Search results for "Information System"

showing 10 items of 2404 documents

Needle-shape quality control by shadowgraphic image processing

2011

International audience; We propose a needle-shape quality-control method. To this end, we have devised a new acquisition system that combines a camera and a backlight. Needle measurements are carried out at a micrometric scale using shadowgraphic image processing. Our method not only distinguishes good needles from bad ones, but also allows classifying flawed needles into various categories of defects. This classification is important because some categories of defects can affect the entire production, whereas others do not. The results of our needle-shape quality-control method are validated using real samples directly off the manufacturing line. Needles are correctly classified at >97%, a…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer scienceImage qualityImage processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyBacklightMathematical morphology[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingneedle020204 information systems[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringComputer visionquality controlImage sensorRadon transformbusiness.industryGeneral EngineeringImage segmentationAtomic and Molecular Physics and Opticsimage processingmetrology[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingShape analysis (digital geometry)Optical Engineering
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Spatial correction in dynamic photon emission by affine transformation matrix estimation

2014

International audience; Photon emission microscopy and Time Resolved Imaging have proved their efficiency for defect localization on VLSI. A common process to find defect candidate locations is to draw a comparison between acquisitions on a normally working device and a faulty one. In order to be accurate and meaningful, this method requires that the acquisition scene remains the same between the two parts. In practice, it can be difficult to set. In this paper, a method to correct position by affine matrix transformation is suggested. It is based on image features detection, description and matching and affine transformation estimation.

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMatching (graph theory)Computer science[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingPosition (vector)020204 information systems0202 electrical engineering electronic engineering information engineeringComputer vision[SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingVery-large-scale integrationHarris affine region detectorbusiness.industryProcess (computing)Affine shape adaptationTransformation (function)020201 artificial intelligence & image processing[ SPI.NANO ] Engineering Sciences [physics]/Micro and nanotechnologies/MicroelectronicsArtificial intelligenceAffine transformationbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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A Use Case of Data Integration in Food Production

2018

International audience; This paper presents a use case about knowledge representation and integration of data from different domains in food science. An ontology named PO 2 DG, the Process and Observation Ontology for the production of Dairy Gels, has been designed in order to provide a shared vocabulary for domain experts. The available data have been semantically structured using PO 2 DG and are stored in an RDF repository named PO 2 DG dataset. This use case identifies some of the challenges when dealing with a multi domain representation problem, gives some hints about possible solutions and suggests some further work.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrievalexperimental observations represen- tationontology based data integrationprocess representation[INFO.INFO-WB] Computer Science [cs]/WebACM: H.: Information Systems[INFO.INFO-WB]Computer Science [cs]/Web[INFO]Computer Science [cs][INFO] Computer Science [cs]food science[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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Bridging Sensing and Decision Making in Ambient Intelligence Environments

2009

Context-aware and Ambient Intelligence environments represent one of the emerging issues in the last decade. In such intelligent environments, information is gathered to provide, on one hand, autonomic and easy to manage applications, and, on the other, secured access controlled environments. Several approaches have been defined in the literature to describe context-aware application with techniques to capture and represent information related to a specified domain. However and to the best of our knowledge, none has questioned the reliability of the techniques used to extract meaningful knowledge needed for decision making especially if the information captured is of multimedia types (image…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Ambient intelligenceComputer science02 engineering and technologycomputer.software_genreBridging (programming)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]uncertainty resolver modelHuman–computer interaction020204 information systemsResolver0202 electrical engineering electronic engineering information engineeringcontext-aware applicationsemantic-based020201 artificial intelligence & image processingData mining[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]computer
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Semantic User Profiling for Digital Advertising

2015

International audience; With the emergence of real-time distribution of online advertising space (“real-time bidding”), user profiling from web navigation traces becomes crucial. Indeed, it allows online advertisers to target customers without interfering with their activities. Current techniques apply traditional methods as statistics and machine learning, but suffer from their limitations. As an answer, the proposed approach aims to develop and evaluate a semantic-based user profiling system for digital advertising.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Data AnalysisBig DataACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.5: Online Information Services[ INFO ] Computer Science [cs]OntologyACM : H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.1: Content Analysis and IndexingACM : H.: Information SystemsUser ProfilingACM: H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONSReasoningACM : H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONS[INFO] Computer Science [cs][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]ACM : H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.4: Systems and Software/H.3.4.5: User profiles and alert servicesACM: H.: Information SystemsInferenceACM : H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.5: Online Information Services[INFO]Computer Science [cs]Logical Rules[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.1: Content Analysis and IndexingSWRLACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.4: Systems and Software/H.3.4.5: User profiles and alert servicesSemantic Web
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Enhancing scientific information systems with semantic annotations

2013

International audience; Scientific Information Systems aim to produce or improve knowledge on a subject through activities of research and development. The management of scientific dat a requires some essential properties. We propose SemLab an architecture that sup ports interoperability, data quality and extensibility through a unique paradigm: semantic annotation. We present two app lications that validate our architecture.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Information retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceInteroperability[INFO.INFO-WB]Computer Science [cs]/Web[ INFO.INFO-WB ] Computer Science [cs]/WebSubject (documents)02 engineering and technologyOntology (information science)Semantic interoperabilityData science[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]020204 information systemsData qualitySemantic computing0202 electrical engineering electronic engineering information engineeringInformation system[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]020201 artificial intelligence & image processing[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]
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Representing and Reasoning for Spatiotemporal Ontology Integration

2004

International audience; The World-Wide Web hosts many autonomous and heterogeneous information sources. In the near future each source may be described by its own ontology. The distributed nature of ontology development will lead to a large number of local ontologies covering overlapping domains. Ontology integration will then become an essential capability for effective interoperability and information sharing. Integration is known to be a hard problem, whose complexity increases particularly in the presence of spatiotemporal information. Space and time entail additional problems such as the heterogeneity of granularity used in representing spatial and temporal features. Spatio-temporal ob…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Information retrieval[INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO]Computer scienceOntologyProcess ontologyOntology-based data integrationSuggested Upper Merged OntologyIntegration[INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]Spatio-Temporal data02 engineering and technologyOntology (information science)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Open Biomedical OntologiesMapping020204 information systemsOntology components0202 electrical engineering electronic engineering information engineeringUpper ontology020201 artificial intelligence & image processingOntology alignment
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Customizing Semantic Profiling for Digital Advertising

2014

International audience; Personalization is the new magic buzzword of application development. To make the complexity of today's application functionalities and information spaces "digestible", customization has become the new go-to technique. But while those technologies aim to ease the consumption of media for their users, they suffer from the same problematic: in the age of Big Data, applications have to cope with a conundrum of heterogeneous information sources that have to be perceived, processed and interpreted. Researchers tend to aim for a maximum degree of integration to create the perfect, all-embracing personalization. The results are wide-range, but overly complex systems that su…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO ] Computer Science [cs]Computer scienceBig dataComplex systemsemantic technologies02 engineering and technology[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]Personalization[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]World Wide Web020204 information systems0202 electrical engineering electronic engineering information engineeringProfiling (information science)Heterogeneous information[ INFO.INFO-CL ] Computer Science [cs]/Computation and Language [cs.CL][INFO]Computer Science [cs]user profiles[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]OWLuser profilingbusiness.industryScalabilitySemantic technology020201 artificial intelligence & image processingbusinessDigital advertisingcustomization
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ArchaeoKM: Realizing Knowledge of the Archaeologists

2010

The potentiality of ontology within the faculty of archaeology has recently been felt. However, the use of ontology is limited either within the data interoperability for data sharing within various heterogeneous platforms or data integration of heterogeneous dataset. Thus the full potentiality of ontology is still to be realized within the community of archaeology. We are developing a system "ArchaeoKM" which uses ontology beyond data integration. It uses the strength of ontology to reason the knowledge presented within. Additionally, ArchaeoKM involves archaeologists to define their knowledge of an excavation site through domain rules which they define through the descriptions and observa…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]information system[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]spatial data[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]ontologyknowledge management[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Industrial archaeology
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An Essay on Denotational Mathematics

2019

Denotational mathematics is a new rigorous discipline of theoretical computer science that springs out from the attempt to provide a suitable mathematical framework in which laid out new algebraic structures formalizing certain formal patterns coming from computational and natural intelligence, software science, cognitive informatics, neuronal networks, and artificial intelligence. In this chapter, a very brief but rigorous exposition of the main formal structures of denotational mathematics is outlined within naive set theory.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO][INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]02 engineering and technology[INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM][MATH.MATH-CT] Mathematics [math]/Category Theory [math.CT][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM]020204 information systems0202 electrical engineering electronic engineering information engineeringMathematics education020201 artificial intelligence & image processing[INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC][INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC]ComputingMilieux_MISCELLANEOUS[MATH.MATH-CT]Mathematics [math]/Category Theory [math.CT]Mathematics
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