Literatura científica selecionada sobre o tema "Distributed environment simulator"
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Artigos de revistas sobre o assunto "Distributed environment simulator"
Park, Seongjoon, Woong Gyu La, Woonghee Lee e Hwangnam Kim . "Devising a Distributed Co-Simulator for a Multi-UAV Network". Sensors 20, n.º 21 (30 de outubro de 2020): 6196. http://dx.doi.org/10.3390/s20216196.
Texto completo da fonteTanveer, Muhammad Hassan, Antony Thomas, Waqar Ahmed e Hongxiao Zhu. "Estimate the Unknown Environment with Biosonar Echoes—A Simulation Study". Sensors 21, n.º 12 (18 de junho de 2021): 4186. http://dx.doi.org/10.3390/s21124186.
Texto completo da fonteLalonde, B. "Converging towards synthetic environment interoperability". Aeronautical Journal 112, n.º 1129 (março de 2008): 171–77. http://dx.doi.org/10.1017/s0001924000002104.
Texto completo da fonteStytz, Martin R., Philip Amburn, Patricia K. Lawlis e Keith Shomper. "Virtual Environments Research in the Air Force Institute of Technology Virtual Environments, 3-D Medical Imaging, and Computer Graphics Laboratory". Presence: Teleoperators and Virtual Environments 4, n.º 4 (janeiro de 1995): 417–30. http://dx.doi.org/10.1162/pres.1995.4.4.417.
Texto completo da fonteRiskhan, Basheer, Halawati Abd Jalil Safuan, Khalid Hussain, Asma Abbas Hassan Elnour, Abdelzahir Abdelmaboud, Fazlullah Khan e Mahwish Kundi. "An Adaptive Distributed Denial of Service Attack Prevention Technique in a Distributed Environment". Sensors 23, n.º 14 (21 de julho de 2023): 6574. http://dx.doi.org/10.3390/s23146574.
Texto completo da fonteMARCHAL, PAUL, MURALI JAYAPALA, SAMUEL XAVIER DE SOUZA, PENG YANG, FRANCKY CATTHOOR e G. DECONINCK. "MATADOR: AN EXPLORATION ENVIRONMENT FOR SYSTEM-DESIGN". Journal of Circuits, Systems and Computers 11, n.º 05 (outubro de 2002): 503–35. http://dx.doi.org/10.1142/s0218126602000598.
Texto completo da fonteAli, Hamid M., Nidhal Ezzat e Wisam F. Kadhim. "DEVELOPMENT OF A LAN SIMULATION TOOL BASED ON WINDOWS ENVIRONMENT". Journal of Engineering 15, n.º 04 (1 de dezembro de 2009): 4364–77. http://dx.doi.org/10.31026/j.eng.2009.04.18.
Texto completo da fonteMeyer, Max-Arno, Lina Sauter, Christian Granrath, Hassen Hadj-Amor e Jakob Andert. "Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests". Automotive Innovation 4, n.º 4 (16 de outubro de 2021): 373–89. http://dx.doi.org/10.1007/s42154-021-00161-1.
Texto completo da fonteGurieiev, V. O., e O. V. Sanginova. "DISTRIBUTED SIMULATION ENVIRONMENT OF MODES FOR FULL-SCALE MODE SIMULATOR FOR UKRAINIAN ENERGY SYSTEMS". Tekhnichna Elektrodynamika 2016, n.º 5 (6 de setembro de 2016): 67–69. http://dx.doi.org/10.15407/techned2016.05.067.
Texto completo da fonteLü, Zhi, Zhan Gao e Yi Lü. "A Flight Simulator that Grouping Aircrafts Simultaneously Take off and Land in Open Grid Computing Environment". Applied Mechanics and Materials 182-183 (junho de 2012): 1292–97. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1292.
Texto completo da fonteTeses / dissertações sobre o assunto "Distributed environment simulator"
Alvarez, Valera Hernan Humberto. "An energy saving perspective for distributed environments : Deployment, scheduling and simulation with multidimensional entities for Software and Hardware". Electronic Thesis or Diss., Pau, 2022. https://theses.hal.science/tel-04116013.
