This thesis investigates the passive magnetic latching mechanism designs for autonomous aerial grasping and programmable self-assembly. The enormous latching potential of neodymium magnets is a well-established fact when it comes to their ability to interact with ferrous surfaces in particular. The force of attraction or repulsion among the magnets is strong enough to keep the levitation trains, and high speed transportation pods off the rails. But such utilization of these desirable magnetic properties in commercial applications, comes at a cost of high power consumption since the magnets used are usually electromagnets. On the other hand, we explore some useful robotic applications of passive (and hence low cost) magnetic latching; which are of vital importance in autonomous aerial transportation, automated drone-based package deliveries, and programmable self-assembly and self-reconfigurable systems. We propose, and implement a novel, attach/detach mechatronic mechanism, based on passive magnetic latching of permanent magnets for usBots; our indige- nously built programmable self-assembly robots, and show that it validates the game theoretic self-assembly algorithms. Another application addressed in this thesis is the utilization of permanent magnets in autonomous aerial grasping for Unmanned Aerial Vehicles (UAVs). We present a novel gripper design for ferrous objects with a passive magnetic pick up and an impulse based drop. For both the applications, we highlight the importance, simplicity and effectiveness of the proposed designs while providing a brief comparison with the other technologies out there.

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Yes / With the increasing complexity of problems in the construction industry, researchers are investigating computationally rigorous intelligent systems with the aim of seeking intelligent solutions. The purpose of this paper is therefore to analyse the research published on ‘intelligent systems in the construction industry’ over the past two decades. This is achieved to observe and understand the historical trends and current patterns in the use of different types of intelligent systems and to exhibit potential directions of further research. Thus, to trace the applications of intelligent systems to research in the construction industry, a profiling approach is employed to analyse 514 publications extracted from the Scopus database. The prime value and uniqueness of this paper lies in analysing and compiling the existing published material by examining variables (such as yearly publications, geographic location of each publication, etc.). This has been achieved by synthesising existing publications using 14 keywords2 ‘Intelligent Systems’, ‘Artificial Intelligence’, ‘Expert Systems’, ‘Fuzzy Systems’, ‘Genetic Algorithms’, ‘Knowledge-Based Systems’, ‘Neural Networks’, ‘Context Aware Applications’, ‘Embedded Systems’, ‘Human–Machine Interface’, ‘Sensing and Multiple Sensor Fusion’, ‘Ubiquitous and Physical Computing’, ‘Case-based Reasoning’ and ‘Construction Industry’. The prime contributions of this research are identified by associating (a) yearly publication and geographic location, (b) yearly publication and the type of intelligent systems employed/discussed, (c) geographic location and the type of research methods employed, and (d) geographic location and the types of intelligent systems employed. These contributions provide a comparison between the two decades and offer insights into the trends in using different intelligent systems types in the construction industry. The analysis presented in this paper has identified intelligent systems studies that have contributed to the development and accumulation of intellectual wealth to the intelligent systems area in the construction industry. This research has implications for researchers, journal editors, practitioners, universities and research institutions. Moreover, it is likely to form the basis and motivation for profiling other database resources and specific types of intelligent systems journals in this area.

Yes / The arrival of artificially intelligent systems into the domain of medical imaging has focused attention and sparked much debate on the role and responsibilities of the radiologist. However, discussion about the impact of such technology on the radiographer role is lacking. This paper discusses the potential impact of artificial intelligence (AI) on the radiography profession by assessing current workflow and cross-mapping potential areas of AI automation such as procedure planning, image acquisition and processing. We also highlight the opportunities that AI brings including enhancing patient-facing care, increased cross-modality education and working, increased technological expertise and expansion of radiographer responsibility into AI-supported image reporting and auditing roles.

