INTELLIGENT VISION-BASED NAVIGATION SYSTEM

This thesis presents a complete vision-based navigation system that can plan and follow an obstacle-avoiding path to a desired destination on the basis of an internal map updated with information gathered from its visual sensor. For vision-based self-localization, the system uses new floor-edges-specific filters for detecting floor edges and their pose, a new algorithm for determining the orientation of the robot, and a new procedure for selecting the initial positions in the self-localization procedure. Self-localization is based on matching visually detected features with those stored in a prior map. For planning, the system demonstrates for the first time a real-world application of the neural-resistive grid method to robot navigation. The neural-resistive grid is modified with a new connectivity scheme that allows the representation of the collision-free space of a robot with finite dimensions via divergent connections between the spatial memory layer and the neuro-resistive grid layer. A new control system is proposed. It uses a Smith Predictor architecture that has been modified for navigation applications and for intermittent delayed feedback typical of artificial vision. A receding horizon control strategy is implemented using Normalised Radial Basis Function nets as path encoders, to ensure continuous motion during the delay between measurements. The system is tested in a simplified environment where an obstacle placed anywhere is detected visually and is integrated in the path planning process. The results show the validity of the control concept and the crucial importance of a robust vision-based self-localization process

Reconnaissance autodidacte de standards à l'aide de réseaux de neurones RBF

L'idée d'un terminal pouvant transmettre n'importe quel type d'informations (voix ou données), de n'importe où, sur n'importe quel réseau commence à faire son chemin. Cela explique le nombre important d'études concernant les terminaux reconfigurables. C'est dans ce contexte, que nous proposons un terminal reconfigurable de manière autoadaptative. Pour réaliser cela, cet article présente une fonction de reconnaissance autodidacte du standard que le terminal désire démoduler. Nous proposons une reconnaissance basée sur l'utilisation de réseau de neurones RBF. Nous avons en particulier défini une nouvelle erreur "l'erreur combinée" qui permet d'obtenir des résultats satisfaisants

Mathematical Modeling and Analysis of Epidemiological and Chemical Systems

This dissertation focuses on three interdisciplinary areas of applied mathematics, mathematical biology/epidemiology, economic epidemiology and mathematical physics, interconnected by the concepts and applications of dynamical systems.;In mathematical biology/epidemiology, a new deterministic SIS modeling framework for the dynamics of malaria transmission in which the malaria vector population is accounted for at each of its developmental stages is proposed. Rigorous qualitative and quantitative techniques are applied to acquire insights into the dynamics of the model and to identify and study two epidemiological threshold parameters reals* and R0 that characterize disease transmission and prevalence, and that can be used for disease control. It is shown that nontrivial disease-free and endemic equilibrium solutions, which can become unstable via a Hopf bifurcation exist. By incorporating vector demography; that is, by interpreting an aspect of the life cycle of the malaria vector, natural fluctuations known to exist in malaria prevalence are captured without recourse to external seasonal forcing and delays. Hence, an understanding of vector demography is necessary to explain the observed patterns in malaria prevalence. Additionally, the model exhibits a backward bifurcation. This implies that simply reducing R0 below unity may not be enough to eradicate the malaria disease. Since, only the female adult mosquitoes involved in disease transmission are identified and fully accounted for, the basic reproduction number (R0) for this model is smaller than that for previous SIS models for malaria. This, and the occurrence of both oscillatory dynamics and a backward bifurcation provide a novel and plausible framework for developing and implementing optimal malaria control strategies, especially those strategies that are associated with vector control.;In economic epidemiology, a deterministic and a stochastic model are used to investigate the effects of determinism, stochasticity, and safety nets on disease-driven poverty traps; that is, traps of low per capita income and high infectious disease prevalence. It is shown that economic development in deterministic models require significant external changes to the initial economic and health care conditions or a change in the parametric structure of the system. Therefore, poverty traps arising from deterministic models lead to more limited policy options. In contrast, there is always some probability that a population will escape or fall into a poverty trap in stochastic models. It is demonstrated that in stochastic models, a safety net can guarantee ultimate escape from the poverty trap, even when it is set within the basin of attraction of the poverty trap or when it is implemented only as an economic or health care intervention. It is also shown that the benefits of safety nets for populations that are close to the poverty trap equilibrium are highest for the stochastic model and lowest for the deterministic model. Based on the analysis of the stochastic model, the following optimal economic development and public health intervention questions are answered: (i) Is it preferable to provide health care, income/income generating resources, or both health care and income/income generating resources to enable populations to break cycles of poverty and disease; that is, escape from poverty traps? (ii) How long will it take a population that is caught in a poverty trap to attain economic development when the initial health and economic conditions are reinforced by safety nets?;In mathematical physics, an unusual form of multistability involving the coexistence of an infinite number of attractors that is exhibited by specially coupled chaotic systems is explored. It is shown that this behavior is associated with generalized synchronization and the emergence of a conserved quantity. The robustness of the phenomenon in relation to a mismatch of parameters of the coupled systems is studied, and it is shown that the special coupling scheme yields a new class of dynamical systems that manifests characteristics of dissipative and conservative systems

