Relevant bibliographies by topics / Accelerating methods

Contents

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

Academic literature on the topic 'Accelerating methods'

Author: Grafiati
Published: 3 June 2023
Last updated: 14 September 2024

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Journal articles on the topic "Accelerating methods"

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Papadrakakis, M. "Accelerating Vector Iteration Methods." Journal of Applied Mechanics 53, no. 2 (June 1, 1986): 291–97. http://dx.doi.org/10.1115/1.3171754.

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This paper describes a technique for accelerating the convergence properties of iterative methods for the solution of large sparse symmetric linear systems that arise from the application of finite element method. The technique is called partial preconditioning process (PPR) and can be combined with pure vector iteration methods, such as the conjugate gradient, the dynamic relaxation, and the Chebyshev semi-iterative methods. The proposed triangular splitting preconditioner combines Evans’ SSOR preconditioner with a drop-off tolerance criterion. The (PPR) is attractive in a FE framework because it is simple and can be implemented at the element level as opposed to incomplete Cholesky preconditioners, which require a sparse assembly. The method, despite its simplicity, is shown to be more efficient on a set of test problems for certain values of the drop-off tolerance parameter than the partial elimination method.
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Zhou, Yu-Long, Fan Xia, Ai-Jun Xie, Hao-Ping Peng, Jian-Hua Wang, and Zhi-Wei Li. "A Review—Effect of Accelerating Methods on Gas Nitriding: Accelerating Mechanism, Nitriding Behavior, and Techno-Economic Analysis." Coatings 13, no. 11 (October 27, 2023): 1846. http://dx.doi.org/10.3390/coatings13111846.

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Gas nitriding, as a surface modification technology to improve the wear resistance of workpiece surfaces, is widely used in wind turbine gears, pressure vessel gears, high-precision die casting abrasives, and other areas. However, the gas nitriding time is too long, reaching 40–60 h, which reduces the efficiency of nitriding and hinders the development of gas nitriding. Therefore, various accelerating methods are born accordingly. This review first introduces the basic principle, microstructure, and process parameters of conventional gas nitriding. Then, five common accelerating methods are summarized: process parameter optimization, surface mechanical nano-crystallization, surface-active catalysis, surface pre-oxidation, and surface laser treatment. Then, the effect of acceleration methods on gas nitriding is analyzed for the acceleration mechanism, nitriding behavior, and nitriding efficiency. Finally, the technical economy of the acceleration methods is compared for three aspects: energy consumption, carbon dioxide emission, and cost. And, the technical maturity of the acceleration methods is compared according to technology readiness level (TRL) technology. Based on the above content, the advantages and disadvantages of the five accelerating methods are reviewed, and the concept of a multi-technology collaborative processing acceleration method is proposed.
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Galletti, Mario, Maria Pia Anania, Sahar Arjmand, Angelo Biagioni, Gemma Costa, Martina Del Giorno, Massimo Ferrario, et al. "Advanced Stabilization Methods of Plasma Devices for Plasma-Based Acceleration." Symmetry 14, no. 3 (February 24, 2022): 450. http://dx.doi.org/10.3390/sym14030450.

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Towards the next generation of compact plasma-based accelerators, useful in several fields, such as basic research, medicine and industrial applications, a great effort is required to control the plasma creation, the necessity of producing a time-jitter free channel, and its stability namely uniformity and reproducibility. In this Letter, we describe an experimental campaign adopting a gas-filled discharge-capillary where the plasma and its generation are stabilized by triggering its ignition with an external laser pulse or an innovative technique based on the primary dark current (DC) in the accelerating structure of a linear accelerator (LINAC). The results show an efficient stabilization of the discharge pulse and plasma density with both pre-ionizing methods turning the plasma device into a symmetrical stable accelerating environment, especially when the external voltage is lowered near the breakdown value of the gas. The development of tens of centimeter long capillaries is enabled and, in turn, longer acceleration lengths can be adopted in a wide range of plasma-based acceleration experiments.
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Hustoft, Hanne Kolsrud, Leon Reubsaet, Tyge Greibrokk, Elsa Lundanes, and Helle Malerod. "Critical assessment of accelerating trypsination methods." Journal of Pharmaceutical and Biomedical Analysis 56, no. 5 (December 2011): 1069–78. http://dx.doi.org/10.1016/j.jpba.2011.08.013.

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Minakov, Artyom Dmitrievich, and Vladimir Anatolievich Sudakov. "Methods for accelerating controlled online experiments." Keldysh Institute Preprints, no. 36 (2023): 1–16. http://dx.doi.org/10.20948/prepr-2023-36.

