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Статті в журналах з теми "Data-driven experiments"
Perng, Sung-Yueh, Rob Kitchin, and Leighton Evans. "Locative media and data-driven computing experiments." Big Data & Society 3, no. 1 (January 5, 2016): 205395171665216. http://dx.doi.org/10.1177/2053951716652161.
Повний текст джерелаCruz, Sérgio Manuel Serra da, and José Antonio Pires do Nascimento. "Towards integration of data-driven agronomic experiments with data provenance." Computers and Electronics in Agriculture 161 (June 2019): 14–28. http://dx.doi.org/10.1016/j.compag.2019.01.044.
Повний текст джерелаDe Persis, Claudio, and Pietro Tesi. "Designing Experiments for Data-Driven Control of Nonlinear Systems." IFAC-PapersOnLine 54, no. 9 (2021): 285–90. http://dx.doi.org/10.1016/j.ifacol.2021.06.085.
Повний текст джерелаKoning, A. J., J. P. Delaroche, and O. Bersillon. "Nuclear data for accelerator driven systems: Nuclear models, experiments and data libraries." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 414, no. 1 (September 1998): 49–67. http://dx.doi.org/10.1016/s0168-9002(98)00528-2.
Повний текст джерелаFrederik, Joeri, Lars Kröger, Gerd Gülker, and Jan-Willem van Wingerden. "Data-driven repetitive control: Wind tunnel experiments under turbulent conditions." Control Engineering Practice 80 (November 2018): 105–15. http://dx.doi.org/10.1016/j.conengprac.2018.08.011.
Повний текст джерелаLin, Shangjing, Jianguo Yu, and Ji Ma. "Big Data Driven Mobile Cellular Networks: Modelling, Experiments, and Applications." IOP Conference Series: Materials Science and Engineering 466 (December 28, 2018): 012074. http://dx.doi.org/10.1088/1757-899x/466/1/012074.
Повний текст джерелаVan Ameijde, Jeroen. "Data-driven Urban Design." SPOOL 9, no. 1 (May 27, 2022): 35–48. http://dx.doi.org/10.47982/spool.2022.1.03.
Повний текст джерелаFiordalis, Andrew, and Christos Georgakis. "Data-driven, using design of dynamic experiments, versus model-driven optimization of batch crystallization processes." Journal of Process Control 23, no. 2 (February 2013): 179–88. http://dx.doi.org/10.1016/j.jprocont.2012.08.011.
Повний текст джерелаMurari, A., E. Peluso, T. Craciunescu, S. Dormido-Canto, M. Lungaroni, R. Rossi, L. Spolladore, J. Vega, and M. Gelfusa. "Frontiers in data analysis methods: from causality detection to data driven experimental design." Plasma Physics and Controlled Fusion 64, no. 2 (December 31, 2021): 024002. http://dx.doi.org/10.1088/1361-6587/ac3ded.
Повний текст джерелаKnox, Joseph E., Kameron Decker Harris, Nile Graddis, Jennifer D. Whitesell, Hongkui Zeng, Julie A. Harris, Eric Shea-Brown, and Stefan Mihalas. "High-resolution data-driven model of the mouse connectome." Network Neuroscience 3, no. 1 (January 2019): 217–36. http://dx.doi.org/10.1162/netn_a_00066.
Повний текст джерелаДисертації з теми "Data-driven experiments"
Cedeno, Vanessa Ines. "Pipelines for Computational Social Science Experiments and Model Building." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/91445.
Повний текст джерелаDoctor of Philosophy
To understand individual and collective behavior, there has been significant interest in using online systems to carry out social science experiments. Considerable work is required for analyzing the data and to uncover interesting insights. In this dissertation, we design and build automated software pipelines for evaluating social phenomena through iterative experiments and modeling. To reason about experiments and models, we design a formal data model. This combined approach of experiments and models has been done in some studies without automation, or purely conceptually. We are motivated by a particular social behavior, namely collective identity (CI). Group or CI is an individual’s cognitive, moral, and emotional connection with a broader community, category, practice, or institution. Extensive experimental research shows that CI influences human decision-making, so there is interest in modeling situations that promote the creation of CI to learn more from the process and to predict human behavior in real life situations. One of our goals in this dissertation is to understand whether a cooperative anagram game can produce CI within a group. With all of the experimental work on anagrams games, it is surprising that very little work has been done in modeling these games. In addition, to identify best explanations for phenomena we use abduction. Abduction is an inference approach that uses data and observations. Abduction has broad application in robotics, genetics, automated systems, and image understanding, but have largely been devoid of human behavior. In a group anagrams web-based networked game setting we do the following. We use these pipelines to understand intra-group cooperation and its effect on fostering CI. We devise and execute an iterative abductive analysis process that is driven by the social sciences. We build and evaluate three agent-based models (ABMs). We analyze experimental data and develop models of human reasoning to predict detailed game player action. We claim our models can explain behavior and provide novel experimental insights into CI, because there is agreement between the model predictions and the experimental data.
