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Статті в журналах з теми "Stochastic simulator":
Chew Hernandez, Mario Luis, Leopoldo Viveros Rosas, and Jose Roberto Perez Torres. "A Stochastic Simulator of a Multi-Component Distillation Tower Built as an Excel Macro." Engineering, Technology & Applied Science Research 13, no. 2 (April 2, 2023): 10222–27. http://dx.doi.org/10.48084/etasr.5563.
Köster, Till, Tom Warnke, and Adelinde M. Uhrmacher. "Generating Fast Specialized Simulators for Stochastic Reaction Networks via Partial Evaluation." ACM Transactions on Modeling and Computer Simulation 32, no. 2 (April 30, 2022): 1–25. http://dx.doi.org/10.1145/3485465.
Guan, Yongtao, and Stephen M. Krone. "WinSSS: Stochastic Spatial Simulator." Bulletin of the Ecological Society of America 85, no. 3 (July 2004): 102–4. http://dx.doi.org/10.1890/0012-9623(2004)85[102:wsss]2.0.co;2.
Lee, Lung-fei. "INTERPOLATION, QUADRATURE, AND STOCHASTIC INTEGRATION." Econometric Theory 17, no. 5 (September 25, 2001): 933–61. http://dx.doi.org/10.1017/s0266466601175043.
Xu, Jinghua, Kathleen L. Hancock, and Frank Southworth. "Simulation of Regional Freight Movement with Trade and Transportation Multinetworks." Transportation Research Record: Journal of the Transportation Research Board 1854, no. 1 (January 2003): 152–61. http://dx.doi.org/10.3141/1854-17.
Amar, Patrick. "Pandæsim: An Epidemic Spreading Stochastic Simulator." Biology 9, no. 9 (September 18, 2020): 299. http://dx.doi.org/10.3390/biology9090299.
Marchetti, Luca, Rosario Lombardo, and Corrado Priami. "HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks." Complexity 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/1232868.
Braun, Willard J. "Assessing a Stochastic Fire Spread Simulator." Journal of Environmental Informatics 22, no. 1 (September 25, 2013): 1–12. http://dx.doi.org/10.3808/jei.201300241.
Ribeiro, A. S., D. A. Charlebois, and J. Lloyd-Price. "CellLine, a stochastic cell lineage simulator." Bioinformatics 23, no. 24 (October 9, 2007): 3409–11. http://dx.doi.org/10.1093/bioinformatics/btm491.
Chen, Yixing, Tianzhen Hong, and Xuan Luo. "An agent-based stochastic Occupancy Simulator." Building Simulation 11, no. 1 (June 1, 2017): 37–49. http://dx.doi.org/10.1007/s12273-017-0379-7.
Дисертації з теми "Stochastic simulator":
Chua, Cheong Wei 1975. "A stochastic pool-based electricity market simulator /." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31045.
In Part II, a stochastic electricity market simulator (SEMS) is designed using elements of Monte Carlo methods and game theory. Each generator is assumed to operate in a stochastic manner, according to a bid strategy composed of a set of pre-established bid instances and a corresponding set of bid probabilities. The Pool dispatches power and defines prices according to either the LED or OPF models from Part I. Generators can update their bidding strategies according to a profit performance index reflecting their degree of risk tolerance, Chicken (risk averse), Average, and Cowboy (risk taker). SEMS can predict issues such as unintended collusion, as well as to evaluate bidding strategies.
Kim, Daniel D. 1982. "A biological simulator using a stochastic approach for synthetic biology." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33307.
Includes bibliographical references (leaves 58-59).
Synthetic Biology is a new engineering discipline created by the development of genetic engineering technology. Part of a new engineering discipline is to create new tools to build an integrated engineering environment. In this thesis, I designed and implemented a biological system simulator that will enable synthetic biologists to simulate their systems before they put time into building actual physical cells. Improvements to the current simulators in use include a design that enables extensions in functionality, external input signals, and a GUI that allows user interaction. The significance of the simulation results was tested by comparing them to actual live cellular experiments. The results showed that the new simulator can successfully simulate the trends of a simple synthetic cell.
by Daniel D. Kim.
M.Eng.
Fan, Futing. "Improving GEMFsim: a stochastic simulator for the generalized epidemic modeling framework." Kansas State University, 2016. http://hdl.handle.net/2097/34564.
