Made available in DSpace on 2016-08-17T14:53:06Z (GMT). No. of bitstreams: 1 UIRATAN ALVES DE SOUSA CAVALCANTE.pdf: 4868154 bytes, checksum: 71d6895bc849a94e0bac2dba2b713adf (MD5) Previous issue date: 2009-12-01 / The agent-oriented development paradigm has achieved a high maturity level over the last decade. However, integrated development environments supporting all phases of agent-oriented development are still missing. MADAE-Pro is a process for the development and reuse of family of multi-agent systems and integrates two complementary process. One is based on Domain Engineering concepts, aiming in creating artifacts of reusable software in the development of an application family in a particular domain problem, and the other is based on Application Engineering, which guides the construction of applications based on reusable software artifacts previously produced in the Domain Engineering process. The environment includes the ONTORMAS ontology and, through inference rules and semantic search over its instances, automates the modeling tasks of the MADAE-Pro process. These features allow for the developer productivity gains and for maintaining the consistence of the knowledge-base, ensuring the success of the reuse in future applications. / O paradigma de desenvolvimento orientado a agentes tem atingido um alto nível de maturidade na última década. Entretanto, ainda faltam ambientes integrados de desenvolvimento de software que suportem todas as fases do desenvolvimento orientado a agentes. MADAE-Pro é um processo para o desenvolvimento e reuso de famílias de sistemas multiagente e integra dois subprocessos complementares. Um é baseado nos conceitos da Engenharia de Domínio, isto é, visa construir artefatos reutilizáveis que representem uma família de aplicações e o outro, baseado na Engenharia de Aplicações, guia o desenvolvimento de uma aplicação específica reutilizando os produtos do primeiro subprocesso. Este trabalho propõe MADAEIDE, um ambiente integrado de desenvolvimento baseado no conhecimento que automatiza o processo desenvolvimento de software multiagente MADAE-Pro. O ambiente agrega a ontologia ONTORMAS e, através de regras de inferência e buscas semânticas diretamente sobre suas instâncias, automatiza as tarefas de modelagem do processo MADAE-Pro. Estas características levam a um ganho de produtividade por parte do desenvolvedor, além de manter uma base de conhecimento consistente, garantido o sucesso do reuso em futuras aplicações.

La modélisation de scènes réelles à travers la capture de données numériques 3D a été prouvée à la fois utile et applicable dans une variété d’applications. Des scènes entières sont généralement numérisées par des scanners laser et représentées par des grands nuages de points non organisés souvent accompagnés de données photogrammétriques. Un problème typique dans le traitement de ces nuages et données réside dans la détection et la classification des objets présents dans la scène. Ces tâches sont souvent entravées par la variabilité des conditions de capture des données, la présence de bruit, les occlusions ainsi que les données manquantes. Compte tenu de la complexité des problèmes sous-jacents, les approches de traitement récentes tentent d’exploiter les connaissances sémantiques pour identifier et classer les objets. Dans cette thèse, nous proposons une nouvelle approche qui fait appel à des stratégies intelligentes de gestion des connaissances pour le traitement des nuages de points 3D ainsi que l’identification et la classification des objets dans les scènes numérisées. Notre approche étend l’utilisation des connaissances sémantiques à toutes les étapes du traitement, y compris le choix et le guidage des algorithmes de traitement axées sur les données individuelles. Notre solution constitue un concept multi-étape itératif sur la base de trois facteurs : la connaissance modélisée, un ensemble d’algorithmes de traitement, et un moteur de classification. L’objectif de ce travail est de sélectionner et d’orienter les algorithmes de manière adaptative et intelligente pour détecter des objets dans les nuages de points. Des expériences avec deux études de cas démontrent l’applicabilité de notre approche. Les études ont été réalisées sur des analyses de la salle d’attente d’un aéroport et le long des voies de chemin de fer. Dans les deux cas, l’objectif était de détecter et d’identifier des objets dans une zone définie. Les résultats montrent que notre approche a réussi à identifier les objets d’intérêt tout en utilisant différents types de données / The modeling of real-world scenes through capturing 3D digital data has proven to be both useful andapplicable in a variety of industrial and surveying applications. Entire scenes are generally capturedby laser scanners and represented by large unorganized point clouds possibly along with additionalphotogrammetric data. A typical challenge in processing such point clouds and data lies in detectingand classifying objects that are present in the scene. In addition to the presence of noise, occlusionsand missing data, such tasks are often hindered by the irregularity of the capturing conditions bothwithin the same dataset and from one data set to another. Given the complexity of the underlyingproblems, recent processing approaches attempt to exploit semantic knowledge for identifying andclassifying objects. In the present thesis, we propose a novel approach that makes use of intelligentknowledge management strategies for processing of 3D point clouds as well as identifying andclassifying objects in digitized scenes. Our approach extends the use of semantic knowledge to allstages of the processing, including the guidance of the individual data-driven processing algorithms.The complete solution consists in a multi-stage iterative concept based on three factors: the modeledknowledge, the package of algorithms, and a classification engine. The goal of the present work isto select and guide algorithms following an adaptive and intelligent strategy for detecting objects inpoint clouds. Experiments with two case studies demonstrate the applicability of our approach. Thestudies were carried out on scans of the waiting area of an airport and along the tracks of a railway.In both cases the goal was to detect and identify objects within a defined area. Results show that ourapproach succeeded in identifying the objects of interest while using various data types