Stabilisation operations as complex systems - order and chaos in the interoperability continuum

There is little knowledge in regards to the influence of complex systems thinking on the strategic modelling of stabilization operations. To better control the impact of information asymmetry in such context, this study focuses on gaining an understanding on how concepts and principles operate in theory and practice. Particularly, this study explores how the complexity of the environmental conditions influences stabilization operations as complex systems. Second, it addresses subsequent influences on a system’s required self-organizing ability to differentiate and integrate its various sub-systems, their organizational resources and competencies. Third, this study regards the development and adjustment of condition-dependent capabilities as key to reaching a state of dynamic equilibrium while processing, distributing and exchanging information. The aim of this study is both theoretical and practical: offering complex systems thinking as an alternative for the strategic modelling of stabilization operations and supporting the debate over the extent to which integration is feasible and desirable

Driver and Sensor Node Selection Strategies Optimizing the Controllability Properties of Complex Dynamical Networks

In recent years, complex networks have attracted the attention of researchers throughout the fields of science due to their ubiquity in natural and artificial settings. While the spontaneous emergence of collective behavior has been thoroughly studied, and has inspired researchers in the design of control strategies able to reproduce it in artificial scenarios, our ability to arbitrarily affect the behavior of complex networks is still limited. To start filling this void, in the past five years, researchers have focused on the preliminary condition of selecting the nodes where input signals have to be injected so to ensure complete controllability of complex networks. Unfortunately, the scale of complex networks is such that more often than not too many input signals are required to arbitrarily modify the behavior of all the nodes of a network. Departing from the idea that achieving complete controllability of complex networks is a chimera, in this thesis, we present a comprehensive toolbox of input selection algorithms so to ensure controllability of the largest number of nodes of a network. Then, we complement this toolbox with algorithms for sensor placement so to also guarantee, when possible, observability of these nodes, thus allowing the implementation of feedback control strategies. Finally, an outlook on the topics that are currently being investigated by researchers working on controllability of complex networks is provided

Implementação de um modelo de geometric semantic genetic programming para aplicação naval