Texto completo da fonteNowadays, strong economic growth and extreme weather conditions increased global electricity demand by more than 6% in 2021 after the COVID pandemic. The fast recovery regarding this demand rapidly increased electricity consumption. Even though renewable sources present a significant growth, electricity production from both coal and gas sources has reached a historical level.On the other hand, the consumption of energy by the digital technology sector depends on its growth and its degree of energy efficiency. On this matter, although devices at all deployment levels are energy efficient today, their massive use means that global energy consumption continues to grow.All these data show the need to use the energy of these devices wisely. For that reason, this thesis work addresses the dynamic (re)deployment of software components (containers or virtual machines) and their data to save energy. To this extent, we designed and developed intelligent distributed scheduling algorithms to decrease global power consumption while preserving the applications' quality of service.Such algorithms execute migrations and duplications procedures considering the natural relation between hardware components' load/features and power consumption. For that, they implement a novel manner of decentralized negotiations based on a distributed middleware we created (Kaligreen) and multidimensional data structures.To operate and assess the algorithms above, appropriate tools regarding hardware and software solutions are essential. Here, our choice was to develop our ownsimulation tool called: PISCO.PISCO is a versatile and straightforward simulator that allows users to concentrate only on their scheduling strategies. It enables network topologies to be abstracted as data structures whose elements are devices indexed by one or more criteria. Additionally, it mimics the execution of microservices by allocating resources according to various scheduling heuristics.We have used PISCO to implement, run and test our scheduling algorithms
Agyeman, Addai Daniel. "A Cloud Based Framework For Managing Requirements Change In Global Software Development". University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593266480093711.
Texto completo da fonteMa, Qingwei. "Distributed Manufacturing Simulation Environment". Ohio University / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1038409280.
Texto completo da fonteYu, Xiaoning. "Distributed interactive simulation". Thesis, Brunel University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310078.
Texto completo da fonteChiou, Jen-Diann. "A distributed simulation environment for multibody physics". Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50509.
Texto completo da fonteIncludes bibliographical references (leaves 128-134).
A distributed simulation environment, which can be used to model multibody physics, is developed. The software design is based on the object oriented paradigm and is implemented in C++ to run on a single workstation or multiple processors in parallel. It provides facilities to set up a multibody physics simulation, including arbitrary 3D geometric representation, particle interactions such as contacts and constraints, and visualization for postprocessing. Contact detection, the process of automatic identifying the geometric overlap between objects, is generally the most time-consuming procedure in the overall discrete element analysis pipeline. The computational cost of contact detection grows as a function of both the number of particles and the complexity of the geometric representation of each body. This thesis presents algorithms that significantly reduce the computational cost of the contact detection problem. The hashtable-based spatial reasoning algorithm demonstrates an O(M) performance, where M is the number of particles in the simulation system for a restricted set of particles. The discrete function representation (DFR) scheme is employed to model the surface geometry of complex 3D objects. DFR-based contact detection between a pair of objects exhibits an O(N) running time performance, where N is the number of surface point used to represent each object. In practice this results in a significant speedup over traditional techniques. A distributed DEM simulation environment is built on top of a set of software tools which exploit the parallelism embedded in the DEM analysis and which take advantage of a high-speed communications network to achieve good parallel performance. The goal is of reducing the entire computing time of of large-scale simulation problems to order O(N) is shown to be achieveable using the algorithms described.
by Jen-Diann Chiou.
Ph.D.
Mao, Wei Ph D. Massachusetts Institute of Technology. "Scalable, probabilistic simulation in a distributed design environment". Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/55254.
Texto completo da fonteCataloged from PDF version of thesis.
Includes bibliographical references (p. 110-114).
