Готові списки джерел за темами / Modeling and parametric calibration

Добірка наукової літератури з теми "Modeling and parametric calibration"

Автор: Grafiati
Опубліковано: 8 червня 2024

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Статті в журналах з теми "Modeling and parametric calibration":

1

Mauro, Francisco, Andrew T. Hudak, Patrick A. Fekety, Bryce Frank, Hailemariam Temesgen, David M. Bell, Matthew J. Gregory, and T. Ryan McCarley. "Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon." Remote Sensing 13, no. 2 (January 13, 2021): 261. http://dx.doi.org/10.3390/rs13020261.

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Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.
2

Lucor, Didier, and Olivier P. Le Maître. "Cardiovascular Modeling With Adapted Parametric Inference." ESAIM: Proceedings and Surveys 62 (2018): 91–107. http://dx.doi.org/10.1051/proc/201862091.

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Computational modeling of the cardiovascular system, promoted by the advance of fluid-structure interaction numerical methods, has made great progress towards the development of patient-specific numerical aids to diagnosis, risk prediction, intervention and clinical treatment. Nevertheless, the reliability of these models is inevitably impacted by rough modeling assumptions. A strong in-tegration of patient-specific data into numerical modeling is therefore needed in order to improve the accuracy of the predictions through the calibration of important physiological parameters. The Bayesian statistical framework to inverse problems is a powerful approach that relies on posterior sampling techniques, such as Markov chain Monte Carlo algorithms. The generation of samples re-quires many evaluations of the cardiovascular parameter-to-observable model. In practice, the use of a full cardiovascular numerical model is prohibitively expensive and a computational strategy based on approximations of the system response, or surrogate models, is needed to perform the data as-similation. As the support of the parameters distribution typically concentrates on a small fraction of the initial prior distribution, a worthy improvement consists in gradually adapting the surrogate model to minimize the approximation error for parameter values corresponding to high posterior den-sity. We introduce a novel numerical pathway to construct a series of polynomial surrogate models, by regression, using samples drawn from a sequence of distributions likely to converge to the posterior distribution. The approach yields substantial gains in efficiency and accuracy over direct prior-based surrogate models, as demonstrated via application to pulse wave velocities identification in a human lower limb arterial network.
3

Mishra, Manish. "Review on Analysis of Free Vibrational Horizontally Curved Bridges." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3463–69. http://dx.doi.org/10.22214/ijraset.2021.36682.

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Curved I-girder concrete bridges give an outstanding answer to urban congestion, traffic, and pollution concerns, but the combined flexibility and torque responses of the bridges make their behavior exceedingly complex. That is why structural design parameters for simplified design procedures are in high demand, as measured by empirical equations. To analyze the effect on the free vibrational reaction of curve composite steel-concreteI-girder bridge with varying vibration parameters, this research employs a sensitivity analysis. To learn the fundamental frequency and the geometric configuration of the model forms, a parametric investigation is performed. Finite element Modelling of composite steel/concrete frameworks, deformable shear model, fine element formula, finite element mounting, finite element calibration, and finite element modeling, etc. Modeling finite element. Sensitivity research to draw the fundamental frequencies for the evaluated bridges. The parametric research outcomes. The results. Curved I-girder bridges of composite steel with single span or multi-span lengths are presented.
4

Rzehak, Roland, and Eckhard Krepper. "CFD for Subcooled Flow Boiling: Parametric Variations." Science and Technology of Nuclear Installations 2013 (2013): 1–22. http://dx.doi.org/10.1155/2013/687494.

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We investigate the present capabilities of CFD for wall boiling. The computational model used combines the Euler/Euler two-phase flow description with heat flux partitioning. Very similar modeling was previously applied to boiling water under high pressure conditions relevant to nuclear power systems. Similar conditions in terms of the relevant nondimensional numbers have been realized in the DEBORA tests using dichlorodifluoromethane (R12) as the working fluid. This facilitated measurements of radial profiles for gas volume fraction, gas velocity, liquid temperature, and bubble size. Robust predictive capabilities of the modeling require that it is validated for a wide range of parameters. It is known that a careful calibration of correlations used in the wall boiling model is necessary to obtain agreement with the measured data. We here consider tests under a variety of conditions concerning liquid subcooling, flow rate, and heat flux. It is investigated to which extent a set of calibrated model parameters suffices to cover at least a certain parameter range.
5

Schepen, Andrew, Yvette Everingham, and Quan J. Wang. "On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs." Monthly Weather Review 148, no. 1 (January 1, 2020): 437–56. http://dx.doi.org/10.1175/mwr-d-19-0046.1.

