Relevant bibliographies by topics / Calibration metrics

Academic literature on the topic 'Calibration metrics'

Author: Grafiati
Published: 18 August 2021
Last updated: 1 February 2022

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Journal articles on the topic "Calibration metrics":

1

Sparnaaij, Martijn, Dorine C. Duives, Victor L. Knoop, and Serge P. Hoogendoorn. "Multiobjective Calibration Framework for Pedestrian Simulation Models: A study on the Effect of Movement Base Cases, Metrics, and Density Levels." Journal of Advanced Transportation 2019 (July 3, 2019): 1–18. http://dx.doi.org/10.1155/2019/5874085.

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Ideally, a multitude of steps has to be taken before a commercial implementation of a pedestrian model is used in practice. Calibration, the main goal of which is to increase the accuracy of the predictions by determining the set of values for the model parameters that allows for the best replication of reality, has an important role in this process. Yet, up to recently, calibration has received relatively little attention within the field of pedestrian modelling. Most studies focus only on one specific movement base case and/or use a single metric. It is questionable how generally applicable a pedestrian simulation model is that has been calibrated using a limited set of movement base cases and one metric. The objective of this research is twofold, namely, to (1) determine the effect of the choice of movement base cases, metrics, and density levels on the calibration results and (2) to develop a multiple-objective calibration approach to determine the aforementioned effects. In this paper a multiple-objective calibration scheme is presented for pedestrian simulation models, in which multiple normalized metrics (i.e., flow, spatial distribution, effort, and travel time) are combined by means of weighted sum method that accounts for the stochastic nature of the model. Based on the analysis of the calibration results, it can be concluded that (1) it is necessary to use multiple movement base cases when calibrating a model to capture all relevant behaviours, (2) the level of density influences the calibration results, and (3) the choice of metric or combinations of metrics influence the results severely.
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Mizukami, Naoki, Oldrich Rakovec, Andrew J. Newman, Martyn P. Clark, Andrew W. Wood, Hoshin V. Gupta, and Rohini Kumar. "On the choice of calibration metrics for “high-flow” estimation using hydrologic models." Hydrology and Earth System Sciences 23, no. 6 (June 17, 2019): 2601–14. http://dx.doi.org/10.5194/hess-23-2601-2019.

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Abstract. Calibration is an essential step for improving the accuracy of simulations generated using hydrologic models. A key modeling decision is selecting the performance metric to be optimized. It has been common to use squared error performance metrics, or normalized variants such as Nash–Sutcliffe efficiency (NSE), based on the idea that their squared-error nature will emphasize the estimates of high flows. However, we conclude that NSE-based model calibrations actually result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. Using three different types of performance metrics, we calibrate two hydrological models at a daily step, the Variable Infiltration Capacity (VIC) model and the mesoscale Hydrologic Model (mHM), and evaluate their ability to simulate high-flow events for 492 basins throughout the contiguous United States. The metrics investigated are (1) NSE, (2) Kling–Gupta efficiency (KGE) and its variants, and (3) annual peak flow bias (APFB), where the latter is an application-specific metric that focuses on annual peak flows. As expected, the APFB metric produces the best annual peak flow estimates; however, performance on other high-flow-related metrics is poor. In contrast, the use of NSE results in annual peak flow estimates that are more than 20 % worse, primarily due to the tendency of NSE to underestimate observed flow variability. On the other hand, the use of KGE results in annual peak flow estimates that are better than from NSE, owing to improved flow time series metrics (mean and variance), with only a slight degradation in performance with respect to other related metrics, particularly when a non-standard weighting of the components of KGE is used. Stochastically generated ensemble simulations based on model residuals show the ability to improve the high-flow metrics, regardless of the deterministic performances. However, we emphasize that improving the fidelity of streamflow dynamics from deterministically calibrated models is still important, as it may improve high-flow metrics (for the right reasons). Overall, this work highlights the need for a deeper understanding of performance metric behavior and design in relation to the desired goals of model calibration.
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Read, Mark N., Kieran Alden, Louis M. Rose, and Jon Timmis. "Automated multi-objective calibration of biological agent-based simulations." Journal of The Royal Society Interface 13, no. 122 (September 2016): 20160543. http://dx.doi.org/10.1098/rsif.2016.0543.