Merikle, Elizabeth Paige 1965. "Facilitation of performance on a picture fragment completion test: Data-driven potentiation in perception." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/277941.
Повний текст джерелаOskarsdottir, Eyglo Myrra. "Towards a Data-Driven Pricing Decision With the Help of A/B Testing." Thesis, KTH, Nationalekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199224.
Повний текст джерелаEmerton, Guy. "Data-driven methods for exploratory analysis in chemometrics and scientific experimentation." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86366.
Повний текст джерелаENGLISH ABSTRACT: Background New methods to facilitate exploratory analysis in scientific data are in high demand. There is an abundance of available data used only for confirmatory analysis from which new hypotheses can be drawn. To this end, two new exploratory techniques are developed: one for chemometrics and another for visualisation of fundamental scientific experiments. The former transforms large-scale multiple raw HPLC/UV-vis data into a conserved set of putative features - something not often attempted outside of Mass-Spectrometry. The latter method ('StatNet'), applies network techniques to the results of designed experiments to gain new perspective on variable relations. Results The resultant data format from un-targeted chemometric processing was amenable to both chemical and statistical analysis. It proved to have integrity when machine-learning techniques were applied to infer attributes of the experimental set-up. The visualisation techniques were equally successful in generating hypotheses, and were easily extendible to three different types of experimental results. Conclusion The overall aim was to create useful tools for hypothesis generation in a variety of data. This has been largely reached through a combination of novel and existing techniques. It is hoped that the methods here presented are further applied and developed.
AFRIKAANSE OPSOMMING: Agtergrond Nuwe metodes om ondersoekende ontleding in wetenskaplike data te fasiliteer is in groot aanvraag. Daar is 'n oorvloed van beskikbaar data wat slegs gebruik word vir bevestigende ontleding waaruit nuwe hipoteses opgestel kan word. Vir hierdie doel, word twee nuwe ondersoekende tegnieke ontwikkel: een vir chemometrie en 'n ander vir die visualisering van fundamentele wetenskaplike eksperimente. Die eersgenoemde transformeer grootskaalse veelvoudige rou HPLC / UV-vis data in 'n bewaarde stel putatiewe funksies - iets wat nie gereeld buite Massaspektrometrie aangepak word nie. Die laasgenoemde metode ('StatNet') pas netwerktegnieke tot die resultate van ontwerpte eksperimente toe om sodoende ân nuwe perspektief op veranderlike verhoudings te verkry. Resultate Die gevolglike data formaat van die ongeteikende chemometriese verwerking was in 'n formaat wat vatbaar is vir beide chemiese en statistiese analise. Daar is bewys dat dit integriteit gehad het wanneer masjienleertegnieke toegepas is om eienskappe van die eksperimentele opstelling af te lei. Die visualiseringtegnieke was ewe suksesvol in die generering van hipoteses, en ook maklik uitbreibaar na drie verskillende tipes eksperimentele resultate. Samevatting Die hoofdoel was om nuttige middele vir hipotese generasie in 'n verskeidenheid van data te skep. Dit is grootliks bereik deur 'n kombinasie van oorspronklike en bestaande tegnieke. Hopelik sal die metodes wat hier aangebied is verder toegepas en ontwikkel word.
Kalibjian, J. R. "A Packet Based, Data Driven Telemetry System for Autonomous Experimental Sub-Orbital Spacecraft." International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/608857.
Повний текст джерелаA data driven telemetry system is described that responds to the rapid nature in which experimental satellite telemetry content is changed during the development process. It also meets the needs of a diverse experiment in which the many phases of a mission may contain radically different types of telemetry data. The system emphasizes mechanisms for achieving high redundancy of critical data. A practical example of such an implementation, Brilliant Pebbles Flight Experiment Three (FE-3), is cited.
