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Artykuły w czasopismach na temat "Distributed environment simulator"
Park, Seongjoon, Woong Gyu La, Woonghee Lee i Hwangnam Kim . "Devising a Distributed Co-Simulator for a Multi-UAV Network". Sensors 20, nr 21 (30.10.2020): 6196. http://dx.doi.org/10.3390/s20216196.
Pełny tekst źródłaTanveer, Muhammad Hassan, Antony Thomas, Waqar Ahmed i Hongxiao Zhu. "Estimate the Unknown Environment with Biosonar Echoes—A Simulation Study". Sensors 21, nr 12 (18.06.2021): 4186. http://dx.doi.org/10.3390/s21124186.
Pełny tekst źródłaLalonde, B. "Converging towards synthetic environment interoperability". Aeronautical Journal 112, nr 1129 (marzec 2008): 171–77. http://dx.doi.org/10.1017/s0001924000002104.
Pełny tekst źródłaStytz, Martin R., Philip Amburn, Patricia K. Lawlis i 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, nr 4 (styczeń 1995): 417–30. http://dx.doi.org/10.1162/pres.1995.4.4.417.
Pełny tekst źródłaRiskhan, Basheer, Halawati Abd Jalil Safuan, Khalid Hussain, Asma Abbas Hassan Elnour, Abdelzahir Abdelmaboud, Fazlullah Khan i Mahwish Kundi. "An Adaptive Distributed Denial of Service Attack Prevention Technique in a Distributed Environment". Sensors 23, nr 14 (21.07.2023): 6574. http://dx.doi.org/10.3390/s23146574.
Pełny tekst źródłaMARCHAL, PAUL, MURALI JAYAPALA, SAMUEL XAVIER DE SOUZA, PENG YANG, FRANCKY CATTHOOR i G. DECONINCK. "MATADOR: AN EXPLORATION ENVIRONMENT FOR SYSTEM-DESIGN". Journal of Circuits, Systems and Computers 11, nr 05 (październik 2002): 503–35. http://dx.doi.org/10.1142/s0218126602000598.
Pełny tekst źródłaAli, Hamid M., Nidhal Ezzat i Wisam F. Kadhim. "DEVELOPMENT OF A LAN SIMULATION TOOL BASED ON WINDOWS ENVIRONMENT". Journal of Engineering 15, nr 04 (1.12.2009): 4364–77. http://dx.doi.org/10.31026/j.eng.2009.04.18.
Pełny tekst źródłaMeyer, Max-Arno, Lina Sauter, Christian Granrath, Hassen Hadj-Amor i Jakob Andert. "Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests". Automotive Innovation 4, nr 4 (16.10.2021): 373–89. http://dx.doi.org/10.1007/s42154-021-00161-1.
Pełny tekst źródłaGurieiev, V. O., i O. V. Sanginova. "DISTRIBUTED SIMULATION ENVIRONMENT OF MODES FOR FULL-SCALE MODE SIMULATOR FOR UKRAINIAN ENERGY SYSTEMS". Tekhnichna Elektrodynamika 2016, nr 5 (6.09.2016): 67–69. http://dx.doi.org/10.15407/techned2016.05.067.
Pełny tekst źródłaLü, Zhi, Zhan Gao i Yi Lü. "A Flight Simulator that Grouping Aircrafts Simultaneously Take off and Land in Open Grid Computing Environment". Applied Mechanics and Materials 182-183 (czerwiec 2012): 1292–97. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1292.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaNowadays, 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.
Pełny tekst źródłaMa, Qingwei. "Distributed Manufacturing Simulation Environment". Ohio University / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1038409280.
Pełny tekst źródłaYu, Xiaoning. "Distributed interactive simulation". Thesis, Brunel University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310078.
Pełny tekst źródłaChiou, Jen-Diann. "A distributed simulation environment for multibody physics". Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50509.
Pełny tekst źródłaIncludes 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.
Pełny tekst źródłaCataloged 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.
Znajdź pełny tekst źródłaJang, 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.
Pełny tekst źródłaMiller, John. "Distributed virtual environment scalability and security". Thesis, University of Cambridge, 2011. https://www.repository.cam.ac.uk/handle/1810/241109.
