Multi-textured 3D humanoid reconstruction through passive and active automatic techniques = Reconstrucción de humanoides 3D multi-texturados mediante técnicas pasivas y activas automáticas

The proliferation of video games and other applications of computer graphics in everyday life demands a much easier way to create animatable virtual human characters. Traditionally, this has been the job of highly skilled artists and animators that painstakingly model, rig and animate their avatars, and usually have to tune them for each application and transmission/rendering platform. The emergence of virtual/mixed reality environments also calls for practical and costeffective ways to produce custom models of actual people. The purpose of the present dissertation is bringing 3D human scanning closer to the average user. For this, two different techniques are presented, one passive and one active. The first one is a fully automatic system for generating statically multi-textured avatars of real people captured with several standard cameras. Our system uses a state-of-the-art shape from silhouette technique to retrieve the shape of subject. However, to deal with the lack of detail that is common in the facial region for these kind of techniques, which do not handle concavities correctly, our system proposes an approach to improve the quality of this region. This face enhancement technique uses a generic facial model which is transformed according to the specific facial features of the subject. Moreover, this system features a novel technique for generating view-independent texture atlases computed from the original images. This static multi-texturing system yields a seamless texture atlas calculated by combining the color information from several photos. We suppress the color seams due to image misalignments and irregular lighting conditions that multi-texturing approaches typically suffer from, while minimizing the blurring effect introduced by color blending techniques. The second technique features a system to retrieve a fully animatable 3D model of a human using a commercial depth sensor. Differently to other approaches in the current state of the art, our system does not require the user to be completely still through the scanning process, and neither the depth sensor is moved around the subject to cover all its surface. Instead, the depth sensor remains static and the skeleton tracking information is used to compensate the user’s movements during the scanning stage. RESUMEN La popularización de videojuegos y otras aplicaciones de los gráficos por ordenador en el día a día requiere una manera más sencilla de crear modelos virtuales humanos animables. Tradicionalmente, estos modelos han sido creados por artistas profesionales que cuidadosamente los modelan y animan, y que tienen que adaptar específicamente para cada aplicación y plataforma de transmisión o visualización. La aparición de los entornos de realidad virtual/mixta aumenta incluso más la demanda de técnicas prácticas y baratas para producir modelos 3D representando personas reales. El objetivo de esta tesis es acercar el escaneo de humanos en 3D al usuario medio. Para ello, se presentan dos técnicas diferentes, una pasiva y una activa. La primera es un sistema automático para generar avatares multi-texturizados de personas reales mediante una serie de cámaras comunes. Nuestro sistema usa técnicas del estado del arte basadas en shape from silhouette para extraer la forma del sujeto a escanear. Sin embargo, este tipo de técnicas no gestiona las concavidades correctamente, por lo que nuestro sistema propone una manera de incrementar la calidad en una región del modelo que se ve especialmente afectada: la cara. Esta técnica de mejora facial usa un modelo 3D genérico de una cara y lo modifica según los rasgos faciales específicos del sujeto. Además, el sistema incluye una novedosa técnica para generar un atlas de textura a partir de las imágenes capturadas. Este sistema de multi-texturización consigue un atlas de textura sin transiciones abruptas de color gracias a su manera de mezclar la información de color de varias imágenes sobre cada triángulo. Todas las costuras y discontinuidades de color debidas a las condiciones de iluminación irregulares son eliminadas, minimizando el efecto de desenfoque de la interpolación que normalmente introducen este tipo de métodos. La segunda técnica presenta un sistema para conseguir un modelo humano 3D completamente animable utilizando un sensor de profundidad. A diferencia de otros métodos del estado de arte, nuestro sistema no requiere que el usuario esté completamente quieto durante el proceso de escaneado, ni mover el sensor alrededor del sujeto para cubrir toda su superficie. Por el contrario, el sensor se mantiene estático y el esqueleto virtual de la persona, que se va siguiendo durante el proceso, se utiliza para compensar sus movimientos durante el escaneado.