Made available in DSpace on 2014-12-17T14:08:33Z (GMT). No. of bitstreams: 1 LeonardoSGR.pdf: 2071138 bytes, checksum: 68ddf1f535ab5dfa9334c250af52a1d6 (MD5) Previous issue date: 2009-06-29 / The petrochemical industry has as objective obtain, from crude oil, some products with a higher commercial value and a bigger industrial utility for energy purposes. These industrial processes are complex, commonly operating with large production volume and in restricted operation conditions. The operation control in optimized and stable conditions is important to keep obtained products quality and the industrial plant safety. Currently, industrial network has been attained evidence when there is a need to make the process control in a distributed way. The Foundation Fieldbus protocol for industrial network, for its interoperability feature and its user interface organized in simple configuration blocks, has great notoriety among industrial automation network group. This present work puts together some benefits brought by industrial network technology to petrochemical industrial processes inherent complexity. For this, a dynamic reconfiguration system for intelligent strategies (artificial neural networks, for example) based on the protocol user application layer is proposed which might allow different applications use in a particular process, without operators intervention and with necessary guarantees for the proper plant functioning / A ind?stria petroqu?mica tem por objetivo obter, a partir do petr?leo bruto, produtos de alto valor comercial e de maior utilidade industrial para fins energ?ticos. Os processos dessa ind?stria s?o complexos, geralmente operam com grandes volumes de produ??o e em condi??es restritas de opera??o. O controle da opera??o em condi??es ?timas e est?veis ? importante para manter a qualidade dos produtos obtidos e a seguran?a da planta. Atualmente, as redes industriais t?m alcan?ado destaque quando h? a necessidade de se realizar o controle do processo de forma distribu?da. O protocolo para redes industriais Foundation Fieldbus, por sua caracter?stica de interoperabilidade e sua interface com usu?rio organizada em blocos de simples configura??o, tem grande notoriedade dentre o grupo de redes para automa??o industrial. O presente trabalho agrega os benef?cios trazidos pela tecnologia de redes industriais ? complexidade inerente dos processos ligados a industria petroqu?mica. Para tal, ? proposto um sistema para reconfigura??o din?mica de estrat?gias inteligentes (redes neurais artificiais, por exemplo) baseado na camada de aplica??o do usu?rio do protocolo, o qual poder? permitir o uso de diferentes aplica??es em um determinado processo, sem interven??es de operadores e com as garantias necess?rias para o bom funcionamento da planta

Identificar e localizar faltas em alimentadores de distribuição representa um passo importante para a melhoria da qualidade de energia, pois proporciona impactos diretos sobre o tempo de inspeção. Na verdade, a duração da inspeção implica consideravelmente no intervalo em que os consumidores estão sem energia elétrica, quando ocorre uma interrupção não programada. O objetivo deste trabalho é fornecer um sistema de detecção automática de curtos-circuitos, permitindo aos profissionais das companhias de distribuição acompanhar e monitorar de maneira on-line a ocorrência de possíveis faltas e transitórios eletromagnéticos observados na rede primária de distribuição. A abordagem de detecção utiliza um sistema híbrido que combina ferramentas inteligentes e convencionais para identificar e localizar faltas em redes primárias. Os resultados que foram compilados demonstram grande potencialidade de aplicação da proposta em sistemas de distribuição. / Efficient faults identification and location in power distribution lines constitute an important step for power quality improvement, since they provide direct impacts on the inspection time. In fact, the duration of inspection implies directly in the time interval where consumers are without power, considering here the occurrence of a non-programmed interruption. The objective of this work is to provide an automated fault detection system, allowing to the power companies engineers to online track and monitor the possible occurrence of faults and electromagnetic transients observed in the primary network for the distribution circuits. The detection approach uses a hybrid system, which combines a set of intelligent and conventional tools to identify and locate faults in the primary networks. Validation results show great application potential in distribution systems.

