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Статті в журналах з теми "FOV PREDICTION"
Batchuluun, Ganbayar, Ja Hyung Koo, Yu Hwan Kim, and Kang Ryoung Park. "Image Region Prediction from Thermal Videos Based on Image Prediction Generative Adversarial Network." Mathematics 9, no. 9 (May 7, 2021): 1053. http://dx.doi.org/10.3390/math9091053.
Повний текст джерелаBatchuluun, Ganbayar, Na Rae Baek, and Kang Ryoung Park. "Enlargement of the Field of View Based on Image Region Prediction Using Thermal Videos." Mathematics 9, no. 19 (September 25, 2021): 2379. http://dx.doi.org/10.3390/math9192379.
Повний текст джерелаLei, Ke, Ali B. Syed, Xucheng Zhu, John M. Pauly, and Shreyas V. Vasanawala. "Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-Stack Attention Neural Network." Bioengineering 10, no. 1 (January 10, 2023): 92. http://dx.doi.org/10.3390/bioengineering10010092.
Повний текст джерелаØygard, Sigrid H., Mélanie Audoin, Andreas Austeng, Erik V. Thomsen, Matthias B. Stuart, and Jørgen A. Jensen. "Accurate prediction of transmission through a lensed row-column addressed array." Journal of the Acoustical Society of America 151, no. 5 (May 2022): 3207–18. http://dx.doi.org/10.1121/10.0010528.
Повний текст джерелаFang, Yuan, Zhang Xiaoyong, Huang Zhiwu, Wentao Yu, and Yabo Wang. "A Switched Extend Kalman-Filter for Visual Servoing Applied in Nonholonomic Robot with the FOV Constraint." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 2 (March 20, 2015): 185–90. http://dx.doi.org/10.20965/jaciii.2015.p0185.
Повний текст джерелаLi, Jie, Ling Han, Cong Zhang, Qiyue Li, and Weitao Li. "Adaptive Panoramic Video Multicast Streaming with Limited FoV Feedback." Complexity 2020 (December 18, 2020): 1–14. http://dx.doi.org/10.1155/2020/8832715.
Повний текст джерелаHuang, Po-Chia, Ho-Hui Hsieh, Ching-Han Hsu, and Ing-Tsung Hsiao. "AN EFFICIENT SENSITIVITY CALCULATION OF TILTED APERTURES FOR PRECLINICAL MULTI-PINHOLE SPECT." Biomedical Engineering: Applications, Basis and Communications 27, no. 01 (February 2015): 1550006. http://dx.doi.org/10.4015/s1016237215500064.
Повний текст джерелаChuang, Shu-Min, Chia-Sheng Chen, and Eric Hsiao-Kuang Wu. "The Implementation of Interactive VR Application and Caching Strategy Design on Mobile Edge Computing (MEC)." Electronics 12, no. 12 (June 16, 2023): 2700. http://dx.doi.org/10.3390/electronics12122700.
Повний текст джерелаLiu, Tailong, Teng Pan, Shuijie Qin, Hui Zhao, and Huikai Xie. "Dynamic Response Analysis of an Immersed Electrothermally Actuated MEMS Mirror." Actuators 12, no. 2 (February 15, 2023): 83. http://dx.doi.org/10.3390/act12020083.
Повний текст джерелаWhang, Allen Jong-Woei, Yi-Yung Chen, Wei-Chieh Tseng, Chih-Hsien Tsai, Yi-Ping Chao, Chieh-Hung Yen, Chun-Hsiu Liu, and Xin Zhang. "Pupil Size Prediction Techniques Based on Convolution Neural Network." Sensors 21, no. 15 (July 21, 2021): 4965. http://dx.doi.org/10.3390/s21154965.
Повний текст джерелаДисертації з теми "FOV PREDICTION"
Björsell, Joachim. "Long Range Channel Predictions for Broadband Systems : Predictor antenna experiments and interpolation of Kalman predictions." Thesis, Uppsala universitet, Signaler och System, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-281058.
Повний текст джерелаKock, Peter. "Prediction and predictive control for economic optimisation of vehicle operation." Thesis, Kingston University, 2013. http://eprints.kingston.ac.uk/35861/.