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Computational agent-based simulation (ABS) is increasingly used to complement laboratory techniques in advancing our understanding of biological systems. Calibration, the identification of parameter values that align simulation with biological behaviours, becomes challenging as increasingly complex biological domains are simulated. Complex domains cannot be characterized by single metrics alone, rendering simulation calibration a fundamentally multi-metric optimization problem that typical calibration techniques cannot handle. Yet calibration is an essential activity in simulation-based science; the baseline calibration forms a control for subsequent experimentation and hence is fundamental in the interpretation of results. Here, we develop and showcase a method, built around multi-objective optimization, for calibrating ABSs against complex target behaviours requiring several metrics (termed objectives ) to characterize. Multi-objective calibration (MOC) delivers those sets of parameter values representing optimal trade-offs in simulation performance against each metric, in the form of a Pareto front. We use MOC to calibrate a well-understood immunological simulation against both established a priori and previously unestablished target behaviours. Furthermore, we show that simulation-borne conclusions are broadly, but not entirely, robust to adopting baseline parameter values from different extremes of the Pareto front, highlighting the importance of MOC's identification of numerous calibration solutions. We devise a method for detecting overfitting in a multi-objective context, not previously possible, used to save computational effort by terminating MOC when no improved solutions will be found. MOC can significantly impact biological simulation, adding rigour to and speeding up an otherwise time-consuming calibration process and highlighting inappropriate biological capture by simulations that cannot be well calibrated. As such, it produces more accurate simulations that generate more informative biological predictions.
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Demirel, Mehmet C., Juliane Mai, Gorka Mendiguren, Julian Koch, Luis Samaniego, and Simon Stisen. "Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model." Hydrology and Earth System Sciences 22, no. 2 (February 20, 2018): 1299–315. http://dx.doi.org/10.5194/hess-22-1299-2018.

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Abstract. Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.
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Bergeron, Jean, Robert Leconte, Mélanie Trudel, and Sepehr Farhoodi. "On the Choice of Metric to Calibrate Time-Invariant Ensemble Kalman Filter Hyper-Parameters for Discharge Data Assimilation and Its Impact on Discharge Forecast Modelling." Hydrology 8, no. 1 (February 24, 2021): 36. http://dx.doi.org/10.3390/hydrology8010036.

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An important step when using some data assimilation methods, such as the ensemble Kalman filter and its variants, is to calibrate its parameters. Also called hyper-parameters, these include the model and observation errors, which have previously been shown to have a strong impact on the performance of the data assimilation method. Many metrics can be used to calibrate these hyper-parameters but may not all yield the same optimal set of values. The current study investigated the importance of the choice of metric used during the hyper-parameter calibration phase and its impact on discharge forecasts. The types of metrics used each focused on discharge accuracy, ensemble spread or observation-minus-background statistics. The calibration was performed for the ensemble square root Kalman filter over two catchments in Canada using two different hydrologic models per catchment. Results show that the optimal set of hyper-parameters depended heavily on the choice of metric used during the calibration phase, where data assimilation was applied. These sets of hyper-parameters in turn produced different hydrologic forecasts. This influence was reduced as the forecast lead time increased, because of not applying data assimilation in the forecast mode, and accordingly, convergence of model state ensembles produced in the calibration phase. However, the influence could remain considerable for a few days up to multiple weeks depending on the catchment and the model. As such, a preliminary analysis would be recommended for future studies to better understand the impact that metrics can have within and outside the bounds of hyper-parameter calibration.
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Koch, Julian, Mehmet Cüneyd Demirel, and Simon Stisen. "The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models." Geoscientific Model Development 11, no. 5 (May 15, 2018): 1873–86. http://dx.doi.org/10.5194/gmd-11-1873-2018.

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Abstract. The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well-tested toolbox of metrics to evaluate temporal model performance. In contrast, spatial performance evaluation does not correspond to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study makes a contribution towards advancing spatial-pattern-oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial-pattern-oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics because stand-alone metrics tend to fail to provide holistic pattern information. The three SPAEF components are found to be independent, which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms, this study suggests applying bias insensitive metrics which further allow for a comparison of variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.8), but we see great potential across disciplines related to spatially distributed earth system modelling.
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da Silva, Diego Corrêa, Marcelo Garcia Manzato, and Frederico Araújo Durão. "Exploiting personalized calibration and metrics for fairness recommendation." Expert Systems with Applications 181 (November 2021): 115112. http://dx.doi.org/10.1016/j.eswa.2021.115112.