Aplicações híbridas entre sistemas multiagentes e técnicas de inteligência artificial para redes inteligentes de distribuição de energia elétrica

Os smart grids representam a nova geração dos sistemas elétricos de potência, combinando avanços em computação, sistemas de comunicação, processos distribuídos e inteligência artificial para prover novas funcionalidades quanto ao acompanhamento em tempo real da demanda e do consumo de energia elétrica, gerenciamento em larga escala de geradores distribuídos, entre outras, a partir de um sistema de controle distribuído sobre a rede elétrica. Esta estrutura modifica profundamente a maneira como se realiza o planejamento e a operação de sistemas elétricos nos dias de hoje, em especial os de distribuição, e há interessantes possibilidades de pesquisa e desenvolvimento possibilitada pela busca da implementação destas funcionalidades. Com esse cenário em vista, o presente trabalho utiliza uma abordagem baseada no uso de sistemas multiagentes para simular esse tipo de sistema de distribuição de energia elétrica, considerando opções de controle distintas. A utilização da tecnologia de sistemas multiagentes para a simulação é baseada na conceituação de smart grids como um sistema distribuído, algo também realizado nesse trabalho. Para validar a proposta, foram simuladas três funcionalidades esperadas dessas redes elétricas: classificação de cargas não-lineares; gerenciamento de perfil de tensão; e reconfiguração topológica com a finalidade de reduzir as perdas elétricas. Todas as modelagens e desenvolvimentos destes estudos estão aqui relatados. Por fim, o trabalho se propõe a identificar os sistemas multiagentes como uma tecnologia a ser empregada tanto para a pesquisa, quanto para implementação dessas redes elétricas.

Arquitetura computacional para redes orgânicas e heterogêneas: plano de controle do sistema operacional swarm.

Computational Swarms (enxames computacionais), consistindo da integração de sensores e atuadores inteligentes no nosso mundo conectado, possibilitam uma extensão da info-esfera no mundo físico. Nós chamamos esta info-esfera extendida, cíber-física, de Swarm. Este trabalho propõe uma visão de Swarm onde dispositivos computacionais cooperam dinâmica e oportunisticamente, gerando redes orgânicas e heterogêneas. A tese apresenta uma arquitetura computacional do Plano de Controle do Sistema Operacional do Swarm, que é uma camada de software distribuída embarcada em todos os dispositivos que fazem parte do Swarm, responsável por gerenciar recursos, definindo atores, como descrever e utilizar serviços e recursos (como divulgá-los e descobrí-los, como realizar transações, adaptações de conteúdos e cooperação multiagentes). O projeto da arquitetura foi iniciado com uma revisão da caracterização do conceito de Swarm, revisitando a definição de termos e estabelecendo uma terminologia para ser utilizada. Requisitos e desafios foram identificados e uma visão operacional foi proposta. Esta visão operacional foi exercitada com casos de uso e os elementos arquiteturais foram extraídos dela e organizados em uma arquitetura. A arquitetura foi testada com os casos de uso, gerando revisões do sistema. Cada um dos elementos arquiteturais requereram revisões do estado da arte. Uma prova de conceito do Plano de Controle foi implementada e uma demonstração foi proposta e implementada. A demonstração selecionada foi o Smart Jukebox, que exercita os aspectos distribuídos e a dinamicidade do sistema proposto. Este trabalho apresenta a visão do Swarm computacional e apresenta uma plataforma aplicável na prática. A evolução desta arquitetura pode ser a base de uma rede global, heterogênea e orgânica de redes de dispositivos computacionais alavancando a integração de sistemas cíber-físicos na núvem permitindo a cooperação de sistemas escaláveis e flexíveis, interoperando para alcançar objetivos comuns.

