Swarm Intelligent Selection and Optimization of Machining System Parameters for Microchannel Fabrication in Medical Devices

Current technology trends in medical device industry calls for fabrication of massive arrays of microfeatures such as microchannels on to nonsilicon material substrates with high accuracy, superior precision, and high throughput. Microchannels are typical features used in medical devices for medication dosing into the human body, analyzing DNA arrays or cell cultures. In this study, the capabilities of machining systems for micro-end milling have been evaluated by conducting experiments, regression modeling, and response surface methodology. In machining experiments by using micromilling, arrays of microchannels are fabricated on aluminium and titanium plates, and the feature size and accuracy (width and depth) and surface roughness are measured. Multicriteria decision making for material and process parameters selection for desired accuracy is investigated by using particle swarm optimization (PSO) method, which is an evolutionary computation method inspired by genetic algorithms (GA). Appropriate regression models are utilized within the PSO and optimum selection of micromilling parameters; microchannel feature accuracy and surface roughness are performed. An analysis for optimal micromachining parameters in decision variable space is also conducted. This study demonstrates the advantages of evolutionary computing algorithms in micromilling decision making and process optimization investigations and can be expanded to other applications

An Analysis of black-box optimization problems in reinsurance: evolutionary-based approaches

Black-box optimization problems (BBOP) are de ned as those optimization problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program). This paper is focussed on BBOPs that arise in the eld of insurance, and more speci cally in reinsurance problems. In this area, the complexity of the models and assumptions considered to de ne the reinsurance rules and conditions produces hard black-box optimization problems, that must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in BBOP, so new computational paradigms must be applied to solve these problems. In this paper we show the performance of two evolutionary-based techniques (Evolutionary Programming and Particle Swarm Optimization). We provide an analysis in three BBOP in reinsurance, where the evolutionary-based approaches exhibit an excellent behaviour, nding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.

Estudi comparatiu de l'algorisme basat en una col��nia artificial d'abelles

La finalitat d'aquest projecte ��s la realitzaci�� d'un estudi comparatiu de l'algoritme basat en una col��nia artificial d'abelles, Artificial Bee Colony (ABC), comparat amb un conjunt d'algoritmes fonamentats en el paradigma de la computaci�� evolutiva. S'utilitzar�� l'efic��cia a l'hora d'optimitzar diverses funcions com a mesura comparativa. Els algoritmes amb els quals es comparara l'algoritme ABC s��n: algoritmes gen��tics, evoluci�� diferencial i optimitzaci�� amb eixam de part��cules.

GOTILWA+: una herramienta para optimizar la gesti��n forestal adaptada al cambio clim��tico

Estudiar el cambio global de origen antropog��nico en los ecosistemas mundiales, y sus efectos sobre los mismos, es y ser�� uno de los principales retos de la ecolog��a del siglo XXI. Los ecosistemas forestales espa��oles ya se encuentran actualmente limitados por el estr��s h��drico. Esta limitaci��n se ver�� agravada por los efectos del cambio clim��tico debido tanto a una reducci��n del agua disponible como a un incremento de la demanda evaporativa. Una gesti��n forestal adecuada puede incrementar la resiliencia de los ecosistemas forestales mediterr��neos al cambio clim��tico. Los modelos de procesos ecofisiol��gicos como GOTILWA+ son herramientas muy potentes a la hora de proyectar los efectos del cambio clim��tico sobre los ecosistemas forestales, asimismo como evaluar la gesti��n forestal. GOTILWA+ incluye un potente motor de optimizaci��n de la gesti��n forestal basado en el "Particle Swarm Algorithm" (PSO) -, que permite proyectar la gesti��n ��ptima en funci��n de las variables ambientales tanto clim��ticas como estructurales y de los objetivos de gesti��n. Una gesti��n adaptativa al cambio clim��tico ser�� imprescindible para combatir los impactos negativos de este sobre los bosques espa��oles. En este art��culo se presentan tres ejemplos de aplicaci��n del modelo GOTILWA+: en el primero se estudia la respuesta de los hayedos (Fagus sylvatica L.) espa��oles a distintos escenarios de cambio clim��tico. En el segundo se eval��an distintos itinerarios de gesti��n de pino carrasco (Pinus halepensis Mill.) en funci��n de distintos objetivos de gesti��n. En el tercero, se aplica el PSO en un rodal de pino silvestre (Pinus sylvestris L.) para obtener la gesti��n ��ptima del rodal. Se concluye que, si bien el cambio clim��tico supondr�� severas constricciones sobre los ecosistemas forestales espa��oles, una gesti��n adaptativa permitir�� en parte mitigar dichos impactos [...].