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Van de Voorde, Tim, Johannes van der Kwast, Frank Canters, Guy Engelen, Marc Binard, Yves Cornet, and Inge Uljee. "A Remote Sensing Based Calibration Framework for the MOLAND Urban Growth Model of Dublin." International Journal of Agricultural and Environmental Information Systems 3, no. 2 (July 2012): 1–21. http://dx.doi.org/10.4018/jaeis.2012070101.

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Land-use change models are useful tools for assessing and comparing the environmental impact of alternative policy scenarios. Their increasing popularity as spatial planning instruments also poses new scientific challenges, such as correctly calibrating the model. The challenge in model calibration is twofold: obtaining a reliable and consistent time series of land-use information and finding suitable measures to compare model output to reality. Both of these issues are addressed in this paper. The authors propose a model calibration framework that is supported by information on urban form and function derived from medium-resolution remote sensing data through newly developed spatial metrics. The remote sensing derived maps are compared to model output of the same date for two model scenarios using well-known spatial metrics. Results demonstrate a good resemblance between the simulation output and the remote sensing derived maps.
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Wang, Dangxiao, Yuru Zhang, Wanlin Zhou, Hui Zhao, and Zhongyuan Chen. "Collocation Accuracy of Visuo-Haptic System: Metrics and Calibration." IEEE Transactions on Haptics 4, no. 4 (October 2011): 321–26. http://dx.doi.org/10.1109/toh.2011.17.

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Kanellopoulos, A., J. C. Gerdessen, and G. D. H. Claassen. "Compromise programming: Non-interactive calibration of utility-based metrics." European Journal of Operational Research 244, no. 2 (July 2015): 519–24. http://dx.doi.org/10.1016/j.ejor.2015.01.031.

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You might also be interested in the extended bibliographies on the topic 'Calibration metrics' for particular source types:
Journal articles

Dissertations / Theses on the topic "Calibration metrics":

1

Tabataba, Farzaneh Sadat. "On the 3 M's of Epidemic Forecasting: Methods, Measures, and Metrics." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/89646.

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Over the past few decades, various computational and mathematical methodologies have been proposed for forecasting seasonal epidemics. In recent years, the deadly effects of enormous pandemics such as the H1N1 influenza virus, Ebola, and Zika, have compelled scientists to find new ways to improve the reliability and accuracy of epidemic forecasts. The improvement and variety of these prediction methods are undeniable. Nevertheless, many challenges remain unresolved in the path of forecasting the outbreaks using surveillance data. Obtaining the clean real-time data has always been an obstacle. Moreover, the surveillance data is usually noisy and handling the uncertainty of the observed data is a major issue for forecasting algorithms. Correct modeling assumptions regarding the nature of the infectious disease is another dilemma. Oversimplified models could lead to inaccurate forecasts, whereas more complicated methods require additional computational resources and information. Without those, the model may not be able to converge to a unique optimum solution. Through the last decade, there has been a significant effort towards achieving better epidemic forecasting algorithms. However, the lack of standard, well-defined evaluating metrics impedes a fair judgment on the proposed methods. This dissertation is divided into two parts. In the first part, we present a Bayesian particle filter calibration framework integrated with an agent-based model to forecast the epidemic trend of diseases like flu and Ebola. Our approach uses Bayesian statistics to estimate the underlying disease model parameters given the observed data and handle the uncertainty in the reasoning. An individual-based model with different intervention strategies could result in a large number of unknown parameters that should be properly calibrated. As particle filter could collapse in very large-scale systems (curse-of-dimensionality problem), achieving the optimum solution becomes more challenging. Our proposed particle filter framework utilizes machine learning concepts to restrain the intractable search space. It incorporates a smart analyzer in the state dynamics unit that examines the predicted and observed data using machine learning techniques to guide the direction and amount of perturbation of each parameter in the searching process. The second part of this dissertation focuses on providing standard evaluation measures for evaluating epidemic forecasts. We present an end-to-end framework that introduces epidemiologically relevant features (Epi-features), error measures, and ranking schema as the main modules of the evaluation process. Lastly, we provide the evaluation framework as a software package named Epi-Evaluator and demonstrate the potentials and capabilities of the framework by applying it to the output of different forecasting methods.
PHD
2

Natarajan, Vishwanath. "Self-healing RF SoCs: low cost built-in test and control driven simultaneous tuning of multiple performance metrics." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37165.

