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21	Conception optimale de circuits magnétiques dédiés à la propulsion spatiale électrique par des méthodes d'optimisation topologique / Optimal design of magnetic circuits dedicated to spatial electric propulsion by topology optimization methods Sanogo, Satafa 01 February 2016 (has links) Dans ces travaux, nous présentons des méthodes d'optimisation théoriques et numériques pour la conception optimale de circuits magnétiques pour propulseurs à effet Hall. Ces problèmes de conception sont des problèmes inverses très difficiles à résoudre que nous formulons sous forme de problèmes d'optimisation topologique. Les problèmes resultant sont non convexes avec des contraintes aux équations différentielles de Maxwell. Au cours de ces travaux, des approches originales ont été proposées afin de résoudre efficacement ces problèmes d'optimisation topologique. L'approche de densité de matériaux SIMP (Solid Isotropic Material with Penalization) qui est une variante de la méthode d'homogénéisation a été privilégiées. De plus, les travaux de ma thèse ont permis la mise en place de codes d'optimisation dénommé ATOP (Algorithm To Optimize Propulsion) utilisant en parallèle les logiciels de calculs scientifiques Matlab et d'élément finis FEMM (Finite Element Method Magnetics). Dans ATOP, nous utilisant à la fois des algorithmes d'optimisation locale de type descente basés sur une analyse de la sensibilité du problème et des algorithmes d'optimisation globale principalement de type Branch and Bound basés sur l'Arithmétique des Intervals. ATOP permettra d'optimiser à la fois la forme topologique des circuits magnétiques mais aussi le temps et le coût de production de nouvelles génération de propulseurs électriques. / In this work, we present theoretical and numerical optimization method for designing magnetic circuits for Hall effect thrusters. These design problems are very difficult inverse ones that we formulate under the form of topology optimization problems. Then, the obtained problems are non convex subject to Maxwell equations like constraints. Some original approaches have been proposed to solve efficiently these topology optimization problems. These approaches are based on the material density model called SIMP approach (Solid Isotropic Material with Penalization) which is a variante of the homogenization method. The results in my thesis allowed to provide optimization source code named ATOP (Algorithm To Optimize Propulsion) unsung in parallel two scientific computing softwares namely Matlab and FEMM (Finite Element Method Magnetics). In ATOP, we use both local optimization algorithms based on sensitivity analysis of the design problem; and global optimization algorithms mainly of type Branch and Bound based on Interval Arithmetic analysis. ATOP will help to optimize both the topological shape of the magnetic circuits and the time and cost of production (design process) of new generations of electrical thrusters. Problème inverse Equation de Maxwell Optimisation topologique Analyse de sensibilité Optimisation numérique Algorithme Branch and Bound Electromagnétisme Propulseur à effet Hall Inverse Problem Maxwell Equation Topology Optimization Sensitivity Analysis Numerical Optimization Branch and Bound Algorithm Electromagnetism Hall effect thruster
22	Planejamento da expansão de sistemas de transmissão usando os modelos CC - CA e tecnicas de programação não-linear / Transmission systems expansion planning using DC-AC models and non-linear programming techniques Rider Flores, Marcos Julio, 1975- 22 February 2006 (has links) Orientador: Ariovaldo Verandio Garcia, Ruben Augusto Romero Lazaro / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-06T06:56:43Z (GMT). No. of bitstreams: 1 RiderFlores_MarcosJulio_D.pdf: 1021887 bytes, checksum: 6000961c2f5457b410ac691912476270 (MD5) Previous issue date: 2006 / Resumo: Neste trabalho são propostos modelos matemáticos e técnicas de solução para resolver o problema de planejamento da expansão de sistemas de transmissão através de três enfoques. a) Usando o modelo de corrente alternada do sistema de transmissão e um algoritmo heurístico construtivo especializado para resolver o problema de planejamento, e, ainda, realiza-se uma primeira tentativa de alocação de fontes de potência reativas; b) Usando o modelo de corrente contínua e técnicas de programação não-linear especializadas. Nesse caso emprega-se uma versão relaxada do problema de planejamento da expansão de sistemas de transmissão usando o modelo de corrente contínua, onde a integralidade das variáveis de investimento é desprezada. Resolve-se o problema de programação não-linear, modelado de forma matricial com um algoritmo de otimização especializado e, além disso, um algoritmo heurístico construtivo especializado é utilizado para resolver o problema de planejamento. c) Usando o modelo de corrente contínua e um algoritmo Branch and Bound (B&B) sem empregar técnicas de decomposição. Para isso foram redefinidos os chamados testes de sondagem no algoritmo B&B e em cada nó da árvore de B&B tem-se um problema de programação não-linear que são resolvidos usando a metodologia desenvolvida no item (b). Os ítens (a), (b) e (c) requerem a solução de problemas de programação não-linear diferenciados. Uma revisão das características principais da resolução iterativa dos métodos de pontos interiores é apresentada. Foi desenvolvida