22,954 research outputs found
Reconstruction of the Antenna Near-Field
CĂlem disertaÄnĂ prĂĄce je navrhnout efektivnÄ pracujĂcĂ algoritmus, kterĂœ na zĂĄkladÄ bezfĂĄzovĂ©ho mÄĆenĂ v blĂzkĂ©m poli antĂ©ny bude schopen zrekonstruovat komplexnĂ blĂzkĂ© pole antĂ©ny resp. vyzaĆovacĂ diagram antĂ©ny ve vzdĂĄlenĂ©m poli. Na zĂĄkladÄ tÄchto Ășvah byly zkoumĂĄny vlastnosti minimalizaÄnĂho algoritmu. ZejmĂ©na byl analyzovĂĄn a vhodnÄ zvolen minimalizaÄnĂ pĆistup, optimalizaÄnĂ metoda a v neposlednĂ ĆadÄ i optimalizaÄnĂ funkce tzv. funkcionĂĄl. DĂĄle pro urychlenĂ celĂ©ho minimalizaÄnĂho procesu byly uvaĆŸovĂĄny prvotnĂ odhady. A na zĂĄvÄr byla do minimalizaÄnĂho algoritmu zahrnuta myĆĄlenka nahrazujĂcĂ hledanĂ© elektrickĂ© pole nÄkolika koeficienty. Na zĂĄkladÄ pĆedchozĂch analĂœz byla navrĆŸenĂĄ bezfĂĄzovĂĄ metoda pro charakterizaci vyzaĆovacĂch vlastnostĂ antĂ©n. Tato metoda kombinuje globĂĄlnĂ optimalizaci s obrazovou kompresnĂ metodou a s lokĂĄlnĂ metodou ve spojenĂ s konveÄnĂm amplitudovĂœm mÄĆenĂm na dvou povrĆĄĂch. V naĆĄem pĆĂpadÄ je globĂĄlnĂ optimalizace pouĆŸita k nalezenĂ globĂĄlnĂho minima minimalizovanĂ©ho funkcionĂĄlu, kompresnĂ metoda k redukci neznĂĄmĂœch promÄnnĂœch na apertuĆe antĂ©ny a lokĂĄlnĂ metoda zajiĆĄĆ„uje pĆesnÄjĆĄĂ nalezenĂ minima. NavrĆŸenĂĄ metoda je velmi robustnĂ a mnohem rychlejĆĄĂ neĆŸ jinĂ© dostupnĂ© minimalizaÄnĂ algoritmy. DalĆĄĂ vĂœzkum byl zamÄĆen na moĆŸnosti vyuĆŸitĂ mÄĆenĂœch amplitud pouze z jednoho mÄĆĂcĂho povrchu pro rekonstrukci vyzaĆovacĂch charakteristik antĂ©n a vyuĆŸitĂ novĂ©ho algoritmu pro rekonstrukci fĂĄze na vĂĄlcovĂ© geometrii.The aim of this dissertation thesis is to design a very effective algorithm, which is able to reconstruct the antenna near-field and radiation patterns, respectively, from amplitude-only measurements. Under these circumstances, the properties of minimization algorithm were researched. The selection of the minimization approach, optimization technique and the appropriate functional were investigated and appropriately chosen. To reveal the global minimum area faster, the possibilities in the form of initial estimates for accelerating minimization algorithm were also considered. And finally, the idea to represent the unknown electric field distribution by a few coefficients was implicated into the minimization algorithm. The designed near-field phaseless approach for the antenna far-field characterization combines a global optimization, an image compression method and a local optimization in conjunction with conventional two-surface amplitude measurements. The global optimization method is used to minimize the functional, the image compression method is used to reduce the number of unknown variables, and the local optimization method is used to improve the estimate achieved by the previous method. The proposed algorithm is very robust and faster than comparable algorithms available. Other investigations were focused on possibilities of using amplitude from only single scanning surface for reconstruction of radiation patterns and the application of the novel phase retrieval algorithm for cylindrical geometry.
An island based hybrid evolutionary algorithm for optimization
This is a post-print version of the article - Copyright @ 2008 Springer-VerlagEvolutionary computation has become an important problem solving methodology among the set of search and optimization techniques. Recently, more and more different evolutionary techniques have been developed, especially hybrid evolutionary algorithms. This paper proposes an island based hybrid evolutionary algorithm (IHEA) for optimization, which is based on Particle swarm optimization (PSO), Fast Evolutionary Programming (FEP), and Estimation of Distribution Algorithm (EDA). Within IHEA, an island model is designed to cooperatively search for the global optima in search space. By combining the strengths of the three component algorithms, IHEA greatly improves the optimization performance of the three basic algorithms. Experimental results demonstrate that IHEA outperforms all the three component algorithms on the test problems.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1
Adaptive multimodal continuous ant colony optimization
Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima
Evolutionary strategy search algorithm for fast block motion estimation
The evolutionary strategy search (ESS) algorithm is a novel method for implementing fast block motion estimation (ME) using evolutionary
strategy (ES). ESS uses a combination of ideas based on existing search strategies and employs a novel (1ĂŸsl) ES implementation. It is essentially a succession of random searches, but by controlling the placement and distribution of these searches in a simple way, it proves
possible to achieve comparable motion vector accuracy to the more established ME strategies, but with enhanced convergence speed
Posing 3D Models from Drawing
Inferring the 3D pose of a character from a drawing is a complex and under-constrained problem. Solving it may help automate various parts of an animation production pipeline such as pre-visualisation. In this paper, a novel way of inferring the 3D pose from a monocular 2D sketch is proposed. The proposed method does not make any external assumptions about the model, allowing it to be used on different types of characters. The inference of the 3D pose is formulated as an optimisation problem and a parallel variation of the Particle Swarm Optimisation algorithm called PARAC-LOAPSO is utilised for searching the minimum. Testing in isolation as well as part of a larger scene, the presented method is evaluated by posing a lamp, a horse and a human character. The results show that this method is robust, highly scalable and is able to be extended to various types of models
- âŠ