Search results for "76"

showing 10 items of 2584 documents

An unsupervised dual-network connectionist model of rule emergence in category learning

2007

We develop an unsupervised dual-network connectionist model of category learning in which rules gradually emerge from a standard Kohonen network. The architecture is based on the interaction of a statistical-learning (Kohonen) network and a competitive-learning rule network. The rules that emerge in the rule network are weightings of individual features according to their importance for categorisation. Once the combined system has learned a particular rule, it de-emphasizes those features that are not sufficient for categorisation, thus allowing correct classification of novel, but atypical, stimuli, for which a standard Kohonen network fails. We explain the principles and architectural det…

QA76
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Trajectory robust control of autonomous quadcopters based on model decoupling and disturbance estimation

2021

In this article, a systematic procedure is given for determining a robust motion control law for autonomous quadcopters, starting from an input–output linearizable model. In particular, the suggested technique can be considered as a robust feedback linearization (FL), where the nonlinear state-feedback terms, which contain the aerodynamic forces and moments and other unknown disturbances, are estimated online by means of extended state observers. Therefore, the control system is made robust against unmodelled dynamics and endogenous as well as exogenous disturbances. The desired closed-loop dynamics is obtained by means of pole assignment. To have a feasible control action, that is, the fo…

QuadcopterDisturbance (geology)Computer sciencelcsh:Electronicslcsh:TK7800-8360Motion controllcsh:QA75.5-76.95Computer Science ApplicationsSettore ING-INF/04 - AutomaticaArtificial IntelligenceControl theoryTrajectorylcsh:Electronic computers. Computer scienceFeedback linearizationdisturbance estimation extended state observers feedback linearization Quadcopter tracking controlRobust controlSoftwareDecoupling (electronics)International Journal of Advanced Robotic Systems
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"Table 19" of "Freeze-out radii extracted from three-pion cumulants in pp, p-Pb and Pb-Pb collisions at the LHC"

2014

Edgeworth radii and intercept parameters in PbPb collisions versus Nch at high KT3.

R lambdaInclusive2760.0PB PB --> PI+ XPB PB --> PI- X
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"Table 16" of "Freeze-out radii extracted from three-pion cumulants in pp, p-Pb and Pb-Pb collisions at the LHC"

2014

Edgeworth radii and intercept parameters in PbPb collisions versus Nch at low KT3.

R lambdaInclusive2760.0PB PB --> PI+ XPB PB --> PI- X
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"Table 10" of "Freeze-out radii extracted from three-pion cumulants in pp, p-Pb and Pb-Pb collisions at the LHC"

2014

Gaussian radii and intercept parameters in PbPb collisions versus Nch at low KT3.

R lambdaInclusive2760.0Physics::Instrumentation and DetectorsAstrophysics::Earth and Planetary AstrophysicsPB PB --> PI+ XNuclear ExperimentPB PB --> PI- XComputer Science::Cryptography and Security
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"Table 13" of "Freeze-out radii extracted from three-pion cumulants in pp, p-Pb and Pb-Pb collisions at the LHC"

2014

Gaussian radii and intercept parameters in PbPb collisions versus Nch at high KT3.

R lambdaInclusive2760.0Physics::Instrumentation and DetectorsAstrophysics::Earth and Planetary AstrophysicsPB PB --> PI+ XNuclear ExperimentPB PB --> PI- XComputer Science::Cryptography and Security
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"Table 22" of "Freeze-out radii extracted from three-pion cumulants in pp, p-Pb and Pb-Pb collisions at the LHC"

2014

Exponential radii scaled down by sqrt(pi) and intercept parameters in PbPb collisions versus Nch at low KT3.

R lambdaInclusiveMathematics::Algebraic Geometry2760.0Physics::Instrumentation and DetectorsPB PB --> PI+ XNuclear ExperimentPB PB --> PI- XMathematics::Geometric TopologyComputer Science::Databases
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"Table 25" of "Freeze-out radii extracted from three-pion cumulants in pp, p-Pb and Pb-Pb collisions at the LHC"

2014

Exponential radii scaled down by sqrt(pi) and intercept parameters in PbPb collisions versus Nch at high KT3.

R lambdaInclusiveMathematics::Algebraic Geometry2760.0Physics::Instrumentation and DetectorsPB PB --> PI+ XNuclear ExperimentPB PB --> PI- XMathematics::Geometric TopologyComputer Science::Databases
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Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images

2020

Background and objective\ud Deep learning approaches are common in image processing, but often rely on supervised learning, which requires a large volume of training images, usually accompanied by hand-crafted labels. As labelled data are often not available, it would be desirable to develop methods that allow such data to be compiled automatically. In this study, we used a Generative Adversarial Network (GAN) to generate realistic B-mode musculoskeletal ultrasound images, and tested the suitability of two automated labelling approaches.\ud \ud Methods\ud We used a model including two GANs each trained to transfer an image from one domain to another. The two inputs were a set of 100 longitu…

RM695_Physicalultrasoundmusclegenerative adversarial networkmedical imagingdeep learningsynthetic imagelihaksetQPQA76koneoppiminenkuvantaminenultraäänitutkimuscycleGAN
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Technical design report for the upgrade of the ALICE inner tracking system

2014

ALICE (A Large Ion Collider Experiment) is studying the physics of strongly interacting matter, and in particular the properties of the Quark–Gluon Plasma (QGP), using proton–proton, proton–nucleus and nucleus–nucleus collisions at the CERN LHC (Large Hadron Collider). The ALICE Collaboration is preparing a major upgrade of the experimental apparatus, planned for installation in the second long LHC shutdown in the years 2018–2019. A key element of the ALICE upgrade is the construction of a new, ultra-light, high- resolution Inner Tracking System (ITS) based on monolithic CMOS pixel detectors. The primary focus of the ITS upgrade is on improving the performance for detection of heavy-flavour…

ROOT-S=2.76 TEV; PP COLLISIONS; DETECTORS; RECONSTRUCTION; ELECTRONICS; SILICON; PHYSICS; MODELPhysics::Instrumentation and DetectorsNuclear TheoryNuclear and High Energy Physics;Tracking (particle physics)01 natural sciences7. Clean energydecaylaw.inventionUpgradeALICElawLHC; ALICE; Inner Tracking SystemNuclear ExperimentGeneralLiterature_REFERENCE(e.g.dictionariesencyclopediasglossaries)ComputingMilieux_MISCELLANEOUSPhysicsLarge Hadron ColliderDetectorSettore FIS/01 - Fisica SperimentaleTracking systemPRIRODNE ZNANOSTI. Fizika.UpgradeLHCParticle physicsNuclear and High Energy PhysicsALICE Inner Tracking SystemROOT-S=2.76 TEV; pp collisions; DETECTORS; RECONSTRUCTION; ELECTRONICS; SILICON; PHYSICS; MODEL; decay[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]Dot pitchPHYSICSELECTRONICS0103 physical sciencesDETECTORSRECONSTRUCTIONCMOS pixel sensors010306 general physicsColliderROOT-S=2.76 TEVSILICONPP COLLISIONSPixel010308 nuclear & particles physicsbusiness.industryALICE experimentInner Tracking SystemTechnical Design ReportNATURAL SCIENCES. Physics.MODELDetector upgradeTechnical Design Report; Upgrade; ALICE Inner Tracking SystemHigh Energy Physics::Experimentbusiness
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