Search results for "Vector"

showing 10 items of 2660 documents

Assouad dimension, Nagata dimension, and uniformly close metric tangents

2013

We study the Assouad dimension and the Nagata dimension of metric spaces. As a general result, we prove that the Nagata dimension of a metric space is always bounded from above by the Assouad dimension. Most of the paper is devoted to the study of when these metric dimensions of a metric space are locally given by the dimensions of its metric tangents. Having uniformly close tangents is not sufficient. What is needed in addition is either that the tangents have dimension with uniform constants independent from the point and the tangent, or that the tangents are unique. We will apply our results to equiregular subRiemannian manifolds and show that locally their Nagata dimension equals the to…

Pure mathematicssub-Riemannian manifoldsGeneral Mathematics54F45 (Primary) 53C23 54E35 53C17 (Secondary)01 natural sciencessymbols.namesakeMathematics - Geometric TopologyDimension (vector space)Mathematics - Metric Geometry0103 physical sciencesFOS: MathematicsMathematics (all)assouad dimensionMathematics::Metric GeometryPoint (geometry)0101 mathematicsMathematics010102 general mathematicsta111TangentMetric Geometry (math.MG)Geometric Topology (math.GT)16. Peace & justiceMetric dimensionAssouad dimension; Metric tangents; Nagata dimension; Sub-Riemannian manifolds; Mathematics (all)Metric spaceBounded functionNagata dimensionMetric (mathematics)symbols010307 mathematical physicsMathematics::Differential Geometrymetric tangentsLebesgue covering dimension
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Instruction-based clinical eye-tracking study on the visual interpretation of divergence : how do students look at vector field plots?

2018

Relating mathematical concepts to graphical representations is a challenging task for students. In this paper, we introduce two visual strategies to qualitatively interpret the divergence of graphical vector field representations. One strategy is based on the graphical interpretation of partial derivatives, while the other is based on the flux concept. We test the effectiveness of both strategies in an instruction-based eye-tracking study with N = 41 physics majors. We found that students’ performance improved when both strategies were introduced (74% correct) instead of only one strategy (64% correct), and students performed best when they were free to choose between the two strategies (88…

QC1-999graafinen esitysUndergraduate StudentsPhysics Education ResearchGeneral Physics and AstronomyResearch MethodologyContext (language use)LernenAssessmentMachine learningcomputer.software_genre01 natural sciencesEducationVisual processingsilmänliikkeetddc:370Concept learning0103 physical sciencesvektorit (matematiikka)ddc:530ta516Wissensrepräsentation010306 general physicsDivergence (statistics)graphical representationsvisual processingeye-trackingLC8-6691studentsopiskelijatbusiness.industryPhysicsMultimethodology05 social sciencesConcepts & Principles050301 educationKognitives LernenSpecial aspects of educationSaccadic maskingPhysikdidaktikEye trackingPartial derivativeArtificial intelligencebusinessvector fields0503 educationcomputer
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Wind gust estimation for precise quasi-hovering control of quadrotor aircraft

2021

Abstract This paper focuses on the control of quadrotor vehicles without wind sensors that are required to accurately track low-speed trajectories in the presence of moderate yet unknown wind gusts. By modeling the wind disturbance as exogenous inputs, and assuming that compensation of its effects can be achieved through quasi-static vehicle motions, this paper proposes an innovative estimation and control scheme comprising a linear dynamic filter for the estimation of such unknown inputs and requiring only position and attitude information. The filter is built upon results from Unknown Input Observer theory and allows estimation of wind and vehicle state without measurement of the wind its…

QuadcopterUnknown Input-state observersOffset (computer science)Observer (quantum physics)Computer scienceRotor (electric)Applied MathematicsRobust controlRoboticsROS/GazeboComputer Science Applicationslaw.inventionCompensation (engineering)Tracking errorQuadrotorsControl and Systems EngineeringControl theoryPosition (vector)Filter (video)lawDisturbance observersElectrical and Electronic EngineeringDisturbance observers; Quadrotors; Robotics; Robust control; ROS/Gazebo; Unknown Input-state observers
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Automated quality control protocol for MR spectra of brain tumors.

2008

Item does not contain fulltext eTUMOUR (http://www.etumour.net/) is acquiring a large database of brain tumor (1)H MR spectra to develop automated pattern recognition methods and decision support system (DSS) for tumor diagnosis. Development of accurate pattern-recognition algorithms requires spectra undistorted by artifacts, low signal-to-noise, or broad lines. eTUMOUR currently uses panels of expert spectroscopists to subjectively grade spectra as being acceptable or unacceptable. Automated quality control (QC) would be more satisfactory for several reasons: 1) to provide a reproducible objective classification of spectrum quality; 2) for use within the future DSS to prevent misdiagnosis …

Quality ControlProtocol (science)Decision support systemMagnetic Resonance SpectroscopyBrain NeoplasmsComputer sciencemedia_common.quotation_subjectFeature extractioncomputer.software_genreIndependent component analysisDecision Support TechniquesPattern Recognition AutomatedTest setPattern recognition (psychology)Support vector machine classifierHumansRadiology Nuclear Medicine and imagingQuality (business)Functional Imaging [UMCN 1.1]Data miningcomputermedia_commonMagnetic Resonance in Medicine
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static optimal estimation of joint accelerations for inverse dynamics problem solution

2002

In inverse dynamics computations, the accuracy of the solution strongly depends on the accuracy of the input data. In particular, estimated joint moments are highly sensitive to uncertainties in acceleration data. The aim of the present work was to improve classical inverse dynamics computations by providing an accurate estimation of accelerations. Accelerations are usually calculated from noise-polluted position data using numerical double differentiation, which amplifies measurement noise. The objective of the present paper is to use all available imperfect position and force measurements to extract optimum acceleration estimations. A weighted least-squares optimisation approach is used t…