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The advent of deep submicron technology coupled with ever increasing demands from the customer for more functionality on a compact silicon real estate has led to a proliferation of highly complex integrated RF system-on-chip (SoC) and system-on-insulator (SoI) solutions. The use of scaled CMOS technologies for high frequency wireless applications is posing daunting technological challenges both in design and manufacturing test. To ensure market success, manufacturers need to ensure the quality of these advanced RF devices by subjecting them to a conventional set of production test routines that are both time consuming and expensive. Typically the devices are tested for parametric specifications such as gain, linearity metrics, quadrature mismatches, phase noise, noise figure (NF) and end-to-end system level specifications such as EVM (error vector magnitude), BER (bit-error-rate) etc. Due to the reduced visibility imposed by high levels of integration, testing for parametric specifications are becoming more and more complex. To offset the yield loss resulting from process variability effects and reliability issues in RF circuits, the use of self-healing/self-tuning mechanisms will be imperative. Such self-healing is typically implemented as a test/self-test and self-tune procedure and is applied post-manufacture. To enable this, simple test routines that can accurately diagnose complex performance parameters of the RF circuits need to be developed first. After diagnosing the performance of a complex RF system appropriate compensation techniques need to be developed to increase or restore the system performance. Moreover, the test, diagnosis and compensation approach should be low-cost with minimal hardware and software overhead to ensure that the final product is economically viable for the manufacturer. The main components of the thesis are as follows: 1) Low-cost specification testing of advanced radio frequency front-ends: Methodologies are developed to address the issue of test cost and test time associated with conventional production testing of advanced RF front-ends. The developed methodologies are amenable for performing self healing of RF SoCs. Test generation algorithms are developed to perform alternate test stimulus generation that includes the artifacts of test signal path such as response capture accuracy, load-board DfT etc. A novel cross loop-back methodology is developed to perform low cost system level specification testing of multi-band RF transceivers. A novel low-cost EVM testing approach is developed for production testing of wireless 802.11 OFDM front-ends. A signal transformation based model extraction technique is developed to compute multiple RF system level specifications of wireless front-ends from a single data capture. The developed techniques are low-cost and facilitate a reduction in the overall contribution of test cost towards the manufacturing cost of advanced wireless products. 2)Analog tuning methodologies for compensating wireless RF front ends: Methodologies for performing low-cost self tuning of multiple impairments of wireless RF devices are developed. This research considers for the first time, multiple analog tuning parameters of a complete RF transceiver system (transmitter and receiver) for tuning purposes. The developed techniques are demonstrated on hardware components and behavioral models to improve the overall yield of integrated RF SoCs.
3

Rydell, Christopher. "Deep Learning for Whole Slide Image Cytology : A Human-in-the-Loop Approach." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-450356.

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With cancer being one of the leading causes of death globally, and with oral cancers being among the most common types of cancer, it is of interest to conduct large-scale oral cancer screening among the general population. Deep Learning can be used to make this possible despite the medical expertise required for early detection of oral cancers. A bottleneck of Deep Learning is the large amount of data required to train a good model. This project investigates two topics: certainty calibration, which aims to make a machine learning model produce more reliable predictions, and Active Learning, which aims to reduce the amount of data that needs to be labeled for Deep Learning to be effective. In the investigation of certainty calibration, five different methods are compared, and the best method is found to be Dirichlet calibration. The Active Learning investigation studies a single method, Cost-Effective Active Learning, but it is found to produce poor results with the given experiment setting. These two topics inspire the further development of the cytological annotation tool CytoBrowser, which is designed with oral cancer data labeling in mind. The proposedevolution integrates into the existing tool a Deep Learning-assisted annotation workflow that supports multiple users.
4

Yeremi, Miayan. "Statistically calibrating comparison metrics of star formation simulations." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44799.

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We have developed an experimentally designed statistical framework, which allows for observation and simulation position-position-velocity cubes to be compared in a systematic manner. The comparison between cubes can be performed using a multitude of metric measures, in particular we have used the following metrics: principal component analysis (PCA), spectral correlation function (SCF), Cramer, and maximum mean discrepancy (MMD). We ran a suite of carefully chosen simulations, and performed a simulation-to-simulation comparison to test the framework in a controlled environment. We have tested the effects of four physical parameters, Mach number, magnetic field, driving scale, and temperature. The developed framework extracted key physics from the optimally designed experiment. In addition, merits of the different measures were highlighted in a standardized manner. After all four metrics were analyzed, we have concluded that PCA, Cramer and MMD respond in a linear manner to changes in factor settings, where as SCF displayed signs of curvature. Furthermore, all four statistics picked out a consistent physical trend, predicting that Mach num- ber, temperature, and Mach-number-temperature interaction are significant dissimilarity drivers. Finally, we propose that this framework will be used in future work for the calibration of simulations to observational data.
5

Zhong, Huang, and 鐘煌. "3D metric reconstruction from uncalibrated circular motion image sequences." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37043791.

