Search results for "Emulation"

showing 10 items of 27 documents

DIGITAL EMULATION OF DIELECTRIC RELAXATION FUNCTIONS FOR CAPACITIVE SENSORS OF NON-DESTRUCTIVE DIELECTRIC SPECTROMETRY

2019

EmulationMaterials sciencebusiness.industryCapacitive sensingNon destructiveOptoelectronicsRelaxation (physics)DielectricbusinessMass spectrometryComputational Methods and Experimental Measurements XIX
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Adaptive Sequential Interpolator Using Active Learning for Efficient Emulation of Complex Systems

2020

Many fields of science and engineering require the use of complex and computationally expensive models to understand the involved processes in the system of interest. Nevertheless, due to the high cost involved, the required study becomes a cumbersome process. This paper introduces an interpolation procedure which belongs to the family of active learning algorithms, in order to construct cheap surrogate models of such costly complex systems. The proposed technique is sequential and adaptive, and is based on the optimization of a suitable acquisition function. We illustrate its efficiency in a toy example and for the construction of an emulator of an atmosphere modeling system.

Emulationexperimental designAtmosphere (unit)010504 meteorology & atmospheric sciencesComputer scienceProcess (engineering)Active learning (machine learning)media_common.quotation_subjectBayesian optimization0211 other engineering and technologiesComplex systemAdaptive interpolation02 engineering and technology01 natural sciencesComputer engineeringactive learningActive learningFunction (engineering)Bayesian optimization021101 geological & geomatics engineering0105 earth and related environmental sciencesmedia_commonInterpolationICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation

2019

Collection of spectroradiometric measurements with associated biophysical variables is an essential part of the development and validation of optical remote sensing vegetation products. However, their quality can only be assessed in the subsequent analysis, and often there is a need for collecting extra data, e.g., to fill in gaps. To generate empirical-like surface reflectance data of vegetated surfaces, we propose to exploit emulation, i.e., reconstruction of spectral measurements through statistical learning. We evaluated emulation against classical interpolation methods using an empirical field dataset with associated hyperspectral spaceborne CHRIS and airborne HyMap reflectance spectra…

Emulationspectroscopy010504 meteorology & atmospheric sciencesComputer scienceScienceQ0211 other engineering and technologiesHyperspectral imagingemulation02 engineering and technology01 natural sciencesReflectivityinterpolationData cubemachine learningscene simulationGeneral Earth and Planetary Sciencesemulation; machine learning; interpolation; spectroscopy; scene simulationSpectral resolutionSpectroscopyHyMap021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingInterpolationRemote Sensing
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PCM Thermal Energy Storage in Buildings: Experimental Study and Applications

2015

Abstract The study aims at analyzing the performance of Phase Change Materials (PCMs) in residential housing for different climates. This paper presents the results of an experiment performed in the Concordia University Solar Simulator and Environmental Chamber research facility (SSEC, Montreal, Canada). PCM boards were embedded on the back wall of a test hut placedin the climatic chamber. Several experiments were performed to explore the potential for verification of the proposed analysis and to produce enough data to perform model calibrations. Results show a strong increase in the apparent thermal inertia of the room allowing for a reduction in daily temperature fluctuations in the test …

EngineeringEmulationSettore ING-IND/11 - Fisica Tecnica AmbientaleMeteorologyThermal inertiabusiness.industryCold climateModel calibrationEnvironmental chamberThermal energy storagePhase-change materialsBuilding simulationMultiple layerEnergy(all)EnergyPlusPhase change materials modelling experiment energy plus parametric analysisThermal massSolar simulatorAerospace engineeringbusinessEnergy Procedia
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Active emulation of computer codes with Gaussian processes – Application to remote sensing

2020

Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest. Nevertheless, the high cost involved hamper reliable and exhaustive simulations. Very often such codes incorporate heuristics that ironically make them less tractable and transparent. This paper introduces an active learning methodology for adaptively constructing surrogate models, i.e. emulators, of such costly computer codes in a multi-output setting. The proposed technique is sequential and adaptive, and is based on the optimization of a suitable acquisition function. It aims to achieve accurate approximations…

FOS: Computer and information sciencesComputer Science - Machine LearningActive learningActive learning (machine learning)Computer sciencemedia_common.quotation_subjectMachine Learning (stat.ML)Radiative transfer model02 engineering and technology01 natural sciencesMachine Learning (cs.LG)symbols.namesakeArtificial IntelligenceStatistics - Machine Learning0103 physical sciences0202 electrical engineering electronic engineering information engineeringCode (cryptography)Emulation010306 general physicsFunction (engineering)Gaussian processGaussian process emulatorGaussian processRemote sensingmedia_commonEmulationbusiness.industrySampling (statistics)Remote sensingSignal ProcessingGlobal Positioning Systemsymbols020201 artificial intelligence & image processingComputer codeComputer Vision and Pattern RecognitionbusinessHeuristicsSoftwareDesign of experimentsPattern Recognition
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Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes