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The paper discusses current methods of hypothesis testing on a large set of data on user actions on a banking site. Methods have been developed and investigated using a consistent approach and Bayesian statistics, and a recommendation for use has been formed. The application of the proposed methods made it possible to reduce the time of conducting controlled online experiments by 25-34%.
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Patra, Tarak K. "Data-Driven Methods for Accelerating Polymer Design." ACS Polymers Au 2, no. 1 (December 28, 2021): 8–26. http://dx.doi.org/10.1021/acspolymersau.1c00035.

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Oh, Se-Chang, Young-Bok Joo, Oh-Young Kwon, and Kyung-Moo Huh. "GPU Accelerating Methods for Pease FFT Processing." Journal of Institute of Control, Robotics and Systems 20, no. 1 (January 1, 2014): 37–41. http://dx.doi.org/10.5302/j.icros.2014.13.1960.

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Ulhaq, Aman, Emma McCrory, and Eleni Besi. "Surgical Methods for Accelerating Orthodontic Tooth Movement." Orthodontic Update 13, no. 4 (October 2, 2020): 170–79. http://dx.doi.org/10.12968/ortu.2020.13.4.170.

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The ability to consistently reduce orthodontic treatment time without adverse outcomes would be an attractive prospect. Several surgical interventions have been described aimed at accelerating orthodontic tooth movement. The aim of this review is to identify and evaluate the current evidence available for surgically-assisted orthodontic tooth movement (OTM). The current evidence suggests that surgical procedures may increase the rate of tooth movement, however, this effect is short lived. Further reporting on total treatment time, and patient centred outcomes, would be beneficial in future studies. CPD/Clinical Relevance: To explain surgical methods for accelerating orthodontic tooth movement.
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Li, Yu, Tao Zhang, Shuyu Sun, and Xin Gao. "Accelerating flash calculation through deep learning methods." Journal of Computational Physics 394 (October 2019): 153–65. http://dx.doi.org/10.1016/j.jcp.2019.05.028.

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Kornfeld, Isaac. "Nonexistence of universally accelerating linear summability methods." Journal of Computational and Applied Mathematics 53, no. 3 (August 1994): 309–21. http://dx.doi.org/10.1016/0377-0427(94)90059-0.

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Dissertations / Theses on the topic "Accelerating methods"

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Kerdreux, Thomas. "Accelerating conditional gradient methods." Thesis, Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLE002.

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Les algorithmes de Frank-Wolfe sont des méthodes d’optimisation de problèmes sous contraintes. Elles décomposent un problème non-linéaire en une série de problèmes linéaires. Cela en fait des méthodes de choix pour l’optimisation en grande dimension et notamment explique leur utilisation dans de nombreux domaines appliqués. Ici nous proposons de nouveaux algorithmes de Frank-Wolfe qui convergent plus rapidement vers la solution du problème d’optimisation sous certaines hypothèses structurelles assez génériques. Nous montrons en particulier, que contrairement à d’autres types d’algorithmes, cette famille s’adapte à ces hypothèses sans avoir à spécifier les paramètres qui les contrôlent
The Frank-Wolfe algorithms, a.k.a. conditional gradient algorithms, solve constrained optimization problems. They break down a non-linear problem into a series of linear minimization on the constraint set. This contributes to their recent revival in many applied domains, in particular those involving large-scale optimization problems. In this dissertation, we design or analyze versions of the Frank-Wolfe algorithms. We notably show that, contrary to other types of algorithms, this family is adaptive to a broad spectrum of structural assumptions, without the need to know and specify the parameters controlling these hypotheses
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Dahlin, Johan. "Accelerating Monte Carlo methods for Bayesian inference in dynamical models." Doctoral thesis, Linköpings universitet, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-125992.

