1,351 research outputs found

    A knowledge graph-supported information fusion approach for multi-faceted conceptual modelling

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    It has become progressively more evident that a single data source is unable to comprehensively capture the variability of a multi-faceted concept, such as product design, driving behaviour or human trust, which has diverse semantic orientations. Therefore, multi-faceted conceptual modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is frequently applied to cope with the high dimensionality and data heterogeneity. The consideration of intra-facets relationships is also indispensable. In this context, a knowledge graph (KG), which can aggregate the relationships of multiple aspects by semantic associations, was exploited to facilitate the multi-faceted conceptual modelling based on heterogeneous and semantic-rich data. Firstly, rules of fault mechanism are extracted from the existing domain knowledge repository, and node attributes are extracted from multi-sourced data. Through abstraction and tokenisation of existing knowledge repository and concept-centric data, rules of fault mechanism were symbolised and integrated with the node attributes, which served as the entities for the concept-centric knowledge graph (CKG). Subsequently, the transformation of process data to a stack of temporal graphs was conducted under the CKG backbone. Lastly, the graph convolutional network (GCN) model was applied to extract temporal and attribute correlation features from the graphs, and a temporal convolution network (TCN) was built for conceptual modelling using these features. The effectiveness of the proposed approach and the close synergy between the KG-supported approach and multi-faceted conceptual modelling is demonstrated and substantiated in a case study using real-world data

    Cybersecurity knowledge graphs

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    Cybersecurity knowledge graphs, which represent cyber-knowledge with a graph-based data model, provide holistic approaches for processing massive volumes of complex cybersecurity data derived from diverse sources. They can assist security analysts to obtain cyberthreat intelligence, achieve a high level of cyber-situational awareness, discover new cyber-knowledge, visualize networks, data flow, and attack paths, and understand data correlations by aggregating and fusing data. This paper reviews the most prominent graph-based data models used in this domain, along with knowledge organization systems that define concepts and properties utilized in formal cyber-knowledge representation for both background knowledge and specific expert knowledge about an actual system or attack. It is also discussed how cybersecurity knowledge graphs enable machine learning and facilitate automated reasoning over cyber-knowledge

    Automatic Generation of Personalized Recommendations in eCoaching

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    Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

    Federated Data Modeling for Built Environment Digital Twins

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    The digital twin (DT) approach is an enabler for data-driven decision making in architecture, engineering, construction, and operations. Various open data models that can potentially support the DT developments, at different scales and application domains, can be found in the literature. However, many implementations are based on organization-specific information management processes and proprietary data models, hindering interoperability. This article presents the process and information management approaches developed to generate a federated open data model supporting DT applications. The business process modeling notation and transaction and interaction modeling techniques are applied to formalize the federated DT data modeling framework, organized in three main phases: requirements definition, federation, validation and improvement. The proposed framework is developed adopting the cross-disciplinary and multiscale principles. A validation on the development of the federated building-level DT data model for the West Cambridge Campus DT research facility is conducted. The federated data model is used to enable DT-based asset management applications at the building and built environment levels

    Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

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    This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023

    Digital Twins of production systems - Automated validation and update of material flow simulation models with real data

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    Um eine gute Wirtschaftlichkeit und Nachhaltigkeit zu erzielen, müssen Produktionssysteme über lange Zeiträume mit einer hohen Produktivität betrieben werden. Dies stellt produzierende Unternehmen insbesondere in Zeiten gesteigerter Volatilität, die z.B. durch technologische Umbrüche in der Mobilität, sowie politischen und gesellschaftlichen Wandel ausgelöst wird, vor große Herausforderungen, da sich die Anforderungen an das Produktionssystem ständig verändern. Die Frequenz von notwendigen Anpassungsentscheidungen und folgenden Optimierungsmaßnahmen steigt, sodass der Bedarf nach Bewertungsmöglichkeiten von Szenarien und möglichen Systemkonfigurationen zunimmt. Ein mächtiges Werkzeug hierzu ist die Materialflusssimulation, deren Einsatz aktuell jedoch durch ihre aufwändige manuelle Erstellung und ihre zeitlich begrenzte, projektbasierte Nutzung eingeschränkt wird. Einer längerfristigen, lebenszyklusbegleitenden Nutzung steht momentan die arbeitsintensive Pflege des Simulationsmodells, d.h. die manuelle Anpassung des Modells bei Veränderungen am Realsystem, im Wege. Das Ziel der vorliegenden Arbeit ist die Entwicklung und Umsetzung eines Konzeptes inkl. der benötigten Methoden, die Pflege und Anpassung des Simulationsmodells an die Realität zu automatisieren. Hierzu werden die zur Verfügung stehenden Realdaten genutzt, die aufgrund von Trends wie Industrie 4.0 und allgemeiner Digitalisierung verstärkt vorliegen. Die verfolgte Vision der Arbeit ist ein Digitaler Zwilling des Produktionssystems, der durch den Dateninput zu jedem Zeitpunkt ein realitätsnahes Abbild des Systems darstellt und zur realistischen Bewertung von Szenarien verwendet werden kann. Hierfür wurde das benötigte Gesamtkonzept entworfen und die Mechanismen zur automatischen Validierung und Aktualisierung des Modells entwickelt. Im Fokus standen dabei unter anderem die Entwicklung von Algorithmen zur Erkennung von Veränderungen in der Struktur und den Abläufen im Produktionssystem, sowie die Untersuchung des Einflusses der zur Verfügung stehenden Daten. Die entwickelten Komponenten konnten an einem realen Anwendungsfall der Robert Bosch GmbH erfolgreich eingesetzt werden und führten zu einer Steigerung der Realitätsnähe des Digitalen Zwillings, der erfolgreich zur Produktionsplanung und -optimierung eingesetzt werden konnte. Das Potential von Lokalisierungsdaten für die Erstellung von Digitalen Zwillingen von Produktionssystem konnte anhand der Versuchsumgebung der Lernfabrik des wbk Instituts für Produktionstechnik demonstriert werden