Повний текст джерелаSchön, Tomas. "Identification for Predictive Control : A Multiple Model Approach." Thesis, Linköping University, Department of Electrical Engineering, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1050.
Повний текст джерелаPredictive control relies on predictions of the future behaviour of the system to be controlled. These predictions are calculated from a model of this system, thus making the model the cornerstone of the predictive controller. Furthermore predictive control is the only advanced control methodology that has managed to become widely used in the industry. The necessity of good models in the predictive control context can thus be motivated both from the very nature of predictive control and from its widespread use in industry.
This thesis is concerned with examining the use of multiple models in the predictive controller. In order to do this the standard predictive control formulation has been extended to incorporate the use of multiple models. The most general case of this new formulation allows the use of an individual model for each prediction horizon.
The models are estimated using measurements of the input and output sequences from the true system. When using this data to find a good model of the system it is important to remember the intended purpose of the model. In this case the model is going to be used in a predictive controller and the most important feature of the models is to deliver good k-step ahead predictions. The identification algorithms used to estimate the models thus strives for estimating models good at calculating these predictions.
Finally this thesis presents some complete simulations of these ideas showing the potential of using multiple models in the predictive control framework.
Shrestha, Rakshya. "Deep soil mixing and predictive neural network models for strength prediction." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607735.
Повний текст джерелаBangalore, Narendranath Rao Amith Kaushal. "Online Message Delay Prediction for Model Predictive Control over Controller Area Network." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78626.
Повний текст джерелаMaster of Science
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.
Ge, Esther. "The query based learning system for lifetime prediction of metallic components." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/18345/4/Esther_Ting_Ge_Thesis.pdf.
Повний текст джерелаGe, Esther. "The query based learning system for lifetime prediction of metallic components." Queensland University of Technology, 2008. http://eprints.qut.edu.au/18345/.
Повний текст джерелаZhu, Zheng. "A Unified Exposure Prediction Approach for Multivariate Spatial Data: From Predictions to Health Analysis." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin155437434818942.
Повний текст джерелаAldars, García Laila. "Predictive mycology as a tool for controlling and preventing the aflatoxin risk in postharvest." Doctoral thesis, Universitat de Lleida, 2017. http://hdl.handle.net/10803/418806.
Повний текст джерелаLas aflatoxinas son potentes carcinógenos que representan una amenaza significativa para la salud humana. La incidencia de estas micotoxinas en los alimentos es alta, por lo que su control y prevención es obligatoria en la industria alimentaria. El desarrollo de modelos predictivos apropiados que nos permitan predecir el crecimiento fúngico y la producción de micotoxinas es de gran utilidad como herramienta para controlar, predecir y prevenir el riesgo de micotoxinas en alimentos. Es importante que los modelos predictivos sean capaces de explicar las condiciones ambientales que se encuentran a lo largo de la cadena alimentaria. Entre tales condiciones encontramos: condiciones subóptimas para el crecimiento y producción de micotoxinas, distribución aleatoria de esporas fúngicas en el alimento, presencia de diferentes cepas de la misma especie o condiciones ambientales dinámicas. El presente trabajo proporciona una base para el desarrollo de modelos científicamente probados, que pueden ser aplicados por la industria alimentaria para mejorar el control de micotoxinas en postcosecha.
Aflatoxins are potent carcinogens that pose a significant threat to human health. Incidence of these mycotoxins in foodstuffs is high, thus their control and prevention is mandatory in the food industry. The development of appropriate predictive models that allow us to predict fungal growth and mycotoxin production will be a valuable tool to monitor, predict and prevent the mycotoxin risk. To develop accurate predictive models it is important to account for the real conditions that we will encounter through the food chain. Such conditions include: suboptimal conditions for growth and mycotoxin production, even distribution of spores across the food matrix, presence of different strains of the same species or dynamic environmental conditions. Given the scope and complexity of the problem the present work provides the basis for scientifically proven models, which can be applied in the food industry in order to improve postharvest control of commodities.
Книги з теми "FOV PREDICTION"
Houston, Walter. Central prediction systems for predicting specific course grades. Iowa City: American College Testing Program, 1988.