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Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. In this thesis, we make use of Bayesian statistics to construct probabilistic models given prior information and historical data, which can be used for decision support and predictions. The main obstacle with this approach is that it often results in mathematical problems lacking analytical solutions. To cope with this, we make use of statistical simulation algorithms known as Monte Carlo methods to approximate the intractable solution. These methods enjoy well-understood statistical properties but are often computational prohibitive to employ. The main contribution of this thesis is the exploration of different strategies for accelerating inference methods based on sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). That is, strategies for reducing the computational effort while keeping or improving the accuracy. A major part of the thesis is devoted to proposing such strategies for the MCMC method known as the particle Metropolis-Hastings (PMH) algorithm. We investigate two strategies: (i) introducing estimates of the gradient and Hessian of the target to better tailor the algorithm to the problem and (ii) introducing a positive correlation between the point-wise estimates of the target. Furthermore, we propose an algorithm based on the combination of SMC and Gaussian process optimisation, which can provide reasonable estimates of the posterior but with a significant decrease in computational effort compared with PMH. Moreover, we explore the use of sparseness priors for approximate inference in over-parametrised mixed effects models and autoregressive processes. This can potentially be a practical strategy for inference in the big data era. Finally, we propose a general method for increasing the accuracy of the parameter estimates in non-linear state space models by applying a designed input signal.
Borde Riksbanken höja eller sänka reporäntan vid sitt nästa möte för att nå inflationsmålet? Vilka gener är förknippade med en viss sjukdom? Hur kan Netflix och Spotify veta vilka filmer och vilken musik som jag vill lyssna på härnäst? Dessa tre problem är exempel på frågor där statistiska modeller kan vara användbara för att ge hjälp och underlag för beslut. Statistiska modeller kombinerar teoretisk kunskap om exempelvis det svenska ekonomiska systemet med historisk data för att ge prognoser av framtida skeenden. Dessa prognoser kan sedan användas för att utvärdera exempelvis vad som skulle hända med inflationen i Sverige om arbetslösheten sjunker eller hur värdet på mitt pensionssparande förändras när Stockholmsbörsen rasar. Tillämpningar som dessa och många andra gör statistiska modeller viktiga för många delar av samhället. Ett sätt att ta fram statistiska modeller bygger på att kontinuerligt uppdatera en modell allteftersom mer information samlas in. Detta angreppssätt kallas för Bayesiansk statistik och är särskilt användbart när man sedan tidigare har bra insikter i modellen eller tillgång till endast lite historisk data för att bygga modellen. En nackdel med Bayesiansk statistik är att de beräkningar som krävs för att uppdatera modellen med den nya informationen ofta är mycket komplicerade. I sådana situationer kan man istället simulera utfallet från miljontals varianter av modellen och sedan jämföra dessa mot de historiska observationerna som finns till hands. Man kan sedan medelvärdesbilda över de varianter som gav bäst resultat för att på så sätt ta fram en slutlig modell. Det kan därför ibland ta dagar eller veckor för att ta fram en modell. Problemet blir särskilt stort när man använder mer avancerade modeller som skulle kunna ge bättre prognoser men som tar för lång tid för att bygga. I denna avhandling använder vi ett antal olika strategier för att underlätta eller förbättra dessa simuleringar. Vi föreslår exempelvis att ta hänsyn till fler insikter om systemet och därmed minska antalet varianter av modellen som behöver undersökas. Vi kan således redan utesluta vissa modeller eftersom vi har en bra uppfattning om ungefär hur en bra modell ska se ut. Vi kan också förändra simuleringen så att den enklare rör sig mellan olika typer av modeller. På detta sätt utforskas rymden av alla möjliga modeller på ett mer effektivt sätt. Vi föreslår ett antal olika kombinationer och förändringar av befintliga metoder för att snabba upp anpassningen av modellen till observationerna. Vi visar att beräkningstiden i vissa fall kan minska ifrån några dagar till någon timme. Förhoppningsvis kommer detta i framtiden leda till att man i praktiken kan använda mer avancerade modeller som i sin tur resulterar i bättre prognoser och beslut.
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Lopes, Antonio Roldao. "Accelerating iterative methods for solving systems of linear equations using FPGAs." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.526401.

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Ghadimi, Euhanna. "Accelerating Convergence of Large-scale Optimization Algorithms." Doctoral thesis, KTH, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-162377.