Department of Electrical and Computer Engineering
Caterina M. Scoglio
The generalized epidemic modeling framework simulator (GEMFsim) is a tool designed by Dr. Faryad Sahneh, former PhD student in the NetSE group. GEMFsim simulates stochastic spreading process over complex networks. It was first introduced in Dr. Sahneh’s doctoral dissertation "Spreading processes over multilayer and interconnected networks" and implemented in Matlab. As limited by Matlab language, this implementation typically solves only small networks; the slow simulation speed is unable to generate enough results in reasonable time for large networks. As a generalized tool, this framework must be equipped to handle large networks and contain sufficient support to provide adequate performance. The C language, a low-level language that effectively maps a program to machine in- structions with efficient execution, was selected for this study. Following implementation of GEMFsim in C, I packed it into Python and R libraries, allowing users to enjoy the flexibility of these interpreted languages without sacrificing performance. GEMFsim limitations are not limited to language, however. In the original algorithm (Gillespie’s Direct Method), the performance (simulation speed) is inversely proportional to network size, resulting in unacceptable speed for very large networks. Therefore, this study applied the Next Reaction Method, making the performance irrelevant of network size. As long as the network fits into memory, the speed is proportional to the average node degree of the network, which is not very large for most real-world networks. This study also applied parallel computing in order to advantageously utilize multiple cores for repeated simulations. Although single simulation can not be paralleled as a Markov process, multiple simulations with identical network structures were run simultaneously, sharing one network description in memory.
Boulianne, Laurier. "An algorithm and VLSI architecture for a stochastic particle based biological simulator." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=96690.
Grâce aux récents progrès en informatique et en biologie, il est maintenant possible de simuler et de visualiser des systèmes biologiques de façon virtuelle. Il est attendu que des simulations réalistes produites par ordinateur, in silico, nous permettront d'améliorer notre connaissance des processus biologiques et de favoriser le développement de traitements thérapeutiques efficaces. Les simulateurs biologiques visent à améliorer notre connaissance de processus biologiques qui, autrement, ne pourraient pas être correctement analysés par des études expérimentales. Cette situation requiert le développement de simulateurs de plus en plus précis qui tiennent compte non seulement de la nature stochastique des systèmes biologiques, mais aussi de l'hétérogénéité spatiale ainsi que des effets causés par la grande densité de particules présentes dans ces systèmes. Ce mémoire présente GridCell, un simulateur biologique stochastique original basé sur une représentation microscopique des particules. Ce mémoire présente aussi une architecture parallèle originale accélérant GridCell par presque deux ordres de magnitude. GridCell est un environnement de simulation tridimensionnel qui permet d'étudier le comportement des réseaux biochimique sous différentes influences spatiales, notamment l'encombrement moléculaire ainsi que les effets de recrutement et de localisation des particules. GridCell traque les particules individuellement, ce qui permet d'explorer le comportement de molécules participants en très petits nombres à divers réseaux de signalisation. L'espace de simulation est divisé en une grille 3D discrète qui permet de générer des collisions entre les particules sans avoir à faire de calculs de distance ni de recherches de particules complexes. La compatibilité avec le format SBML permet à des réseaux déjà existants d'être simulés et visualisés. L'interface visuelle permet à l'utilisateur de naviguer de façon intuitive dans la simulation afin d'observer le comportement des espèces à travers le temps et l'espace. Des effets d'encombrement moléculaire sur un système enzymatique de type Michaelis-Menten sont simulés, et les résultats montrent un effet important sur le taux de formation du produit. Tenir compte de millions de particules à la fois est extrêmement demandant pour un ordinateur et, pour pouvoir simuler des cellules complètes avec une résolution spatiale moléculaire en moins d'une journée, un but souvent exprimé en biologie des systèmes, il est essentiel d'accélérer GridCell à l'aide de matériel informatique fonctionnant en parallèle. On propose une architecture sur FPGA combinant le traitement en pipeline, le fonctionnement en mode continu ainsi que l'exécution parallèle. L'architecture peut supporter plusieurs FPGA et l'approche en mode continu permet à l'architecture de supporter très grands systèmes. Une architecture comprenant 25 unités de traitement sur chaque étage du pipeline est synthétisée sur un seul FPGA Virtex-6 XC6VLX760, ce qui permet d'obtenir des gains de performance 76 fois supérieurs à l'implémentation séquentielle de l'algorithme. Ce gain de performance réduit l'écart entre la complexité de la simulation des cellules biologiques et la puissance de calcul des simulateurs avancés. Des travaux futurs sur GridCell pourraient avoir pour objectif de supporter des compartiments de forme très complexe ainsi que des particules haute définition.