Pełny tekst źródłaChen, Min. "A distributed object-oriented discrete event-driven simulation environment-DODESE". FIU Digital Commons, 1991. http://digitalcommons.fiu.edu/etd/2140.
Pełny tekst źródłaKsiążki na temat "Distributed environment simulator"
Ikonen, Jouni. Improving distributed simulation in a workstation environment. Lappeenranta: Lappeenranta University of Technology, 2001.
Znajdź pełny tekst źródłaUnited States. Congress. Office of Technology Assessment., red. Distributed interactive simulation of combat. Washington, DC: Office of Technology Assessment, Congress of the U.S., 1995.
Znajdź pełny tekst źródłaPorras, Jari. Developing a distributed simulation environment on a cluster of workstations. Lappeenranta, Finland: Lappeenranta University of Technology, 1998.
Znajdź pełny tekst źródłaU.S. Army Research Institute for the Behavioral and Social Sciences. ARI Field Unit at Fort Knox, red. 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.
Znajdź pełny tekst źródłaKapp, John J. Utilization of a virtual environment for combat information center training. Monterey, Calif: Naval Postgraduate School, 1997.
Znajdź pełny tekst źródłaL, Clarke Thomas, i Society of Photo-optical Instrumentation Engineers., red. 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.
Znajdź pełny tekst źródłaDesign and Prototype of the AFIT Virtual Emergency Room: A Distributed Virtual Environment for Emergency Medical Simulation. Storming Media, 1996.
Znajdź pełny tekst źródłaCzęści książek na temat "Distributed environment simulator"
Samridhi i Ramiro Liscano. "Performance Evaluation of SDN-WISE Against RPL-Based Ad-Hoc Wireless Sensor Network Using the Cooja Simulator". W 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.
Pełny tekst źródłaOhwada, Hayato, i Fumio Mizoguchi. "A Qualitative Quantitative Simulator Based on Constraint Logic Programming". W Distributed Environments, 107–22. Tokyo: Springer Japan, 1991. http://dx.doi.org/10.1007/978-4-431-68144-1_8.
Pełny tekst źródłaLees, Michael, Brian Logan, Rob Minson, Ton Oguara i Georgios Theodoropoulos. "Modelling Environments for Distributed Simulation". W 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.
Pełny tekst źródłaStraßburger, Steffen, Thomas Schulze i Richard Fujimoto. "Future Trends in Distributed Simulation and Distributed Virtual Environments". W Advancing the Frontiers of Simulation, 231–61. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/b110059_11.
Pełny tekst źródłaIgbe, Damian, N. Kalantery, S. E. Ijaha i S. C. Winter. "Parallel Traffic Simulation in Spider Programming Environment". W Distributed and Parallel Systems, 165–72. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1167-0_20.
Pełny tekst źródłaTolk, Andreas. "Modeling the Environment". W 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.
Pełny tekst źródłaScahill, Mark. "Distributed Individual-Based Environmental Simulation". W 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.
Pełny tekst źródłaSantos, Arlindo, i Helena Rodrigues. "Evaluating Ubiquitous Computing Environments Using 3D Simulation". W Distributed, Ambient, and Pervasive Interactions, 109–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20804-6_10.
Pełny tekst źródłaJamshidi, M., S. Sheikh-Bahaei, J. Kitzinger, P. Sridhar, S. Xia, Y. Wang, J. Liu i in. "A Distributed Intelligent Discrete-Event Environment For Autonomous Agents Simulation". W Applied System Simulation, 241–74. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9218-5_11.
Pełny tekst źródłaKim, Chang-Hoon, Tae-Dong Lee, Sun-Chul Hwang i Chang-Sung Jeong. "Grid-Based Parallel and Distributed Simulation Environment". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Distributed environment simulator"
Rodrigues, Cristiano, Daniel Castro Silva, Rosaldo J. F. Rossetti i Eugenio Oliveira. "Distributed flight simulation environment using flight simulator X". W 2015 10th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2015. http://dx.doi.org/10.1109/cisti.2015.7170615.
Pełny tekst źródłaJanacik, Peter, Johannes Lessmann i Michael Karch. "Distributed Simulation Environment for the ShoX Network Simulator". W 2010 Sixth International Conference on Networking and Services (ICNS). IEEE, 2010. http://dx.doi.org/10.1109/icns.2010.35.