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Tohme, Tony. "The Bayesian validation metric : a framework for probabilistic model calibration and validation." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/126919.

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Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 109-114).
In model development, model calibration and validation play complementary roles toward learning reliable models. In this thesis, we propose and develop the "Bayesian Validation Metric" (BVM) as a general model validation and testing tool. We show that the BVM can represent all the standard validation metrics - square error, reliability, probability of agreement, frequentist, area, probability density comparison, statistical hypothesis testing, and Bayesian model testing - as special cases while improving, generalizing and further quantifying their uncertainties. In addition, the BVM assists users and analysts in designing and selecting their models by allowing them to specify their own validation conditions and requirements. Further, we expand the BVM framework to a general calibration and validation framework by inverting the validation mathematics into a method for generalized Bayesian regression and model learning. We perform Bayesian regression based on a user's definition of model-data agreement. This allows for model selection on any type of data distribution, unlike Bayesian and standard regression techniques, that "fail" in some cases. We show that our tool is capable of representing and combining Bayesian regression, standard regression, and likelihood-based calibration techniques in a single framework while being able to generalize aspects of these methods. This tool also offers new insights into the interpretation of the predictive envelopes in Bayesian regression, standard regression, and likelihood-based methods while giving the analyst more control over these envelopes.
by Tony Tohme.
S.M.
S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program
7

De, Vaal Jan Hendrik. "Metric reconstruction of multiple rigid objects." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/2892.

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Thesis (MScEng (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2009.
Engineers struggle to replicate the capabilities of the sophisticated human visual system. This thesis sets out to recover motion and 3D structure of multiple rigid objects up to a similarity. The motion of these objects are either recorded in a single video sequence, or images of the objects are recorded on multiple, di erent cameras. We assume a perspective camera model with optional provision for calibration information. The Structure from Motion (SfM) problem is addressed from a matrix factorization point of view. This leads to a reconstruction correct up to a projectivity of little use in itself. Using techniques from camera autocalibration the projectivity is upgraded to a similarity. This reconstruction is also applied to multiple objects through motion segmentation. The SfM system developed in this thesis is a batch-processing algorithm, requiring few frames for a solution and readily accepts images from very di erent viewpoints. Since a solution can be obtained with just a few frames, it can be used to initialize sequential methods with slower convergence rates, such as the Kalman lter. The SfM system is critically evaluated against an extensive set of motion sequences.
8

Bouafia, Philippe. "Representation operators of metric and Euclidian charges." Phd thesis, Université Paris Sud - Paris XI, 2014. http://tel.archives-ouvertes.fr/tel-01015943.

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We study multiple valued functions with values in a Hilbert space. We introduce a possibledefinition of Sobolev spaces and the rightful notion of p energy. We prove the existence of pminimizers. Then we consider two-valued real functions of two variables which are stationarywith respect to both domain and range transformations. We prove their local Lipschitzcontinuity and use it to establish strong convergence in W1,2 to their unique blow-up at anypoint. We claim that the branch set of any such function consists of finitely many real analyticcurves meeting at nod points with equal angles. We also provide an example showing thatstationarity with respect to domain transformations only does not imply continuity.In a second part, we prove that there does not exist a uniformly continuous retractionfrom the space of continuous vector fields onto the subspace of vector fields whose divergencevanishes in the distributional sense. We then generalise this result using the concept of mcharges on any subset X _ Rn satisfying a mild geometric condition, there is no uniformlycontinuous representation operator for mcharges in X.
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Zhu, Nanhao. "Simulation and optimization of energy consumption in wireless sensor networks." Phd thesis, Ecole Centrale de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-01002108.

