Search results for "Structural knowledge"

showing 3 items of 3 documents

A structural comparison of halloysite nanotubes of different origin by Small-Angle Neutron Scattering (SANS) and Electric Birefringence

2018

The structure of halloysite nanotubes (Hal) from different mines was investigated by Small-Angle Neutron Scattering (SANS) and Electric Birefringence (EBR) experiments. The analysis of the SANS curves allowed us to correlate the sizes and polydispersity and the specific surfaces (obtained by a Porod analysis of the SANS data) of the nanotubes with their specific geological setting. Contrast matching measurements were performed on patch Hal (from Western Australia) in order to determine their experimental scattering length density for a more precise analysis. Further characterization of the mesoscopic structure of Hal was carried out by Electric Birefringence (EBR), which allowed to study th…

Yarn Electric birefringenceSuperconducting materialPatch halloysiteHalloysite nanotube02 engineering and technologyengineering.materialNeutron scattering010402 general chemistry01 natural sciencesHalloysiteMolecular physicsPlants (botany)Structural descriptionGeochemistry and PetrologyKaoliniteRotational diffusion coefficientSettore CHIM/02 - Chimica FisicaMesoscopic physicsBirefringenceBirefringenceScattering length densityStructural analysiElectric BirefringenceRotational diffusionStructural comparisonScattering lengthGeologyHalloysite021001 nanoscience & nanotechnologySmall-angle neutron scattering0104 chemical sciencesCharacterization (materials science)NanotubeStructural knowledge Neutron scatteringengineeringSmall Angle Neutron Scattering0210 nano-technology
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Structural Knowledge Extraction from Mobility Data

2016

Knowledge extraction has traditionally represented one of the most interesting challenges in AI; in recent years, however, the availability of large collections of data has increased the awareness that “measuring” does not seamlessly translate into “understanding”, and that more data does not entail more knowledge. We propose here a formulation of knowledge extraction in terms of Grammatical Inference (GI), an inductive process able to select the best grammar consistent with the samples. The aim is to let models emerge from data themselves, while inference is turned into a search problem in the space of consistent grammars, induced by samples, given proper generalization operators. We will …

Process (engineering)Computer scienceGeneralizationmedia_common.quotation_subjectInference02 engineering and technologyMachine learningcomputer.software_genreTheoretical Computer ScienceGrammatical inferenceKnowledge extractionRule-based machine translation020204 information systems0202 electrical engineering electronic engineering information engineeringSearch problemmedia_commonStructural knowledgeGrammarbusiness.industryMobility dataComputer Science (all)020207 software engineeringGrammar inductionArtificial intelligencebusinesscomputerNatural language processing
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Structural Knowledge Extraction and Representation in Sensory Data

During the last decades the availability of increasingly cheaper technology for pervasive monitoring has boosted the creation of systems able to automatically comprehend the events occurring in the monitored area, in order to plan a set of actions to bring the environment closer to the user's preferences. These systems must inevitably process a great amount of raw data - sensor measurements - and need to summarize them in a high-level representation to accomplish their tasks. An implicit requirement is the need to learn from experience, in order to be able to capture the hidden structure of the data, in terms of relations between its key components. The availability of large collections of …

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniStructural Knowledge sensor data machine learning data mining
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