248 research outputs found
Characterization and surface reconstruction of objects in tomographic images of composite materials
Dissertação para obtenção do Grau de Mestre em
Engenharia InformĂĄticaIn the scope of the project Tomo-GPU supported by FCT / MCTES the aim is to build
an interactive graphical environment that allows a Materials specialist to define their
own programs for analysis of 3D tomographic images. This project aims to build a tool
to characterize and investigate the identified objects, where the user can define search criteria such as size, orientation, bounding boxes, among others. All this processing will be done on a desktop computer equipped with a graphics card with some processing power.
On the proposed solution the modules for characterizing objects, received from the
identification phase, will be implemented using some existing software libraries, most
notably the CGAL library. The characterization modules with bigger execution times will be implemented using OpenCL and GPUs. With this work the characterization and reconstruction of objects and their research can now be done on conventional machines, using GPUs to accelerate the most time-consuming computations. After the conclusion of this thesis, new tools that will help to improve the current development cycle of new materials will be available for Materials Science specialists
Design and Implementation of a Multi-Purpose Object-Orientated Spatio-Temporal (MPooST) Data Model for Cadastral and Land Information Systems (C/LIS)
The application of the object-oriented methodology in geospatial information management has significantly increased during the last 10 years and tends to gradually replace the status quo relational technology. In general, object orientation offers a flexible and adaptable modelling framework to satisfy the most demanding complex data structuring requirements. The objective of this thesis is to determine how a modern Land Information System used for cadastral purposes can benefit from an object-oriented methodology. To this aim, a Multi-Purpose, Object-Oriented Spatio-Temporal (abbreviated as MPOOST) data model has been developed. In brief, the MPOOST data model embodies spatial data and their temporal reference in the form of objects which contain their attributes as well as their behaviour. The design of the MPOOST data model has been specified in such a way that it enables other data models to exploit its functionality, therefore enabling the multi-purpose aspect. At first, the requirements of Land Information Systems are being examined. Next, the functionality that is offered by the object-oriented methodology is being analysed in detail. Even if the bibliography is quite rich in relevant research, however there seems to be no starting point regarding the application of OO in LIS. Hence, a whole chapter of this thesis has been dedicated in an extended bibliographic research. Finally, the OO methodology is applied for the design and implementation of the MPOOST data model. The outcome of the design and the implementation is the first version of the MPOOST data model written using the Java object-oriented programming language. In this way, it is proven that: the relational technology has significant drawbacks which prohibit it from being applied in conceptually demanding information systems; and that object-orientation can fully satisfy the most complex data structuring requirements posed in modern geographic information systems
The exploration of a category theory-based virtual Geometrical product specification system for design and manufacturing
In order to ensure quality of products and to facilitate global outsourcing, almost all
the so-called âworld-classâ manufacturing companies nowadays are applying various
tools and methods to maintain the consistency of a productâs characteristics
throughout its manufacturing life cycle. Among these, for ensuring the consistency of
the geometric characteristics, a tolerancing language â the Geometrical Product
Specification (GPS) has been widely adopted to precisely transform the functional
requirements from customers into manufactured workpieces expressed as tolerance
notes in technical drawings. Although commonly acknowledged by industrial users as
one of the most successful efforts in integrating existing manufacturing life-cycle
standards, current GPS implementations and software packages suffer from several
drawbacks in their practical use, possibly the most significant, the difficulties in
inferring the data for the âbestâ solutions. The problem stemmed from the foundation
of data structures and knowledge-based system design. This indicates that there need
to be a ânewâ software system to facilitate GPS applications.
The presented thesis introduced an innovative knowledge-based system â the
VirtualGPS â that provides an integrated GPS knowledge platform based on a stable
and efficient database structure with knowledge generation and accessing facilities.
The system focuses on solving the intrinsic product design and production problems
by acting as a virtual domain expert through translating GPS standards and rules into
the forms of computerized expert advices and warnings. Furthermore, this system can
be used as a training tool for young and new engineers to understand the huge amount
of GPS standards in a relative âquickerâ manner.