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Abstract Multivariate seasonal climate forecasts are increasingly required for quantitative modeling in support of natural resources management and agriculture. GCM forecasts typically require postprocessing to reduce biases and improve reliability; however, current seasonal postprocessing methods often ignore multivariate dependence. In low-dimensional settings, fully parametric methods may sufficiently model intervariable covariance. On the other hand, empirical ensemble reordering techniques can inject desired multivariate dependence in ensembles from template data after univariate postprocessing. To investigate the best approach for seasonal forecasting, this study develops and tests several strategies for calibrating seasonal GCM forecasts of rainfall, minimum temperature, and maximum temperature with intervariable dependence: 1) simultaneous calibration of multiple climate variables using the Bayesian joint probability modeling approach; 2) univariate BJP calibration coupled with an ensemble reordering method (the Schaake shuffle); and 3) transformation-based quantile mapping, which borrows intervariable dependence from the raw forecasts. Applied to Australian seasonal forecasts from the ECMWF System4 model, univariate calibration paired with empirical ensemble reordering performs best in terms of univariate and multivariate forecast verification metrics, including the energy and variogram scores. However, the performance of empirical ensemble reordering using the Schaake shuffle is influenced by the selection of historical data in constructing a dependence template. Direct multivariate calibration is the second-best method, with its far superior performance in in-sample testing vanishing in cross validation, likely because of insufficient data relative to the number of parameters. The continued development of multivariate forecast calibration methods will support the uptake of seasonal climate forecasts in complex application domains such as agriculture and hydrology.
6

Ghnatios, Chady, Khalil El Rai, Nicolas Hascoet, Pierre-Adrien Pires, Jean-Louis Duval, Jon Lambarri, Jean-Yves Hascoet, and Francisco Chinesta. "Reduced order modeling of selective laser melting: from calibration to parametric part distortion." International Journal of Material Forming 14, no. 5 (March 31, 2021): 973–86. http://dx.doi.org/10.1007/s12289-021-01613-z.

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AbstractAdditive manufacturing is an appealing solution to produce geometrically complex parts, difficult to manufacture using traditional technologies. The extreme process conditions, in particular the high temperature, complex interactions and couplings, and rich metallurgical transformations that this process entails, are at the origin of numerous process defects. Therefore, the numerical simulation of the process is gaining the interest of both the scientific and the industrial communities. However, simulating that process demands impressive computational resources, limiting high resolution simulations to the microscopic and mesoscopic scales. This paper proposes a thermo-mechanical modeling framework at the process scale as well as its associated reduced order simulation counterpart, enabling the parametric evaluation of the part distortion. It deeply addresses the process calibration using a high-resolution computational procedure based on the use of an in-plane-out-of-plane separated representation at the heart of the so-called Proper Generalized Decomposition (PGD), as well as the analysis of the transient thermal effects, defining the conditions in which the thermal and mechanical analyses can be decoupled.
7

Chen, Hua-Zhou, Kai Shi, Ken Cai, Li-Li Xu, and Quan-Xi Feng. "Investigation of sample partitioning in quantitative near-infrared analysis of soil organic carbon based on parametric LS-SVR modeling." RSC Advances 5, no. 98 (2015): 80612–19. http://dx.doi.org/10.1039/c5ra12468a.

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A framework for sample partitioning is proposed to take into account the tunable ratio of numbers of calibration and prediction samples, in consideration with the randomness, stability and robustness of calibration models.
8

Mang, Andreas, Spyridon Bakas, Shashank Subramanian, Christos Davatzikos, and George Biros. "Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology." Annual Review of Biomedical Engineering 22, no. 1 (June 4, 2020): 309–41. http://dx.doi.org/10.1146/annurev-bioeng-062117-121105.

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Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved ( a) detection/segmentation of tumor subregions and ( b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of ( a) biophysical growth modeling and simulation,( b) inverse problems for model calibration, ( c) these models' integration with imaging workflows, and ( d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.
9

Ghnatios, Chady, Khalil El Rai, Nicolas Hascoet, Pierre-Adrien Pires, Jean-Louis Duval, Jon Lambarri, Jean-Yves Hascoet, and Francisco Chinesta. "Correction to: reduced order modeling of selective laser melting: from calibration to parametric part distortion." International Journal of Material Forming 14, no. 5 (August 4, 2021): 987. http://dx.doi.org/10.1007/s12289-021-01638-4.

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10

Sheng, J., S.-Y. Yang, and W. Tong. "Implicitization of the Vegter Yield Criterion." IOP Conference Series: Materials Science and Engineering 1238, no. 1 (May 1, 2022): 012007. http://dx.doi.org/10.1088/1757-899x/1238/1/012007.

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Abstract An advanced yield criterion plays a central role in rate-independent plasticity modeling of sheet metals in automotive and other industrial applications. Different from many non-quadratic yield criteria based on implicit polynomials, Vegter yield criterion is piecewise and quadratic as it is originally constructed by interpolation of several biaxial stress states using multiple second-order Bézier curves. Additional efforts in recent years have made Vetger yield criterion more user-friendly, flexible and robust for sheet metal forming applications. Nevertheless, Vetger yield criterion has only been presented in its parametric formulation in the literature. This work describes a method of reformulating the existing Vetger Lite yield criterion into the implicit form that is more commonly used in the conventional sheet metal plasticity modeling. The potential benefits of using both the original parametric and the new implicit forms of Vegter yield criterion for material parameter calibration, convexity certification, and computational simulations are discussed.
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Статті в журналах Дисертації Книги

Дисертації з теми "Modeling and parametric calibration":

1

Wan, Shuang. "Parametric array calibration." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/4902.