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Several recent engineering applications in multi-agent systems, communication networks, and machine learning deal with decision problems that can be formulated as optimization problems. For many of these problems, new constraints limit the usefulness of traditional optimization algorithms. In some cases, the problem size is much larger than what can be conveniently dealt with using standard solvers. In other cases, the problems have to be solved in a distributed manner by several decision-makers with limited computational and communication resources. By exploiting problem structure, however, it is possible to design computationally efficient algorithms that satisfy the implementation requirements of these emerging applications. In this thesis, we study a variety of techniques for improving the convergence times of optimization algorithms for large-scale systems. In the first part of the thesis, we focus on multi-step first-order methods. These methods add memory to the classical gradient method and account for past iterates when computing the next one. The result is a computationally lightweight acceleration technique that can yield significant improvements over gradient descent. In particular, we focus on the Heavy-ball method introduced by Polyak. Previous studies have quantified the performance improvements over the gradient through a local convergence analysis of twice continuously differentiable objective functions. However, the convergence properties of the method on more general convex cost functions has not been known. The first contribution of this thesis is a global convergence analysis of the Heavy- ball method for a variety of convex problems whose objective functions are strongly convex and have Lipschitz continuous gradient. The second contribution is to tailor the Heavy- ball method to network optimization problems. In such problems, a collection of decision- makers collaborate to find the decision vector that minimizes the total system cost. We derive the optimal step-sizes for the Heavy-ball method in this scenario, and show how the optimal convergence times depend on the individual cost functions and the structure of the underlying interaction graph. We present three engineering applications where our algorithm significantly outperform the tailor-made state-of-the-art algorithms. In the second part of the thesis, we consider the Alternating Direction Method of Multipliers (ADMM), an alternative powerful method for solving structured optimization problems. The method has recently attracted a large interest from several engineering communities. Despite its popularity, its optimal parameters have been unknown. The third contribution of this thesis is to derive optimal parameters for the ADMM algorithm when applied to quadratic programming problems. Our derivations quantify how the Hessian of the cost functions and constraint matrices affect the convergence times. By exploiting this information, we develop a preconditioning technique that allows to accelerate the performance even further. Numerical studies of model-predictive control problems illustrate significant performance benefits of a well-tuned ADMM algorithm. The fourth and final contribution of the thesis is to extend our results on optimal scaling and parameter tuning of the ADMM method to a distributed setting. We derive optimal algorithm parameters and suggest heuristic methods that can be executed by individual agents using local information. The resulting algorithm is applied to distributed averaging problem and shown to yield substantial performance improvements over the state-of-the-art algorithms.

QC 20150327

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Singh, Karanpreet. "Accelerating Structural Design and Optimization using Machine Learning." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/104114.

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Machine learning techniques promise to greatly accelerate structural design and optimization. In this thesis, deep learning and active learning techniques are applied to different non-convex structural optimization problems. Finite Element Analysis (FEA) based standard optimization methods for aircraft panels with bio-inspired curvilinear stiffeners are computationally expensive. The main reason for employing many of these standard optimization methods is the ease of their integration with FEA. However, each optimization requires multiple computationally expensive FEA evaluations, making their use impractical at times. To accelerate optimization, the use of Deep Neural Networks (DNNs) is proposed to approximate the FEA buckling response. The results show that DNNs obtained an accuracy of 95% for evaluating the buckling load. The DNN accelerated the optimization by a factor of nearly 200. The presented work demonstrates the potential of DNN-based machine learning algorithms for accelerating the optimization of bio-inspired curvilinearly stiffened panels. But, the approach could have disadvantages for being only specific to similar structural design problems, and requiring large datasets for DNNs training. An adaptive machine learning technique called active learning is used in this thesis to accelerate the evolutionary optimization of complex structures. The active learner helps the Genetic Algorithms (GA) by predicting if the possible design is going to satisfy the required constraints or not. The approach does not need a trained surrogate model prior to the optimization. The active learner adaptively improve its own accuracy during the optimization for saving the required number of FEA evaluations. The results show that the approach has the potential to reduce the total required FEA evaluations by more than 50%. Lastly, the machine learning is used to make recommendations for modeling choices while analyzing a structure using FEA. The decisions about the selection of appropriate modeling techniques are usually based on an analyst's judgement based upon their knowledge and intuition from past experience. The machine learning-based approach provides recommendations within seconds, thus, saving significant computational resources for making accurate design choices.
Doctor of Philosophy
This thesis presents an innovative application of artificial intelligence (AI) techniques for designing aircraft structures. An important objective for the aerospace industry is to design robust and fuel-efficient aerospace structures. The state of the art research in the literature shows that the structure of aircraft in future could mimic organic cellular structure. However, the design of these new panels with arbitrary structures is computationally expensive. For instance, applying standard optimization methods currently being applied to aerospace structures to design an aircraft, can take anywhere from a few days to months. The presented research demonstrates the potential of AI for accelerating the optimization of an aircraft structures. This will provide an efficient way for aircraft designers to design futuristic fuel-efficient aircraft which will have positive impact on the environment and the world.
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Bryan, Paul David. "Accelerating microarchitectural simulation via statistical sampling principles." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47715.