Zhao, He Sokhansanj Bahrad. "Systematic data-driven modeling of cellular systems for experimental design and hypothesis evaluation /." Philadelphia, Pa. : Drexel University, 2009. http://hdl.handle.net/1860/3133.
Повний текст джерелаGuerrero, Ludueña Richard E. "Data Driven Approach to Enhancing Efficiency and Value in Healthcare." Doctoral thesis, Universitat de Barcelona, 2017. http://hdl.handle.net/10803/456670.
Повний текст джерелаLa asistencia sanitaria está cambiando y la era de las organizaciones sanitarias basadas en datos es cada vez más popular. Los enfoques basados en datos (por ejemplo, Aprendizaje Automático; Meta-heurísticas; Modelamiento y Simulación; y Análisis y Visualización de datos) pueden utilizarse para aumentar la eficiencia y el valor en los servicios sanitarios. A pesar de la amplia investigación y el desarrollo tecnológico, la evidencia sobre el impacto de estas metodologías en el sector sanitario es limitada. En esta tesis argumentamos que un enfoque sin fronteras en términos de sociedades académicas y campo de estudio podría ayudar a abordar esta falta de impacto para aumentar la eficiencia y el valor en la asistencia sanitaria. Esta tesis se basa en la resolución de problemas prácticos en el sector sanitario, con un enfoque tanto teórico como práctico. La investigación se organizó en cuatro etapas. En la primera, una variedad de técnicas de modelamiento y simulación fueron estudiadas y aplicadas en el análisis y simulación de mejores y más eficientes configuraciones de sistemas sanitarios. El objetivo fue un análisis de capacidad, demanda, actividad y listas de esperas a nivel hospitalario y poblacional. En la segunda parte, un Algoritmo Genético fue implementado para resolver un problema de ruteo de personal sanitario encargado de atención de salud en el hogar. En la tercera parte, Análisis de Redes Sociales fue utilizado para visualizar y analizar una red de usuarios de correos electrónicos. En la etapa final, se propone una nueva métrica para evaluar el rendimiento de sistemas sanitarios, la cual fue implementada a través de un caso de estudio. Diferentes marcos de referencia para la implementación de estas metodologías en problemas reales se presentan a lo largo de la tesis.
Deshpande, Shubhangi. "Data Driven Surrogate Based Optimization in the Problem Solving Environment WBCSim." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/35901.
Повний текст джерелаMaster of Science
Jha, Rajesh. "Combined Computational-Experimental Design of High-Temperature, High-Intensity Permanent Magnetic Alloys with Minimal Addition of Rare-Earth Elements." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2621.
Повний текст джерелаMarín, de Mas Igor Bartolomé. "Development and application of novel model-driven and data-driven approaches to study metabolism in the framework of systems medicine." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/296313.
Повний текст джерелаLa presente tesis doctoral se centra en el desarrollo de herramientas computacionales que permitan el estudio de los mecanismos moleculares que ocurren dentro de la célula. Mas específicamente estudia el metabolismo celular desde diferentes puntos de vista usando y desarrollando métodos computacionales basados en diversas metodologías. Así pues en un primer capitulo se desarrolla un método basado en el analista de los flujos metabólicos en estado no estacional isotópico utilizando modelos cinéticos para estudiar el fenómeno de la canalización metabólica en hepatocitos. Este fenómeno modifica la topología metabólica alterando el fenotipo. Nuestro método nos permitió discriminar varios modelos con distintas topología prediciendo la existencia de canalización metabólica en la glucólisis. En el segundo capitulo se desarrolló un método para analizar el metabolismo tumoral teniendo en cuenta la heterogeneidad de poblaciones. En concreto estudiamos dos subpoblaciones extraídas de una linea celular de cáncer de próstata. Para ello utilizamos un modelo a gran escala de todo el metabolismo celular humano. El análisis reflejó la existencia de diferencias notables a nivel de vías metabólicas concretas, confiriendo a cada subpoblacion sensibilidades distintas a diferentes fármacos. En esta linea se demostró que mientras las células PC-3M eran sensibles al etomoxir e insensibles al calcitriol, las PC-3S presentaban una sensibilidad opuesta. En el tercero y ultimo capitulo de la tesis desarrollamos un nuevo método computacional que integra aproximaciones probabilísticas y mecanicistas para integrar diferentes tipos de datos en un análisis basado en modelos discretos. Para ello utilizamos como caso de concepto el estudio de la adaptación anómala al entrenamiento de pacientes con EPOC. El análisis reveló diferencias importantes a nivel de metabolismo energético en comparación con el grupo control.