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The subject of this thesis is the development of parametric methods for the calibration of array shape errors. Two physical scenarios are considered, the online calibration (self-calibration) using far-field sources and the offline calibration using near-field sources. The maximum likelihood (ML) estimators are employed to estimate the errors. However, the well-known computational complexity in objective function optimization for the ML estimators demands effective and efficient optimization algorithms. A novel space-alternating generalized expectation-maximization (SAGE)-based algorithm is developed to optimize the objective function of the conditional maximum likelihood (CML) estimator for the far-field online calibration. Through data augmentation, joint direction of arrival (DOA) estimation and array calibration can be carried out by a computationally simple search procedure. Numerical experiments show that the proposed method outperforms the existing method for closely located signal sources and is robust to large shape errors. In addition, the accuracy of the proposed procedure attains the Cram´er-Rao bound (CRB). A global optimization algorithm, particle swarm optimization (PSO) is employed to optimize the objective function of the unconditional maximum likelihood (UML) estimator for the farfield online calibration and the near-field offline calibration. A new technique, decaying diagonal loading (DDL) is proposed to enhance the performance of PSO at high signal-to-noise ratio (SNR) by dynamically lowering it, based on the counter-intuitive observation that the global optimum of the UML objective function is more prominent at lower SNR. Numerical simulations demonstrate that the UML estimator optimized by PSO with DDL is optimally accurate, robust to large shape errors, and free of the initialization problem. In addition, the DDL technique is applicable to a wide range of array processing problems where the UML estimator is employed and can be coupled with different global optimization algorithms.
2

Osborne, Christine. "Non-parametric calibration." Thesis, University of Bath, 1990. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293248.

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3

Wenger, Jonathan. "Non-Parametric Calibration for Classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262652.

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Many applications for classification methods not only require high accuracy but also reliable estimation of predictive uncertainty. This is of particular importance in fields such as computer vision or robotics, where safety-critical decisions are made based on classification outcomes. However, while many current classification frameworks, in particular deep neural network architectures, provide very good results in terms of accuracy, they tend to incorrectly estimate their predictive uncertainty. In this thesis we focus on probability calibration, the notion that a classifier’s confidence in a prediction matches the empirical accuracy of that prediction. We study calibration from a theoretical perspective and connect it to over- and underconfidence, two concepts first introduced in the context of active learning. The main contribution of this work is a novel algorithm for classifier calibration. We propose a non-parametric calibration method which is, in contrast to existing approaches, based on a latent Gaussian process and specifically designed for multiclass classification. It allows for the incorporation of prior knowledge, can be applied to any classification method that outputs confidence estimates and is not limited to neural networks. We demonstrate the universally strong performance of our method across different classifiers and benchmark data sets from computer vision in comparison to existing classifier calibration techniques. Finally, we empirically evaluate the effects of calibration on querying efficiency in active learning.
Många applikationer för klassificeringsmetoder kräver inte bara hög noggrannhet utan även tillförlitlig uppskattning av osäkerheten av beräknat utfall. Detta är av särskild betydelse inom områden som datorseende eller robotik, där säkerhetskritiska beslut fattas utifrån klassificeringsresultat. Medan många av de nuvarande klassificeringsverktygen, i synnerhet djupa neurala nätverksarkitekturer, ger resultat när det gäller noggrannhet, tenderar de att felaktigt uppskatta strukturens osäkerhet. I detta examensarbete fokuserar vi på sannolikhetskalibrering, d.v.s. hur väl en klassificerares förtroende för ett resultat stämmer överens med den faktiska empiriska säkerheten. Vi studerar kalibrering ur ett teoretiskt perspektiv och kopplar det till över- och underförtroende, två begrepp som introducerades första gången i samband med aktivt lärande. Huvuddelen av arbetet är framtagandet av en ny algoritm för klassificeringskalibrering. Vi föreslår en icke-parametrisk kalibreringsmetod som, till skillnad från befintliga tillvägagångssätt, bygger på en latent Gaussisk process och som är specielltutformad för klassificering av flera klasser. Algoritmen är inte begränsad till neurala nätverk utan kan tillämpas på alla klassificeringsmetoder som ger konfidensberäkningar. Vi demonstrerar vår metods allmänt starka prestanda över olika klassifikatorer och kända datamängder från datorseende i motsats till befintliga klassificeringskalibreringstekniker. Slutligen utvärderas effektiviteten av kalibreringen vid aktivt lärande.
4

Tachet, des combes Rémi. "Non-parametric model calibration in finance." Phd thesis, Ecole Centrale Paris, 2011. http://tel.archives-ouvertes.fr/tel-00658766.