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Les grandes évolutions de la technique de systèmes embarqués au cours des dernières années ont permis avec succès la combinaison de la détection, le traitement des données, et diverses technologies de communication sans fil tout en un nœud. Les réseaux de capteurs sans fil (WSN) qui se composent d'un grand nombre de ces nœuds ont attiré l'attention du monde entier sur les établissements scolaires et les communautés industrielles, puisque leurs applications sont très répandues dans des domaines tels que la surveillance de l'environnement, le domaine militaire, le suivi des événements et la détection des catastrophes. En raison de la dépendance sur la batterie, la consommation d'énergie des réseaux de capteurs a toujours été la préoccupation la plus importante. Dans cet article, une méthode mixte est utilisée pour l'évaluation précise de l'énergie sur les réseaux de capteurs, ce qui inclut la conception d'un environnement de SystemC simulation base au niveau du système et au niveau des transactions pour l'exploration de l'énergie, et la construction d'une plate-forme de mesure d'énergie pour les mesures de nœud banc d'essai dans le monde réel pour calibrer et valider à la fois le modèle de simulation énergétique de nœud et le modèle de fonctionnement. La consommation d'énergie élaborée de plusieurs différents réseaux basés sur la plate-forme de nœud sont étudiées et comparées dans différents types de scénarios, et puis des stratégies globales d'économie d'énergie sont également données après chaque scénario pour les développeurs et les chercheurs qui se concentrent sur la conception des réseaux de capteurs efficacité énergétique. Un cadre de l'optimisation basée sur un algorithme génétique est conçu et mis en œuvre à l'aide de MATLAB pour les réseaux de capteurs conscients de l'énergie. En raison de la propriété de recherche global des algorithmes génétiques, le cadre de l'optimisation peut automatiquement et intelligemment régler des centaines de solutions possibles pour trouver le compromis le plus approprié entre la consommation d'énergie et d'autres indicateurs de performance. Haute efficacité et la fiabilité du cadre de la recherche des solutions de compromis entre l'énergie de nœud, la perte de paquets réseau et la latence ont été prouvés par réglage paramètres de l'algorithme CSMA / CA de unslotted (le mode non-beacon de IEEE 802.15.4) dans notre simulation basé sur SystemC via une fonction de coût de la somme pondérée. En outre, le cadre est également disponible pour la tâche d'optimisation basée sur multi-scénarios et multi-objectif par l'étude d'une application médicale typique sur le corps humain.
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Earnest, Steven F. P. "Integrating GIS with Benthic Metrics: Calibrating a Biotic Index to Effectively Discriminate Stream Impacts in Urban Areas of the Blackland Prairie Eco-Region." Thesis, University of North Texas, 2003. https://digital.library.unt.edu/ark:/67531/metadc4425/.

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Rapid Bioassessment Protocols integrate a suite of community, population, and functional metrics, determined from the collection of benthic macroinvertebrates or fish, into a single assessment. This study was conducted in Dallas County Texas, an area located in the blackland prairie eco-region that is semi-arid and densely populated. The objectives of this research were to identify reference streams and propose a set of metrics that are best able to discriminate between differences in community structure due to natural variability from those caused by changes in water quality due to watershed impacts. Using geographic information systems, a total of nine watersheds, each representing a different mix of land uses, were chosen for evaluation. A total of 30 metrics commonly used in RBP protocols were calculated. Efficacy of these metrics to distinguish change was determined using several statistical techniques. Ten metrics were used to classify study area watersheds according to stream quality. Many trends, such as taxa presence along habitat quality gradients, were observed. These gradients coincided with expected responses of stream communities to landscape and habitat variables.

Books on the topic "Calibration metrics":

1

Campbell, Paul D. Q. An introduction to measuration and calibration. New York: Industrial Press, 1995.

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J, Sovers O., and Jet Propulsion Laboratory (U.S.), eds. A comparative survey of current and proposed tropospheric refraction-delay models for DSN radio metric data calibration. Pasadena, Calif: National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1994.

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Comité international des poids et mesures., International Bureau of Weights and Measures., and Organisation intergouvernementale de la convention du mètre., eds. Reconnaissance mutuelle des étalons nationaux de mesure et des certificats d'étalonnage et de mesurage émis par les laboratoires nationaux de métrologie, Paris, le 14 Octobre 1999 =: Mutual recognition of national measurement standards and of calibration and measurement certificates issued by national metrology institutes, Paris, 14 October 1999. Sèvres, France: BIPM, 1999.

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Book chapters on the topic "Calibration metrics":

1

Siblini, Wissam, Jordan Fréry, Liyun He-Guelton, Frédéric Oblé, and Yi-Qing Wang. "Master Your Metrics with Calibration." In Lecture Notes in Computer Science, 457–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44584-3_36.

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Lépine, P., S. Cogan, E. Foltête, and M. O. Parent. "Robust Model Calibration Using Determinist and Stochastic Performance Metrics." In Model Validation and Uncertainty Quantification, Volume 3, 185–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29754-5_18.

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Piórkowski, Rafał, and Radosław Mantiuk. "Calibration of Structural Similarity Index Metric to Detect Artefacts in Game Engines." In Computer Vision and Graphics, 86–94. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46418-3_8.