Quality ControlShoulderMovementAcceleration0206 medical engineeringBiomedical EngineeringBiophysics02 engineering and technologyResidualModels BiologicalSensitivity and SpecificityInverse dynamics03 medical and health sciencesAcceleration0302 clinical medicinePosition (vector)Control theory[SPI.MECA.BIOM] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph]HumansTorqueOrthopedics and Sports Medicine[PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph]ComputingMilieux_MISCELLANEOUSMathematicsHipOptimal estimationShoulder JointRehabilitation[PHYS.MECA.BIOM] Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph]Reproducibility of Results[SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph]020601 biomedical engineeringElasticityBiomechanical PhenomenaMoment (mathematics)NoiseTorqueHip JointJointsStress MechanicalAnkleAnkle Joint030217 neurology & neurosurgery
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Computer simulations of DNA stretching

2006

Abstract In this chapter we will give short review of computer modelling/simulations of DNA manipulation as a complementary tool to current single molecule manipulation experiments in order to follow the impact on molecular structure during the manipulation experiments. As an example we report molecular dynamics simulations of a 22 base-pair DNA fragment in an explicit water solution with counter-ions to mimic a torsionally unconstrained single-molecule stretching experiment. Positions of the O5′ and O3′ atoms at one end of the 22-mer were fixed while an external linearly increasing tensile force was applied on the corresponding atoms at the other end. Changes in the intramolecular potentia…

Quantitative Biology::BiomoleculesCrystallographyMolecular dynamicsStack (abstract data type)Chemical physicsChemistryPosition (vector)Intramolecular forceMoleculeTwistPotential energyGroove (engineering)
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Structure and dynamics of polymer brushes near the Θ point: A Monte Carlo simulation

1992

Grafted polymer layers under variable solvent conditions are studied by Monte Carlo simulations using the bond fluctuation model. Structural information such as monomer density profiles, brush thickness, mean‐square displacement of monomers, and positions of the monomers along the chain are obtained for temperatures above, at, and below the Θ point. In particular, the scaling of the brush thickness is formulated and verified by the simulation data. At the Θ point, more extensive simulations are performed to investigate the structural and dynamical properties. While the brush thickness at the Θ point agrees very well with the scaling and self‐consistent field predictions, the latter deviate …

Quantitative Biology::BiomoleculesField (physics)ChemistryRelaxation (NMR)Monte Carlo methodGeneral Physics and AstronomyPolymer brushMolecular physicsDisplacement (vector)Condensed Matter::Soft Condensed MatterDistribution functionExponentStatistical physicsPhysical and Theoretical ChemistryScalingThe Journal of Chemical Physics
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A benchmark for protein dynamics: Ribonuclease A measured by neutron scattering in a large wavevector-energy transfer range

2008

The dynamics of Ribonuclease A was explored in the full range of time and length-scales accessible by neutron spectroscopy, on time-of-flight, backscattering and spin-echo spectrometers. Samples were examined in dry and hydrated powder forms and in concentrated and dilute solutions. The aim of the study was an experimental characterisation of the full variety of protein dynamics arising from stabilisation forces. The results provide a benchmark against which other sample dynamics can be compared.

Quantitative Biology::BiomoleculesRange (particle radiation)SpectrometerChemistryProtein dynamicsDynamics (mechanics)General Physics and AstronomyNeutron scatteringMolecular physicsNeutron spectroscopyBenchmark (computing)Wave vectorPhysical and Theoretical ChemistryAtomic physicsChemical Physics
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Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.

2017

The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to determine correctly the mechanism of action of a compound, quantitative structure-activity relationship (QSAR) methods, which have proved their interest in toxicity prediction, can be used. In this work, several QSAR models for the prediction of MOA of 221 phenols to the ciliated protozoan Tetrahymena pyriformis, using Chemistry Development Kit descriptors, are reported. Four machine learning techniques (ML), k-nearest neighbours, support vector…

Quantitative structure–activity relationshipAntiprotozoal AgentsQuantitative Structure-Activity RelationshipBioengineeringModes of toxic action010501 environmental sciencesMachine learningcomputer.software_genre01 natural sciencesMachine Learningchemistry.chemical_compoundPhenolsMolecular descriptorDrug DiscoveryPhenols0105 earth and related environmental sciencesCiliated protozoanArtificial neural networkbusiness.industryTetrahymena pyriformisGeneral Medicine0104 chemical sciencesSupport vector machine010404 medicinal & biomolecular chemistrychemistryTetrahymena pyriformisMolecular MedicineArtificial intelligenceNeural Networks ComputerbusinesscomputerSAR and QSAR in environmental research
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<strong>Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction </strong>

2015

The atom-based quadratic indices are used in this work together with some machine learning techniques that includes: support vector machine, artificial neural network, random forest and k-nearest neighbor. This methodology is used for the development of two quantitative structure-activity relationship (QSAR) studies for the prediction of proteasome inhibition. A first set consisting of active and non-active classes was predicted with model performances above 85% and 80% in training and validation series, respectively. These results provided new approaches on proteasome inhibitor identification encouraged by virtual screenings procedures. .

Quantitative structure–activity relationshipArtificial neural networkSeries (mathematics)Computer sciencebusiness.industryMachine learningcomputer.software_genreRandom forestSupport vector machineSet (abstract data type)Quadratic equationProteasome inhibitormedicineArtificial intelligencebusinesscomputermedicine.drugProceedings of MOL2NET, International Conference on Multidisciplinary Sciences
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