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Consistently fitting vanilla option surfaces is an important issue when it comes to modelling in finance. In three different models: local and stochastic volatility, local correlation and hybrid local volatility with stochstic rates, this calibration boils down to the resolution of a nonlinear partial integro-differential equation. In a first part, we give existence results of solutions for the calibration equation. They are based upon fixed point methods in Hölder spaces and short-time a priori estimates. We then apply those existence results to the three models previously mentioned and give the calibration obtained when solving the pde numerically. At last, we focus on the algorithm used for the resolution: an ADI predictor/corrector scheme that needs to be modified to take into account the nonlinear term. We also study an instability phenomenon that occurs in certain cases for the local and stochastic volatility model. Using Hadamard's theory, we try to offer a theoretical explanation to the instability
5

Xiang, Yi. "Implied volatility smirk and non-parametric calibration /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?MATH%202004%20XIANG.

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Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2004.
Includes bibliographical references (leaves 107-114). Also available in electronic version. Access restricted to campus users.
6

COELHO, LUIZ CRISTOVAO GOMES. "SHELL MODELING WITH PARAMETRIC INTERSECTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1998. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2780@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Apresenta-se uma metodologia para modelagem de cascas para elementos finitos definidas em superfícies paramétricas. A metodologia consiste na criação de curvas e geração de malhas sobre os retalhos paramétricos constru´ıdos com base nestas curvas, que também são usadas para a conexão de malhas adjacentes. O modelo final é uma representação de todas as malhas combinadas em uma única estrutura de dados. As ferramentas básicas para geração de tais malhas são uma interface para modelagem de curvas espaciais e os algoritmos geom´etricos para construcão de mapeamentos nos domínios elementares. O problema central em modelagens compostas é o tratamento dado às malhas em superfícies que se interceptam. Um algoritmo capaz de modelar com precisão as curvas de interseção e de ajustar as duas malhas para as novas restrições geradas é apresentado neste trabalho. O algoritmo é parte de um programa completo para modelagem interativa de cascas, que tem sido usado no projeto de grandes sistemas flutuantes para explotação de petróleo em águas profundas. O uso de uma variante da estrutura de dados DCEL, que usa árvores de ordenação espacial para armazenar as entidades topol´ogicas ao invés de listas ou vetores, permite que malhas bastante refinadas sejam reconstru´ıdas em tempo compatível com o trabalho interativo. Estas árvores aceleram os cálculos de interseção necessários à determinação dos pontos de interpolação das curvas de trimming, permitindo tamb´em a reconstrução das malhas usando-se apenas consultas locais.
We present a methodology for modeling finite-element meshes defined on parametric surface patches. The idea is to build curves and generate meshes over the parametric patches built with these curves, which also connect adjacent meshes. The final model is a representation of all meshes combined into a single data structure. The basic tools to generate such meshes are the user interface to model space curves and the geometric algorithms to construct the elementary domain mappings. The main problem in composite modeling is how to handle mesh surfaces that intersect each other. We present an algorithm that models the intersection curves precisely and adjusts both meshes to the newly formed borders. The algorithm is part of an interactive shell modeling program, which has been used in the design of large offshore oil structures. We avoid unacceptable interaction delays by using a variant of the DCEL data structure that stores topological entities in spatial indexing trees instead of linked lists. These trees speed up the intersection computations required to determine points of the trimming curves, and also allows mesh reconstruction using only local queries.
7

Hoare, Armando. "Parametric, non-parametric and statistical modeling of stony coral reef data." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002470.

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8

Basso, Filippo. "A non-parametric Calibration Algorithm for Depth Sensors Exploiting RGB Cameras." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3424206.