2018

Computationally expensive radiative transfer models (RTMs) are widely used to realistically reproduce the light interaction with the earth surface and atmosphere. Because these models take long processing time, the common practice is to first generate a sparse look-up table (LUT) and then make use of interpolation methods to sample the multidimensional LUT input variable space. However, the question arise whether common interpolation methodsperform most accurate. As an alternative to interpolation, this paper proposes to use emulation, i.e., approximating the RTM output by means of the statistical learning. Two experiments were conducted to assess the accuracy in delivering spectral outputs…

FOS: Computer and information sciencesComputer Science - Machine LearningAtmospheric Science010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologiesFOS: Physical sciences02 engineering and technologyStatistics - Applications01 natural sciencesArticleMachine Learning (cs.LG)Sampling (signal processing)KrigingInverse distance weightingApplications (stat.AP)Computers in Earth Sciences021101 geological & geomatics engineering0105 earth and related environmental sciencesEmulationArtificial neural networkMODTRANComputational Physics (physics.comp-ph)Physics - Atmospheric and Oceanic PhysicsAtmospheric and Oceanic Physics (physics.ao-ph)Lookup tablePhysics - Computational PhysicsAlgorithmInterpolationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Deep Importance Sampling based on Regression for Model Inversion and Emulation

2021

Understanding systems by forward and inverse modeling is a recurrent topic of research in many domains of science and engineering. In this context, Monte Carlo methods have been widely used as powerful tools for numerical inference and optimization. They require the choice of a suitable proposal density that is crucial for their performance. For this reason, several adaptive importance sampling (AIS) schemes have been proposed in the literature. We here present an AIS framework called Regression-based Adaptive Deep Importance Sampling (RADIS). In RADIS, the key idea is the adaptive construction via regression of a non-parametric proposal density (i.e., an emulator), which mimics the posteri…

FOS: Computer and information sciencesComputer Science - Machine LearningImportance samplingComputer scienceMonte Carlo methodPosterior probabilityBayesian inferenceInferenceContext (language use)Machine Learning (stat.ML)02 engineering and technologyEstadísticaStatistics - ComputationMachine Learning (cs.LG)symbols.namesakeSurrogate modelStatistics - Machine LearningArtificial Intelligence0202 electrical engineering electronic engineering information engineeringAdaptive regressionEmulationElectrical and Electronic EngineeringModel inversionGaussian processComputation (stat.CO)EmulationApplied Mathematics020206 networking & telecommunicationsRemote sensingComputational Theory and MathematicsSignal Processingsymbols020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionStatistics Probability and UncertaintyAlgorithmImportance sampling
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AI for Resource Allocation and Resource Allocation for AI: a two-fold paradigm at the network edge

2022

5G-and-beyond and Internet of Things (IoT) technologies are pushing a shift from the classic cloud-centric view of the network to a new edge-centric vision. In such a perspective, the computation, communication and storage resources are moved closer to the user, to the benefit of network responsiveness/latency, and of an improved context-awareness, that is, the ability to tailor the network services to the live user's experience. However, these improvements do not come for free: edge networks are highly constrained, and do not match the resource abundance of their cloud counterparts. In such a perspective, the proper management of the few available resources is of crucial importance to impr…

Internet Of ThingMINLPIoTEdge NetworkPerformance EvaluationLow Power Wide Area NetworkSystem ModelingSettore ING-INF/03 - TelecomunicazioniUAVSoftware Defined RadioReal TestbedVehicular NetworkMLLoRaReinforcement LearningResource AllocationMachine LearningGame TheoryArtificial IntelligenceAILPWANColosseum Channel EmulatorChannel EmulationEmulationSDR
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Venerari Contendere Adicere: Roman Emulation, Intergenerational Reciprocity, and the Ancient Idea of Progress

2019

Over the past few decades, the successful emergence of intertextuality, with its careful investigation of the dynamics of imitation, allusion, and emulation, has effectively challenged the Romantic notions of creativity and individual authorship. In the wide-open field left by the postmodern ‘death of the author’, however, the territory of culture as a network of patterns hiding behind the text has often been restricted within the boundaries of literary culture. In this paper, I will attempt to enlarge such a text-centred perspective by highlighting the often neglected connections between family education, intergenerational reciprocity, and aesthetic thought in Roman culture. Indeed, long b…

Seneca QuintiliansociologyHoraceprogreCicerofamily memorySettore L-FIL-LET/04 - Lingua E Letteratura Latinaintergenerational reciprocityintertextualitygenreanthropology of the ancient worldimitatio/aemulatioliterary emulationliterary theorySallustkinshipcanonicity
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Unknown order process emulation

2002

Approaches the emulation problem using feedforward neural networks of single input single output (SISO) processes, applying a backpropagation method with a higher convergence rate. In this kind of application, difficult problems appear when the system's order is a priori unknown. A search through the SISO processes space is proposed, aiming to find a favorable neural emulator over the training examples set.

Set (abstract data type)EmulationRate of convergenceTime delay neural networkComputer scienceControl theoryComputer Science::Neural and Evolutionary ComputationLinear systemFeedforward neural networkBackpropagationIJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)
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