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The design and evaluation of computer systems rely heavily upon simulation. Simulation is also a major bottleneck in the iterative design process. Applications that may be executed natively on physical systems in a matter of minutes may take weeks or months to simulate. As designs incorporate increasingly higher numbers of processor cores, it is expected the times required to simulate future systems will become an even greater issue. Simulation exhibits a tradeoff between speed and accuracy. By basing experimental procedures upon known statistical methods, the simulation of systems may be dramatically accelerated while retaining reliable methods to estimate error. This thesis focuses on the acceleration of simulation through statistical processes. The first two techniques discussed in this thesis focus on accelerating single-threaded simulation via cluster sampling. Cluster sampling extracts multiple groups of contiguous population elements to form a sample. This thesis introduces techniques to reduce sampling and non-sampling bias components, which must be reduced for sample measurements to be reliable. Non-sampling bias is reduced through the Reverse State Reconstruction algorithm, which removes ineffectual instructions from the skipped instruction stream between simulated clusters. Sampling bias is reduced via the Single Pass Sampling Regimen Design Process, which guides the user towards selected representative sampling regimens. Unfortunately, the extension of cluster sampling to include multi-threaded architectures is non-trivial and raises many interesting challenges. Overcoming these challenges will be discussed. This thesis also introduces thread skew, a useful metric that quantitatively measures the non-sampling bias associated with divergent thread progressions at the beginning of a sampling unit. Finally, the Barrier Interval Simulation method is discussed as a technique to dramatically decrease the simulation times of certain classes of multi-threaded programs. It segments a program into discrete intervals, separated by barriers, which are leveraged to avoid many of the challenges that prevent multi-threaded sampling.
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Parks, Paula L. "Moving at the speed of potential| A mixed-methods study of accelerating developmental students in a California community college." Thesis, Capella University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3611804.

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Most developmental community college students are not completing the composition sequence successfully. This mixed-methods study examined acceleration as a way to help developmental community college students complete the composition sequence more quickly and more successfully. Acceleration is a curricular redesign that includes challenging readings and assignments and reduces the number of required classes in the developmental composition sequence. Developmental students taking an accelerated composition class at the California community college studied were as successful as developmental students taking the traditional segmented basic skills course. Students who pass the accelerated course skip a developmental class and are eligible to take the college-level course, which saves them time and money. The students who were interviewed cited the main factors leading to their success: the academic support from faculty, academic support from fellow students, the personality/caring of the teacher, and an interest in the class theme. Data were from the first semester the college offered this class. Findings from the study indicate that the college studied should continue offering accelerated composition classes and should encourage attendance at professional development meetings so that all parts of the accelerated curriculum will be implemented in the future. Implementing all parts of the accelerated curriculum may increase the success rates. The college studied should also re-examine its traditional basic skills curriculum and the timed writing departmental final exam, which causes unnecessary stress and lowers expectations. More effort could be made to include readings from minority authors and to provide support, such as through learning communities.

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O'Brien, Gerard. "Comparison and evaluation of United Nations and ARC based test methods for the determination of self-accelerating decomposition temperatures." Thesis, London South Bank University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388169.

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Drzisga, Daniel [Verfasser], Barbara [Akademischer Betreuer] Wohlmuth, Matthias [Gutachter] Möller, Barbara [Gutachter] Wohlmuth, and Giancarlo [Gutachter] Sangalli. "Accelerating Isogeometric Analysis and Matrix-free Finite Element Methods Using the Surrogate Matrix Methodology / Daniel Drzisga ; Gutachter: Matthias Möller, Barbara Wohlmuth, Giancarlo Sangalli ; Betreuer: Barbara Wohlmuth." München : Universitätsbibliothek der TU München, 2020. http://d-nb.info/122693434X/34.

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Macedo, Alves de Lima Jean. "Développement et validation d'un nouveau critère de déformation progressive pour les REPs." Electronic Thesis or Diss., Ecully, Ecole centrale de Lyon, 2023. http://www.theses.fr/2023ECDL0011.

