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About 10,000 papers for “simulation framework”
Prof Dr, Faruk Polat, Prof Dr et al. · 2008
hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work.
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Anna Schlösser · Lecture notes in economics and mathematical systems · 2010
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Frank Bohn · Contributions to economics · 2000
Within the framework of part II (sections 2, 3, and 4), this section outlines the methodical basis for this treatise. In subsection 2.1, the approach and core elements of the underlying model are summarized. In subsection 2.2, the strategy used for conducting comparative simulation exercises is discussed. Finally, subsection 2.3 sets out to justify the strategy of constructing counterfactuals in an ex post analysis based on historical data.
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Qingfeng Li (1524259), Yajing Huang (3457778) · Figshare · 2022
<p>Note: the global map was made using the R package “maps”. The original source of the base layer is Natural Earth, whose Terms of Use explicitly state that "No permission is needed to use Natural Earth. Crediting the authors is unnecessary." <a href="https://www.naturalearthdata.com/about/terms-of-use/" target="_blank">https://www.naturalearthdata.com/about/terms-of-use/</a>.</p>
Cited by 0Open access
Donald A. Berry (9239313), Scott Berry (32788), Peter Hale (9915165) et al. · Figshare · 2020
<p>For each Monte Carlo simulation path, we simulate patient-level infections data based on input trial design assumptions and attack rates from the population epidemiological model (for an RCT, ORCT, and ARCT). At the end of the trial (or, at each interim analysis for an ARCT), we determine if the vaccine candidate is approved under superiority or superiority-by-margin testing. Finally, we compute the expected net value of the trial design over 100,000 simulation paths.</p>
Cited by 0Open access
Simon Boutry (10956118), Raphaël Helaers (796995), Tom Lenaerts (21921) et al. · Figshare · 2023
<p>Schema of the code structure (independent modules represented in blue or green boxes) and data flow (black arrows) of our simulation framework. The green boxes represent steps that are specific to empirical power simulations.</p> <p>(PNG)</p>
Cited by 0Open access
Yanan Dai · Springer theses · 2020
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Carlos Bragança Oliveira, António Carvalho Brito · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2017
The performance of a company depends on the quality of its strategic decisions but depends also on how those decisions are implemented. Companies are composed of different units that can be geographically distributed with different levels of autonomy and local resources. Due to the complexity of the problem many companies use simulation to analyse the performance of critical sectors based on a number of scenarios. This reality requires new approaches that, taking advantage of existing models enables their integration with new models, facilitate their upgrade and allow a distributed execution. In this work we developed a Framework to integrate distributed agent-based simulators (ABMS) with a mechanism of communication TCP/IP to exchange synchronization messages and business messages based on the OASIS standard. The XML standard format was used for coding of all the messages. The communication mechanism of the Framework was developed in order to benefit from distributed computing.
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Ole Bialas (21416664), Edmund C. Lalor (11450457) · Figshare · 2025
<div> Background: In recent decades, studies modeling the neural processing of continuous, naturalistic, speech provided new insights into how speech and language are represented in the brain. However, the linear encoder models commonly used in such studies assume that the underlying data are stationary, varying to a fixed degree around a constant mean. Long, continuous, neural recordings may violate this assumption leading to impaired model performance. We aimed to examine the effect of non-stationary trends in continuous neural recordings on the performance of linear speech encoding models. Methods: We used temporal response functions (TRFs) to predict continuous neural responses to speech while splitting the data into segments of varying length, prior to model fitting. Our Hypothesis was that if the data were non-stationary, segmentation should improve model performance by making individual segments approximately stationary. We simulated and predicted stationary and non-stationary recordings to test our hypothesis under a known ground truth and predicted the brain activity of participants who listened to a narrated story, to test our hypothesis on actual neural recordings. Results: Simulations showed that, for stationary data, increasing segmentation steadily decreased model performance. For non-stationary data however, segmentation initially improved model performance. Modeling of neural recordings yielded similar results: segments of intermediate length (5–15 s) led to improved model performance compared to very short (1–2 s) and very long (30–120 s) segments. Conclusions: We showed that data segmentation improves the performance of encoding models for both simulated and real neural data and that this can be explained by the fact that shorter segments approximate stationarity more closely. Thus, the common practice of applying encoding models to long continuous segments of data is suboptimal and recordings should be segmented prior to modeling. </div>
Cited by 0Open access
2010
Simulation models of complex systems can be implemented as stand-alone applications dedicated to a particular domain with dedicated and optimised GUIs and supporting tools. Alternatively, the same effect can be achieved by building a generic simulation framework and then implementing the simulation model as a specific instance of that framework. Our framework, build primarily as a testing environment for robotic applications, consists of the repository store and the set of controllers: internal, rule module, tapestry, sensor, output. It follows the standard client server model where the clients store their private states and the server is a repository of shared states for simulation entities and the
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Randolph R. Settgast, J. A. White, Benjamin C. Corbett et al. · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2018
General multi-physics simulation framework targeting exascale computing platforms.