Stability and asymptotic analysis of a fluid-particle interaction model

We are interested in coupled microscopic/macroscopic models describing the evolution of particles dispersed in a fluid. The system consists in a Vlasov-Fokker-Planck equation to describe the microscopic motion of the particles coupled to the Euler equations for a compressible fluid. We investigate dissipative quantities, equilibria and their stability properties and the role of external forces. We also study some asymptotic problems, their equilibria and stability and the derivation of macroscopic two-phase models.

Unconstrained optimization with MINTOOLKIT for GNU octave

The paper documents MINTOOLKIT for GNU Octave. MINTOOLKIT provides functions for minimization and numeric differentiation. The main algorithms are BFGS, LBFGS, and simulated annealing. Examples are given.

Simulaci��n de fluidos inmiscibles con el Particle Finite Element Method (PFEM)

Proyecto de investigaci��n realizado a partir de una estancia en el Centro Internacional de M��todos Computacionales en Ingenier��a (CIMEC), Argentina, entre febrero y abril del 2007. La simulaci��n num��rica de problemas de mezclas mediante el Particle Finite Element Method (PFEM) es el marco de estudio de una futura tesis doctoral. ��ste es un m��todo desarrollado conjuntamente por el CIMEC y el Centre Internacional de M��todos Num��rics en l'Enginyeria (CIMNE-UPC), basado en la resoluci��n de las ecuaciones de Navier-Stokes en formulaci��n Lagrangiana. El mallador ha sido implementado y desarrollado por Dr. Nestor Calvo, investigador del CIMEC. El desarrollo del m��dulo de c��lculo corresponde al trabajo de tesis de la beneficiaria. La correcta interacci��n entre ambas partes es fundamental para obtener resultados v��lidos. En esta memoria se explican los principales aspectos del mallador que fueron modificados (criterios de refinamiento geom��trico) y los cambios introducidos en el m��dulo de c��lculo (librer��a PETSc, algoritmo predictor-corrector) durante la estancia en el CIMEC. Por ��ltimo, se muestran los resultados obtenidos en un problema de dos fluidos inmiscibles con transferencia de calor.

Analysis and optimization of the satellite-to-plane link of an aeronautical global system

En aquest projecte s���ha analitzat i optimitzat l���enlla�� sat��l��lit amb avi�� per a un sistema aeron��utic global. Aquest nou sistema anomenat ANTARES est�� dissenyat per a comunicar avions amb estacions base mitjan��ant un sat��l��lit. Aquesta ��s una iniciativa on hi participen institucions oficials en l���aviaci�� com ara l���ECAC i que ��s desenvolupat en una col��laboraci�� europea d���universitats i empreses. El treball dut a terme en el projecte compren b��sicament tres aspectes. El disseny i an��lisi de la gesti�� de recursos. La idone��tat d���utilitzar correcci�� d���errors en la capa d���enlla�� i en cas que sigui necess��ria dissenyar una opci�� de codificaci�� preliminar. Finalment, estudiar i analitzar l���efecte de la interfer��ncia co-canal en sistemes multifeix. Tots aquests temes s��n considerats nom��s per al ���forward link���. L���estructura que segueix el projecte ��s primer presentar les caracter��stiques globals del sistema, despr��s centrar-se i analitzar els temes mencionats per a poder donar resultats i extreure conclusions.

