gains.

Particle swarm optimization (PSO) has previously been parallelized primarily by distributing the computation corresponding to particles across multiple processors. In this thesis we present a speculative approach to the parallelization of PSO that we refer to as SEPSO. In our approach, we refactor PSO such that the computation needed for iteration t+1 can be done concurrently with the computation needed for iteration t. Thus we can perform two iterations of PSO at once. Even with some amount of wasted computation, we show that this approach to parallelization in PSO often outperforms the standard parallelization of simply adding particles to the swarm. SEPSO produces results that are exactly equivalent to PSO; this is not a new algorithm or variant, only a new method of parallelization. However, given this new parallelization model we can relax the requirement of exactly reproducing PSO in an attempt to produce better results. We present several such relaxations, including keeping the best speculative position evaluated instead of the one corresponding to the standard behavior of PSO, and speculating several iterations ahead instead of just one. We show that these methods dramatically improve the performance of parallel PSO in many cases, giving speed ups of up to six times compared to previous parallelization techniques.

<p dir="ltr">Neural networks (NNs) effectively address the challenges of engineering design optimization by using data-driven models, thus reducing computational demands. However, their effectiveness depends heavily on hyperparameter optimization (HPO), which is a global optimization problem. While traditional HPO methods, such as manual, grid, and random search, are simple, they often fail to navigate the vast hyperparameter (HP) space efficiently. This work examines the effectiveness of integrating Bayesian optimization (BO) with multi-armed bandit (MAB) optimization for HPO in NNs. The thesis initially addresses HPO in one-shot sampling, where NNs are trained using datasets of varying sample sizes. It compares the performance of NNs optimized through traditional HPO techniques and a combination of BO and MAB optimization on the analytical Branin function and aerodynamic shape optimization (ASO) of an airfoil in transonic flow. Findings from the optimization of the Branin function indicate that the combined BO and MAB optimization approach leads to simpler NNs and reduces the sample size by approximately 10 to 20 compared to traditional HPO methods, all within half the time. This efficiency improvement is even more pronounced in ASO, where the BO and MAB optimization use about 100 fewer samples than the traditional methods to achieve the optimized airfoil design. The thesis then expands on adaptive HPOs within the framework of efficient global optimization (EGO) using a NN-based prediction and uncertainty (EGONN) algorithm. It employs the BO and MAB optimization for tuning HPs during sequential sampling, either every iteration (HPO-1itr) or every five iterations (HPO-5itr). These strategies are evaluated against the EGO as a benchmark method. Through experimentation with the analytical three-dimensional Hartmann function and ASO, assessing both comprehensive and selective tunable HP sets, the thesis contrasts adaptive HPO approaches with a static HPO method (HPO-static), which uses the initial HP settings throughout. Initially, a comprehensive set of the HPs is optimized and evaluated, followed by an examination of selectively chosen HPs. For the optimization of the three-dimensional Hartmann function, the adaptive HPO strategies surpass HPO-static in performance in both cases, achieving optimal convergence and sample efficiency comparable to EGO. In ASO, applying the adaptive HPO strategies reduces the baseline airfoil's drag coefficient to 123 drag counts (d.c.) for HPO-1itr and 120 d.c. for HPO-5itr when tuning the full set of the HPs. For a selected subset of the HPs, 123 d.c. and 121 d.c. are achieved by HPO-1itr and HPO-5itr, respectively, which are comparable to the minimum achieved by EGO. While the HPO-static method reduces the drag coefficient to 127 d.c. by tuning a subset of the HPs, which is a 15 d.c. reduction from its full set case, it falls short of the minimum of adaptive HPO strategies. Focusing on a subset of the HPs reduces time costs and enhances the convergence rate without sacrificing optimization efficiency. The time reduction is more significant with higher HPO frequencies as HPO-1itr cuts time by 66%, HPO-5itr by 38%, and HPO-static by 2%. However, HPO-5itr still requires 31% of the time needed by HPO-1itr for the full HP tuning and 56% for the subset HP tuning.</p>