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Range sensors are common devices on modern robotic platforms. They endow the robot with information about distance and shape of the objects in the sensors field of view. In particular, the advent in the last few years of consumer RGB- D sensors such as the Microsoft Kinect, has greatly fostered the development of depth-based algorithms for robotics. In fact, such sensors can provide a large quantity of data at a relatively low price. In this thesis three different calibration problems for depth sensors are tackled. The first original contribution to the state of the art is an algorithm to recover the axis of rotation of a 2D laser range finder (LRF) mounted on a rotating support. The key difference with other approaches is the use of kinematics point-plane constraints to estimate the pose of the LRF with respect to a static camera, and screw decomposition to recover the axis of rotation. The correct reconstruction of a small indoor environment after calibration validates the proposed algorithm. The second and most important original contribution of the thesis is a fully automatic two-steps calibration algorithm for structured-light depth sensors (e.g. Kinect). The key novelty of this work is the separation of the depth error into two components, corrected with functions estimated on a pixel-basis. This separation, validated by experimental observations, allows to dramatically reduce the number of parameters in the final non-linear minimization and, consequently, the time for the solution to converge to the global minimum. The depth images of a test set corrected using the obtained calibration parameters are analyzed and compared to the ground truth. The comparison shows that they differ from the real ones just for an unpredictable noise. A qualitative analysis of the fusion between depth and RGB data further confirms the effectiveness of the approach. Moreover, a ROS package for both calibrating and correcting the Kinect data has been released as open source. The third contribution reported in the thesis is a new distributed calibration algorithm for networks composed by cameras and already-calibrated depth sensors. A ROS package implementing the proposed approach has been developed and is available for free as a part of a big open source project for people tracking: OpenPTrack. The developed package is able to calibrate networks composed by a dozen sensors in real-time (i.e., batch processing is not needed), exploiting plane- to-plane constraints and non-linear least squares optimization.
I sensori di profondità sono dispositivi comuni sui robot moderni. Essi forniscono al robot informazioni sulla distanza e sulla forma degli oggetti nel loro campo di visione, permettendogli di agire di conseguenza. In particolare, l’arrivo negli ultimi anni di sensori RGB-D di consumo come Microsoft Kinect, ha favorito lo sviluppo di algoritmi per la robotica basati su dati di profondità. Di fatto, questi sensori sono in grado di generare una grande quantità di dati ad un prezzo relativamente basso. In questa tesi vengono affrontati tre diversi problemi riguardanti la calibrazione di sensori di profondità. Il primo contributo originale allo stato dell’arte è un algoritmo per stimare l’asse di rotazione di un laser range finder (LRF) 2D montato su un supporto rotante. La differenza chiave con gli altri approcci è l’utilizzo di vincoli punto-piano derivanti dalla cinematica per stimare la posizione del LRF rispetto ad una videocamera fissa, e l’uso di una screw decomposition per stimare l’asse di rotazione. La corretta ricostruzione di una stanza dopo la calibrazione valida l’algoritmo proposto. Il secondo e più importante contributo originale di questa tesi è un algoritmo completamente automatico per la calibrazione di sensori di profondità a luce strut- turata (ad esempio Kinect). La chiave di questo lavoro è la separazione dell’errore di profondità in due componenti, entrambe corrette pixel a pixel. Questa separa- zione, validata da osservazioni sperimentali, permette di ridurre sensibilmente il numero di parametri nell’ottimizzazione finale e, di conseguenza, il tempo neces- sario affinché la soluzione converga al minimo globale. Il confronto tra le immagini di profondità di un test set, corrette con i parametri di calibrazione ottenuti, e quelle attese, dimostra che la differenza tra le due è solamente di una quantità ca- suale. Un’analisi qualitativa della fusione tra dati di profondità e RGB conferma ulteriormente l’efficacia dell’approccio. Inoltre, un pacchetto ROS per calibrare e correggere i dati generati da Kinect è disponibile open source. Il terzo contributo riportato nella tesi è un nuovo algoritmo distribuito per la calibrazione di reti composte da videocamere e sensori di profondità già calibrati. Un pacchetto ROS che implementa l’algoritmo proposto è stato rilasciato come parte di un grande progetto open source per il tracking di persone: OpenPTrack. Il pacchetto sviluppato è in grado di calibrare reti composte da una decina di sensori in tempo reale (non è necessario processare i dati in un secondo tempo), sfruttando vincoli piano-piano e un’ottimizzazione non lineare.
9

Holden, Christian. "Modeling and Control of Parametric Roll Resonance." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-12736.

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Parametric roll resonance is a dangerous resonance phenomenon affecting several types of ships, such as destroyers, RO-RO paxes, cruise ships, fishing vessels and especially container ships. Worst case, parametric roll is capable of causing roll angles of at least 50 degrees, and damage in the tens of millions of US dollars. Empirical and mathematical investigations have concluded that parametric roll occurs due to periodic changes in the waterplane area of the ship. If the vessel is sailing in longitudinal seas, with waves of approximately the same length as the ship, and encounter frequency of about twice the natural roll frequency, then parametric resonance can occur. While there is a significant amount of literature on the hydrodynamics of parametric roll, there is less on controlling and stopping the phenomenon through active control. The main goal of this thesis has been to develop controllers capable of stopping parametric roll. Two main results on control are presented. To derive, analyze and simulate the controllers, it proved necessary to develop novel models. The thesis thus contains four major contributions on modeling. The main results are (presented in order of appearance in the thesis): Six-DOF computer model for parametric roll One-DOF model of parametric roll for non-constant velocity Three-DOF model of parametric roll Seven-DOF model for ships with u-tanks of arbitrary shape Frequency detuning controller Active u-tank based controller for parametric roll
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Qvarngård, Daniel. "Modeling Optical Parametric Generation in Inhomogeneous Media." Thesis, Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74256.

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Книги з теми "Modeling and parametric calibration":

1

American Society of Mechanical Engineers., ed. Parametric calibration of coordinate measuring machines. New York: ASME, 1991.

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2

Shih, Randy H. Parametric modeling with solidworks 2010. [Place of publication not identified]: Schroff Development Corp, 2010.

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3

John, Wilson. Mechanical desktop 5: Parametric modeling. Lawrence, Kan: CMP Books, 2001.

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4

Shih, Randy H. Parametric Modeling with SolidWorks 2013. Mission, Kan: SDC Publications, 2013.

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5

Shih, Randy H. Parametric modeling with Autodesk Inventor 2011. Mission, Kansas: Schroff Development Corporation, 2010.

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6

Shih, Randy H. Parametric modeling with Autodesk Inventor 2013. Mission, KS: SDC Publications, 2012.

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7

Shih, Randy H. Parametric modeling with Autodesk Inventor 2016. Mission, Kan: SDC Publications, 2015.