Integrated simulations have been used to predict and analyze the integrated behavior of large, complex product and technology systems throughout their design cycles. During the process of integration, uncertainties arise from many sources, such as material properties, manufacturing variations, inaccuracy of models and so on. Concerns about uncertainty and robustness in large-scale integrated design can be significant, especially under the situations where the system performance is sensitive to the variations. Probabilistic simulation can be an important tool to enable uncertainty analysis, sensitivity analysis, risk assessment and reliability-based design in integrated simulation environments. Monte Carlo methods have been widely used to resolve probabilistic simulation problems. To achieve desired estimation accuracy, typically a large number of samples are needed. However, large integrated simulation systems are often computationally heavy and time-consuming due to their complexity and large scale, making the conventional Monte Carlo approach computationally prohibitive. This work focuses on developing an efficient and scalable approach for probabilistic simulations in integrated simulation environments. A predictive machine learning and statistical approach is proposed in this thesis.
(cont.) Using random sampling of the system input distributions and running the integrated simulation for each input state, a random sample of limited size can be attained for each system output. Based on this limited output sample, a multilayer, feed-forward neural network is constructed as an estimator for the underlying cumulative distribution function. A mathematical model for the cumulative probability distribution function is then derived and used to estimate the underlying probability density function using differentiation. Statistically processing the sample used by the neural network is important so as to provide a good training set to the neural network estimator. Combining the statistical information from the empirical output distribution and the kernel estimation, a training set containing as much information about the underlying distribution as possible is attained. A back-propagation algorithm using adaptive learning rates is implemented to train the neural network estimator. To incorporate a required cumulative probability distribution function monotonicity hint into the learning process, a novel hint-reinforced back-propagation approach is created. The neural network estimator trained by empirical and kernel information (NN-EK estimator) can then finally be attained. To further improve the estimation, the statistical method of bootstrap aggregating (Bagging) is used. Multiple versions of the estimator are generated using bootstrap resampling and are aggregated to improve the estimator. A prototype implementation of the proposed approach is developed and test results on different models show its advantage over the conventional Monte Carlo approach in reducing the time by tens of times to achieve the same level of estimation accuracy.
by Wei Mao.
Ph.D.
Lopes, Diaz Adriana Carleton University Dissertation Computer Science. "An Object-oriented reflective simulation environment for distributed algorithms". Ottawa, 1996.
Encontre o texto completo da fonteJang, Duh 1957. "Realization of distributed experimental frame in DEVS-SCHEME and simulation environment". Thesis, The University of Arizona, 1988. http://hdl.handle.net/10150/276665.
Texto completo da fonteMiller, John. "Distributed virtual environment scalability and security". Thesis, University of Cambridge, 2011. https://www.repository.cam.ac.uk/handle/1810/241109.
Texto completo da fonteChen, Min. "A distributed object-oriented discrete event-driven simulation environment-DODESE". FIU Digital Commons, 1991. http://digitalcommons.fiu.edu/etd/2140.
Texto completo da fonteLivros sobre o assunto "Distributed environment simulator"
Ikonen, Jouni. Improving distributed simulation in a workstation environment. Lappeenranta: Lappeenranta University of Technology, 2001.
Encontre o texto completo da fonteUnited States. Congress. Office of Technology Assessment., ed. Distributed interactive simulation of combat. Washington, DC: Office of Technology Assessment, Congress of the U.S., 1995.
Encontre o texto completo da fontePorras, Jari. Developing a distributed simulation environment on a cluster of workstations. Lappeenranta, Finland: Lappeenranta University of Technology, 1998.
Encontre o texto completo da fonteU.S. Army Research Institute for the Behavioral and Social Sciences. ARI Field Unit at Fort Knox, ed. Catalog of training tools for use in Distributed Interactive Simulation (DIS) environments. [Fort Knox, Ky.]: Fort Knox Field Unit, Training Systems Research Division, U.S. Army Research Institute for the Behavioral and Social Sciences, 1994.
Encontre o texto completo da fonteKapp, John J. Utilization of a virtual environment for combat information center training. Monterey, Calif: Naval Postgraduate School, 1997.