The thesis started with a detailed discussion of the proposed categorical modelling
mechanism, which has been devised based on the Category Theory. It provided a
unified mechanism for knowledge acquisition and representation, knowledge-based
system design, and database schema modelling. As a core part for assessing this
knowledge-based system, the implementation of the categorical Database
Management System (DBMS) is also presented in this thesis. The focus then moved
on to demonstrate the design and implementation of the proposed VirtualGPS system.
The tests and evaluations of this system were illustrated in Chapter 6. Finally, the
thesis summarized the contributions to knowledge in Chapter 7.
After thoroughly reviewing the project, the conclusions reached construe that the
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entire VirtualGPS system was designed and implemented to conform to Category
Theory and object-oriented programming rules. The initial tests and performance
analyses show that the system facilitates the geometric product manufacturing
operations and benefits the manufacturers and engineers alike from function designs,
to a manufacturing and verification
Le nuage de point intelligent
Discrete spatial datasets known as point clouds often lay the groundwork for decision-making applications. E.g., we can use such data as a reference for autonomous cars and robotâs navigation, as a layer for floor-planâs creation and buildingâs construction, as a digital asset for environment modelling and incident prediction... Applications are numerous, and potentially increasing if we consider point clouds as digital reality assets. Yet, this expansion faces technical limitations mainly from the lack of semantic information within point ensembles. Connecting knowledge sources is still a very manual and time-consuming process suffering from error-prone human interpretation. This highlights a strong need for domain-related data analysis to create a coherent and structured information. The thesis clearly tries to solve automation problematics in point cloud processing to create intelligent environments, i.e. virtual copies that can be used/integrated in fully autonomous reasoning services. We tackle point cloud questions associated with knowledge extraction â particularly segmentation and classification â structuration, visualisation and interaction with cognitive decision systems. We propose to connect both point cloud properties and formalized knowledge to rapidly extract pertinent information using domain-centered graphs. The dissertation delivers the concept of a Smart Point Cloud (SPC) Infrastructure which serves as an interoperable and modular architecture for a unified processing. It permits an easy integration to existing workflows and a multi-domain specialization through device knowledge, analytic knowledge or domain knowledge. Concepts, algorithms, code and materials are given to replicate findings and extend current applications.Les ensembles discrets de donnĂ©es spatiales, appelĂ©s nuages de points, forment souvent le support principal pour des scĂ©narios dâaide Ă la dĂ©cision. Par exemple, nous pouvons utiliser ces donnĂ©es comme rĂ©fĂ©rence pour les voitures autonomes et la navigation des robots, comme couche pour la crĂ©ation de plans et la construction de bĂątiments, comme actif numĂ©rique pour la modĂ©lisation de l'environnement et la prĂ©diction dâincidents... Les applications sont nombreuses et potentiellement croissantes si l'on considĂšre les nuages de points comme des actifs de rĂ©alitĂ© numĂ©rique. Cependant, cette expansion se heurte Ă des limites techniques dues principalement au manque d'information sĂ©mantique au sein des ensembles de points. La crĂ©ation de liens avec des sources de connaissances est encore un processus trĂšs manuel, chronophage et liĂ© Ă une interprĂ©tation humaine sujette Ă l'erreur. Cela met en Ă©vidence la nĂ©cessitĂ© d'une analyse automatisĂ©e des donnĂ©es relatives au domaine Ă©tudiĂ© afin de crĂ©er une information