GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.

Negative non-ground queries in well founded semantics

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Computational Logic

Problemas associados à instalação de subestações de energia em malha urbana. Caso de estudo da subestação de parceiros em Leiria

No âmbito do desenvolvimento da dissertação do Mestrado de Engenharia Eletrotécnica – Sistemas Elétricos de Energia, surgiu a oportunidade de participar no desenvolvimento de um projeto integrante numa subestação de energia. O presente caso de estudo tem em vista a resolução de condicionantes na conceção desta subestação, tais como cumprir requisitos estéticos projetando todo o tipo de equipamentos dentro de edifícios, a ventilação das salas de transformação assim como insonorização de todo o ruído produzido de forma a cumprir os limites legais e não perturbar a vizinhança. A presente subestação de energia está situada numa zona urbana da cidade de Leiria, localidade de Parceiros, dando origem ao nome Subestação de Energia de Parceiros. Esta subestação pertence ao cliente Energias de Portugal, conhecida como EDP, e visa o melhoramento da distribuição do serviço eléctrico. As tradicionais subestações de energia, com aparência bastante desenquadrada dos meios urbanos, representam um entrave ao nível da aproximação destes centros de produção energética às zonas urbanas, comprometendo a melhoria da rede elétrica. Desta forma, foi implementada a tecnologia Gas-Insolated Switchgear e permite o enquadramento destes centros energéticos em zonas urbanas, constituindo mais um edifício urbano na zona onde se insere. Esta substitui os convencionais barramentos existentes nos Parques Exteriores de Aparelhagem das subestações e apresenta-se com dimensões muito reduzidas quando comparadas com as estruturas instaladas nestes parques. Dado que esta tecnologia é desenvolvida no sentido de permitir a construção destes centros energéticos em zonas urbanas, podem ser alojadas dentro de edifícios produzindo assim vantagens ao nível estético, não perturbando a paisagem. Dado que os principais equipamentos de funcionamento na subestação de Parceiros, nomeadamente o Transformador de Potência, se encontram instalados num edifício completamente fechado, foram construídas duas salas de ventilação, na parte superior deste edifício, cada uma adjacente a uma sala de transformação. O transformador instalado possui elevadas dimensões, pesando 53000 kg e contendo 11000 kg de óleo que em estado normal de funcionamento circula por quase todo o interior da máquina a elevadas temperaturas, provocando um aquecimento elevado no interior do edifício o que condiciona o bom funcionamento do transformador. Para ultrapassar esta condicionante foi realizado um estudo de um sistema de ventilação capaz de avaliar e controlar os valores térmicos existentes e proceder à circulação de ar, que será movimentado ou bloqueado, recorrendo a um sistema autónomo, mantendo a temperatura ideal nas salas de transformação. Este autómato é o cérebro de toda a cadeia lógica que, mediante as leituras efetuadas irá dar ordens de atuação aos diversos equipamentos. Apesar dos TP estarem protegidos dentro do edifício, estes produzem um maior ruído. A necessidade da existência do referido sistema de ventilação das divisões de funcionamento destas máquinas, implica a utilização de aparelhos que, apesar da sua evolução tecnológica os torna cada vez menos ruidosos, mas geram sempre alguma perturbação, o que pode representar um problema no cumprimento do regulamento geral do ruído.

Efficient Legendre spectral tau algorithm for solving two-sided space-time Caputo fractional advection-dispersion equation

In this paper we present the operational matrices of the left Caputo fractional derivative, right Caputo fractional derivative and Riemann–Liouville fractional integral for shifted Legendre polynomials. We develop an accurate numerical algorithm to solve the two-sided space–time fractional advection–dispersion equation (FADE) based on a spectral shifted Legendre tau (SLT) method in combination with the derived shifted Legendre operational matrices. The fractional derivatives are described in the Caputo sense. We propose a spectral SLT method, both in temporal and spatial discretizations for the two-sided space–time FADE. This technique reduces the two-sided space–time FADE to a system of algebraic equations that simplifies the problem. Numerical results carried out to confirm the spectral accuracy and efficiency of the proposed algorithm. By selecting relatively few Legendre polynomial degrees, we are able to get very accurate approximations, demonstrating the utility of the new approach over other numerical methods.

