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Статті в журналах з теми "Dynaic prediction"
Daniele, Mario, and Elisa Raoli. "Early Warning Systems for financial crises prediction in private companies: Evidence from the Italian context." FINANCIAL REPORTING, no. 2 (December 2024): 133–61. https://doi.org/10.3280/fr2024-002006.
Повний текст джерелаLin, Huan, Weiye Yu, and Zhan Lian. "Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories." Journal of Marine Science and Engineering 12, no. 11 (October 28, 2024): 1933. http://dx.doi.org/10.3390/jmse12111933.
Повний текст джерелаStoodley, Catherine J., and Peter T. Tsai. "Adaptive Prediction for Social Contexts: The Cerebellar Contribution to Typical and Atypical Social Behaviors." Annual Review of Neuroscience 44, no. 1 (July 8, 2021): 475–93. http://dx.doi.org/10.1146/annurev-neuro-100120-092143.
Повний текст джерелаOh, Cheol, Stephen G. Ritchie, and Jun-Seok Oh. "Exploring the Relationship between Data Aggregation and Predictability to Provide Better Predictive Traffic Information." Transportation Research Record: Journal of the Transportation Research Board 1935, no. 1 (January 2005): 28–36. http://dx.doi.org/10.1177/0361198105193500104.
Повний текст джерелаSiek, M., and D. P. Solomatine. "Nonlinear chaotic model for predicting storm surges." Nonlinear Processes in Geophysics 17, no. 5 (September 6, 2010): 405–20. http://dx.doi.org/10.5194/npg-17-405-2010.
Повний текст джерелаPrasanna, Christopher, Jonathan Realmuto, Anthony Anderson, Eric Rombokas, and Glenn Klute. "Using Deep Learning Models to Predict Prosthetic Ankle Torque." Sensors 23, no. 18 (September 6, 2023): 7712. http://dx.doi.org/10.3390/s23187712.
Повний текст джерелаBisola Oluwafadekemi Adegoke, Tolulope Odugbose, and Christiana Adeyemi. "Data analytics for predicting disease outbreaks: A review of models and tools." International Journal of Life Science Research Updates 2, no. 2 (April 30, 2024): 001–9. http://dx.doi.org/10.53430/ijlsru.2024.2.2.0023.
Повний текст джерелаZhang, Xiaopeng. "Paris House Rental Price Index Prediction-A Classical Statistical Model Approach." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 294–99. http://dx.doi.org/10.54097/q6kz2d72.
Повний текст джерелаNik Nurul Hafzan, Mat Yaacob, Deris Safaai, Mat Asiah, Mohamad Mohd Saberi, and Safaai Siti Syuhaida. "Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment." MATEC Web of Conferences 255 (2019): 03002. http://dx.doi.org/10.1051/matecconf/201925503002.
Повний текст джерелаKim, Jeonghun, and Ohbyung Kwon. "A Model for Rapid Selection and COVID-19 Prediction with Dynamic and Imbalanced Data." Sustainability 13, no. 6 (March 11, 2021): 3099. http://dx.doi.org/10.3390/su13063099.
Повний текст джерелаДисертації з теми "Dynaic prediction"
Chabeau, Lucas. "Développement et validation d’un outil multivarié de prédiction dynamique d’un échec de greffe rénale." Electronic Thesis or Diss., Nantes Université, 2024. http://www.theses.fr/2024NANU1033.
Повний текст джерелаFor many chronic diseases, dynamic prediction of a clinical event of interest can be useful in personalised medicine. In this context, prognoses can be updated throughout the patient's follow-up, as new information becomes available. This CIFRE doctoral thesis, in collaboration with Sêmeia, involves developing and validating a dynamic prediction tool for kidney graft failure. The proposed tool predicts kidney graft failure, in competition with death with a functional graft, over a five-year time horizon. The prediction is based on information available at patient inclusion and three biological markers collected during follow-up (serum creatinine, proteinuria and type II donor-specific antibodies). allowing to update the prognosis. This tool has been validated for prediction times of between 1 and 6 years post-transplant. This doctoral thesis was the subject of three original projects. The first involved developing an inference procedure to estimate a joint model for longitudinal and survival data compatible with the prediction tool. Secondly, we examined the heterogeneity in the definition of prediction horizon in the dynamic prediction literature. Finally, we present the construction and validation of a dynamic prediction model for renal transplant failure. The model showed good discrimination and calibration
Greco, Antonino. "The role of task relevance in the modulation of brain dynamics during sensory predictions." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/307050.