cohĂ©rente et structurĂ©e. La thĂšse tente clairement de rĂ©soudre les problĂšmes d'automatisation dans le traitement des nuages de points pour crĂ©er des environnements intelligents, c'est-Ă dire des copies virtuelles qui peuvent ĂȘtre utilisĂ©es/intĂ©grĂ©es dans des services de raisonnement totalement autonomes. Nous abordons plusieurs problĂ©matiques liĂ©es aux nuages de points et associĂ©es Ă l'extraction des connaissances - en particulier la segmentation et la classification - la structuration, la visualisation et l'interaction avec les systĂšmes cognitifs de dĂ©cision. Nous proposons de relier Ă la fois les propriĂ©tĂ©s des nuages de points et les connaissances formalisĂ©es pour extraire rapidement les informations pertinentes Ă l'aide de graphes centrĂ©s sur le domaine. La dissertation propose le concept d'une infrastructure SPC (Smart Point Cloud) qui sert d'architecture interopĂ©rable et modulaire pour un traitement unifiĂ©. Elle permet une intĂ©gration facile aux flux de travail existants et une spĂ©cialisation multidomaine grĂące aux connaissances liĂ©e aux capteurs, aux connaissances analytiques ou aux connaissances de domaine. Plusieurs concepts, algorithmes, codes et supports sont fournis pour reproduire les rĂ©sultats et Ă©tendre les applications actuelles.Diskrete rĂ€umliche DatensĂ€tze, so genannte Punktwolken, bilden oft die Grundlage fĂŒr Entscheidungsanwendungen. Beispielsweise können wir solche Daten als Referenz fĂŒr autonome Autos und Roboternavigation, als Ebene fĂŒr die Erstellung von Grundrissen und GebĂ€udekonstruktionen, als digitales Gut fĂŒr die Umgebungsmodellierung und Ereignisprognose verwenden... Die Anwendungen sind zahlreich und nehmen potenziell zu, wenn wir Punktwolken als Digital Reality Assets betrachten. Allerdings stöĂt diese Erweiterung vor allem durch den Mangel an semantischen Informationen innerhalb von Punkt-Ensembles auf technische Grenzen. Die Verbindung von Wissensquellen ist immer noch ein sehr manueller und zeitaufwendiger Prozess, der unter fehleranfĂ€lliger menschlicher Interpretation leidet. Dies verdeutlicht den starken Bedarf an domĂ€nenbezogenen Datenanalysen, um eine kohĂ€rente und strukturierte Information zu schaffen. Die Arbeit versucht eindeutig, Automatisierungsprobleme in der Punktwolkenverarbeitung zu lösen, um intelligente Umgebungen zu schaffen, d.h. virtuelle Kopien, die in vollstĂ€ndig autonome Argumentationsdienste verwendet/integriert werden können. Wir befassen uns mit Punktwolkenfragen im Zusammenhang mit der Wissensextraktion - insbesondere Segmentierung und Klassifizierung - Strukturierung, Visualisierung und Interaktion mit kognitiven Entscheidungssystemen. Wir schlagen vor, sowohl Punktwolkeneigenschaften als auch formalisiertes Wissen zu verbinden, um schnell relevante Informationen mithilfe von domĂ€nenzentrierten Grafiken zu extrahieren. Die Dissertation liefert das Konzept einer Smart Point Cloud (SPC) Infrastruktur, die als interoperable und modulare Architektur fĂŒr eine einheitliche Verarbeitung dient. Es ermöglicht eine einfache Integration in bestehende Workflows und eine multidimensionale Spezialisierung durch GerĂ€tewissen, analytisches Wissen oder DomĂ€nenwissen. Konzepte, Algorithmen, Code und Materialien werden zur VerfĂŒgung gestellt, um Erkenntnisse zu replizieren und aktuelle Anwendungen zu erweitern
An intelligent Geographic Information System for design
Recent advances in geographic information systems (GIS) and artificial
intelligence (AI) techniques have been summarised, concentrating on the theoretical
aspects of their construction and use. Existing projects combining AI and GIS have also
been discussed, with attention paid to the interfacing methods used and problems
uncovered by the approaches. AI and GIS have been combined in this research to create
an intelligent GIS for design. This has been applied to off-shore pipeline route design.
The system was tested using data from a real pipeline design project. [Continues.
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