uma técnica baseada em uma combinação de métodos de pontos interiores de alta ordem (MPI-AO) para resolver os problemas de programação não-linear de forma rápida, eficiente e robusta. Essa combinação dos MPI-AO tem como objetivo colocar num único método as características particulares de cada um dos MPI-AO e melhorar o desempenho computacional comparado com os MPI-AO de forma individual / Abstract: In this work mathematical models and solution techniques are proposed to solve the power system transmission expansion planning problem through three approaches: a) Using the nonlinear model ofthe transmission system (AC model) and a specialized constructive heuristic algorithm to solve the problem and, yet, a first attempt to allocate reactive power sources is also considered; b) Using the direct-current (DC) model and specialized techniques of nonlinear programming. In this case a version of the power system transmission expansion planning problem using the DC model where the integrality of the investment variables is relaxed is used. The nonlinear programming problem is solved with a specialized optimization algorithm and, moreover, a constructive heuristic algorithm is employed to solve the planning problem. c) Using the DC model and Branch and Bound (B&B) algorithm without the use of decomposition techniques. The so called fathoming tests of the B&B were redefined and at each node of the tree a nonlinear programming problem is solved using the method developed in b). Items a), b) and c) require the solution of distinct problems of nonlinear programming. A revision of the main characteristics of the iterative solution of the interior points methods is presented. An optimization technique based on a combination of the higher order interior point methods (HO-IPM) had been developed to solve the nonlinear programming problems in a fast, efficient and robust way. This combination of the HO-IPM has as objective to explore the particular characteristics of each method in a single one and to improve the comparative computational performance with the HO-IPM of individual form / Doutorado / Energia Eletrica / Doutor em Engenharia Elétrica Energia elétrica - Transmissão Otimização combinatória Programação não-linear Power transmission planning Branch and bound algorithm Higher order interior point method Mixed integer nonlinear programming DC model
23	Parallelisation of hybrid metaheuristics for COP solving / Parallélisation de métaheuristiques hybrides pour la résolution de POC Labidi, Mohamed Khalil 20 September 2018 (has links) L’Optimisation Combinatoire (OC) est un domaine de recherche qui est en perpétuel changement. Résoudre un problème d’optimisation combinatoire (POC) consiste essentiellement à trouver la ou les meilleures solutions dans un ensemble des solutions réalisables appelé espace de recherche qui est généralement de cardinalité exponentielle en la taille du problème. Pour résoudre des POC, plusieurs méthodes ont été proposées dans la littérature. On distingue principalement les méthodes exactes et les méthodes d’approximation. Ne pouvant pas viser une résolution exacte de problèmes NP-Complets lorsque la taille du problème dépasse une certain seuil, les chercheurs on eu de plus en plus recours, depuis quelques décennies, aux algorithmes dits hybrides (AH) ou encore à au calcul parallèle. Dans cette thèse, nous considérons la classe POC des problèmes de conception d'un réseau fiable. Nous présentons un algorithme hybride parallèle d'approximation basé sur un algorithme glouton, un algorithme de relaxation Lagrangienne et un algorithme génétique, qui produit des bornes inférieure et supérieure pour les formulations à base de flows. Afin de valider l'approche proposée, une série d'expérimentations est menée sur plusieurs applications: le Problème de conception d'un réseau k-arête-connexe avec contrainte de borne (kHNDP) avec L=2,3, le problème de conception d'un réseau fiable Steiner k-arête-connexe (SkESNDP) et ensuite deux problèmes plus généraux, à savoir le kHNDP avec L >= 2 et le problème de conception d'un réseau fiable k-arête-connexe (kESNDP). L'étude expérimentale de la parallélisation est présentée après cela. Dans la dernière partie de ce travail, nous présentons deux algorithmes parallèles exactes: un Branch-and-Bound distribué et un Branch-and-Cut distribué. Une série d'expérimentation a été menée sur une grappe de 128 processeurs, et des accélération intéressantes ont été atteintes pour la résolution du problèmes kHNDP avec k=3 et L=3. / Combinatorial Optimization (CO) is an area of research that is in a constant progress. Solving a Combinatorial Optimization Problem (COP) consists essentially in finding the best solution (s) in a set of feasible solutions called a search space that is usually exponential in cardinality in the size of the problem. To solve COPs, several methods have been proposed in the literature. A distinction is made mainly between exact methods and approximation methods. Since it is not possible to aim for an exact resolution of NP-Complete problems when the size of the problem exceeds a certain threshold, researchers have increasingly used Hybrid (HA) or parallel computing algorithms in recent decades. In this thesis we consider the COP class of Survivability Network Design Problems. We present an approximation parallel hybrid algorithm based on a greedy algorithm, a Lagrangian relaxation algorithm and a genetic algorithm