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Lee, John D. "Demand Calibration in Multitask Environments: Interactions of Micro and Macrocognition." In Macrocognition Metrics and Scenarios, 95–108. CRC Press, 2018. http://dx.doi.org/10.1201/9781315593173-9.

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Politis, Dionysios, Sophia Aidona, Petros Stagiopoulos, Georgios Kyriafinis, and Jannis Constantinidis. "Self-Determination Calibration for Cochlear Implant Rehabilitation." In Advances in Psychology, Mental Health, and Behavioral Studies, 121–54. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7513-9.ch007.

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Cochlear implantation is a well-established therapeutic approach for deaf or hearing-impaired patients. After the medical intervention, which aims to restore hearing, subjects undergo rehabilitation procedures in order to cure instructional disadvantages, problematic schooling circumstances, or deficits in their sociability. Essential physical, mental, social, and cognitive skills are taken into perspective, as the prerequisite of a notable aptitude determines the suitability of a subject to get professional and communal roles. Quality of life, as an indicator, provides the metrics that demonstrate the level of adoption with established norms.
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Politis, Dionysios, Sophia Aidona, Petros Stagiopoulos, Georgios Kyriafinis, and Jannis Constantinidis. "Self-Determination Calibration for Cochlear Implant Rehabilitation." In Research Anthology on Emerging Technologies and Ethical Implications in Human Enhancement, 582–604. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8050-9.ch031.

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Cochlear implantation is a well-established therapeutic approach for deaf or hearing-impaired patients. After the medical intervention, which aims to restore hearing, subjects undergo rehabilitation procedures in order to cure instructional disadvantages, problematic schooling circumstances, or deficits in their sociability. Essential physical, mental, social, and cognitive skills are taken into perspective, as the prerequisite of a notable aptitude determines the suitability of a subject to get professional and communal roles. Quality of life, as an indicator, provides the metrics that demonstrate the level of adoption with established norms.
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Woodcock, Mike, and Dave Francis. "Calibrating Myself: A Team Feedback Exercise." In Team Metrics, 291–98. Routledge, 2017. http://dx.doi.org/10.4324/9781315087429-27.

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BENOIT, ERIC. "CALIBRATION OF ORDINAL METRICAL SCALE." In Series on Advances in Mathematics for Applied Sciences, 43–50. WORLD SCIENTIFIC, 2012. http://dx.doi.org/10.1142/9789814397957_0006.

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Dial, G., and J. Grodecki. "Test ranges for metric calibration and validation of high-resolution satellite imaging systems." In International Society for Photogrammetry and Remote Sensing (ISPRS) Book Series, 171–79. CRC Press, 2004. http://dx.doi.org/10.1201/9780203026830.ch17.

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Boufama, B., and S. Bouakaz. "THE USE OF CONSTRAINTS FOR CALIBRATION-FREE 3D METRIC RECONSTRUCTION: FROM THEORY TO APPLICATIONS." In Emerging Topics in Computer Vision and its Applications, 337–56. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814343008_0017.

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Conference papers on the topic "Calibration metrics":

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Hayden, Leonard. "Calibration errors when neglecting crosstalk." In 2005 66th ARFTG Conference Measurement Conference Digital Communication System Metrics (ARFTG). IEEE, 2005. http://dx.doi.org/10.1109/arftg.2005.8373125.

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Larochelle, Pierre M., and Andrew P. Murray. "Projection Metrics for Rigid-Body Displacements." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84698.

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An open research question is how to define a useful metric on SE(n) with respect to (1) the choice of coordinate frames and (2) the units used to measure linear and angular distances. We present two techniques for approximating elements of the special Euclidean group SE(n) with elements of the special orthogonal group SO(n+1). These techniques are based on the singular value and polar decompositions (denoted as SVD and PD respectively) of the homogeneous transform representation of the elements of SE(n). The projection of the elements of SE(n) onto SO(n+1) yields hyperdimensional rotations that approximate the rigid-body displacements (hence the term projection metric. A bi-invariant metric on SO(n+1) may then be used to measure the distance between any two spatial displacements. The results are PD and SVD based projection metrics on SE(n). These metrics have applications in motion synthesis, robot calibration. motion interpolation, and hybrid robot control.
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Narayanan, Sankaran, Vamsi Sai Kommuri, Sethu N. Subramanian, and Kamal Bijlani. "Question bank calibration using unsupervised learning of assessment performance metrics." In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2017. http://dx.doi.org/10.1109/icacci.2017.8125810.