Знайти повний текст джерелаPredicting Prehistory: Predictive models and field research methods for detecting prehistoric contexts. Firenze: Museo e istituto fiorentino di preistoria "Paolo Graziosi,", 2015.
Знайти повний текст джерелаCherdanceva, Tat'yana, Vladimir Klimechev, and Igor' Bobrov. Pathological and molecular biological analysis of renal cell carcinoma. Diagnosis and prognosis. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1020785.
Повний текст джерелаAltmisdort, F. Nadir. Development of a new prediction algorithm and a simulator for the Predictive Read Cache (PRC). Monterey, Calif: Naval Postgraduate School, 1996.
Знайти повний текст джерелаCasey, Douglas R. Predictions for 1988. 2nd ed. Alexandria, VA: KCI Communications, 1988.
Знайти повний текст джерелаUnited States. National Weather Service, ed. National Centers for Environmental Prediction. [Silver Spring, Md.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, 1996.
Знайти повний текст джерелаVasil'eva, Natal'ya. Mathematical models in the management of copper production: ideas, methods, examples. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014071.
Повний текст джерелаRathore, Santosh Singh, and Sandeep Kumar. Fault Prediction Modeling for the Prediction of Number of Software Faults. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7131-8.
Повний текст джерелаHanson, R. Karl. Prediction statistics for psychological assessment. Washington: American Psychological Association, 2022. http://dx.doi.org/10.1037/0000275-000.
Повний текст джерелаBolfarine, Heleno, and Shelemyahu Zacks. Prediction Theory for Finite Populations. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2904-9.
Повний текст джерелаЧастини книг з теми "FOV PREDICTION"
Li, Yunqiao, Yiling Xu, Shaowei Xie, Liangji Ma, and Jun Sun. "Two-Layer FoV Prediction Model for Viewport Dependent Streaming of 360-Degree Videos." In Communications and Networking, 501–9. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06161-6_49.
Повний текст джерелаPourbafrani, 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 джерелаLiu, Wendi, Léan E. Garland, Jesus Ochoa, and Michael J. Pyrcz. "A Geostatistical Heterogeneity Metric for Spatial Feature Engineering." In Springer Proceedings in Earth and Environmental Sciences, 3–19. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_1.
Повний текст джерелаFani Sani, Mohammadreza, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, and Wil M. P. van der Aalst. "Event Log Sampling for Predictive Monitoring." In Lecture Notes in Business Information Processing, 154–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_12.
Повний текст джерелаLee, Suhwan, Marco Comuzzi, and Xixi Lu. "Continuous Performance Evaluation for Business Process Outcome Monitoring." In Lecture Notes in Business Information Processing, 237–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_18.
Повний текст джерелаWarmuth, Christian, and Henrik Leopold. "On the Potential of Textual Data for Explainable Predictive Process Monitoring." In Lecture Notes in Business Information Processing, 190–202. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_14.
Повний текст джерелаMontesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Linear Mixed Models." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 141–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_5.
Повний текст джерелаBautista-Hernández, Jorge, and María Ángeles Martín-Prats. "Monte Carlo Simulation Applicable for Predictive Algorithm Analysis in Aerospace." In Technological Innovation for Connected Cyber Physical Spaces, 243–56. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36007-7_18.
Повний текст джерелаOnishi, Ryo, Joe Hirai, Dmitry Kolomenskiy, and Yuki Yasuda. "Real-Time High-Resolution Prediction of Orographic Rainfall for Early Warning of Landslides." In Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022, 237–48. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16898-7_17.
Повний текст джерелаSpenrath, Yorick, Marwan Hassani, and Boudewijn F. van Dongen. "Online Prediction of Aggregated Retailer Consumer Behaviour." In Lecture Notes in Business Information Processing, 211–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_16.
Повний текст джерелаТези доповідей конференцій з теми "FOV PREDICTION"
Zhang, Zhihao, Haipeng Du, Shouqin Huang, Weizhan Zhang, and Qinghua Zheng. "VRFormer: 360-Degree Video Streaming with FoV Combined Prediction and Super resolution." In 2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2022. http://dx.doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom57177.2022.00074.
Повний текст джерелаDeshwal, Aryan, Janardhan Rao Doppa, and Dan Roth. "Learning and Inference for Structured Prediction: A Unifying Perspective." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/878.