Повний текст джерелаGreco, Antonino. "The role of task relevance in the modulation of brain dynamics during sensory predictions." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/307050.
Повний текст джерелаCurrier, Patrick Norman. "A Method for Modeling and Prediction of Ground Vehicle Dynamics and Stability in Autonomous Systems." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/27632.
Повний текст джерелаPh. D.
Chen, Yutao. "Algorithms and Applications for Nonlinear Model Predictive Control with Long Prediction Horizon." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421957.
Повний текст джерелаImplementazioni rapide di NMPC sono importanti quando si affronta il controllo in tempo reale di sistemi che presentano caratteristiche come dinamica veloce, ampie dimensioni e orizzonte di predizione lungo, poiché in tali situazioni il carico di calcolo dell'MNPC può limitare la larghezza di banda di controllo ottenibile. A tale scopo, questa tesi riguarda sia gli algoritmi che le applicazioni. In primo luogo, sono stati sviluppati algoritmi veloci NMPC per il controllo di sistemi dinamici a tempo continuo che utilizzano un orizzonte di previsione lungo. Un ponte tra MPC lineare e non lineare viene costruito utilizzando linearizzazioni parziali o aggiornamento della sensibilità. Al fine di aggiornare la sensibilità solo quando necessario, è stata introdotta una misura simile alla curva di non linearità (CMoN) per i sistemi dinamici e applicata agli algoritmi NMPC esistenti. Basato su CMoN, sono state sviluppate logiche di aggiornamento intuitive e avanzate per diverse prestazioni numeriche e di controllo. Pertanto, il CMoN, insieme alla logica di aggiornamento, formula uno schema di aggiornamento della sensibilità parziale per NMPC veloce, denominato CMoN-RTI. Gli esempi di simulazione sono utilizzati per dimostrare l'efficacia e l'efficienza di CMoN-RTI. Inoltre, un'analisi rigorosa sull'ottimalità e sulla convergenza locale di CMoN-RTI viene fornita ed illustrata utilizzando esempi numerici. Algoritmi di condensazione parziale sono stati sviluppati quando si utilizza lo schema di aggiornamento della sensibilità parziale proposto. La complessità computazionale è stata ridotta poiché parte delle informazioni di condensazione sono sfruttate da precedenti istanti di campionamento. Una logica di aggiornamento della sensibilità insieme alla condensazione parziale viene proposta con una complessità lineare nella lunghezza della previsione, che porta a una velocità di un fattore dieci. Sono anche proposti algoritmi di fattorizzazione parziale della matrice per sfruttare l'aggiornamento della sensibilità parziale. Applicando metodi di suddivisione a problemi a più stadi, è necessario aggiornare solo parte del sistema KKT risultante, che è computazionalmente dominante nell'ottimizzazione online. Un miglioramento significativo è stato dimostrato dando operazioni in virgola mobile (flop). In secondo luogo, sono state realizzate implementazioni efficienti di NMPC sviluppando un pacchetto basato su Matlab chiamato MATMPC. MATMPC ha due modalità operative: quella si basa completamente su Matlab e l'altra utilizza l'API del linguaggio MATLAB C. I vantaggi di MATMPC sono che gli algoritmi sono facili da sviluppare e eseguire il debug grazie a Matlab e le librerie e le toolbox di Matlab possono essere utilizzate direttamente. Quando si lavora nella seconda modalità, l'efficienza computazionale di MATMPC è paragonabile a quella del software che utilizza la generazione di codice ottimizzata. Le realizzazioni in tempo reale sono ottenute per un simulatore di guida dinamica di nove gradi di libertà e per il movimento multisensoriale con sedile attivo.
Garside, Simon. "Dynamic prediction of road traffic networks." Thesis, Lancaster University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387431.
Повний текст джерелаChoudhury, Nazim Ahmed. "Mining Time-aware Actor-level Evolution Similarity for Link Prediction in Dynamic Network." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18640.
Повний текст джерелаPiccinini, Federico. "Dynamic load balancing based on latency prediction." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143333.
Повний текст джерелаAlkindi, Ahmed Bin Masoud Bin Ali. "Performance optimisation through modelling and dynamic prediction." Thesis, University of Warwick, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399475.
Повний текст джерелаSomoye, Adesina Eniari. "A computer prediction of robot dynamic performance." Thesis, University of Surrey, 1985. http://epubs.surrey.ac.uk/848049/.
Повний текст джерелаКниги з теми "Dynaic prediction"
Building and Fire Research Laboratory (U.S.) and Factory Mutual Research Corporation, eds. Prediction of fire dynamics. Gaithersburg, MD: The Institute, 1997.