which produces both lower and upper bounds for flow-based formulations. In order to validate the proposed approach, a series of experiments is carried out on several applications: the k-Edge-Connected Hop-Constrained Network Design Problem (kHNDP) when L = 2,3, The problem of the Steiner k-Edge-Connected Network Design Problem (SkESNDP) and then, two more general problems namely the kHNDP when L >= 2 and the k-Edge-Connected Network Design Problem (kESNDP). The experimental study of the parallelisation is presented after that. In the last part of this work, we present a two parallel exact algorithms: a distributed Branch-and-Bound and a distributed Branch-and-Cut. A series of experiments has been made on a cluster of 128 processors and interesting speedups has been reached in kHNDP resolution when k=3 and L=3. Calcul parallèle Métaheuristique Optimisation Combinatoire Algorithme de coupes et branchements Hybridation Conception de réseaux Algorithme de séparation et évaluation Parallel computing Metaheuristic Combinatorial Optimization Branch-And-Cut algorithm Hybridization Network Design Branch-And-Bound algorithm 004
24	GIS-based Episode Reconstruction Using GPS Data for Activity Analysis and Route Choice Modeling / GIS-based Episode Reconstruction Using GPS Data Dalumpines, Ron 26 September 2014 (has links) Most transportation problems arise from individual travel decisions. In response, transportation researchers had been studying individual travel behavior – a growing trend that requires activity data at individual level. Global positioning systems (GPS) and geographical information systems (GIS) have been used to capture and process individual activity data, from determining activity locations to mapping routes to these locations. Potential applications of GPS data seem limitless but our tools and methods to make these data usable lags behind. In response to this need, this dissertation presents a GIS-based toolkit to automatically extract activity episodes from GPS data and derive information related to these episodes from additional data (e.g., road network, land use). The major emphasis of this dissertation is the development of a toolkit for extracting information associated with movements of individuals from GPS data. To be effective, the toolkit has been developed around three design principles: transferability, modularity, and scalability. Two substantive chapters focus on selected components of the toolkit (map-matching, mode detection); another for the entire toolkit. Final substantive chapter demonstrates the toolkit’s potential by comparing route choice models of work and shop trips using inputs generated by the toolkit. There are several tools and methods that capitalize on GPS data, developed within different problem domains. This dissertation contributes to that repository of tools and methods by presenting a suite of tools that can extract all possible information that can be derived from GPS data. Unlike existing tools cited in the transportation literature, the toolkit has been designed to be complete (covers preprocessing up to extracting route attributes), and can work with GPS data alone or in combination with additional data. Moreover, this dissertation contributes to our understanding of route choice decisions for work and shop trips by looking into the combined effects of route attributes and individual characteristics. / Dissertation / Doctor of Philosophy (PhD) GPS time use diary episode extraction multinomial logit travel behavior mode detection episode reconstruction GIS map-matching route choice path size logit potential path area scale estimation Python ArcGIS activity analysis trip reconstruction smartphone global positioning system geographic information system toolkit work trip shop trip potential activity location land use activity episode travel episode stop episode transferability scalability modularity scripting big data travel survey respondent burden preprocessing multipath error tracking purpose detection segmentation data filter data smoothing fuzzy logic neural network decision tree rule-based algorithm trajectory point road network network dataset gateway shortest path horizontal dilution of precision HDOP commonality factor mode transfer point Halifax Nova Scotia Space-Time Activity Research variance inflation factor shapefile comma-separated values traveling salesman problem data mining branch-and-bound algorithm pedestrian network alternative route observed route route efficiency route attributes distance time heading bearing duration acceleration latitude longitude coordinate overlay analysis intersect shopping module data logger walk likelihood ratio test classification table path generation kappa statistic degrees data collection transportation research automate framework ArcToolbox spatio-temporal navigation positioning trace path spatial data topology horizontal accuracy SPSS Stata spatial resolution temporal resolution household survey trip reporting proximity analysis DMTI Desktop Mapping Technologies Inc. road intersection route overlap left turn right turn location analysis time geography spatial statistics buffer analysis cycling bus transit public transportation GEOIDE AGILE data need raw data urban canyon endpoint data cleaning elevation satellite outliers automatic processing nearest node classifier classification method short trip multi-point

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