Uma tendência crescente entre os pesquisadores da Robótica Móvel é a elaboração de sistemas robóticos descentralizados denominados enxames de robôs, nos quais a ação conjunta de cada agente leva à execução de tarefas de maneira mais robusta que quando realizada por um único robô. Um acréscimo adicional à robustez é conveniente em tais sistemas para que eles sejam de maior confiabilidade no mundo real. Neste trabalho, uma rede neural hierárquica desenvolvida para o aprendizado em tempo real inicialmente elaborada para o aprendizado de navegação de um único robô será estendida para controlar um enxame de robôs. O sistema realiza um balanceamento da influência de comportamentos implementados previamente em um robô de acordo com conhecimentos obtidos através da interação do mesmo com o ambiente. Cada robô possui sua própria rede neural, adquirindo seu conhecimento tanto independentemente quanto com o compartilhamento de informações com outros robôs. Espera-se que o uso de tal arquitetura permita uma adaptação mais rápida dos robôs ao ambiente, permitindo uma mudança em tempo real de seus parâmetros de acordo com as peculiaridades do ambiente no qual os robôs estão inseridos. A tarefa de escolta de um robô pelos demais é adotada para a avaliação de desempenho do modelo de rede neural proposto. Dois comportamentos são ponderados pela rede neural hierárquica: o de manutenção de uma distância preestabelecida a um agente e um outro de cobertura de área baseado em Diagramas Centroidais de Voronoi. Os testes foram feitos nos ambientes Player/Stage e indicam que a rede neural hierárquica torna os robôs capazes não apenas de aprender à medida que interagem com ambiente como de utilizar este conhecimento em tempo real para realizar a escolta de forma bem sucedida / A growing trend among Mobile Robotics researchers is developing robot swarms, in which a decentralized robot team solves tasks by combining simple behaviors. It is convenient to have mechanisms to increase a robot systems robustness. In this work, a neural network inspired in behavioral analysis is used to make robots from a swarm to learn how to act propoerly. This network combines two innate behaviors and, according to its experience, learns with the robots mistakes how to make this combination. Each robot has access to its own independent neural network, and can share its knowledge with neighboring robots. It is expected that such architecture learns by itself when to stimulate or supress each behaviors influence as it interacts with the environment. The task chosen to evaluate the proposed system is the escorting of a mobile agent. Two behaviors are balanced to achieve an escorting behavior: maintenance of a minimum distance between a robot and the escort target and an area coverage method based on Centroidal Voronoi Tessellations. Tests were meade using the Player/Stage simulator, and they show that the robots not only are capable of adapting themselves but also are able to use the stored knowledge to improve their effectiveness in doing the desired task

A extração e comercialização das castanhas do Brasil (Bertholletia excelsa H.B.K.) é uma importante fonte de renda na região norte do Brasil. O processamento das castanhas nas indústrias ainda necessita de melhorias. Por isso, o Brasil está atrasado na questão da qualidade do produto em relação às exigências feitas pelo mercado externo. A Bolívia, que é a maior exportadora de amêndoas, utiliza tecnologia para processamento das amêndoas e com isso consegue satisfazer as exigências impostas pelo mercado internacional, sendo mais competitiva do que o Brasil nesse segmento. Sistemas de visão computacional e sistemas inteligentes estão sendo amplamente utilizados para melhoria dos processos de produção e dos produtos em diversas áreas do conhecimento. Visando a melhoria dos processos nas indústrias brasileiras de beneficiamento das castanhas, este trabalho utilizou conceitos de visão computacional com foco em duas das várias etapas de beneficiamento: classificação das castanhas e seleção de suas amêndoas. Assim, esta dissertação apresenta o software desenvolvido para seleção das amêndoas e também uma metodologia de classificação por origem. O software desenvolvido para a seleção das amêndoas apresentou na distinção entre intactas e quebradas uma média de identificações corretas de 95,7%. Já para a metodologia de classificação, teve 84% de identificações corretas na identificação das origens. / Extraction and trading of the Brazil nuts (Bertholletia excelsa H.B.K.) is an important source of income for the northern region of Brazil. The factory processing of the Brazil nuts still needs improvements. This is in the mean reason why Brazil is losing ground in the foreign market due to the demands made on the issue of product quality. Bolivia, today is the largest exporter of nuts, and uses technology for nuts processing satisfying the requirements imposed by the international market, being more competitive than Brazil in this segment. Computer vision and intelligent systems are being widely used to improve production processes and products in many areas of technology. Aiming to improve the Brazilian industrial nuts processing, this study used computer vision concepts with focus on two of the various stages of processing: classification of nuts and selection of its almonds. Thus, this dissertation presents the software developed for selection of almonds and also a method of classification by origin. The software developed for the selection of almonds showed the distinction between intact and broken with an average accuracy of 95.7%. As for the methodology of classification, this had 84% accuracy in identifying the sources.