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementRecaí sob a responsabilidade da Marinha Portuguesa a gestão da Zona Económica Exclusiva de Portugal, assegurando a sua segurança da mesma face a atividades criminosas. Para auxiliar a tarefa, é utilizado o sistema Oversee, utilizado para monitorizar a posição de todas as embarcações presentes na área afeta, permitindo a rápida intervenção da Marinha Portuguesa quando e onde necessário. No entanto, o sistema necessita de transmissões periódicas constantes originadas nas embarcações para operar corretamente – casos as transmissões sejam interrompidas, deliberada ou acidentalmente, o sistema deixa de conseguir localizar embarcações, dificultando a intervenção da Marinha. A fim de colmatar esta falha, é proposto adicionar ao sistema Oversee a capacidade de prever as posições futuras de uma embarcação com base no seu trajeto até à cessação das transmissões. Tendo em conta os grandes volumes de dados gerados pelo sistema (históricos de posições), a área de Inteligência Artificial apresenta uma possível solução para este problema. Atendendo às necessidades de resposta rápida do problema abordado, o algoritmo de Geometric Semantic Genetic Programming baseado em referências de Vanneschi et al. apresenta-se como uma possível solução, tendo já produzido bons resultados em problemas semelhantes. O presente trabalho de tese pretende integrar o algoritmo de Geometric Semantic Genetic Programming desenvolvido com o sistema Oversee, a fim de lhe conceder capacidades preditivas. Adicionalmente, será realizado um processo de análise de desempenho a fim de determinar qual a ideal parametrização do algoritmo. Pretende-se com esta tese fornecer à Marinha Portuguesa uma ferramenta capaz de auxiliar o controlo da Zona Económica Exclusiva Portuguesa, permitindo a correta intervenção da Marinha em casos onde o atual sistema não conseguiria determinar a correta posição da embarcação em questão

Clustering Behavior in Systems of Coupled Oscillators and Networks of Mutually Attracting Agents

In systemen van gekoppelde oscillatoren, zoals zwermen vuurvliegjes of groepen pacemakercellen in het menselijk hart, kunnen aan de afzonderlijke oscillatoren verschillende natuurlijke frequenties geassocieerd worden. Indien de interacties tussen de oscillatoren voldoende groot zijn, kunnen deze ervoor zorgen dat de oscillatoren aan een gemeenschappelijke frequentie bewegen. Interacties tussen pacemakercellen leiden op die manier tot een sterk, éénduidig signaal, met een regelmatige hartslag als gevolg. In het eerste deel van dit proefschrift wordt een wiskundig model voor gekoppelde oscillatoren (het Kuramoto-model) bestudeerd, en worden een aantal analytische resultaten afgeleid met betrekking tot het optreden van gesynchroniseerde groepen, en dit zowel voor systemen met een eindig aantal oscillatoren als voor systemen met een oneindig aantal oscillatoren. In het tweede deel wordt een model geïntroduceerd dat gelijkaardig gedrag vertoont als het Kuramoto-model, maar meer wiskundige resultaten toelaat. Het model is ook toepasbaar op systemen waarin clustergedrag optreedt, maar die geen verband houden met gekoppelde oscillatoren. De verschillende gesynchroniseerde groepen worden daarom clusters genoemd, en het blijkt o.a. mogelijk te zijn de clusterstructuur volledig te karakteriseren aan de hand van een stel ongelijkheden in de parameters van het model. Verschillende uitbreidingen van het model zijn mogelijk, waarbij ook de analytische resultaten kunnen worden veralgemeend

Smart grid framework analysis and artificial neutral network in load forecast

Power system is the one of the most critical parts of the whole energy utilization around the world. Recently people pay more attention to the energy utilization, new types of generations, storages and power utilization need to increase energy efficiency and reduce carbon emission. Due to the power grid currently is still mainly under the old-designed approach, it is increasingly exposed limitation on efficiency enhancement, security and reliability improvement, new technologies compatibility and meeting larger power capacity requirements. Thus, Smart Grid is 'born' to improve power grid for these requirements. It is an overlapping area between power system and digital technology, intelligent technology, communication technology and so on. Smart Grid can provide updates for nearly all sections of traditional power grid. It is a systematic framework that new technologies integration, system development strategy and planning, customers' awareness improvements and supports from all relevant areas. The areas must be operated in coordination and parallel. Firstly, this thesis introduces Smart Grid and Smart Metering on its definition, characteristics and deployment. Secondly, this thesis describes a load forecasting system for macro-grid. Artificial Neural Network (ANN) was introduced to achieve this work for its excellent mapping approximation ability. In the third section, thesis focuses on load forecasting for micro-grid. BackPropagation method is used to train the Multi-layer Perceptron (MLP) ANN and its results were compared to that from Radial Basis Function (RBF) ANN. Analysis was focused not only on the two networks but also ANN generalization problems and differences between micro-grid load and macro-grid load prediction