Line search multilevel optimization as computational methods for dense optical flow

We evaluate the performance of different optimization techniques developed in the context of optical flowcomputation with different variational models. In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we develop the use of efficient multilevel schemes for computing the optical flow. More precisely, we evaluate the performance of a standard unidirectional multilevel algorithm - called multiresolution optimization (MR/OPT), to a bidrectional multilevel algorithm - called full multigrid optimization (FMG/OPT). The FMG/OPT algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. Experimental results on different image sequences using four models of optical flow computation show that the FMG/OPT algorithm outperforms both the TN and MR/OPT algorithms in terms of the computational work and the quality of the optical flow estimation.

An algorithm for semi-infinite polynomial optimization

"Vegeu el resum a l'inici del document del fitxer adjunt."

A linear optimization technique for graph pebbling

Graph pebbling is a network model for studying whether or not a given supply of discrete pebbles can satisfy a given demand via pebbling moves. A pebbling move across an edge of a graph takes two pebbles from one endpoint and places one pebble at the other endpoint; the other pebble is lost in transit as a toll. It has been shown that deciding whether a supply can meet a demand on a graph is NP-complete. The pebbling number of a graph is the smallest t such that every supply of t pebbles can satisfy every demand of one pebble. Deciding if the pebbling number is at most k is NP 2 -complete. In this paper we develop a tool, called theWeight Function Lemma, for computing upper bounds and sometimes exact values for pebbling numbers with the assistance of linear optimization. With this tool we are able to calculate the pebbling numbers of much larger graphs than in previous algorithms, and much more quickly as well. We also obtain results for many families of graphs, in many cases by hand, with much simpler and remarkably shorter proofs than given in previously existing arguments (certificates typically of size at most the number of vertices times the maximum degree), especially for highly symmetric graphs. Here we apply theWeight Function Lemma to several specific graphs, including the Petersen, Lemke, 4th weak Bruhat, Lemke squared, and two random graphs, as well as to a number of infinite families of graphs, such as trees, cycles, graph powers of cycles, cubes, and some generalized Petersen and Coxeter graphs. This partly answers a question of Pachter, et al., by computing the pebbling exponent of cycles to within an asymptotically small range. It is conceivable that this method yields an approximation algorithm for graph pebbling.

A parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics

This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex conguration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of cases.

On the stability of the optimal value and the optimal set in optimization problems

The paper develops a stability theory for the optimal value and the optimal set mapping of optimization problems posed in a Banach space. The problems considered in this paper have an arbitrary number of inequality constraints involving lower semicontinuous (not necessarily convex) functions and one closed abstract constraint set. The considered perturbations lead to problems of the same type as the nominal one (with the same space of variables and the same number of constraints), where the abstract constraint set can also be perturbed. The spaces of functions involved in the problems (objective and constraints) are equipped with the metric of the uniform convergence on the bounded sets, meanwhile in the space of closed sets we consider, coherently, the Attouch-Wets topology. The paper examines, in a unified way, the lower and upper semicontinuity of the optimal value function, and the closedness, lower and upper semicontinuity (in the sense of Berge) of the optimal set mapping. This paper can be seen as a second part of the stability theory presented in [17], where we studied the stability of the feasible set mapping (completed here with the analysis of the Lipschitz-like property).

Route optimization and customization using real geographical data in Android mobile devices

Nowadays, there are several services and applications that allow users to locate and move to different tourist areas using a mobile device. These systems can be used either by internet or downloading an application in concrete places like a visitors centre. Although such applications are able to facilitate the location and the search for points of interest, in most cases, these services and applications do not meet the needs of each user. This paper aims to provide a solution by studying the main projects, services and applications, their routing algorithms and their treatment of the real geographical data in Android mobile devices, focusing on the data acquisition and treatment to improve the routing searches in off-line environments.

Xarxa social (Swarm)

Aquesta mem��ria presenta les l��nies generals que s'han seguit per tal d'implementar una aplicaci�� anomenada SWARM. En aquest document es recullen les bases del nostre projecte utilitzant el llenguatge de programaci�� C# i fent servir altres eines i frameworks per les diferents capes de qu�� consta el projecte, com poden ser Silverlight o WCF.