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OFlaherty, Kevin, Zachary Graves, Lie Xiong, and Mark Andrews. "Using Statistical Calibration for Model Verification and Validation, Diagnosis of Model Inadequacy, and Improving Simulation Accuracy." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75807.

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The paper presents an application of statistical calibration techniques to a bracket design fatigue model simulated in COMSOL Multiphysics®. The calibration will tune the bracket’s material properties and fatigue characteristics. For illustrative purposes, the test data used to calibrate the simulation model will be generated from the same simulation routine with the addition of an intentionally applied bias and random noise to simulate model form and physical testing errors. The accuracy and conclusions from the statistically calibrated model will be compared with the uncalibrated model as well as a model calibrated with conventional error minimization methods. Multiple metrics will be shown which can be used for model validation, including a discrepancy map which characterizes inadequacies in the simulation. The metrics used in the comparison will also include results from optimization, sensitivity analysis, and propagation of uncertainties motivated by manufacturing variations during bracket fabrication. The results will demonstrate the importance of calibrating a model before drawing design conclusions.
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Aguero-Martinez, Domingo, and D. S. Reis. "Evaluation of Multiobjective Optimization Algorithms in Calibration of Hydrologic Models Using Proposed Metrics." In World Environmental and Water Resources Congress 2017. Reston, VA: American Society of Civil Engineers, 2017. http://dx.doi.org/10.1061/9780784480601.058.

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van der Kwast, Johannes, Inge Uljee, Guy Engelen, Tim Van de Voorde, Frank Canters, and Carlo Lavalle. "Using remote sensing derived spatial metrics for the calibration of land-use change models." In 2009 Joint Urban Remote Sensing Event. IEEE, 2009. http://dx.doi.org/10.1109/urs.2009.5137596.

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Gardner, Paul, Charles Lord, and Robert J. Barthorpe. "Sequential Bayesian History Matching for Model Calibration." In ASME 2019 Verification and Validation Symposium. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/vvs2019-5149.

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Abstract Computer models, whilst frequently utilised for many complex engineering tasks, suffer from model form errors due to some level of simplification and/or absence of certain physics. These model form errors lead to a mismatch between model outputs and observational data when the ‘true’ parameters are known; a phenomenon known as model discrepancy. Calibration of a computer model without consideration of this type of uncertainty therefore leads to biased estimates of system parameters. Bayesian history matching (BHM) is one such method of calibrating a computer model whilst accounting for uncertainties associated with model discrepancy. The ‘likelihood-free’ technique assesses the system parameter domain using an emulator of the complex computer model in order to discard parameter combinations based on how unlikely they were to have produced a known observation response. BHM can be approached in an iterative manner, allowing sequential-based approaches to be used in selecting new computer model evaluations that will maximise the improvement in emulator performance. This paper develops techniques for sequentially selecting new computer model evaluations, reducing the total number of evaluations and increasing improvements in the emulator. The developed metrics and criteria are outlined with a demonstration on a numerical case study in order to visually demonstrate their applicability and increase in computational efficiency.
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Himmelsbach, Michael, Sebastian Schneider, and Hans-Joachim Wuensche. "A Comparison of Error Metrics for Extrinsic Calibration and Fusion of Camera and Multi-Layer LIDAR." In Signal Processing, Pattern Recognition, and Applications / Computer Graphics and Imaging. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.721-111.

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Lopez-Dekker, P., G. Farquharson, and S. Frasier. "A Comparison of Contrast Metrics for Contrast-based Phase Calibration of Digital Beamforming Remote Sensing Systems." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.779.

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Shang, Mingyue, Zhenxin Fu, Nanyun Peng, Yansong Feng, Dongyan Zhao, and Rui Yan. "Learning to Converse with Noisy Data: Generation with Calibration." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/603.

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The availability of abundant conversational data on the Internet brought prosperity to the generation-based open domain conversation systems. In the training of the generation models, existing methods generally treat all the training data equivalently. However, the data crawled from the websites may contain many noises. Blindly training with the noisy data could harm the performance of the final generation model. In this paper, we propose a generation with calibration framework, that allows high- quality data to have more influences on the generation model and reduces the effect of noisy data. Specifically, for each instance in training set, we employ a calibration network to produce a quality score for it, then the score is used for the weighted update of the generation model parameters. Experiments show that the calibrated model outperforms baseline methods on both automatic evaluation metrics and human annotations.

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