The effect of the CaO/SiO2 ratio on the phase equilibria in the ZnO-“Fe2O3”-(PbO + CaO + SiO2) system in air: CaO/SiO2 = 0.1, PbO/(CaO + SiO2) = 6.2, and CaO/SiO2 = 0.6, PbO/(CaO + SiO2) = 4.3

Experimental studies on phase equilibria in the multi-component system PbO-ZnO-CaO-SiO2-FeO-Fe2O3 in air have been conducted to characterize the phase relations of a complex slag system used in the oxidation smelting of lead and in typical lead blast furnace sinters. The liquidus in two pseudoternary sections ZnO-Fe2O3-(PbO + CaO + SiO2) with the CaO/SiO2 weight ratio of 0.1 and the PbO/(CaO + SiO2) weight ratio of 6.2, and with CaO/SiO2 weight ratio of 0.6 and the PbO/(CaO + SiO2) weight ratio of 4.3, have been constructed.

Modeling and exploiting behavior patterns in dynamic environments

This paper presents a new approach to improving the effectiveness of autonomous systems that deal with dynamic environments. The basis of the approach is to find repeating patterns of behavior in the dynamic elements of the system, and then to use predictions of the repeating elements to better plan goal directed behavior. It is a layered approach involving classifying, modeling, predicting and exploiting. Classifying involves using observations to place the moving elements into previously defined classes. Modeling involves recording features of the behavior on a coarse grained grid. Exploitation is achieved by integrating predictions from the model into the behavior selection module to improve the utility of the robot's actions. This is in contrast to typical approaches that use the model to select between different strategies or plays. Three methods of adaptation to the dynamic features of the environment are explored. The effectiveness of each method is determined using statistical tests over a number of repeated experiments. The work is presented in the context of predicting opponent behavior in the highly dynamic and multi-agent robot soccer domain (RoboCup)

Multi-robot control in highly dynamic, competitive environments

The control and coordination of multiple mobile robots is a challenging task; particularly in environments with multiple, rapidly moving obstacles and agents. This paper describes a robust approach to multi-robot control, where robustness is gained from competency at every layer of robot control. The layers are: (i) a central coordination system (MAPS), (ii) an action system (AES), (iii) a navigation module, and (iv) a low level dynamic motion control system. The multi-robot coordination system assigns each robot a role and a sub-goal. Each robot’s action execution system then assumes the assigned role and attempts to achieve the specified sub-goal. The robot’s navigation system directs the robot to specific goal locations while ensuring that the robot avoids any obstacles. The motion system maps the heading and speed information from the navigation system to force-constrained motion. This multi-robot system has been extensively tested and applied in the robot soccer domain using both centralized and distributed coordination.

Classifying an opponent's behaviour in robot soccer

This paper illustrates the prediction of opponent behaviour in a competitive, highly dynamic, multi-agent and partially observableenvironment, namely RoboCup small size league robot soccer. The performance is illustrated in the context of the highly successful robot soccer team, the RoboRoos. The project is broken into three tasks; classification of behaviours, modelling and prediction of behaviours and integration of the predictions into the existing planning system. A probabilistic approach is taken to dealing with the uncertainty in the observations and with representing the uncertainty in the prediction of the behaviours. Results are shown for a classification system using a Naïve Bayesian Network that determines the opponent’s current behaviour. These results are compared to an expert designed fuzzy behaviour classification system. The paper illustrates how the modelling system will use the information from behaviour classification to produce probability distributions that model the manner with which the opponents perform their behaviours. These probability distributions are show to match well with the existing multi-agent planning system (MAPS) that forms the core of the RoboRoos system.

Genetics and competing strategies in a threshold model for mail processing

Multi-agent algorithms inspired by the division of labour in social insects are applied to a problem of distributed mail retrieval in which agents must visit mail producing cities and choose between mail types under certain constraints.The efficiency (i.e. the average amount of mail retrieved per time step), and the flexibility (i.e. the capability of the agents to react to changes in the environment) are investigated both in static and dynamic environments. New rules for mail selection and specialisation are introduced and are shown to exhibit improved efficiency and flexibility compared to existing ones. We employ a genetic algorithm which allows the various rules to evolve and compete. Apart from obtaining optimised parameters for the various rules for any environment, we also observe extinction and speciation. From a more theoretical point of view, in order to avoid finite size effects, most results are obtained for large population sizes. However, we do analyse the influence of population size on the performance. Furthermore, we critically analyse the causes of efficiency loss, derive the exact dynamics of the model in the large system limit under certain conditions, derive theoretical upper bounds for the efficiency, and compare these with the experimental results.