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8

Nardi Rezende, Rene, ed. Liquid Rocket Engine Thrust Chamber Parametric Modeling. Warrendale, PA: SAE International, 2018. http://dx.doi.org/10.4271/0768093333.

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9

LoFaso, James P. Parametric modeling using Inventor: Autodesk Inventor 11. Tinley Park, Ill: Goodheart-Willcox Co., 2007.

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10

LoFaso, James P. Parametric modeling using Inventor: Autodesk inventor 2008. Tinley Park, Ill: The Goodheart-Willcox Company, 2007.

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Частини книг з теми "Modeling and parametric calibration":

1

Horejš, Otakar, Martin Mareš, Michal Straka, Jiří Švéda, and Tomáš Kozlok. "Adaptive Thermal Error Compensation Model of a Horizontal Machining Centre." In Lecture Notes in Production Engineering, 83–98. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34486-2_7.

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AbstractThe state-of-the-art method to reduce CNC machine tool thermal errors is real-time error compensation based on the thermal error estimation models. However, it is difficult to establish a thermal error compensation model with good versatility, high accuracy, and strong robustness due to various manufacturing conditions and a thermally varying surrounding environment. It causes that thermal behaviour of the machine tools is nonlinear and varying in real time. Consequently, the pre-trained and non-adaptive model may not be accurate and robust enough for long-term application. The presented research shows a systematic adaptation technique to update the thermal error compensation model of a horizontal machining centre under varying conditions, which differ from the calibration test. System identification theory is applied to build a dynamic thermal error model for a horizontal machining centre based on calibration test. Linear parametric models of autoregressive with external input (ARX) present an established dynamic method, and its modelling and calculation speed are suitable for real-time applications. Additionally, process-intermittent probing and thermal error model are integrated into the machine management software of the horizontal machining centre to monitor and compensate for thermal errors at the tool centre point (TCP) in real time using C#/C++ programming language. The results show that the prediction accuracy measured as peak-to-peak values and the normalized root mean squared error of the thermal error compensation models are improved by up to 33% and 51%, respectively, when adaptive compensation model is applied.
2

Mareš, Martin, Otakar Horejš, and Pravoslav Nykodym. "An Indicative Model Considering Part of the Thermo-Mechanical Behaviour of a Large Grinding Machine." In Lecture Notes in Production Engineering, 54–66. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34486-2_5.

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AbstractMachine tool (MT) thermal errors are an important element in ma-chined workpiece inaccuracies. In the past few decades, thermal errors associated mainly with one particular source (e.g. spindle or environment), have been successfully reduced by SW compensation techniques such as multiple linear re-gression analysis, finite element method, neural network, transfer function (TF) within similar calibration and verification conditions. An approach based on TFs is used for thermal error modelling in this research. This method respects basic heat transfer mechanisms in the MT and requires a minimum of additional gauges. The approach provides insight into the share of each source in the total machine thermal error through a combination of linear parametric models. The aim of this research is to develop an indicative model for a large grinding machine with predictive functionality focused on part of the thermo-mechanical behaviour within different configurations of the headstock, tailstock and workpiece. Unlike a compensation model, an indicative model has no connection to the MT feed drives and can only provide the machine operator with information regarding the actual direction and relative magnitude along with prediction of the time constant and steady state of the non-stationary thermal error. The second aim is to compare the difficulty of measuring at the stator and rotating machine part levels, the thermal behaviour linearity at both levels and the possibility of upgrading the indicative model to a compensation model to extend industrial applicability.
3

Reiss, Rolf-Dieter, and Michael Thomas. "Parametric Modeling." In Statistical Analysis of Extreme Values, 3–36. Basel: Birkhäuser Basel, 1997. http://dx.doi.org/10.1007/978-3-0348-6336-0_1.

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4

Ahmed, Mukhtar, Shakeel Ahmad, Muhammad Ali Raza, Uttam Kumar, Muhammad Ansar, Ghulam Abbas Shah, David Parsons, Gerrit Hoogenboom, Taru Palosuo, and Sabine Seidel. "Models Calibration and Evaluation." In Systems Modeling, 151–78. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4728-7_5.

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5

Harrell, Frank E. "Parametric Survival Models." In Regression Modeling Strategies, 423–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19425-7_18.

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Harrell, Frank E. "Parametric Survival Models." In Regression Modeling Strategies, 413–42. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3462-1_17.

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7

Kahle, Waltraud, and Heide Wendt. "Parametric Shock Models." In Advances in Degradation Modeling, 77–104. Boston, MA: Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4924-1_6.

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8

Roller, Dieter. "Advanced Methods for Parametric Design." In Geometric Modeling, 251–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76404-2_11.

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9

Wu, Lixin. "Calibration of LIBOR Market Model." In Interest Rate Modeling, 189–224. 2nd edition. | Boca Raton, Florida : CRC Press, [2019]: CRC Press, 2019. http://dx.doi.org/10.1201/9781351227421-7.

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10

Johora, Fatema Tuj. "Calibration Methodology." In Modeling Interactions among Pedestrians and Cars in Shared Spaces, 95–107. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-38345-9_6.