Книги з теми "Data-driven experiments"
Bazerman, Max H., and Michael Luca. Power of Experiments: Decision Making in a Data-Driven World. MIT Press, 2020.
Знайти повний текст джерелаBazerman, Max H., and Michael Luca. Power of Experiments: Decision Making in a Data-Driven World. MIT Press, 2020.
Знайти повний текст джерелаBazerman, Max H., and Michael Luca. Power of Experiments - Decision Making in a Data-Driven World. MIT Press, 2020.
Знайти повний текст джерелаBazerman, Max H., and Michael Luca. Power of Experiments: Decision Making in a Data-Driven World. MIT Press, 2021.
Знайти повний текст джерелаScaletti, Carla. Sonification ≠ Music. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.9.
Повний текст джерелаRissen, Paul. Experiment-Driven Product Development: How to Use a Data-Lnformed Approach to Learn, Iterate, and Succeed Faster. Apress L. P., 2019.
Знайти повний текст джерелаWhitehouse, Harvey. The Ritual Animal. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780199646364.001.0001.
Повний текст джерелаThomsen, Bodil Marie Stavning, ed. Affects, Interfaces, Events. Imbricate! Press, 2021. http://dx.doi.org/10.22387/imbaie.
Повний текст джерелаCampbell, John, Joey Huston, and Frank Krauss. Soft QCD. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199652747.003.0007.
Повний текст джерелаBucy, Erik P., and Patrick Stewart. The Personalization of Campaigns: Nonverbal Cues in Presidential Debates. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228637.013.52.
Повний текст джерелаЧастини книг з теми "Data-driven experiments"
Jiang, Lili, and Vicenç Torra. "On the Effects of Data Protection on Multi-database Data-Driven Models." In Lecture Notes in Computer Science, 226–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98018-4_19.
Повний текст джерелаConcilio, Grazia, and Paola Pucci. "The Data Shake: An Opportunity for Experiment-Driven Policy Making." In The Data Shake, 3–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63693-7_1.
Повний текст джерелаGenç, Zülküf, Michel Oey, Hendrik van Antwerpen, and Frances Brazier. "Dynamic Data-Driven Experiments in the Smart Grid Domain with a Multi-agent Platform." In Multi-Agent Based Simulation XVI, 121–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31447-1_8.
Повний текст джерелаStarke, Ludger, Thoralf Niendorf, and Sonia Waiczies. "Data Preparation Protocol for Low Signal-to-Noise Ratio Fluorine-19 MRI." In Methods in Molecular Biology, 711–22. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_43.
Повний текст джерелаTomaselli, Venera, and Giulio Giacomo Cantone. "Multipoint vs slider: a protocol for experiments." In Proceedings e report, 91–96. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.19.
Повний текст джерелаHarlander, Uwe, Thomas von Larcher, Grady B. Wright, Michael Hoff, Kiril Alexandrov, and Christoph Egbers. "Orthogonal Decomposition Methods to Analyze PIV, LDV, and Thermography Data of Thermally Driven Rotating Annulus Laboratory Experiments." In Modeling Atmospheric and Oceanic Flows, 315–36. Hoboken, NJ: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118856024.ch17.
Повний текст джерелаParker, Austin, Gerardo I. Simari, Amy Sliva, and V. S. Subrahmanian. "Experimental Evaluation." In Data-driven Generation of Policies, 37–45. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4939-0274-3_5.
Повний текст джерелаWang, Jing, Jinglin Zhou, and Xiaolu Chen. "Simulation Platform for Fault Diagnosis." In Intelligent Control and Learning Systems, 45–58. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_4.
Повний текст джерелаKhandelwal, Dhruv. "Experimental Results." In Automating Data-Driven Modelling of Dynamical Systems, 173–220. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90343-5_7.
Повний текст джерелаSartirana, A., P. Hennion, P. Mora de Freitas, I. Semenjuk, P. Busson, M. Jouvin, G. Philippon, et al. "Grid Computing Operations for the CMS Experiment at the GRIF Tier-2." In Data Driven e-Science, 347–56. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8014-4_27.