Soltani-Moghaddam, Alireza. "Network simulator design with extended object model and generalized stochastic petri-net /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9999317.
Taleb, B. "The theory and design of a stochastic reliability simulator for large scale systems." Thesis, Open University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383689.
PINTO, ROBERTO JOSE. "STOCHASTIC SIMULATOR TO CALCULATE THE AGENTS FINANCIAL FLOW AT BRAZILIAN WHOLESALE ENERGY MARKET." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2876@1.
No novo modelo de livre concorrência do Setor Elétrico Nacional,o Mercado Atacadista de Energia (MAE) foi criado para ser o ambiente onde se processam as compras e vendas de energia de curto prazo. Logo, os agentes que possuem excedentes de energia, provenientes de excesso de geração ou de sobra de contrato, poderão vendê-los no MAE. A situação inversa também pode ocorrer, ou seja, o agente que necessitar de energia para cobrir um deficit de energia ou honrar contratos também poderá comprar energia no MAE. Em cada instante de tempo, os montantes de energia que cada agente poderá comercializar no MAE, assim como o preço de liquidação, não podem ser previstos com exatidão, pois dependem, por exemplo, das condições hidrológicas futuras. Isto acarreta incertezas com relação ao fluxo de caixa futuro dos agentes.No presente trabalho é apresentado um modelo de simulador estocástico capaz de fornecer estimativas futuras do fluxo financeiro de um agente no MAE, considerando-se em detalhe as regras vigentes, analisando- se diversos cenários hidrológicos.
In the new trading model for the Brazilian electricity sector, the Wholesale Energy Market -Mercado Atacadista de Energia - MAE- is the place where all buyers and sellers of electricity can trade and in which the spot price of energy will be determined. In this market the agents can sell the excess of generation or the positive net energy of bilateral contracts. However, lack of generation or negative net energy of bilateral contracts will be exposured to spot market price.The market price and the energy amount that each agent can trade at MAE depends on many factors, such as future hydrological conditions, for example.This fact causes financial flow uncertainties to all market agents. Then, this dissertation shows a model to make the market accounts using the MAE rules and future estimation of generations and consumptions energies. The results of this model could help the agents to forecast the payments and receipts at MAE.
Olsén, Jörgen. "Stochastic Modeling and Simulation of the TCP protocol." Doctoral thesis, Uppsala University, Mathematical Statistics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3534.
The success of the current Internet relies to a large extent on a cooperation between the users and the network. The network signals its current state to the users by marking or dropping packets. The users then strive to maximize the sending rate without causing network congestion. To achieve this, the users implement a flow-control algorithm that controls the rate at which data packets are sent into the Internet. More specifically, the Transmission Control Protocol (TCP) is used by the users to adjust the sending rate in response to changing network conditions. TCP uses the observation of packet loss events and estimates of the round trip time (RTT) to adjust its sending rate.
In this thesis we investigate and propose stochastic models for TCP. The models are used to estimate network performance like throughput, link utilization, and packet loss rate. The first part of the thesis introduces the TCP protocol and contains an extensive TCP modeling survey that summarizes the most important TCP modeling work. Reviewed models are categorized as renewal theory models, fixed-point methods, fluid models, processor sharing models or control theoretic models. The merits of respective category is discussed and guidelines for which framework to use for future TCP modeling is given.
The second part of the thesis contains six papers on TCP modeling. Within the renewal theory framework we propose single source TCP-Tahoe and TCP-NewReno models. We investigate the performance of these protocols in both a DropTail and a RED queuing environment. The aspects of TCP performance that are inherently depending on the actual implementation of the flow-control algorithm are singled out from what depends on the queuing environment.
Using the fixed-point framework, we propose models that estimate packet loss rate and link utilization for a network with multiple TCP-Vegas, TCP-SACK and TCP-Reno on/off sources. The TCP-Vegas model is novel and is the first model capable of estimating the network's operating point for TCP-Vegas sources sending on/off traffic. All TCP and network models in the contributed research papers are validated via simulations with the network simulator ns-2.
This thesis serves both as an introduction to TCP and as an extensive orientation about state of the art stochastic TCP models.
Erben, Vojtěch. "Návrh a testování stochastické navigace v TRASI." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-219900.