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Тези доповідей конференцій з теми "Modeling and parametric calibration":

1

HAMM, CA, and P. HINES. "MEASUREMENTS AND MODELLING ON A HIGH-POWER, LOW FREQUENCY PARAMETRIC ARRAY." In Underwater Acoustic Calibration and Measurements 1998. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/18897.

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2

Yang, Bin, Tomas McKelvey, Mats Viberg, and Guanghan Xu. "Array calibration using array response interpolation and parametric modeling." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362601.

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3

Moebus, Marco, Holger Degenhardt, and Abdelhak Zoubir. "Local array calibration using parametric modeling of position errors and a sparse calibration grid." In 2009 IEEE/SP 15th Workshop on Statistical Signal Processing (SSP). IEEE, 2009. http://dx.doi.org/10.1109/ssp.2009.5278542.

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4

Wang, Feng, Mengzhen Kang, Qi Lu, Hui Han, Véronique Letort, Yan Guo, Philippe de Reffye, and Baoguo Li. "Calibration of Topological Development in the Procedure of Parametric Identification: Application of the Stochastic GreenLab Model for Pinus sylvestris var. mongolica." In 2009 Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA). IEEE, 2009. http://dx.doi.org/10.1109/pma.2009.35.

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5

Carter, Jordan, and Aikaterini Genikomsou. "Finite element modeling of concrete beams reinforced with basalt FRP bars." In IABSE Congress, New York, New York 2019: The Evolving Metropolis. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/newyork.2019.0460.

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<p>Fiber-reinforced polymer (FRP) bars can replace conventional steel reinforcing rebars to prevent from corrosion in reinforced concrete structures exposed to highly corrosive environments. In this contribution, three tested concrete beams reinforced with BFRP (Basalt Fiber Reinforced Polymer) bars are analyzed using three-dimensional finite element methods. In the numerical analyses, concrete is modeled as nonlinear using plasticity and damage principles, while BFRP is modeled as linear elastic material. The main focus of this research is to present the calibration process that should take place prior to any parametric studies. This calibration suggests that the concrete model should be regularized using a characteristic length and material post-yield fracture energies in both tension and compression to provide mesh-size independent results. The numerical results are compared to the test results with regard to failure load and cracking.</p>
6

Petrella, Orsola, Salvatore Ameduri, Vincenzo Quaranta, Giovanni Betta, and Marco Laracca. "Modelling, parametric analysis, and optimization of an experimental set-up for acoustic antenna calibration." In 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2013. http://dx.doi.org/10.1109/i2mtc.2013.6555698.

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7

Nannapaneni, Saideep, and Sankaran Mahadevan. "Manufacturing Process Evaluation Under Uncertainty: A Hierarchical Bayesian Network Approach." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59226.

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This paper proposes a Hierarchical Bayesian Network (HBN) approach to estimate the uncertainty in performance prediction of manufacturing processes by aggregating the uncertainty arising from multiple models at multiple levels. A HBN is an extension of a Bayesian network (BN) for modeling hierarchical or multi-level systems where each node may represent a lower-level BN. The BNs at different levels can be constructed either using physics-based models or available data or by a hybrid approach through a combination of physics-based models and data. An improved BN learning algorithm is presented where the topology is learnt using an existing algorithm but different parametric and non-parametric models are fit to represent the conditional probabilities. Data for model calibration may be available at multiple levels such as at the unit process level or line level or sometimes at the factory level. Using all the data for calibration can be computationally expensive; therefore, a multi-level segmented approach for model calibration is developed. The injection molding process is used to demonstrate the proposed methodologies for uncertainty prediction in its energy consumption.
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Schoenhardt, Matthew, Vachel Pardais, Cheryl Fortin, Brent Kitson, and Jay Hanzel. "Enbridge Cost and Schedule Contingency Assessments." In 2012 9th International Pipeline Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/ipc2012-90259.

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All capital projects have an element of risk and uncertainty. In today’s business environment this requires more than just simply adding 10 percent contingency to the cost estimate to cover off project unknowns. Before sanctioning a project for hundreds of millions of dollars, Board of Directors need to know what possible cost and schedule outcomes exist in order to safe-guard shareholders’ investments. Contingency assessments must be: • Risk-based • Project Specific • Repeatable • Defendable • Cost effective Six years ago, Enbridge grappled with these issues and realized it needed to adopt a new method of assessing both cost and schedule contingencies. After evaluating options, Enbridge set upon developing an in-house parametric modeling solution for its contingency assessment needs. This paper will: • Identify various options for assessing contingency • Review the Enbridge process • Demonstrate the value of a simplified risk register • Identify required data inputs • Illustrate calibration and accuracy of assessments • Discuss business advantages of parametric modelling
9

Pereira, Israel Cubas, and Diego F. B. Sarzosa. "Calibration and Verification of XFEM Model to Predict Ductile Fracture." In ASME 2022 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/pvp2022-84341.