Mathematical methods in power system stability studies

In this thesis various mathematical methods of studying the transient and dynamic stabiIity of practical power systems are presented. Certain long established methods are reviewed and refinements of some proposed. New methods are presented which remove some of the difficulties encountered in applying the powerful stability theories based on the concepts of Liapunov. Chapter 1 is concerned with numerical solution of the transient stability problem. Following a review and comparison of synchronous machine models the superiority of a particular model from the point of view of combined computing time and accuracy is demonstrated. A digital computer program incorporating all the synchronous machine models discussed, and an induction machine model, is described and results of a practical multi-machine transient stability study are presented. Chapter 2 reviews certain concepts and theorems due to Liapunov. In Chapter 3 transient stability regions of single, two and multi~machine systems are investigated through the use of energy type Liapunov functions. The treatment removes several mathematical difficulties encountered in earlier applications of the method. In Chapter 4 a simple criterion for the steady state stability of a multi-machine system is developed and compared with established criteria and a state space approach. In Chapters 5, 6 and 7 dynamic stability and small signal dynamic response are studied through a state space representation of the system. In Chapter 5 the state space equations are derived for single machine systems. An example is provided in which the dynamic stability limit curves are plotted for various synchronous machine representations. In Chapter 6 the state space approach is extended to multi~machine systems. To draw conclusions concerning dynamic stability or dynamic response the system eigenvalues must be properly interpreted, and a discussion concerning correct interpretation is included. Chapter 7 presents a discussion of the optimisation of power system small sjgnal performance through the use of Liapunov functions.

Agent Based Models of Competition and Collaboration

Swarm intelligence is a popular paradigm for algorithm design. Frequently drawing inspiration from natural systems, it assigns simple rules to a set of agents with the aim that, through local interactions, they collectively solve some global problem. Current variants of a popular swarm based optimization algorithm, particle swarm optimization (PSO), are investigated with a focus on premature convergence. A novel variant, dispersive PSO, is proposed to address this problem and is shown to lead to increased robustness and performance compared to current PSO algorithms. A nature inspired decentralised multi-agent algorithm is proposed to solve a constrained problem of distributed task allocation. Agents must collect and process the mail batches, without global knowledge of their environment or communication between agents. New rules for specialisation are proposed and are shown to exhibit improved eciency and exibility compared to existing ones. These new rules are compared with a market based approach to agent control. The eciency (average number of tasks performed), the exibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved eciency and robustness. Evolutionary algorithms are employed, both to optimize parameters and to allow the various rules to evolve and compete. We also observe extinction and speciation. In order to interpret algorithm performance we analyse the causes of eciency loss, derive theoretical upper bounds for the eciency, as well as a complete theoretical description of a non-trivial case, and compare these with the experimental results. Motivated by this work we introduce agent "memory" (the possibility for agents to develop preferences for certain cities) and show that not only does it lead to emergent cooperation between agents, but also to a signicant increase in efficiency.

The effect of load on agent-based algorithms for distributed task allocation

Multi-agent algorithms inspired by the division of labour in social insects and by markets, are applied to a constrained problem of distributed task allocation. The efficiency (average number of tasks performed), the flexibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved efficiency and robustness. We employ nature inspired particle swarm optimisation to obtain optimised parameters for all algorithms in a range of representative environments. Although results are obtained for large population sizes to avoid finite size effects, the influence of population size on the performance is also analysed. From a theoretical point of view, we analyse the causes of efficiency loss, derive theoretical upper bounds for the efficiency, and compare these with the experimental results.