Повний текст джерелаRohani, Muhammad Joehan Bin, Azam Bin A. Rahman, M. Syazwan Kamil Bin Abdullah, M. Nazmi Bin Ali, I. Wayan Eka Putra, Hazwani Binti Hidzir, and Ehsan Amirian. "IMGESA (Integrated Meteorological and Geohazard System Advisory) as Predictive Analytics Tool for Managing Geohazard Impacts to Pipeline." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211292-ms.
Повний текст джерелаAssaf, Roy, and Anika Schumann. "Explainable Deep Neural Networks for Multivariate Time Series Predictions." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/932.
Повний текст джерелаColban, Will F., Karen A. Thole, and David Bogard. "A Film-Cooling Correlation for Shaped Holes on a Flat-Plate Surface." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50121.
Повний текст джерелаJakša, Rudolf, Martina Zeleňáková, Juraj Koščák, and Helena Hlavatá. "Local Prediction of Precipitation Based on Neural Network." In Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.079.
Повний текст джерелаLu, Ziqi, Shixiao Fu, Mengmeng Zhang, Haojie Ren, and Leijian Song. "A Non-Iterative Method for Vortex Induced Vibration Prediction of Marine Risers." In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/omae2017-61216.
Повний текст джерелаLauerova, Dana, Vladislav Pistora, Milan Brumovsky, and Milos Kytka. "Warm Pre-Stressing Tests for WWER 440 Reactor Pressure Vessel Material." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77287.
Повний текст джерелаDhakksinesh, A., Olivia R. Katherine, and V. S. Pooja. "Crime Analysis and Prediction Based on Machine Learning Algorithm." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-y21866.
Повний текст джерелаTong, Michael T. "A Machine-Learning Approach to Assess Aircraft Engine System Performance." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14661.
Повний текст джерелаЗвіти організацій з теми "FOV PREDICTION"
Roberson, Madeleine, Kathleen Inman, Ashley Carey, Isaac Howard, and Jameson Shannon. Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history. Engineer Research and Development Center (U.S.), June 2022. http://dx.doi.org/10.21079/11681/44483.
Повний текст джерелаKumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, March 2023. http://dx.doi.org/10.31979/mti.2023.2211.
Повний текст джерелаZhu, Xian-Kui, Brian Leis, and Tom McGaughy. PR-185-173600-R01 Reference Stress for Metal-loss Assessment of Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 2018. http://dx.doi.org/10.55274/r0011516.
Повний текст джерелаVecherin, Sergey, Stephen Ketcham, Aaron Meyer, Kyle Dunn, Jacob Desmond, and Michael Parker. Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45300.
Повний текст джерелаPeterson, Warren. PR-663-19600-Z01 Develop Guidance for Calculation of HCDP in Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2020. http://dx.doi.org/10.55274/r0011659.
Повний текст джерелаMartin, Marcus G., Edward J. Maginn, Robin D. Rogers, Greg Voth, and Mark S. Gordon. Technologies for Developing Predictive Atomistic and Coarse-Grained Force Fields for Ionic Liquid Property Prediction. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada485626.
Повний текст джерелаOliver, Amanda, Catherine Murphy, Edmund Howe, and John Vest. Comparing methods for estimating water surface elevation between gages in the Lower Mississippi River. Engineer Research and Development Center (U.S.), April 2023. http://dx.doi.org/10.21079/11681/46915.
Повний текст джерелаBuchanan, Randy, Christina Rinaudo, George Gallarno, and M. Lagarde. Early life-cycle prediction of reliability. Engineer Research and Development Center (U.S.), April 2023. http://dx.doi.org/10.21079/11681/46919.
Повний текст джерелаPanek, Krol, and Huth. PR-312-12208-R03 USEPA AERMOD Plume Rise and Volume Formulations and Implications for Existing RICE. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), February 2016. http://dx.doi.org/10.55274/r0010858.
Повний текст джерелаWei, Dongmei, Yang Sun, and Rongtao Chen. Risk prediction model for ISR after coronary stenting-a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2023. http://dx.doi.org/10.37766/inplasy2023.4.0014.
Повний текст джерела