Activities of the Remote Sensing Information Sciences Research Group

Topics on the analysis and processing of remotely sensed data in the areas of vegetation analysis and modelling, georeferenced information systems, machine assisted information extraction from image data, and artificial intelligence are investigated. Discussions on support field data and specific applications of the proposed technologies are also included

Foundations of Fuzzy Logic and Semantic Web Languages

This book is the first to combine coverage of fuzzy logic and Semantic Web languages. It provides in-depth insight into fuzzy Semantic Web languages for non-fuzzy set theory and fuzzy logic experts. It also helps researchers of non-Semantic Web languages get a better understanding of the theoretical fundamentals of Semantic Web languages. The first part of the book covers all the theoretical and logical aspects of classical (two-valued) Semantic Web languages. The second part explains how to generalize these languages to cope with fuzzy set theory and fuzzy logic

Foundations of Fuzzy Logic and Semantic Web Languages

This book is the first to combine coverage of fuzzy logic and Semantic Web languages. It provides in-depth insight into fuzzy Semantic Web languages for non-fuzzy set theory and fuzzy logic experts. It also helps researchers of non-Semantic Web languages get a better understanding of the theoretical fundamentals of Semantic Web languages. The first part of the book covers all the theoretical and logical aspects of classical (two-valued) Semantic Web languages. The second part explains how to generalize these languages to cope with fuzzy set theory and fuzzy logic

Knowledge-based product support systems

This research helps bridge the gap between conventional product support, where the support system is considered as a stand-alone application, and the new paradigm of responsive one, where the support system frequently communicates with its environment and reacts to stimuli. This new paradigm would enable product support knowledge to be captured, stored, processed, and updated automatically, being delivered to the users when, where and in the form they need it. The research reported in this thesis first defines Product Support Systems (PRSSs) as electronic means that provide accurate and up-to-date information to the user in a coherent and personalised manner. Product support knowledge is then identified as the integration of product, task, user, and support documentation knowledge. Next, the thesis focuses on an ontology-based model of the structure, relations, and attributes of product support knowledge. In that model product support virtual documentation (PSVD) is presented as an aggregation of Information Objects (IOs) and Information Object Clusters (IOCs). The description of PSVD is followed by an analysis of the relation between IOs, IOCs, and domain knowledge. Then, the thesis builds on the ontology-based representation of product support knowledge and explores the synergy between product support, problem solving, and knowledge engineering. As a result, a structured problem solving approach is introduced that combines case-based adaptation and model-based generation techniques. Based on that approach a knowledge engineering framework for product support systems is developed. A conceptual model of context-aware product support systems that extends the framework is then introduced. The conceptual model includes an ontology-based representation of knowledge related to the users, their activities, the support environment, and the device being used. An approach to semi-automatically integrating design and documentation data is also proposed as part of context-aware product support systems development process.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Knowledge-based product support systems

This research helps bridge the gap between conventional product support, where the support system is considered as a stand-alone application, and the new paradigm of responsive one, where the support system frequently communicates with its environment and reacts to stimuli. This new paradigm would enable product support knowledge to be captured, stored, processed, and updated automatically, being delivered to the users when, where and in the form they need it. The research reported in this thesis first defines Product Support Systems (PRSSs) as electronic means that provide accurate and up-to-date information to the user in a coherent and personalised manner. Product support knowledge is then identified as the integration of product, task, user, and support documentation knowledge. Next, the thesis focuses on an ontology-based model of the structure, relations, and attributes of product support knowledge. In that model product support virtual documentation (PSVD) is presented as an aggregation of Information Objects (IOs) and Information Object Clusters (IOCs). The description of PSVD is followed by an analysis of the relation between IOs, IOCs, and domain knowledge. Then, the thesis builds on the ontology-based representation of product support knowledge and explores the synergy between product support, problem solving, and knowledge engineering. As a result, a structured problem solving approach is introduced that combines case-based adaptation and model-based generation techniques. Based on that approach a knowledge engineering framework for product support systems is developed. A conceptual model of context-aware product support systems that extends the framework is then introduced. The conceptual model includes an ontology-based representation of knowledge related to the users, their activities, the support environment, and the device being used. An approach to semi-automatically integrating design and documentation data is also proposed as part of context-aware product support systems development process