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Lors de la conception, la construction et l'exploitation d'un composant nucléaire, il est nécessaire d’assurer l’intégrité de celui-ci quelles que soient les conditions de fonctionnement, nominales ou accidentelles. Dans le cadre de la sûreté, la démonstration de la résistance des composants fondamentaux des circuits primaires et secondaires aux modes de ruine est une nécessité pour valider le dimensionnement de ces structures. Parmi les modes possibles de ruine figure le phénomène de déformation progressive. De manière générale, le dimensionnement en déformation progressive des composants de centrales nucléaires est réalisé par des méthodes simplifiées ou des analyses inélastiques complètes. D’un point de vue industriel, les deux types d’évaluation ne sont pas satisfaisantes car elles sont soit trop sévères soit trop complexes à mettre en œuvre. Dans ce contexte, ces travaux de thèse visent à développer un nouveau critère industriel et/ou une nouvelle méthodologie de calcul applicable sur des structures complexes. Le premier chapitre est consacré à l'étude bibliographique, notamment à l’étude du phénomène de déformation progressive. Le deuxième chapitre présente la modélisation du comportement des matériaux métalliques et les méthodes numériques pour simuler les calculs cycliques. Nous proposons une nouvelle méthode d'accélération des calculs cycliques afin de rendre la méthode d'intégration pas à pas plus rapide. Le troisième chapitre est consacré à la modélisation des essais COTHAA. Des modèles de comportement sont évalués afin de proposer un modèle capable de décrire la déformation progressive observée sur des structures. Les résultats prédits par une version simplifiée du modèle de Chaboche sont en bon accord avec les mesures expérimentales. Nous montrons également l'aptitude de la nouvelle méthode d'accélération à simuler ces essais. Le quatrième chapitre est dédié à l'étude expérimentale. Dans un premier temps, nous proposons un nouvel essai de déformation progressive : l'essai DEFPROG. Dans un second temps, nous validons le modèle proposé dans le troisième chapitre sur ces résultats expérimentaux. Le cinquième, et dernier chapitre, est consacré à la proposition de la nouvelle méthode pour se prémunir contre le risque de déformation progressive. Nous proposons et validons une nouvelle méthode simplifiée, tout en s'appuyant sur des résultats expérimentaux et des modélisations
During the design, construction and operation of a nuclear component, it is necessary to ensure its integrity whatever the operating conditions : nominal or accidental. The demonstration of the components’ resistance of the primary and secondary circuits to failure modes is necessary in order to validate the design of these structures. Among the possible failure modes is the phenomenon of ratcheting. The ratcheting check of nuclear power plant structures is mainly investigated by means of simplified methods or a complete inelastic analysis. Nevertheless, these methods are either conservatives or complex to use and implement. In this context, the aim of this thesis is to develop a new industrial design rule and/or new calculation methodology that is applicable to complex structures.The first chapter is addressed to the state of the art, in particular to the ratcheting phenomenon. The second chapter presents the modeling of metallic materials and the numerical methods to simulate cyclic calculations. We propose a new method for accelerating cyclic calculations in order to make the step-by-step integration method faster.The third chapter is devoted to the modeling of COTHAA tests. Constitutive models are evaluated in order to propose a robust model capable of simulating ratcheting. Results predicted by a simplified version of Chaboche model are found in good agreement as compared to experimental measurements. We also show the ability of the new acceleration method to simulate these tests. The fourth chapter is dedicated to the experimental study. We propose a new structural ratcheting test: the DEFPROG test. Secondly, we validate the model proposed in the third chapter on these experimental results. The fifth and last chapter is devoted to the proposal of the new design rule to forecast the risk of ratcheting. We propose and validate a new simplified method, while relying on experimental results and modeling
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Books on the topic "Accelerating methods"

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Tan, Yao-Hua. Accelerating global supply chains with IT-innovation: ITAIDE tools and methods. Heidelberg: Springer, 2011.

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Slomski, J. F. Effectiveness of multigrid in accelerating convergence of multidimensional flows in chemical nonequilibrium. New York: American Institute of Aeronautics and Astronautics, 1990.

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Proch, D. Transparencies from the Workshop on Thin Film Coating Methods for Superconducting Accelerating Cavities. Hamburg: Deutsches Elektronen-Synchrotron DESY, MHF-SL Group, 2000.

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Moyer, Brian. Aggregation issues in integrating and accelerating BEA's accounts: Improved methods for calculating GDP by industry. Cambridge, MA: National Bureau of Economic Research, 2005.

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Maxwell, Wendy. Accelerating fluency: A holistic approach to the teaching of French through the integration of the gesture approach, drama and music. Bowen Island, B.C: Muffin Rhythm Co., 2003.

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Yu, Wenhua. Advanced FDTD methods: Parallelization, acceleration, and engineering applications. Boston: Artech House, 2011.

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Nikitchenko, Maxim V. Inference of functional neural connectivity and convergence acceleration methods. [New York, N.Y.?]: [publisher not identified], 2013.

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Valcartier, Canada Defence Research Establishment. Acceleration-Invariant Approximation Method For Recursive Digital Filters. S.l: s.n, 1985.

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Demuren, A. O. Convergence acceleration of the proteus computer code with multigrid methods. Norfolk, Va: Old Dominion University Research Foundation of Mechanical Engineering & Mechanics, College of Engineering & Technology, Old Dominion University, 1992.