    Object and Pattern Association for Robot Localization

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    Methodological approaches and techniques for designing ontologies in information systems requirements engineering

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    Programa doutoral em Information Systems and TechnologyThe way we interact with the world around us is changing as new challenges arise, embracing innovative business models, rethinking the organization and processes to maximize results, and evolving change management. Currently, and considering the projects executed, the methodologies used do not fully respond to the companies' needs. On the one hand, organizations are not familiar with the languages used in Information Systems, and on the other hand, they are often unable to validate requirements or business models. These are some of the difficulties encountered that lead us to think about formulating a new approach. Thus, the state of the art presented in this paper includes a study of the models involved in the software development process, where traditional methods and the rivalry of agile methods are present. In addition, a survey is made about Ontologies and what methods exist to conceive, transform, and represent them. Thus, after analyzing some of the various possibilities currently available, we began the process of evolving a method and developing an approach that would allow us to design ontologies. The method we evolved and adapted will allow us to derive terminologies from a specific domain, aggregating them in order to facilitate the construction of a catalog of terminologies. Next, the definition of an approach to designing ontologies will allow the construction of a domain-specific ontology. This approach allows in the first instance to integrate and store the data from different information systems of a given organization. In a second instance, the rules for mapping and building the ontology database are defined. Finally, a technological architecture is also proposed that will allow the mapping of an ontology through the construction of complex networks, allowing mapping and relating terminologies. This doctoral work encompasses numerous Research & Development (R&D) projects belonging to different domains such as Software Industry, Textile Industry, Robotic Industry and Smart Cities. Finally, a critical and descriptive analysis of the work done is performed, and we also point out perspectives for possible future work.A forma como interagimos com o mundo à nossa volta está a mudar à medida que novos desafios surgem, abraçando modelos empresariais inovadores, repensando a organização e os processos para maximizar os resultados, e evoluindo a gestão da mudança. Atualmente, e considerando os projetos executados, as metodologias utilizadas não respondem na totalidade às necessidades das empresas. Por um lado, as organizações não estão familiarizadas com as linguagens utilizadas nos Sistemas de Informação, por outro lado, são muitas vezes incapazes de validar requisitos ou modelos de negócio. Estas são algumas das dificuldades encontradas que nos levam a pensar na formulação de uma nova abordagem. Assim, o estado da arte apresentado neste documento inclui um estudo dos modelos envolvidos no processo de desenvolvimento de software, onde os métodos tradicionais e a rivalidade de métodos ágeis estão presentes. Além disso, é efetuado um levantamento sobre Ontologias e quais os métodos existentes para as conceber, transformar e representar. Assim, e após analisarmos algumas das várias possibilidades atualmente disponíveis, iniciou-se o processo de evolução de um método e desenvolvimento de uma abordagem que nos permitisse conceber ontologias. O método que evoluímos e adaptamos permitirá derivar terminologias de um domínio específico, agregando-as de forma a facilitar a construção de um catálogo de terminologias. Em seguida, a definição de uma abordagem para conceber ontologias permitirá a construção de uma ontologia de um domínio específico. Esta abordagem permite em primeira instância, integrar e armazenar os dados de diferentes sistemas de informação de uma determinada organização. Num segundo momento, são definidas as regras para o mapeamento e construção da base de dados ontológica. Finalmente, é também proposta uma arquitetura tecnológica que permitirá efetuar o mapeamento de uma ontologia através da construção de redes complexas, permitindo mapear e relacionar terminologias. Este trabalho de doutoramento engloba inúmeros projetos de Investigação & Desenvolvimento (I&D) pertencentes a diferentes domínios como por exemplo Indústria de Software, Indústria Têxtil, Indústria Robótica e Smart Cities. Finalmente, é realizada uma análise critica e descritiva do trabalho realizado, sendo que apontamos ainda perspetivas de possíveis trabalhos futuros
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