Main aim of this thesis is to study existing systems of faceted search and to design own system based on faceted search in the index of Wikipedia. In this thesis we can meet with existing solutions of faceted search. From mistakes and failures of existing solutions was designed our own system, that is output of this thesis. Designed system is described in way of design and implementation. Product of thesis is application and graphical interface. Application interface can be integrated into existing informational system, where it can be used as multidimensional filter. Graphical interface provides option how can application interface be used in real system. System was created focusing on usefullness and simplicity, for using in existing information systems.

The activated sludge and anaerobic digestion processes have been modelled in widely accepted models. Nevertheless, these models still have limitations when describing operational problems of microbiological origin. The aim of this thesis is to develop a knowledge-based model to simulate risk of plant-wide operational problems of microbiological origin.For the risk model heuristic knowledge from experts and literature was implemented in a rule-based system. Using fuzzy logic, the system can infer a risk index for the main operational problems of microbiological origin (i.e. filamentous bulking, biological foaming, rising sludge and deflocculation). To show the results of the risk model, it was implemented in the Benchmark Simulation Models. This allowed to study the risk model's response in different scenarios and control strategies. The risk model has shown to be really useful providing a third criterion to evaluate control strategies apart from the economical and environmental criteria. / Els processos de fangs activats i digestió anaeròbia estan descrits en models àmpliament acceptats. No obstant, aquests encara tenen limitacions a l'hora de descriure problemes operacionals d'origen microbiològic. L'objectiu és desenvolupar un model basat en el coneixement per simular el risc de problemes operacionals d'origen microbiològic en planta completa. Per al model de risc es va utilitzar coneixement d'experts i de la bibliografia implementat després en un sistema basat en regles. Utilitzant la lògica difusa, el sistema pot inferir un índex de risc per a problemes operacionals d'origen microbiològic (esponjament, escumes, desnitrificació incontrolada i desflocul•lació). Per a mostrar els resultats del model de risc es va implementar i aplicar en els diferents Benchmark Simulation Models. Això ha permés estudiar la resposta del model de risc en diferents escenaris i estratègies de control. El model de risc ha mostrat ser molt útil proporcionant un tercer criteri per a l'avaluació d'estratègies de control a part dels criteris econòmics i ambientals.

This International Workshop is a high quality seminar providing a forum for the exchange of scientific achievements between research communities of different universities and research institutes in the area of innovation information technologies. It is a continuation of the Russian-German Workshops that have been organized by the universities in Dresden, Karlsruhe and Ufa before. The workshop was arranged in 9 sessions covering the major topics: Modern Trends in Information Technology, Knowledge Based Systems and Semantic Modelling, Software Technology and High Performance Computing, Geo-Information Systems and Virtual Reality, System and Process Engineering, Process Control and Management and Corporate Information Systems.

<p dir="ltr">This dissertation explores the integration of remote sensing and artificial intelligence (AI) in geospatial mapping, specifically through the development of knowledge-based mapping systems. Remote sensing has revolutionized Earth observation by providing data that far surpasses traditional in-situ measurements. Over the last decade, significant advancements in inferential capabilities have been achieved through the fusion of geospatial sciences and AI (GeoAI), particularly with the application of deep learning. Despite its benefits, the reliance on data-driven AI has introduced challenges, including unpredictable errors and biases due to imperfect labeling and the opaque nature of the processes involved.</p><p dir="ltr">The research highlights the limitations of solely using data-driven AI methods for geospatial mapping, which tend to produce spatially heterogeneous errors and lack transparency, thus compromising the trustworthiness of the outputs. In response, it proposes novel knowledge-based mapping systems that prioritize transparency and scalability. This research has developed comprehensive techniques to extract key Earth and urban features and has introduced a 3D urban land cover mapping system, including a 3D Landscape Clustering framework aimed at enhancing urban climate studies. The developed systems utilize universally applicable physical knowledge of targets, captured through remote sensing, to enhance mapping accuracy and reliability without the typical drawbacks of data-driven approaches.</p><p dir="ltr">The dissertation emphasizes the importance of moving beyond mere accuracy to consider the broader implications of error patterns in geospatial mappings. It demonstrates the value of integrating generalizable target knowledge, explicitly represented in remote sensing data, into geospatial mapping to address the trustworthiness challenges in AI mapping systems. By developing mapping systems that are open, transparent, and scalable, this work aims to mitigate the effects of spatially heterogeneous errors, thereby improving the trustworthiness of geospatial mapping and analysis across various fields. Additionally, the dissertation introduces methodologies to support urban pathway accessibility and flood management studies through dependable geospatial systems. These efforts aim to establish a robust foundation for informed urban planning, efficient resource allocation, and enriched environmental insights, contributing to the development of more sustainable, resilient, and smart cities.</p>