Echavarria, Gregory Maria Angelica. "Predictive Data-Derived Bayesian Statistic-Transport Model and Simulator of Sunken Oil Mass." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/471.
Книги з теми "Stochastic simulator":
Canada. Dept. of the Environment. National Hydrology Research Institute. A numerical simulator for flow and transport in stochastic discrete fracture networks. S.l: s.n, 1988.
Directorate, Canada Inland Waters. A numerical simulator for flow and transport in stochastic discrete fracture networks. Saskatoon, Sask: Inland Waters Directorate, National Hydrology Research Institute, National Hydrology Research Centre, 1988.
Greboval, Dominique Franc ʹois. Fisheries management under stochastic conditions: A bioeconomic simulator of the New England groundfishfishery. Ann Arbor, Mich: University Microfilms International, 1988.
Kelley, Neil D. Turbulence-turbine interaction: The basis for the development of the TurbSim Stochastic Simulator. Golden, CO: National Renewable Energy Laboratory, 2011.
Ripley, Brian D. Stochastic simulation. New York: Wiley, 1987.
Ripley, Brian D., ed. Stochastic Simulation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1987. http://dx.doi.org/10.1002/9780470316726.
Shedler, G. S. Regenerative stochastic simulation. Boston: Academic Press, 1993.
sentralbyrå, Norway Statistisk, ed. Stochastic simulation of KVARTS91. Oslo: Statistisk sentralbyrå, 1993.
MacKeown, P. K. Stochastic simulation in physics. New York: Springer, 1997.
Nelson, Barry L. Stochastic modeling: Analysis & simulation. Mineloa, N.Y: Dover Publications, 2002.
Частини книг з теми "Stochastic simulator":
Segovia-Hernández, Juan Gabriel, and Fernando Israel Gómez-Castro. "The Simulator Aspen Plus®." In Stochastic Process Optimization using Aspen Plus®, 55–59. Boca Raton : Taylor & Francis, CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315155739-4.
Cannon, Robert. "PSICS: the Parallel Stochastic Ion Channel Simulator." In Encyclopedia of Computational Neuroscience, 2531–32. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_260.
Cannon, Robert. "PSICS: The Parallel Stochastic Ion Channel Simulator." In Encyclopedia of Computational Neuroscience, 1–2. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7320-6_260-1.
Cannon, Robert. "PSICS: The Parallel Stochastic Ion Channel Simulator." In Encyclopedia of Computational Neuroscience, 1–2. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_260-2.
Santner, Thomas J., Brian J. Williams, and William I. Notz. "Stochastic Process Models for Describing Computer Simulator Output." In Springer Series in Statistics, 27–66. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8847-1_2.
Köppen, Veit, Marina Allgeier, and Hans-J. Lenz. "Balanced Scorecard Simulator — A Tool for Stochastic Business Figures." In Studies in Classification, Data Analysis, and Knowledge Organization, 457–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-70981-7_52.
Andrews, Steven S. "Spatial and Stochastic Cellular Modeling with the Smoldyn Simulator." In Bacterial Molecular Networks, 519–42. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-61779-361-5_26.
Montagna, Sara, and Andrea Roli. "Parameter Tuning of a Stochastic Biological Simulator by Metaheuristics." In AI*IA 2009: Emergent Perspectives in Artificial Intelligence, 466–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10291-2_47.
Bernardeschi, Cinzia, Andrea Domenici, and Maurizio Palmieri. "Towards Stochastic FMI Co-Simulations: Implementation of an FMU for a Stochastic Activity Networks Simulator." In Software Technologies: Applications and Foundations, 34–44. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04771-9_3.
Yamamoto, O., Yuichiro Shibata, Hitoshi Kurosawa, and Hideharu Amano. "A Reconfigurable Stochastic Model Simulator for Analysis of Parallel Systems." In Lecture Notes in Computer Science, 475–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44614-1_52.
Тези доповідей конференцій з теми "Stochastic simulator":
Thornburg, Jesse, Bruce Krogh, and Taha Selim Ustun. "Stochastic Simulator for Smart Microgrid Planning." In ACM DEV '16: Annual Symposium on Computing for Development. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/3001913.3006631.
Theofanis, Patrick L., and Oleg Tazetdinov. "Monte Carlo EUV stochastic simulator (MESS): a chemistry-oriented lithography simulator." In Optical and EUV Nanolithography XXXV, edited by Anna Lio and Martin Burkhardt. SPIE, 2022. http://dx.doi.org/10.1117/12.2617292.