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McGhee, D. S. The effect of acceleration versus displacement methods on steady-state boundary forces. Marshall Space Flight Center, Ala: George C. Marshall Space Flight Center, 1992.

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Book chapters on the topic "Accelerating methods"

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Robert, Christian P., and George Casella. "Controlling and Accelerating Convergence." In Introducing Monte Carlo Methods with R, 89–124. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1576-4_4.

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Liu, Jianwei, Wout Hofman, and Yao-Hua Tan. "Procedure Redesign Methods." In Accelerating Global Supply Chains with IT-Innovation, 223–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15669-4_13.

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Teixeira, Cristina C., Edmund Khoo, and Mani Alikhani. "Different Methods of Accelerating Tooth Movement." In Clinical Guide to Accelerated Orthodontics, 19–31. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-43401-8_2.

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Koirala, Kushal, Keya Joshi, Victor Adediwura, Jinan Wang, Hung Do, and Yinglong Miao. "Accelerating Molecular Dynamics Simulations for Drug Discovery." In Methods in Molecular Biology, 187–202. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3441-7_11.

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Zhang, Pei. "Analysis Methods for Beam Position Extraction from HOM." In Beam Diagnostics in Superconducting Accelerating Cavities, 43–60. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00759-5_4.

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Benvenuti, C., Ph Bernard, D. Bloess, E. Chiaveri, C. Hauviller, and W. Weingarten. "Various Methods of Manufacturing Superconducting Accelerating Cavities." In A Cryogenic Engineering Conference Publication, 885–93. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0373-2_114.

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Heifetz, Alexander. "Accelerating COVID-19 Drug Discovery with High-Performance Computing." In Methods in Molecular Biology, 405–11. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3449-3_19.

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Fasman, Kenneth H. "Managing Accelerating Data Growth in the Genome Database." In Theoretical and Computational Methods in Genome Research, 145–51. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5903-0_11.

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Aguilera-Puga, Mariana d. C., Natalia L. Cancelarich, Mariela M. Marani, Cesar de la Fuente-Nunez, and Fabien Plisson. "Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence." In Methods in Molecular Biology, 329–52. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3441-7_18.

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Monza, Emanuele, Victor Gil, and Maria Fatima Lucas. "Computational Enzyme Design at Zymvol." In Methods in Molecular Biology, 249–59. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1826-4_13.

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AbstractDirected evolution is the most recognized methodology for enzyme engineering. The main drawback resides in its random nature and in the limited sequence exploration; both require screening of thousands (if not millions) of variants to achieve a target function. Computer-driven approaches can limit laboratorial screening to a few hundred candidates, enabling and accelerating the development of industrial enzymes. In this book chapter, the technology adopted at Zymvol is described. An overview of the current development and future directions in the company is also provided.
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Conference papers on the topic "Accelerating methods"

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Madhukar, Kumar, Bjorn Wachter, Daniel Kroening, Matt Lewis, and Mandayam Srivas. "Accelerating invariant generation." In 2015 Formal Methods in Computer-Aided Design (FMCAD). IEEE, 2015. http://dx.doi.org/10.1109/fmcad.2015.7542259.

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Shmueli, Yaniv, Gil Shabat, Amit Bermanis, and Amir Averbuch. "Accelerating Particle filter using multiscale methods." In 2012 IEEE 27th Convention of Electrical & Electronics Engineers in Israel (IEEEI 2012). IEEE, 2012. http://dx.doi.org/10.1109/eeei.2012.6377009.

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Kamvar, Sepandar D., Taher H. Haveliwala, Christopher D. Manning, and Gene H. Golub. "Extrapolation methods for accelerating PageRank computations." In the twelfth international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/775152.775190.

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Tse, Anson H. T., David B. Thomas, and Wayne Luk. "Accelerating Quadrature Methods for Option Valuation." In 2009 17th IEEE Symposium on Field Programmable Custom Computing Machines. IEEE, 2009. http://dx.doi.org/10.1109/fccm.2009.36.

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Anwar Atif, Touheed, Uchenna Chukwu, Jesse Berwald, and Raouf Dridi. "Accelerating NISQ variational methods using geometry." In Quantum Computing, Communication, and Simulation III, edited by Philip R. Hemmer and Alan L. Migdall. SPIE, 2023. http://dx.doi.org/10.1117/12.2655793.

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Weinstock, Jan Henrik, Rainer Leupers, and Gerd Ascheid. "Accelerating MPSoC Simulation Using Parallel SystemC and Processor Sleep Models." In RAPIDO '17: Methods and Tools. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3023973.3023975.