An efficient numerical scheme for solving multi-dimensional fractional optimal control problems with a quadratic performance index

The shifted Legendre orthogonal polynomials are used for the numerical solution of a new formulation for the multi-dimensional fractional optimal control problem (M-DFOCP) with a quadratic performance index. The fractional derivatives are described in the Caputo sense. The Lagrange multiplier method for the constrained extremum and the operational matrix of fractional integrals are used together with the help of the properties of the shifted Legendre orthonormal polynomials. The method reduces the M-DFOCP to a simpler problem that consists of solving a system of algebraic equations. For confirming the efficiency and accuracy of the proposed scheme, some test problems are implemented with their approximate solutions.

Probabilistic constraint reasoning

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Informática, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Constraint-based modelling of mixed microbial populations: Application to polyhydroxyalkanoates production

Dissertação para obtenção do Grau de Doutor em Engenharia Química e Bioquímica

Constraint reasoning with local search for continuous optimization

Optimization is a very important field for getting the best possible value for the optimization function. Continuous optimization is optimization over real intervals. There are many global and local search techniques. Global search techniques try to get the global optima of the optimization problem. However, local search techniques are used more since they try to find a local minimal solution within an area of the search space. In Continuous Constraint Satisfaction Problems (CCSP)s, constraints are viewed as relations between variables, and the computations are supported by interval analysis. The continuous constraint programming framework provides branch-and-prune algorithms for covering sets of solutions for the constraints with sets of interval boxes which are the Cartesian product of intervals. These algorithms begin with an initial crude cover of the feasible space (the Cartesian product of the initial variable domains) which is recursively refined by interleaving pruning and branching steps until a stopping criterion is satisfied. In this work, we try to find a convenient way to use the advantages in CCSP branchand- prune with local search of global optimization applied locally over each pruned branch of the CCSP. We apply local search techniques of continuous optimization over the pruned boxes outputted by the CCSP techniques. We mainly use steepest descent technique with different characteristics such as penalty calculation and step length. We implement two main different local search algorithms. We use “Procure”, which is a constraint reasoning and global optimization framework, to implement our techniques, then we produce and introduce our results over a set of benchmarks.

Probabilistic constraint reasoning with Monte Carlo integration to robot localization

This work studies the combination of safe and probabilistic reasoning through the hybridization of Monte Carlo integration techniques with continuous constraint programming. In continuous constraint programming there are variables ranging over continuous domains (represented as intervals) together with constraints over them (relations between variables) and the goal is to find values for those variables that satisfy all the constraints (consistent scenarios). Constraint programming “branch-and-prune” algorithms produce safe enclosures of all consistent scenarios. Special proposed algorithms for probabilistic constraint reasoning compute the probability of sets of consistent scenarios which imply the calculation of an integral over these sets (quadrature). In this work we propose to extend the “branch-and-prune” algorithms with Monte Carlo integration techniques to compute such probabilities. This approach can be useful in robotics for localization problems. Traditional approaches are based on probabilistic techniques that search the most likely scenario, which may not satisfy the model constraints. We show how to apply our approach in order to cope with this problem and provide functionality in real time.

Otimização com restrições de complementaridade: algoritmos e aplicações

Tese de Doutoramento em Engenharia Industrial e de Sistemas (PDEIS)

In silico constraint-based strain optimization methods: the quest for optimal cell factories

Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

A firefly dynamic penalty approach for solving engineering design problems

Firefly Algorithm is a recent swarm intelligence method, inspired by the social behavior of fireflies, based on their flashing and attraction characteristics [1, 2]. In this paper, we analyze the implementation of a dynamic penalty approach combined with the Firefly algorithm for solving constrained global optimization problems. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.

Kinematic constraint equations

This chapter presents a general methodology for the formulation of the kinematic constraint equations at position, velocity and acceleration levels. Also a brief characterization of the different type of constraints is offered, namely the holonomic and nonholonomic constraints. The kinematic constraints described here are formulated using generalized coordinates. The chapter ends with a general approach to deal with the kinematic analysis of multibody systems.