Alemany, Kristina, and John Olds. "LASSO - Lunar Architecture Stochastic Simulator and Optimizer." In AIAA Modeling and Simulation Technologies Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2005. http://dx.doi.org/10.2514/6.2005-6415.
Xue, Xinfeng, Liang Zheng, and Jin Ye. "A Stochastic Simulation-Based Optimization Method for Calibrating VISSIM Simulator under Uncertainties." In 19th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2019. http://dx.doi.org/10.1061/9780784482292.260.
Asadi, Hirad, and Johan Schubert. "A stochastic discrete event simulator for effects-based planning." In 2013 Winter Simulation Conference - (WSC 2013). IEEE, 2013. http://dx.doi.org/10.1109/wsc.2013.6721654.
Skrebtsov, Andrey, Zijian Bai, Guido H. Bruck, and Peter Jung. "A novel network simulator based on stochastic spatial models." In 2013 7th International Conference on Signal Processing and Communication Systems (ICSPCS). IEEE, 2013. http://dx.doi.org/10.1109/icspcs.2013.6723927.
Xue, Mengran, Sandip Roy, Stephen Zobell, Yan Wan, Christine Taylor, and Craig Wanke. "A Stochastic Spatiotemporal Weather-Impact Simulator: Representative Scenario Selection." In 11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-6812.
Lecca, Paola, Lorenzo Dematte, Adaoha E. C. Ihekwaba, and Corrado Priami. "Redi: A Simulator of Stochastic Biochemical Reaction-Diffusion Systems." In 2010 Second International Conference on Advances in System Simulation (SIMUL). IEEE, 2010. http://dx.doi.org/10.1109/simul.2010.14.
Varol, Huseyin Atakan. "MOSES: A Matlab-based open-source stochastic epidemic simulator." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7591271.
Luboschik, Martin, Stefan Rybacki, Roland Ewald, Benjamin Schwarze, Heidrun Schumann, and Adelinde M. Uhrmacher. "Interactive visual exploration of simulator accuracy: A case study for stochastic simulation algorithms." In 2012 Winter Simulation Conference - (WSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/wsc.2012.6465190.
Звіти організацій з теми "Stochastic simulator":
Ringhand, Madlen, Maximilian Bäumler, Christian Siebke, Marcus Mai, and Felix Elrod. Report on validation of the stochastic traffic simulation (Part A). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.242.
Bäumler, Maximilian, Madlen Ringhand, Christian Siebke, Marcus Mai, Felix Elrod, and Günther Prokop. Report on validation of the stochastic traffic simulation (Part B). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.243.
Kelley, N. D., and B. J. Jonkman. Overview of the TurbSim Stochastic Inflow Turbulence Simulator. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/15020329.
Frazier, John, Yaroslav Chusak, and Brent Foy. Stochastic Simulation of Biomolecular Reaction Networks Using the Biomolecular Network Simulator Software. Fort Belvoir, VA: Defense Technical Information Center, February 2008. http://dx.doi.org/10.21236/ada484775.
Kelley, N. D., and B. J. Jonkman. Overview of the TurbSim Stochastic Inflow Turbulence Simulator: Version 1.10. Office of Scientific and Technical Information (OSTI), September 2006. http://dx.doi.org/10.2172/891590.
Kelley, Neil D. Turbulence-Turbine Interaction: The Basis for the Development of the TurbSim Stochastic Simulator. Office of Scientific and Technical Information (OSTI), November 2011. http://dx.doi.org/10.2172/1031981.
Kelley, N. D., and B. J. Jonkman. Overview of the TurbSim Stochastic Inflow Turbulence Simulator: Version 1.21 (Revised February 1, 2001). Office of Scientific and Technical Information (OSTI), April 2007. http://dx.doi.org/10.2172/903073.
Siebke, Christian, Maximilian Bäumler, Madlen Ringhand, Marcus Mai, Felix Elrod, and Günther Prokop. Report on integration of the stochastic traffic simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.246.
James Glimm and Xiaolin Li. Multiscale Stochastic Simulation and Modeling. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/862194.
Field, Richard V. ,. Jr. Stochastic models: theory and simulation. Office of Scientific and Technical Information (OSTI), March 2008. http://dx.doi.org/10.2172/932886.