7 research outputs found

    Multisource Data Integration in Remote Sensing

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    Papers presented at the workshop on Multisource Data Integration in Remote Sensing are compiled. The full text of these papers is included. New instruments and new sensors are discussed that can provide us with a large variety of new views of the real world. This huge amount of data has to be combined and integrated in a (computer-) model of this world. Multiple sources may give complimentary views of the world - consistent observations from different (and independent) data sources support each other and increase their credibility, while contradictions may be caused by noise, errors during processing, or misinterpretations, and can be identified as such. As a consequence, integration results are very reliable and represent a valid source of information for any geographical information system

    NASA Tech Briefs, April 1992

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    Topics covered include: New Product Ideas; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences

    Multispace & Multistructure. Neutrosophic Transdisciplinarity (100 Collected Papers of Sciences), Vol. IV

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    The fourth volume, in my book series of “Collected Papers”, includes 100 published and unpublished articles, notes, (preliminary) drafts containing just ideas to be further investigated, scientific souvenirs, scientific blogs, project proposals, small experiments, solved and unsolved problems and conjectures, updated or alternative versions of previous papers, short or long humanistic essays, letters to the editors - all collected in the previous three decades (1980-2010) – but most of them are from the last decade (2000-2010), some of them being lost and found, yet others are extended, diversified, improved versions. This is an eclectic tome of 800 pages with papers in various fields of sciences, alphabetically listed, such as: astronomy, biology, calculus, chemistry, computer programming codification, economics and business and politics, education and administration, game theory, geometry, graph theory, information fusion, neutrosophic logic and set, non-Euclidean geometry, number theory, paradoxes, philosophy of science, psychology, quantum physics, scientific research methods, and statistics. It was my preoccupation and collaboration as author, co-author, translator, or cotranslator, and editor with many scientists from around the world for long time. Many topics from this book are incipient and need to be expanded in future explorations

    Integrative risk-based assessment modelling of safety-critical marine and offshore applications

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    This research has first reviewed the current status and future aspects of marine and offshore safety assessment. The major problems identified in marine and offshore safety assessment in this research are associated with inappropriate treatment of uncertainty in data and human error issues during the modelling process. Following the identification of the research needs, this thesis has developed several analytical models for the safety assessment of marine and offshore systems/units. Such models can be effectively integrated into a risk-based framework using the marine formal safety assessment and offshore safety case concepts. Bayesian network (BN) and fuzzy logic (FL) approaches applicable to marine and offshore safety assessment have been proposed for systematically and effectively addressing uncertainty due to randomness and vagueness in data respectively. BN test cases for both a ship evacuation process and a collision scenario between the shuttle tanker and Floating, Production, Storage and Offloading unit (FPSO) have been produced within a cause-effect domain in which Bayes' theorem is the focal mechanism of inference processing. The proposed FL model incorporating fuzzy set theory and an evidential reasoning synthesis has been demonstrated on the FPSO-shuttle tanker collision scenario. The FL and BN models have been combined via mass assignment theory into a fuzzy-Bayesian network (FBN) in which the advantages of both are incorporated. This FBN model has then been demonstrated by addressing human error issues in a ship evacuation study using performance-shaping factors. It is concluded that the developed FL, BN and FBN models provide a flexible and transparent way of improving safety knowledge, assessments and practices in the marine and offshore applications. The outcomes have the potential to facilitate the decision-making process in a risk-based framework. Finally, the results of the research are summarised and areas where further research is required to improve the developed methodologies are outline

    Proceedings of the NASA Conference on Space Telerobotics, volume 1

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    The theme of the Conference was man-machine collaboration in space. Topics addressed include: redundant manipulators; man-machine systems; telerobot architecture; remote sensing and planning; navigation; neural networks; fundamental AI research; and reasoning under uncertainty

    Sensor data validation for nuclear power plants through bayesian conditioning and dempster's rule of combination

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    Sensor data fusion and interpretation, sensor failure detection, isolation and identification are extremely important activities for the safety of a nuclear power plant. In particular, they become critical in case of conflicts among the data. If the monitored system's description  model is correct and its components work properly, then incompatibilities among data may only be attributed to temporary deterioration or permanent breakage of one or more sensors.  This paper introduces and discusses three simple ideas: 1. classical "model-based diagnosis" can be extended straightforwardly to encompass the sensors models into the system's description in order to diagnose even their own faults 2. from the "log-file" of the diagnosed minimal conflicts among the sensors, one can draw interesting conclusion regarding their relative reliability (e.g., through Bayesian conditioning) 3. the estimated reliability of the sensors is useful when assessing (e.g. through Dempster's Rule of Combination) the actual state of the monitored physical system, even in cases of conflicting data. These ideas lead to the conception of a distributed monitoring system able to attach to each sensor a statistically evaluated relative reliability, which is especially useful for devices situated in dangerous zones or areas, difficult to reach inside huge and complex power plants

    Sensor data validation for nuclear power plants through Bayesian conditioning and Dempster's rule of combination

    No full text
    Sensor data fusion and interpretation, sensor failure detection, isolation and identification are extremely important activities for the safety of a nuclear power plant. In particular, they become critical in case of conflicts among the data. If the monitored system's description model is correct and its components work properly, then incompatibilities among data may only be attributed to temporary deterioration or permanent breakage of one or more sensors. This paper introduces and discusses three simple ideas: 1. classical model-based diagnosis can be extended straightforwardly to encompass the sensors' models into the system's description in order to diagnose even their own faults 2. from the log-file of the diagnosed minimal conflicts among the sensors, one can draw interesting conclusion regarding their relative reliability (e.g., through Bayesian conditioning) 3. the estimated reliability of the sensors is useful when assessing (e.g., through Dempster's Rule of Combination) the actual state of the monitored physical system, even in cases of conflicting data. These ideas lead to the conception of a distributed monitoring system able to attach to each sensor a statistically evaluated relative reliability, which is especially useful for devices situated in dangerous zones or areas, difficult to reach inside huge and complex power plants
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