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Kassahun, Yohannes, Jose de Gea, Mark Edgington, Jan Hendrik Metzen, and Frank Kirchner. "Accelerating neuroevolutionary methods using a Kalman filter." In the 10th annual conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1389095.1389365.

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Gholoobi, Amin, and Stavros Stavrou. "Accelerating TOA/TDOA packet based localization methods." In 2014 IEEE Conference on Wireless Sensors (ICWiSe). IEEE, 2014. http://dx.doi.org/10.1109/icwise.2014.7042657.

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Metsch, Jan-Henrik, Jonathan Neuhauser, Jerome Jouffroy, Taous-Meriem Laleg-Kirati, and Johann Reger. "Accelerating Extremum Seeking Convergence by Richardson Extrapolation Methods." In 2022 IEEE 61st Conference on Decision and Control (CDC). IEEE, 2022. http://dx.doi.org/10.1109/cdc51059.2022.9992618.

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Lin, Hsien-I., and Chung-Sheng Cheng. "A study on accelerating convolutional neural networks." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2019 (ICCMSE-2019). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5138068.

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Reports on the topic "Accelerating methods"

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Moyer, Brian, Marshall Reinsdorf, and Robert Yuskavage. Aggregation Issues in Integrating and Accelerating BEA's Accounts: Improved Methods for Calculating GDP by Industry. Cambridge, MA: National Bureau of Economic Research, January 2005. http://dx.doi.org/10.3386/w11073.

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Sands, Anna, Julia Turner, and Amrita Saha. Trade Policy for Sustainable and Inclusive Agriculture. Institute of Development Studies, January 2023. http://dx.doi.org/10.19088/ids.2023.010.

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Trade policy provides a powerful set of levers for accelerating a transition to more inclusive and sustainable agricultural practices. Yet, trade in agriculture is often reliant on unsustainable methods of production, misaligned to tackling hunger, inadequate in support for decent farmer livelihoods, with negative climate and environmental impacts. Several countries are pioneering efforts to reform agricultural support schemes. This briefing highlights two key priorities in aligning trade policy with efforts to reform support for a transition to sustainable and inclusive food systems: core environmental standards coupled with scaling finance to support global South producer compliance.
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Alexander, Francis, Tammie Borders, Angie Sheffield, and Marc Wonders. Workshop Report for Next-Gen AI for Proliferation Detection: Accelerating the Development and Use of Explainability Methods to Design AI Systems Suitable for Nonproliferation Mission Applications. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1768761.

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Nation, John A. Novel Methods of Acceleration. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada204929.

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Nation, John A. Novel Methods of Acceleration. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada264828.

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Ruggiero, A. Tracking of Acceleration with HNJ Method. Office of Scientific and Technical Information (OSTI), February 2008. http://dx.doi.org/10.2172/939969.

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Ruggiero A. Tracking of Acceleration with HNJ Method. Office of Scientific and Technical Information (OSTI), February 2008. http://dx.doi.org/10.2172/1061891.

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Boyd, J. Spectral methods and sum acceleration algorithms. Final report. Office of Scientific and Technical Information (OSTI), March 1995. http://dx.doi.org/10.2172/52830.

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Watts, Benjamin, and Danielle Kennedy. Additive regulated concrete for thermally extreme conditions. Engineer Research and Development Center (U.S.), May 2024. http://dx.doi.org/10.21079/11681/48510.

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This study details a multiprong effort to validate the Cold Regions Research and Engineering Laboratory’s solution for concrete construction and repair in cold weather, Additive Regulated Concrete for Thermally Extreme Conditions (ARCTEC). ARCTEC is the product of several years of research and consists of a testing and simulation workflow which generates scenario-sensitive guidance for use of accelerating admixtures in concrete. This report details efforts to validate ARCTEC using real-world, full-scale, field demonstrations. These demonstrations were used to collect data on the behavior of concrete obtained through conventional supply chains, to assess the accuracy of the simulation component of the workflow, and test efficacy of ARCTEC guidance in achieving frost protection. Results indicate that ARCTEC is at a high level of maturity, and provides additive dosage guidance that ensures frost protection and strength development in concrete placed where overnight lows fall as low as 0°F. The effort and cost required to implement ARCTEC as a cold weather protection strategy is minimal, and significantly less burdensome than conventional methods. Any cold region installation with a winter construction or repair needs and access to conventional concrete supply chains could field ARCTEC, and reduce the cost and schedule constraints associated with winter construction.
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Urbatsch, T. J. Iterative acceleration methods for Monte Carlo and deterministic criticality calculations. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/212566.

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