Encontre o texto completo da fonteL, Clarke Thomas, e Society of Photo-optical Instrumentation Engineers., eds. Distributed interactive simulation systems for simulation and training in the aerospace environment: Proceedings of a conference held 19-20 April 1995, Orlando, Florida. Bellingham, Wash: SPIE Optical Engineering Press, 1995.
Encontre o texto completo da fonteDesign and Prototype of the AFIT Virtual Emergency Room: A Distributed Virtual Environment for Emergency Medical Simulation. Storming Media, 1996.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Distributed environment simulator"
Samridhi e Ramiro Liscano. "Performance Evaluation of SDN-WISE Against RPL-Based Ad-Hoc Wireless Sensor Network Using the Cooja Simulator". In 3rd International Conference on Wireless, Intelligent and Distributed Environment for Communication, 31–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44372-6_3.
Texto completo da fonteOhwada, Hayato, e Fumio Mizoguchi. "A Qualitative Quantitative Simulator Based on Constraint Logic Programming". In Distributed Environments, 107–22. Tokyo: Springer Japan, 1991. http://dx.doi.org/10.1007/978-4-431-68144-1_8.
Texto completo da fonteLees, Michael, Brian Logan, Rob Minson, Ton Oguara e Georgios Theodoropoulos. "Modelling Environments for Distributed Simulation". In Environments for Multi-Agent Systems, 150–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32259-7_8.
Texto completo da fonteStraßburger, Steffen, Thomas Schulze e Richard Fujimoto. "Future Trends in Distributed Simulation and Distributed Virtual Environments". In Advancing the Frontiers of Simulation, 231–61. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/b110059_11.
Texto completo da fonteIgbe, Damian, N. Kalantery, S. E. Ijaha e S. C. Winter. "Parallel Traffic Simulation in Spider Programming Environment". In Distributed and Parallel Systems, 165–72. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1167-0_20.
Texto completo da fonteTolk, Andreas. "Modeling the Environment". In Engineering Principles of Combat Modeling and Distributed Simulation, 93–111. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118180310.ch6.
Texto completo da fonteScahill, Mark. "Distributed Individual-Based Environmental Simulation". In IFIP Advances in Information and Communication Technology, 269–76. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-5041-2869-8_35.
Texto completo da fonteSantos, Arlindo, e Helena Rodrigues. "Evaluating Ubiquitous Computing Environments Using 3D Simulation". In Distributed, Ambient, and Pervasive Interactions, 109–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20804-6_10.
Texto completo da fonteJamshidi, M., S. Sheikh-Bahaei, J. Kitzinger, P. Sridhar, S. Xia, Y. Wang, J. Liu et al. "A Distributed Intelligent Discrete-Event Environment For Autonomous Agents Simulation". In Applied System Simulation, 241–74. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9218-5_11.
Texto completo da fonteKim, Chang-Hoon, Tae-Dong Lee, Sun-Chul Hwang e Chang-Sung Jeong. "Grid-Based Parallel and Distributed Simulation Environment". In Lecture Notes in Computer Science, 503–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45145-7_46.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Distributed environment simulator"
Rodrigues, Cristiano, Daniel Castro Silva, Rosaldo J. F. Rossetti e Eugenio Oliveira. "Distributed flight simulation environment using flight simulator X". In 2015 10th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2015. http://dx.doi.org/10.1109/cisti.2015.7170615.
Texto completo da fonteJanacik, Peter, Johannes Lessmann e Michael Karch. "Distributed Simulation Environment for the ShoX Network Simulator". In 2010 Sixth International Conference on Networking and Services (ICNS). IEEE, 2010. http://dx.doi.org/10.1109/icns.2010.35.
Texto completo da fonteChandramohan, D., S. K. V. Jayakumar, Shailesh Khapre e M. S. Nanda Kishore. "Dwse-simulator for distributed web service environment". In 2011 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, 2011. http://dx.doi.org/10.1109/icrtit.2011.5972294.