Знайти повний текст джерелаHein, Putter, ed. Dynamic prediction in clinical survival analysis. Boca Raton: CRC Press, 2012.
Знайти повний текст джерелаLalanne, M. Rotordynamics prediction in engineering. Chichester: Wiley, 1990.
Знайти повний текст джерелаA, Ladd J., Yuhas A. J, and United States. National Aeronautics and Space Administration., eds. Dynamic inlet distortion prediction with a combined computational fluid dynamics and distortion synthesis approach. [Washington, DC: National Aeronautics and Space Administration, 1996.
Знайти повний текст джерелаHiermaier, Stefan, ed. Predictive Modeling of Dynamic Processes. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0727-1.
Повний текст джерелаLughofer, Edwin, and Moamar Sayed-Mouchaweh, eds. Predictive Maintenance in Dynamic Systems. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2.
Повний текст джерелаSreeramesh, Kalluri, Bonacuse Peter J. 1960-, and Symposium on Multiaxial Fatigue and Deformation: Testing and Prediction (1999 : Seattle, Wash.), eds. Multiaxial fatigue and deformation: Testing and prediction. W. Conshohocken, PA: ASTM, 2000.
Знайти повний текст джерелаSebastian, James W. Parametric prediction of the transverse dynamic stability of ships. Monterey, Calif: Naval Postgraduate School, 1998.
Знайти повний текст джерелаPhillips, Norman A. Dispersion processes in large-scale weather prediction. Geneva, Switzerland: Secretariat of the World Meteorological Organization, 1990.
Знайти повний текст джерелаPhillips, Norman A. Dispersion processes in large scale weather prediction. [Geneva]: World Meteorological Organization, 1990.
Знайти повний текст джерелаЧастини книг з теми "Dynaic prediction"
Pourbafrani, Mahsa, Shreya Kar, Sebastian Kaiser, and Wil M. P. van der Aalst. "Remaining Time Prediction for Processes with Inter-case Dynamics." In Lecture Notes in Business Information Processing, 140–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_11.
Повний текст джерелаShao, Yaping, Gongbing Peng, and Lance M. Leslie. "The Environmental Dynamic System." In Environmental Modelling and Prediction, 21–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04868-9_2.
Повний текст джерелаGarcia, Angel E. "Molecular Dynamics Simulations of Protein Folding." In Protein Structure Prediction, 315–30. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_12.
Повний текст джерелаPrincipe, Jose C., Ludong Wang, and Jyh-Ming Kuo. "Non-Linear Dynamic Modelling with Neural Networks." In Signal Analysis and Prediction, 275–90. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_20.
Повний текст джерелаDodla, Venkata Bhaskar Rao. "Weather Prediction — Lorenz Chaos Theory — Nonlinear Dynamics, Ensemble Prediction." In Numerical Weather Prediction, 189–205. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003354017-6.
Повний текст джерелаDietrich, William E., Dino G. Bellugi, Leonard S. Sklar, Jonathan D. Stock, Arjun M. Heimsath, and Joshua J. Roering. "Geomorphic Transport Laws for Predicting Landscape form and Dynamics." In Prediction in Geomorphology, 103–32. Washington, D. C: American Geophysical Union, 2013. http://dx.doi.org/10.1029/135gm09.
Повний текст джерелаHirsch, Markus, Klaus Oppenauer, and Luigi del Re. "Dynamic Engine Emission Models." In Automotive Model Predictive Control, 73–87. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-071-7_5.
Повний текст джерелаSao, Ashutosh, Simon Gottschalk, Nicolas Tempelmeier, and Elena Demidova. "MetaCitta: Deep Meta-Learning for Spatio-Temporal Prediction Across Cities and Tasks." In Advances in Knowledge Discovery and Data Mining, 70–82. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33383-5_6.
Повний текст джерелаO’Neil, Patrick. "Dynamic, Covert Network Simulation." In Social Computing, Behavioral - Cultural Modeling and Prediction, 239–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29047-3_29.
Повний текст джерелаAng, Zhendong, and Umang Mathur. "Predictive Monitoring with Strong Trace Prefixes." In Computer Aided Verification, 182–204. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65630-9_9.
Повний текст джерелаТези доповідей конференцій з теми "Dynaic prediction"
Abras, Jennifer, and Nathan Hariharan. "A Dual-Step Deep Learning-Based Surrogate Model for Dynamic Stall Predictions." In Vertical Flight Society 80th Annual Forum & Technology Display, 1–10. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1325.