Cited by 4Open access
Radek Šindelář, Petr Novák · 2012
Integration of simulators, data sources, optimizers and other tools is discussed in this paper. More exacting requirements on the quality of the plant operation together with environmental limits and lowering energy intensity bring more complex control systems into the everyday life. When controlling the complex multi-variable systems usually variety of control techniques can be applied in all layers of control systems. The term Advanced Process Control (APC) is used for collaboration of different techniques originate from disciplines like statistics, artificial intelligence, signal processing etc. The most important goals of APC targeted by plant operators are to increase product yield, lower power consumption. The benefits of Advanced Process Control (APC) has been clearly shown in the last years. Mostly used approaches in this area rely heavily upon a study of system behavior and process model and cannot be developed and operated without access to online and historical data. This paper presents an integration platform for simulations and SCADA systems. The main idea is to connect simulations and more complex calculations into the control system architecture. The different control techniques can be utilized by using different tools and algorithms.
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Ethers Lab · OSF Preprints (OSF Preprints) · 2025
Public theory paper proposing a falsifiable simulation detection method based on truncation error and entropy limits. Published by Ethers Engineering & Research Lab. This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). https://creativecommons.org/licenses/by-nc/4.0/
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Morsch, Andreas · CERN Bulletin · 1999
<P>Proposal of abstract for CHEP2000<P>ALICE uses an object oriented framework for simulation and reconstruction based on ROOT (AliRoot).<BR>Here, we describe those components of the class design that represent common concepts of simulation, such as particle generation and transport, detector response and detector segmentation. Applications for fast and slow physics and detector performance simulations as well as the relevance of the design for visualisation and the link of simulation to reconstruction are discussed.
Cited by 0Open access
Aaron Epiney, Cristian Rabiti, Jong Suk Kim et al. · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2021
HYBRID is a modeling toolset to assess the economic viability of Nuclear-Renewable Integrated Energy Systems (N-R IES). The frameworks enabling this toolset are INL’s RAVEN, its CashFlow plugin and the Modelica language. The toolset includes sample RAVEN workflows performing economic assessments. These workflows consist of: generation of stochastic time series and application of probabilistic analysis and optimization algorithms (RAVEN); a library of Modelica models representing the physical behavior of N-R IES; and the CashFlow plugin mapping physical performance to economic performance. The toolset allows assembling existing and new models such as nuclear reactors, renewable energy sources, energy storage, gas turbines, industrial processes, etc. into an N-R IES. The toolset workflows evaluate the dynamics of the N-R IES responding to stochastic conditions (electricity demand, price, etc.) and optimize the dispatch economics as well as N-R IES capacity planning.
Cited by 1Open access
Andreas Alfons · 2009
A general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort. The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results. It follows a clear object-oriented design and supports parallel computing to increase computational performance.
Cited by 9Open access
Fangqu Niu · Urban sustainability · 2024
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Stephen Nayfach (5616590), Patrick H. Bradley (267452), Stacia K. Wyman (264810) et al. · Figshare · 2015
<p>1) Taxonomic profiles from real metagenomes are used to construct mock microbial communities. 2) Protein family annotations for SFams [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004573#pcbi.1004573.ref023" target="_blank">23</a>] are transferred to genes present in community members’ genomes. 3) The expected relative abundance of protein families is computed for the mock community. 4) Metagenomic reads are simulated from reference genomes that correspond to community members. 5) Simulated metagenomic reads are translated into predicted peptides. 6) Peptides are searched against the database of protein families used in (2) using the alignment tool RAPsearch2 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004573#pcbi.1004573.ref024" target="_blank">24</a>] 7) Metagenomic peptides are classified into protein families according to their top-hit. Hits that do not satisfy the classification threshold are removed. 8) Classified metagenomic reads are used to estimate the relative abundance of protein families in the mock community. 9) Estimation accuracy is computed using an L1 distance between the expected and estimated relative abundance profiles.</p>
Cited by 0Open access
Valur Hauksson · Skemman · 2004
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I. Hrivnacova, A. Morsch · 2015
ALICE uses an object-oriented framework for simulation and reconstruction (AliRoot) based on ROOT. Here, we describe those components of the class design that represent com-mon concepts of simulation such as particle generation and transport, detector response and detector segmentation. The smooth way of transition from existing GEANT 3 simulation soft-ware to GEANT 4 adopted by ALICE is explained. As a client of the Monte Carlo interface (pure abstract class) AliRoot is used both for GEANT 3 and GEANT 4 simulations. Applica-tions for fast and slow physics and detector performance simulations as well as the relevance of the design for visualization and the link of simulation to reconstruction are discussed.
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