Geometric guides for interactive evolutionary design

This thesis describes the addition of novel Geometric Guides to a generative Computer-Aided Design (CAD) application that supports early-stage concept generation. The application generates and evolves abstract 3D shapes, used to inspire the form of new product concepts. It was previously a conventional Interactive Evolutionary system where users selected shapes from evolving populations. However, design industry users wanted more control over the shapes, for example by allowing the system to influence the proportions of evolving forms. The solution researched, developed, integrated and tested is a more cooperative human-machine system combining classic user interaction with innovative geometric analysis. In the literature review, different types of Interactive Evolutionary Computation (IEC), Pose Normalisation (PN), Shape Comparison, and Minimum-Volume Bounding Box approaches are compared, with some of these technologies identified as applicable for this research. Using its Application Programming Interface, add-ins for the Siemens NX CAD system have been developed and integrated with an existing Interactive Evolutionary CAD system. These add-ins allow users to create a Geometric Guide (GG) at the start of a shape exploration session. Before evolving shapes can be compared with the GG, they must be aligned and scaled (known as Pose Normalisation in the literature). Computationally-efficient PN has been achieved using geometric functions such as Bounding Box for translation and scaling, and Principle Axes for the orientation. A shape comparison algorithm has been developed that is based on the principle of non-intersecting volumes. This algorithm is also implemented with standard, readily available geometric functions, is conceptually simple, accessible to other researchers and also offers appropriate efficacy. Objective geometric testing showed that the PN and Shape Comparison methods developed are suitable for this guiding application and can be efficiently adapted to enhance an Interactive Evolutionary Design system. System performance with different population sizes was examined to indicate how best to use the new guiding capabilities to assist users in evolutionary shape searching. This was backed up by participant testing research into two user interaction strategies. A Large Background Population (LBP) approach where the GG is used to select a sub-set of shapes to show to the user was shown to be the most effective. The inclusion of Geometric Guides has taken the research from the existing aesthetic focused tool to a system capable of application to a wider range of engineering design problems. This system supports earlier design processes and ideation in conceptual design and allows a designer to experiment with ideas freely to interactively explore populations of evolving solutions. The design approach has been further improved, and expanded beyond the previous quite limited scope of form exploration

Estudio comparativo de algoritmos de agrupación basados en la ley de gravitación universal aplicados a la segmentación de imágenes

Esta investigación tuvo como objetivo principal resolver el problema de la segmentación de imágenes a través del uso del algoritmo de agrupación basados en la ley de gravitación universal llamado “Algoritmo de búsqueda gravitacional”, se realizó un análisis comparativo entre los resultados obtenidos por este y los obtenidos por los algoritmos de agrupación “convencionales”, cuyo propósito fue evaluar si este resuelve las debilidades identificadas en los algoritmos convencionales para este campo de aplicación.This study's main objective was to solve the problem of image segmentation through the use of clustering algorithms based on the law of universal gravitation called "gravitational search algorithm with heuristics" and a comparative analysis of the results was carried out by this and those obtained by conventional algorithms, whose purpose was to assess whether this resolves the weaknesses identified in conventional algorithms for this field of application

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate