Search results for "component"

showing 10 items of 1682 documents

Multipactor Effect in a Parallel-Plate Waveguide Partially Filled With Magnetized Ferrite

2014

The aim of this paper is the analysis of the multipactor effect in a parallel-plate waveguide when a ferrite slab, transversally magnetized by a static magnetic field parallel to the waveguide walls, is present. Employing an in-house developed code, numerical simulations are performed to predict the multipactor radio frequency voltage threshold in such a ferrite-loaded waveguide. Variations of the ferrite magnetization field strength and the ferrite slab height are analyzed. Effective electron trajectories are also shown for a better understanding of the breakdown phenomenon, finding different multipactor regimes.

Multipactor effectFerrite componentsMaterials scienceCondensed matter physicsPhysics::Instrumentation and DetectorsPhysics::OpticsRF breakdownField strengthMagnetostaticsElectronic Optical and Magnetic MaterialsCondensed Matter::Materials ScienceMagnetizationRadio frequency (RF) breakdownParallel-plate waveguideTEORIA DE LA SEÑAL Y COMUNICACIONESElectronic engineeringSlabFerrite (magnet)Multipactor effectRadio frequencyPhysics::Chemical PhysicsElectrical and Electronic EngineeringVoltageIEEE Transactions on Electron Devices
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Analysis of compatibility between lighting devices and descriptive features using Parzen’s kernel: application to flaw inspection by artificial vision

2000

We present a supervised method, developed for industrial inspections by artificial vision, to obtain an adapted combination of descriptive features and a lighting device. This method must be implemented under real-time constraints and therefore a minimal number of features must be selected. The method is based on the assessment of the discrimination power of many descriptive features. The objective is to select the combination of descriptive features and lighting system best able to discriminate flawed classes from defect-free classes. In the first step, probability densities are computed for flawed and defect-free classes and for each tested combination. The discrimination power of the fea…

Multiple discriminant analysisbusiness.industryMachine visionComputer scienceGeneral EngineeringImage processingPattern recognitionFeature selectionMachine learningcomputer.software_genreAtomic and Molecular Physics and OpticsKernel (image processing)Compatibility (mechanics)Principal component analysisArtificial intelligencebusinesscomputerOptical Engineering
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Adaptation of the Presentation in a Multi-tenant Web Information System

2012

We introduced a Web Information System (WIS) adaptation architecture that is based on Software as a Service (SaaS) ideas. It includes adaptation components, which allow adaptation in two levels: the organizations and the users get their own adapted instance of the WIS. The user interface in case of multi-tenancy should be dynamically adapted according to the particular organization and user. The same application component that contains a set of fields, controls and other interface elements should be varied according to the usage context. In this paper we provide a method for adapting the user interface within the proposed adaptation architecture that uses a set of rules describing the seque…

MultitenancyComputer scienceHuman–computer interactionbusiness.industryInterface (computing)Software as a serviceComponent (UML)Context (language use)User interfaceAdaptation (computer science)businessWeb information system
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A Multivariate Analysis on Non-nucleoside HIV-1 Reverse Transcriptase Inhibitors and Resistance Induced by Mutation

2003

This paper describes the use of multivariate statistical procedure PCA as a tool to explore the inhibitory activity of classes of NNRTIs against HIV-1 viruses (wild type and more frequent mutants, Y181C, V106A, K103N, L100I) and against RT enzyme. The analysis of correlations between biological activity and molecular descriptors or similarity indexes allowed a reliable classification of the fifty five derivatives considered in this study. The best results were obtained in the case of L100I and K103N mutants for which the higher number of assignments was found when the principal components derived from the descriptors were used. On this basis this statistical approach is proposed as a reliab…

Multivariate analysisOrganic ChemistryMutantWild typevirus diseasesBiological activityComputational biologyBiologyBioinformaticsSettore CHIM/08 - Chimica FarmaceuticaReverse transcriptaseComputer Science ApplicationsMolecular descriptorDrug DiscoveryMutation (genetic algorithm)Principal component analysisNNRTIs PCA DA resistance mutation
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Comparison of different predictive models for nutrient estimation in a sequencing batch reactor for wastewater treatment

2006

Abstract In this paper different predictive models for nutrient estimation in a sequencing batch reactor (SBR) for wastewater treatment are compared: principal component regression (PCR), partial least squares (PLS), and artificial neural networks (ANNs). Two unfolding procedures were used: batch-wise and variable-wise. For the latter unfolding method, X and Y matrix augmentation with lagged variables were used in some models to incorporate process dynamics. The results have shown that batch-wise unfolding PLS models outperform the other approaches. The ANN models are good predictive models, but in this particular case-study, they do not outperform those multivariate projection models that …

Multivariate statisticsArtificial neural networkbusiness.industryComputer scienceProcess Chemistry and TechnologySequencing batch reactorSoft sensorMachine learningcomputer.software_genreMissing dataComputer Science ApplicationsAnalytical ChemistryPartial least squares regressionPrincipal component regressionArtificial intelligenceData miningbusinesscomputerModel buildingSpectroscopySoftwareChemometrics and Intelligent Laboratory Systems
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Comparison of canonical variate analysis and principal component analysis on 422 descriptive sensory studies

2015

International audience; Although Principal Component Analysis (PCA) of product mean scores is most often used to generate a product map from sensory profiling data, it does not take into account variance of product mean scores due to individual variability. Canonical Variate Analysis (CVA) of the product effect in the two-way (product and subject) multivariate ANOVA model is the natural extension of the classical univariate approach consisting of ANOVAs of every attribute. CVA generates successive components maximizing the ANOVA F-criterion. Thus, CVA is theoretically more adapted than PCA to represent sensory data. However, CVA requires a matrix inversion which can result in computing inst…

Multivariate statisticsCVAPCANutrition and DieteticsComputer scienceUnivariateSenso BaseSensory systemCovarianceMeta-analysisStimulus modalityStatisticsPrincipal component analysis[SDV.IDA]Life Sciences [q-bio]/Food engineeringProduct topology[SPI.GPROC]Engineering Sciences [physics]/Chemical and Process EngineeringAnalysis of varianceFood Science
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A multivariate approach to the study of orichalcum ingots from the underwater Gela's archaeological site

2017

Abstract In this work a careful ICP-OES and ICP-MS investigation of 38 ancient ingots has been performed to determine both major components and trace elements content to find a correlation between the observed different features and the composition. The ingots, recovered in an underwater archaeological site of various finds near Gela (CL, Italy), were previously investigated by X-Ray Fluorescence (XRF) spectroscopy to know the composition of the alloy and it was found that the major elements were copper and zinc, in a ratio compatible with the famous orichalcum similar to the contemporary brass that was considered a precious metal in ancient times. The discovery of huge amount this alloy is…

Multivariate statisticsChemometric approach010401 analytical chemistryMetallurgyMineralogy02 engineering and technologyOrichalcum ingot021001 nanoscience & nanotechnologyLinear discriminant analysis01 natural sciencesArchaeology0104 chemical sciencesAnalytical ChemistryBrassvisual_artPrincipal component analysisOutliervisual_art.visual_art_mediumICP-OESICP-MSUnderwaterIngot0210 nano-technologyGeologySpectroscopy
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On the internal multivariate quality control of analytical laboratories. A case study: the quality of drinking water

2001

Abstract Multivariate statistical process control (MSPC) tools, based on principal component analysis (PCA), partial least squares (PLS) regression and other regression models, are used in the present study for automatic detection of possible errors in the methods used for routine multiparametric analysis in order to design an internal Multivariate Analytical Quality Control (iMAQC) program. Such tools could notice possible failures in the analytical methods without resorting to any external reference since they use their own analytical results as a source for the diagnosis of the method's quality. Pseudo-univariate control charts provide an attractive alternative to traditional univariate …

Multivariate statisticsComputer scienceMultiparametric AnalysisProcess Chemistry and TechnologyUnivariateRegression analysiscomputer.software_genreComputer Science ApplicationsAnalytical ChemistryAnalytical quality controlStatisticsPrincipal component analysisPartial least squares regressionControl chartData miningcomputerSpectroscopySoftwareChemometrics and Intelligent Laboratory Systems
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Application of multivariate statistics to the problems of upper palaeolithic and mesolithic samples

1987

Multivariate statistics (discriminant function analysis and principal component analysis) have been applied to a broad sample of Upper Paleolithic and mesolithic skulls. In addition to some methodological problems concerning the evaluation of missing data by principal component analysis, we discussed the possibility of misclassifications (14%).

Multivariate statisticsGeographyDiscriminant function analysisAnthropologyStatisticsPrincipal component analysisUpper PaleolithicSample (statistics)Missing dataMesolithicHuman Evolution
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Multivariate Exploratory Comparative Analysis of LaLiga Teams: Principal Component Analysis

2021

The use of principal component analysis (PCA) provides information about the main characteristics of teams, based on a set of indicators, instead of displaying individualized information for each of these indicators. In this work we have considered reducing an extensive data matrix to improve interpretation, using PCA. Subsequently, with new components and with multiple linear regression, we have carried out a comparative analysis between the best and bottom teams of LaLiga. The sample consisted of the matches corresponding to the 2015/16, 2016/17 and 2017/18 seasons. The results showed that the best teams were characterized and differentiated from bottom teams in the realization of a great…

Multivariate statisticsMultivariate analysisComputer scienceprincipal component analysisHealth Toxicology and MutagenesisFootballPrincipal component analysiselite footballlcsh:MedicineSample (statistics)FootballAthletic Performance050105 experimental psychologyArticle5899 Otras Especialidades Pedagógicas03 medical and health sciences0302 clinical medicineStatisticsSoccerLaLigaAnàlisi multivariable0501 psychology and cognitive sciencesperformance analysisEspanyaSet (psychology)05 social scienceslcsh:RPerformance analysisPublic Health Environmental and Occupational HealthOffensiveElite footballEquips de futbol030229 sport sciencesmultivariate analysisFutbolMultivariate analysisSpainPrincipal component analysisPerformance indicatorSoccer team
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