Pełny tekst źródłaChandramohan, D., S. K. V. Jayakumar, Shailesh Khapre i M. S. Nanda Kishore. "Dwse-simulator for distributed web service environment". W 2011 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, 2011. http://dx.doi.org/10.1109/icrtit.2011.5972294.
Pełny tekst źródłaRieck, David, Björn Schünemann, Ilja Radusch i Christoph Meinel. "Efficient traffic simulator coupling in a distributed V2X simulation environment". W 3rd International ICST Conference on Simulation Tools and Techniques. ICST, 2010. http://dx.doi.org/10.4108/icst.simutools2010.8640.
Pełny tekst źródłaMontoya, Juan, Ron Brandl, Mike Vogt, Frank Marten, Marios Maniatopoulos i Alejandra Fabian. "Asynchronous Integration of a Real-Time Simulator to a Geographically Distributed Controller Through a Co-Simulation Environment". W IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2018. http://dx.doi.org/10.1109/iecon.2018.8591486.
Pełny tekst źródłaAksu, Murat, John L. Michaloski i Frederick M. Proctor. "Virtual Experimental Investigation for Industrial Robotics in Gazebo Environment". W ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87686.
Pełny tekst źródłaHeshmat, Hooshang, i James F. Walton. "On the Development of an Oil-Free, High-Speed and High-Temperature Turboalternator". W ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-22852.
Pełny tekst źródłaThompson, Thomas V., Donald D. Nelson, Elaine Cohen i John Hollerbach. "Maneuverable NURBS Models Within a Haptic Virtual Environment". W ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0375.
Pełny tekst źródłaChen, F. X., X. N. Wang, J. Zhu i P. Jiang. "Path planning in distributed intelligent environment". W 2012 International Conference on System Simulation (ICUSS 2012). IET, 2012. http://dx.doi.org/10.1049/cp.2012.0486.
Pełny tekst źródłaLally Singh, H., Denis Gracanin i Kresimir Matkovic. "Controlling scalability of Distributed Virtual Environment systems". W 2014 Winter Simulation Conference - (WSC 2014). IEEE, 2014. http://dx.doi.org/10.1109/wsc.2014.7020184.
Pełny tekst źródłaRaporty organizacyjne na temat "Distributed environment simulator"
Fujimoto, Richard M. Distributed Simulation of Synthetic Environments and Wireless Networks. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 1999. http://dx.doi.org/10.21236/ada369488.
Pełny tekst źródłaBajaj, Chandrajit L. Modeling and Simulation in a Reconfigurable Distributed Virtual Environment. Fort Belvoir, VA: Defense Technical Information Center, grudzień 1996. http://dx.doi.org/10.21236/ada330023.
Pełny tekst źródłaPullen, M., M. Myjak i C. Bouwens. Limitations of Internet Protocol Suite for Distributed Simulation the Large Multicast Environment. RFC Editor, luty 1999. http://dx.doi.org/10.17487/rfc2502.
Pełny tekst źródłaAtwood, N. K., B. J. Winsch, K. A. Quinkert i C. K. Heiden. Catalog of Training Tools for Use in Distributed Interactive Simulation (DIS) Environments. Fort Belvoir, VA: Defense Technical Information Center, lipiec 1993. http://dx.doi.org/10.21236/ada282759.
Pełny tekst źródłaAyoul-Guilmard, Q., R. Badia, J. Ejarque, S. Ganesh, F. Nobile, M. Nuñez, C. Soriano, C. Roig, R. Rossi i R. Tosi. D1.3 First public Release of the solver. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.007.
Pełny tekst źródłaAyoul-Guilmard, Q., S. Ganesh, F. Nobile, R. Badia, J. Ejarque, L. Cirrottola, A. Froehly i in. D1.4 Final public Release of the solver. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.009.
Pełny tekst źródłaMosalam, Khalid, Amarnath Kasalanati i Grace Kang. PEER Annual Report 2016. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, styczeń 2017. http://dx.doi.org/10.55461/anra5954.
Pełny tekst źródłaMiller, Mr Michael J. DTPH56-06-T-000017 In-Field Welding and Coating Protocols. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), maj 2009. http://dx.doi.org/10.55274/r0012117.
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