Texto completo da fonteRieck, David, Björn Schünemann, Ilja Radusch e Christoph Meinel. "Efficient traffic simulator coupling in a distributed V2X simulation environment". In 3rd International ICST Conference on Simulation Tools and Techniques. ICST, 2010. http://dx.doi.org/10.4108/icst.simutools2010.8640.
Texto completo da fonteMontoya, Juan, Ron Brandl, Mike Vogt, Frank Marten, Marios Maniatopoulos e Alejandra Fabian. "Asynchronous Integration of a Real-Time Simulator to a Geographically Distributed Controller Through a Co-Simulation Environment". In IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2018. http://dx.doi.org/10.1109/iecon.2018.8591486.
Texto completo da fonteAksu, Murat, John L. Michaloski e Frederick M. Proctor. "Virtual Experimental Investigation for Industrial Robotics in Gazebo Environment". In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87686.
Texto completo da fonteHeshmat, Hooshang, e James F. Walton. "On the Development of an Oil-Free, High-Speed and High-Temperature Turboalternator". In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-22852.
Texto completo da fonteThompson, Thomas V., Donald D. Nelson, Elaine Cohen e John Hollerbach. "Maneuverable NURBS Models Within a Haptic Virtual Environment". In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0375.
Texto completo da fonteChen, F. X., X. N. Wang, J. Zhu e P. Jiang. "Path planning in distributed intelligent environment". In 2012 International Conference on System Simulation (ICUSS 2012). IET, 2012. http://dx.doi.org/10.1049/cp.2012.0486.
Texto completo da fonteLally Singh, H., Denis Gracanin e Kresimir Matkovic. "Controlling scalability of Distributed Virtual Environment systems". In 2014 Winter Simulation Conference - (WSC 2014). IEEE, 2014. http://dx.doi.org/10.1109/wsc.2014.7020184.
Texto completo da fonteRelatórios de organizações sobre o assunto "Distributed environment simulator"
Fujimoto, Richard M. Distributed Simulation of Synthetic Environments and Wireless Networks. Fort Belvoir, VA: Defense Technical Information Center, setembro de 1999. http://dx.doi.org/10.21236/ada369488.
Texto completo da fonteBajaj, Chandrajit L. Modeling and Simulation in a Reconfigurable Distributed Virtual Environment. Fort Belvoir, VA: Defense Technical Information Center, dezembro de 1996. http://dx.doi.org/10.21236/ada330023.
Texto completo da fontePullen, M., M. Myjak e C. Bouwens. Limitations of Internet Protocol Suite for Distributed Simulation the Large Multicast Environment. RFC Editor, fevereiro de 1999. http://dx.doi.org/10.17487/rfc2502.
Texto completo da fonteAtwood, N. K., B. J. Winsch, K. A. Quinkert e C. K. Heiden. Catalog of Training Tools for Use in Distributed Interactive Simulation (DIS) Environments. Fort Belvoir, VA: Defense Technical Information Center, julho de 1993. http://dx.doi.org/10.21236/ada282759.
Texto completo da fonteAyoul-Guilmard, Q., R. Badia, J. Ejarque, S. Ganesh, F. Nobile, M. Nuñez, C. Soriano, C. Roig, R. Rossi e R. Tosi. D1.3 First public Release of the solver. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.007.
Texto completo da fonteAyoul-Guilmard, Q., S. Ganesh, F. Nobile, R. Badia, J. Ejarque, L. Cirrottola, A. Froehly et al. D1.4 Final public Release of the solver. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.009.
Texto completo da fonteMosalam, Khalid, Amarnath Kasalanati e Grace Kang. PEER Annual Report 2016. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, janeiro de 2017. http://dx.doi.org/10.55461/anra5954.
Texto completo da fonteMiller, Mr Michael J. DTPH56-06-T-000017 In-Field Welding and Coating Protocols. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), maio de 2009. http://dx.doi.org/10.55274/r0012117.
Texto completo da fonte