Multibody System Mechanics: Modelling, Stability, Control, and Robustness by V. A. Konoplev and A. Cheremensky

BOOK REVIEWS Multibody System Mechanics: Modelling, Stability, Control, and Ro- bustness, by V. A. Konoplev and A. Cheremensky, Mathematics and its Appli- cations Vol. 1, Union of Bulgarian Mathematicians, Sofia, 2001, XXII + 288 pp., $ 65.00, ISBN 954-8880-09-01

A New Class of Processes for Formalizing and Generalizing Individual-Based Models: The Semi-Semi-Markov Processes

2000 Mathematics Subject Classification: 60K15, 60K20, 60G20,60J75, 60J80, 60J85, 60-08, 90B15.

Development of distributed generation infrastructure for microgrid connectivity to operational power systems

Distributed Generation (DG) from alternate sources and smart grid technologies represent good solutions for the increase in energy demands. Employment of these DG assets requires solutions for the new technical challenges that are accompanied by the integration and interconnection into operational power systems. A DG infrastructure comprised of alternate energy sources in addition to conventional sources, is developed as a test bed. The test bed is operated by synchronizing, wind, photovoltaic, fuel cell, micro generator and energy storage assets, in addition to standard AC generators. Connectivity of these DG assets is tested for viability and for their operational characteristics. The control and communication layers for dynamic operations are developed to improve the connectivity of alternates to the power system. A real time application for the operation of alternate sources in microgrids is developed. Multi agent approach is utilized to improve stability and sequences of actions for black start are implemented. Experiments for control and stability issues related to dynamic operation under load conditions have been conducted and verified.

Design and implementation of a relative localization system for ground and aerial robotic teams

The main focus of this thesis is to address the relative localization problem of a heterogenous team which comprises of both ground and micro aerial vehicle robots. This team configuration allows to combine the advantages of increased accessibility and better perspective provided by aerial robots with the higher computational and sensory resources provided by the ground agents, to realize a cooperative multi robotic system suitable for hostile autonomous missions. However, in such a scenario, the strict constraints in flight time, sensor pay load, and computational capability of micro aerial vehicles limits the practical applicability of popular map-based localization schemes for GPS denied navigation. Therefore, the resource limited aerial platforms of this team demand simpler localization means for autonomous navigation. Relative localization is the process of estimating the formation of a robot team using the acquired inter-robot relative measurements. This allows the team members to know their relative formation even without a global localization reference, such as GPS or a map. Thus a typical robot team would benefit from a relative localization service since it would allow the team to implement formation control, collision avoidance, and supervisory control tasks, independent of a global localization service. More importantly, a heterogenous team such as ground robots and computationally constrained aerial vehicles would benefit from a relative localization service since it provides the crucial localization information required for autonomous operation of the weaker agents. This enables less capable robots to assume supportive roles and contribute to the more powerful robots executing the mission. Hence this study proposes a relative localization-based approach for ground and micro aerial vehicle cooperation, and develops inter-robot measurement, filtering, and distributed computing modules, necessary to realize the system. The research study results in three significant contributions. First, the work designs and validates a novel inter-robot relative measurement hardware solution which has accuracy, range, and scalability characteristics, necessary for relative localization. Second, the research work performs an analysis and design of a novel nonlinear filtering method, which allows the implementation of relative localization modules and attitude reference filters on low cost devices with optimal tuning parameters. Third, this work designs and validates a novel distributed relative localization approach, which harnesses the distributed computing capability of the team to minimize communication requirements, achieve consistent estimation, and enable efficient data correspondence within the network. The work validates the complete relative localization-based system through multiple indoor experiments and numerical simulations. The relative localization based navigation concept with its sensing, filtering, and distributed computing methods introduced in this thesis complements system limitations of a ground and micro aerial vehicle team, and also targets hostile environmental conditions. Thus the work constitutes an essential step towards realizing autonomous navigation of heterogenous teams in real world applications.