Повний текст джерелаТези доповідей конференцій з теми "Data-driven experiments"
Deng, Alex, and Xiaolin Shi. "Data-Driven Metric Development for Online Controlled Experiments." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939700.
Повний текст джерелаSilfverberg, Miikka, and Mans Hulden. "Initial Experiments in Data-Driven Morphological Analysis for Finnish." In Proceedings of the Fourth International Workshop on Computatinal Linguistics of Uralic Languages. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-0209.
Повний текст джерелаPareek, Kaushal Arun, Daniel May, Mohamad Abo Ras, and Bernhard Wunderle. "Towards Data Driven Failure Analysis Using Infrared Thermography." In 2021 22nd International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE). IEEE, 2021. http://dx.doi.org/10.1109/eurosime52062.2021.9410885.
Повний текст джерелаDong, Hua, and Xueying Yang. "The project management of corneal transplantation in animal experiments based on BP neural network." In 2017 IEEE 6th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2017. http://dx.doi.org/10.1109/ddcls.2017.8068121.
Повний текст джерелаAjusha, P. V., and P. Babu Anto. "Experiments on Malayalam Language using Graph-based Data-driven Dependency Parser." In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2019. http://dx.doi.org/10.1109/icicict46008.2019.8993315.
Повний текст джерелаPhung, Chi-Dung, Nour-El-Houda Yellas, Salah Bin Ruba, and Stefano Secci. "An Open Dataset for Beyond-5G Data-driven Network Automation Experiments." In 2022 1st International Conference on 6G Networking (6GNet). IEEE, 2022. http://dx.doi.org/10.1109/6gnet54646.2022.9830292.
Повний текст джерелаKhanam, Mayana Humera, Palli Suryachandra, and Kv Madhumurthy. "Experiments on POS tagging and data driven dependency parsing for Telugu language." In the International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2345396.2345567.
Повний текст джерелаRadac, Mircea-Bogdan, Raul-Cristian Roman, Radu-Emil Precup, and Emil M. Petriu. "Data-driven model-free control of twin rotor aerodynamic systems: Algorithms and experiments." In 2014 IEEE International Symposium on Intelligent Control (ISIC). IEEE, 2014. http://dx.doi.org/10.1109/isic.2014.6967639.
Повний текст джерелаWeiderhold, Joseph, David E. Lambert, and Michael Hopson. "Experimental Design and Data Collection for Dynamic Fragmentation Experiments." In ASME 2010 Pressure Vessels and Piping Division/K-PVP Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/pvp2010-25163.
Повний текст джерелаHickmann, Kyle S., Deborah Shutt, Andrew Robinson, and Jonathan Lind. "Data-driven learning of impactor strength properties from shock experiments with additively-manufactured materials." In Applications of Machine Learning 2021, edited by Michael E. Zelinski, Tarek M. Taha, and Jonathan Howe. SPIE, 2021. http://dx.doi.org/10.1117/12.2594898.
Повний текст джерелаЗвіти організацій з теми "Data-driven experiments"
Kurbanov, Serdar. Data Driven Trigger Design and Analysis for the NOvA Experiment. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1334263.
Повний текст джерелаO'neil, Ryunosuke. Data-driven Alignment of the Tracker for the Mu2e Experiment. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1769399.
Повний текст джерелаZevotek, Robin, Keith Stakes, and Joseph Willi. Impact of Fire Attack Utilizing Interior and Exterior Streams on Firefighter Safety and Occupant Survival: Full-Scale Experiments. UL Firefighter Safety Research Institute, January 2018. http://dx.doi.org/10.54206/102376/dnyq2164.
Повний текст джерелаSnyder, Victor A., Dani Or, Amos Hadas, and S. Assouline. Characterization of Post-Tillage Soil Fragmentation and Rejoining Affecting Soil Pore Space Evolution and Transport Properties. United States Department of Agriculture, April 2002. http://dx.doi.org/10.32747/2002.7580670.bard.
Повний текст джерелаvan den Boogaard,, Vanessa, and Fabrizio Santoro. Co-Financing Community-Driven Development Through Informal Taxation: Experimental Evidence from South-Central Somalia. Institute of Development Studies (IDS), September 2021. http://dx.doi.org/10.19088/ictd.2021.016.
Повний текст джерела