Повний текст джерелаMadenci, Erdogan, Nam Phan, and Atila Barut. "Multi-Body Peridynamics for Failure Prediction in Rotating Thick Composites." In Vertical Flight Society 71st Annual Forum & Technology Display, 1–7. The Vertical Flight Society, 2015. http://dx.doi.org/10.4050/f-0071-2015-10266.
Повний текст джерелаJackson, Ryan, Michael Jump, and Peter Green. "Towards Gaussian Process Models of Complex Rotorcraft Dynamics." In Vertical Flight Society 74th Annual Forum & Technology Display, 1–11. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12828.
Повний текст джерелаGennaretti, Massimo, Roberto Celi, Claudio Pasquali, Felice Cardito, Jacopo Serafini, and Giovanni Bernardini. "Dynamic Wake Inflow Modeling in Ground Effect for Flight Dynamics Applications." In Vertical Flight Society 73rd Annual Forum & Technology Display, 1–12. The Vertical Flight Society, 2017. http://dx.doi.org/10.4050/f-0073-2017-12059.
Повний текст джерелаThornburgh, Robert, Andrew Kreshock, and Matthew Wilbur. "A Dynamic Calibration Method for Experimental and Analytical Hub Load Comparison." In Vertical Flight Society 71st Annual Forum & Technology Display, 1–14. The Vertical Flight Society, 2015. http://dx.doi.org/10.4050/f-0071-2015-10271.
Повний текст джерелаHyeon, Eunjeong, Youngki Kim, Niket Prakash, and Anna G. Stefanopoulou. "Influence of Speed Forecasting on the Performance of Ecological Adaptive Cruise Control." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9046.
Повний текст джерелаKemmerer, Julian, and Baris Taskin. "Range-based Dynamic Routing of Hierarchical On Chip Network Traffic." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633953.
Повний текст джерелаCameron, Fraser, and Gu¨nter Niemeyer. "Predicting Blood Glucose Levels Around Meals for Patients With Type I Diabetes." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4060.
Повний текст джерелаHendricks, Terry J., Chendong Huang, and Joseph C. Giglio. "Experimental Correlation of a Thermal / Fluid Dynamic / Electrical Performance Model of a Multiple-Tube, Vapor-Anode AMTEC Cell." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-1007.
Повний текст джерелаLeu, Jessica, and Masayoshi Tomizuka. "Motion Planning for Industrial Mobile Robots With Closed-Loop Stability Enhanced Prediction." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9208.
Повний текст джерелаЗвіти організацій з теми "Dynaic prediction"
Tuniki, Himanshu Patel, Gabriel Bekö, and Andrius Jurelionis. Using Adaptive Behaviour Patterns of Open Plan Office Occupants in Energy Consumption Predictions. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541563857.
Повний текст джерелаGallagher, B., and T. Eliassi-Rad. API Requirements for Dynamic Graph Prediction. Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/1036864.
Повний текст джерелаPfeffer, Richard L. Nonlinear Dynamics Underlying Atmospheric Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada305478.
Повний текст джерелаСоловйов, В. М., та В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.
Повний текст джерелаDavis, Steven J., and Pawel M. Krolikowski. Reservation Wages Revisited: Empirics with the Canonical Model. Federal Reserve Bank of Cleveland, October 2024. http://dx.doi.org/10.26509/frbc-wp-202423.
Повний текст джерелаVas, Dragos, Elizabeth Corriveau, Lindsay Gaimaro, and Robyn Barbato. Challenges and limitations of using autonomous instrumentation for measuring in situ soil respiration in a subarctic boreal forest in Alaska, USA. Engineer Research and Development Center (U.S.), December 2023. http://dx.doi.org/10.21079/11681/48018.
Повний текст джерелаTrott, Kevin C. Simulation Development for Dynamic Situation Awareness and Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada440082.
Повний текст джерелаTrott, Kevin. Simulation for Dynamic Situation Awareness and Prediction III. Fort Belvoir, VA: Defense Technical Information Center, March 2010. http://dx.doi.org/10.21236/ada516768.
Повний текст джерелаKimagai, Toru, and Motoyuki Akamatsu. Human Driving Behavior Prediction Using Dynamic Bayesian Networks. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0305.
Повний текст джерелаPerdigão, Rui A. P. Earth System Dynamic Intelligence with Quantum Technologies: Seeing the “Invisible”, Predicting the “Unpredictable” in a Critically Changing World. Meteoceanics, October 2021. http://dx.doi.org/10.46337/211028.
Повний текст джерела