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Abstract The XFEM (Extended Finite Element Method) has emerged as a reliable tool for structural engineers to study fracture problems. This method was introduced in 1999 as an alternative to the solution of models with inclusions and discontinuities. As it is a recent method and despite being available in most of the commercial software, the modeling with XFEM lacks assessment on the sensitivity of the method in terms of mesh refinement and other parameters that need to be suitable for building the model. This experimental and numerical study explores the Extended Finite Element Method to predict ductile crack propagation of typical fracture specimens made of a pressure vessel ASTM A285 steel. First, a detailed parametric study is conducted to reproduce the load versus displacement curve obtained from a fracture toughness test using deep crack bend samples. Then, after calibrating the model parameters, the model is used to predict the response of specimens with different levels of crack-tip triaxiality. For this purpose, shallow crack bend specimens with and without side grooves are modeled and compared to experimental toughness tests. Overall, a good agreement between experimental and numerical responses was observed.
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Durocher, Antoine, Gilles Bourque, and Jeffrey M. Bergthorson. "Bayesian Calibration of Kinetic Parameters in the CH Chemistry Towards Accurate Prompt-NO Modelling." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-78466.

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Abstract Significant efforts made by the gas turbine industry have helped reduce nitrogen oxides (NOx) emissions considerably. To meet and surpass the increasingly stringent regulations, accurate and robust thermochemical mechanisms are needed to help design future sub-10 ppm combustion systems. Uncertainty in kinetic modelling, however, can result in large prediction uncertainty and significant discrepancy between models that hinder the identification of promising combustors with confidence. Direct reaction rate measurements are seldom available for some reactions, especially when involving short-lived radicals like methylidyne, CH. As the main precursor to the prompt-NO formation pathway, its large parametric uncertainty directly propagates through the nitrogen chemistry preventing accurate and precise emissions predictions. Recent independent CH concentration measurements obtained at various operating conditions are used as indirect rate measurements to perform statistical, or Bayesian, calibration. A subset of important reactions in the CH chemistry affecting peak-CH concentration is identified through uncertainty-weighted sensitivity analysis to first constrain the parametric space of this prompt-NO precursor. Spectral expansion provides the surrogate model used in the Markov-Chain Monte Carlo method to evaluate the posterior kinetic distribution. The resulting constrained CH-chemistry better captures experimental measurements while providing smaller prediction uncertainty of a similar order as the uncertainty of the measurements, which can increase the confidence in simulation results to identify promising future low-emissions configurations. For the quasi-steady state species CH, fuel decomposition reactions leading to CH production are constrained while little impact is observed for intermediate reactions within the CH-chemistry. The reduction in prediction uncertainty results mainly from the constrained correlations between parameters which greatly limit the set of feasible reaction rate combinations. Additional independent direct and indirect measurements would be necessary to further constrain rate parameters in the CH chemistry, but this calibration demonstrates that predictions of radical species can be improved by assimilating enough data.

Звіти організацій з теми "Modeling and parametric calibration":

1

Proctor, Camron, and Steven Wiryadinata. Parametric Vehicle Choice Modeling: ParaChoice. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1598100.

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2

Moses, Randolph L. Parametric Signature Modeling for Target Identification. Fort Belvoir, VA: Defense Technical Information Center, May 1995. http://dx.doi.org/10.21236/ada296734.

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3

Golish, L. M., and Eric D. Griffith. Analysis of Parametric Design Applications for Modeling Military Standard Facilities. Fort Belvoir, VA: Defense Technical Information Center, July 1992. http://dx.doi.org/10.21236/ada255740.

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Nordham, David J. Automated Ship Auxiliary Systems Design Process -- Ship Parametric Modeling Program. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada261834.

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Kay, Steven, and Debasis Sengupta. Detection in Incompletely Characterized Colored Non-Gaussian Noise via Parametric Modeling. Fort Belvoir, VA: Defense Technical Information Center, August 1986. http://dx.doi.org/10.21236/ada175402.

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Shi, Liang, Xiaobing Liu, Ming Qu, Mingkan Zhang, and Lingshi Wang. Numerical Modeling and Parametric Study of a Dual Purpose Underground Thermal Battery. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1818730.

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Baker, Benjamin, Javier Ortensi, and Mark DeHart. FY 2017 Modeling of the M8 Calibration Series using MAMMOTH. Office of Scientific and Technical Information (OSTI), June 2017. http://dx.doi.org/10.2172/1408512.

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Goodman, Carolyn, Laura Hinkle, Trevor Hardy, and Hayden Reeve. Building Model Calibration: Validation of GridLAB-D Thermal Dynamics Modeling. Office of Scientific and Technical Information (OSTI), November 2022. http://dx.doi.org/10.2172/1897180.

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Peterson, John R., P. J. Marshall, and K. Andersson. Smoothed Particle Inference: A Kilo-Parametric Method for X-ray Galaxy Cluster Modeling. Office of Scientific and Technical Information (OSTI), August 2005. http://dx.doi.org/10.2172/878813.

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Goodhue, Rachael, and Carlo Russo. Modeling Processor Market Power and the Incidence of Agricultural Policy: A Non-parametric Approach. Cambridge, MA: National Bureau of Economic Research, January 2011. http://dx.doi.org/10.3386/w16706.

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