Origen y evolución del magma granítico en el Batolito de Achala: Fundamentos petrológicos, geoquímicos y estructurales para la preconcentración uranífera en el área de Cañada del Puerto. Origin and evolution of the granitic magma in the Achala Batholith: petrological, geochemical and structural constraint for the uranium preconcentration in the Cañada del Puerto area.

El batolito de Achala es uno de los macizos graníticos más grandes de las Sierras Pampeanas, el cual se localiza en las Sierras Grandes de Córdoba. Si bien el batolito de Achala ha sido objeto de diversos estudios geológicos, principalmente debido a sus yacimientos de uranio, el mismo todavía no posee un inequívoco modelo petrogéntico. Tampoco existe, en la actualidad, un inequívoco modelo que explique la preconcentración de uranio en las rocas graníticas portadores de este elemento. Este Proyecto tiene como objetivo general realizar estudios petrológicos y geoquímicos en la región conocida como CAÑADA del PUERTO, un lugar estratégicamente definido debido a la abundancia de granitos equigranulares de grano fino y/o grano medio biotíticos, emplazados durante el desarrollo de cizallas magmáticas tardías, y que constituirían las rocas fuentes de uranio. El objetivo específico requiere estudios detallados de las diferentes facies del batolito de Achala en el área seleccionada, incluyendo investigaciones petrológicas, geoquímicas de roca total, geoquímica de isótopos radiactivos y química mineral, con el fin de definir un MODELO PETROGENÉTICO que permita explicar: (a) el origen del magma padre y el subsiguiente proceso de cristalización de las diferentes facies graníticas aflorantes en el área de estudio, (b) identificar el proceso principal que condujo a la PRECONCENTRACIÓN uranífera de los magmas graníticos canalizados en las cizallas magmáticas tardías. Ambos objetivos se complementan y no son compartimentos estancos, ya que el logro combinado de estos objetivos permitirá comprender de mejor manera el proceso geoquímico que gobernó la distribución y concentración del U. De esta manera, se intentará definir un MODELO de PRECONCENTRACIÓN URANÍFERA EXTRAPOLABLE a otras áreas graníticas enriquecidas en uranio, constituyendo una poderosa herramienta de investigación aplicada a la exploración uranífera. En particular, el conocimiento de los recursos uraníferos es parte de una estrategia nacional con vistas a triplicar antes del 2025 la disponibilidad energética actual, en cuyo caso, el uranio constituye la materia prima de las centrales nucleares que se están planificando y en construcción. Por otro lado, la Argentina adhirió al Protocolo de Kioto y, junto a los países adherentes, deben disminuir de manera progresiva el uso de combustibles fósiles (que producen gases de efecto invernadero), reemplazándola por otras fuentes de energía, entre ellas, la ENERGÍA NUCLEAR. Este Proyecto, si bien NO es un Proyecto de exploración y/o prospección minera, es totalmente consistente con la política energética nacional promocionada desde el Ministerio de Planificación Federal, Inversión Pública y Servicios (v. sitio WEB CNEA), que ha invertido, desde 2006, importantes sumas de dinero, en el marco del Programa de Reactivación de la Actividad Nuclear.Los estudios referidos serán conducidos por los Drs. Dahlquist (CONICET-UNC) y Zarco (CNEA) quienes integrarán sus experiencias desarrolladas en el campo de las Ciencias Básicas con aquel logrado en el campo de las Ciencias Aplicadas, respectivamente. Se pretende, por tanto, aplicar conocimientos académicos-científicos a un problema de geología con potencial significado económico-energético, vinculando las instituciones referidas, esto es, CONICET-UNC y CNEA, con el fin de contribuir a la actividad socioeconómica de la provincia de Córdoba en particular y de Argentina en general.Finalmente, convencidos de que el progreso de la Ciencia y el Desarrollo Tecnológico está íntimamente vinculada con la sólida Formación de Recursos Humanos se pretende que este Proyecto contribuya SIGNIFICATIVAMENTE a las investigaciones de Doctorado que iniciará la Geóloga Carina Bello, actual Becaria de la CNEA.