Littérature scientifique sur le sujet « FOV PREDICTION »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Sommaire
Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « FOV PREDICTION ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Articles de revues sur le sujet "FOV PREDICTION"
Batchuluun, Ganbayar, Ja Hyung Koo, Yu Hwan Kim et Kang Ryoung Park. « Image Region Prediction from Thermal Videos Based on Image Prediction Generative Adversarial Network ». Mathematics 9, no 9 (7 mai 2021) : 1053. http://dx.doi.org/10.3390/math9091053.
Texte intégralBatchuluun, Ganbayar, Na Rae Baek et Kang Ryoung Park. « Enlargement of the Field of View Based on Image Region Prediction Using Thermal Videos ». Mathematics 9, no 19 (25 septembre 2021) : 2379. http://dx.doi.org/10.3390/math9192379.
Texte intégralLei, Ke, Ali B. Syed, Xucheng Zhu, John M. Pauly et Shreyas V. Vasanawala. « Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-Stack Attention Neural Network ». Bioengineering 10, no 1 (10 janvier 2023) : 92. http://dx.doi.org/10.3390/bioengineering10010092.
Texte intégralØygard, Sigrid H., Mélanie Audoin, Andreas Austeng, Erik V. Thomsen, Matthias B. Stuart et Jørgen A. Jensen. « Accurate prediction of transmission through a lensed row-column addressed array ». Journal of the Acoustical Society of America 151, no 5 (mai 2022) : 3207–18. http://dx.doi.org/10.1121/10.0010528.
Texte intégralFang, Yuan, Zhang Xiaoyong, Huang Zhiwu, Wentao Yu et 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 (20 mars 2015) : 185–90. http://dx.doi.org/10.20965/jaciii.2015.p0185.
Texte intégralLi, Jie, Ling Han, Cong Zhang, Qiyue Li et Weitao Li. « Adaptive Panoramic Video Multicast Streaming with Limited FoV Feedback ». Complexity 2020 (18 décembre 2020) : 1–14. http://dx.doi.org/10.1155/2020/8832715.
Texte intégralHuang, Po-Chia, Ho-Hui Hsieh, Ching-Han Hsu et Ing-Tsung Hsiao. « AN EFFICIENT SENSITIVITY CALCULATION OF TILTED APERTURES FOR PRECLINICAL MULTI-PINHOLE SPECT ». Biomedical Engineering : Applications, Basis and Communications 27, no 01 (février 2015) : 1550006. http://dx.doi.org/10.4015/s1016237215500064.
Texte intégralChuang, Shu-Min, Chia-Sheng Chen et Eric Hsiao-Kuang Wu. « The Implementation of Interactive VR Application and Caching Strategy Design on Mobile Edge Computing (MEC) ». Electronics 12, no 12 (16 juin 2023) : 2700. http://dx.doi.org/10.3390/electronics12122700.
Texte intégralLiu, Tailong, Teng Pan, Shuijie Qin, Hui Zhao et Huikai Xie. « Dynamic Response Analysis of an Immersed Electrothermally Actuated MEMS Mirror ». Actuators 12, no 2 (15 février 2023) : 83. http://dx.doi.org/10.3390/act12020083.
Texte intégralWhang, Allen Jong-Woei, Yi-Yung Chen, Wei-Chieh Tseng, Chih-Hsien Tsai, Yi-Ping Chao, Chieh-Hung Yen, Chun-Hsiu Liu et Xin Zhang. « Pupil Size Prediction Techniques Based on Convolution Neural Network ». Sensors 21, no 15 (21 juillet 2021) : 4965. http://dx.doi.org/10.3390/s21154965.
Texte intégralThèses sur le sujet "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.
Texte intégralKock, Peter. « Prediction and predictive control for economic optimisation of vehicle operation ». Thesis, Kingston University, 2013. http://eprints.kingston.ac.uk/35861/.
Texte intégralSchön, 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.
Texte intégralPredictive 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.
Texte intégralBangalore, 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.
Texte intégralMaster 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.
Texte intégralImplementazioni 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.
Texte intégralGe, Esther. « The query based learning system for lifetime prediction of metallic components ». Queensland University of Technology, 2008. http://eprints.qut.edu.au/18345/.
Texte intégralZhu, 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.
Texte intégralAldars, 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.
Texte intégralLas 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.
Livres sur le sujet "FOV PREDICTION"
Houston, Walter. Central prediction systems for predicting specific course grades. Iowa City : American College Testing Program, 1988.
Trouver le texte intégralPredicting Prehistory : Predictive models and field research methods for detecting prehistoric contexts. Firenze : Museo e istituto fiorentino di preistoria "Paolo Graziosi,", 2015.
Trouver le texte intégralCherdanceva, Tat'yana, Vladimir Klimechev et 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.
Texte intégralAltmisdort, F. Nadir. Development of a new prediction algorithm and a simulator for the Predictive Read Cache (PRC). Monterey, Calif : Naval Postgraduate School, 1996.
Trouver le texte intégralCasey, Douglas R. Predictions for 1988. 2e éd. Alexandria, VA : KCI Communications, 1988.
Trouver le texte intégralUnited States. National Weather Service, dir. National Centers for Environmental Prediction. [Silver Spring, Md.?] : U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, 1996.
Trouver le texte intégralVasil'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.
Texte intégralRathore, Santosh Singh, et 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.
Texte intégralHanson, R. Karl. Prediction statistics for psychological assessment. Washington : American Psychological Association, 2022. http://dx.doi.org/10.1037/0000275-000.
Texte intégralBolfarine, Heleno, et 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.
Texte intégralChapitres de livres sur le sujet "FOV PREDICTION"
Li, Yunqiao, Yiling Xu, Shaowei Xie, Liangji Ma et Jun Sun. « Two-Layer FoV Prediction Model for Viewport Dependent Streaming of 360-Degree Videos ». Dans Communications and Networking, 501–9. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06161-6_49.
Texte intégralPourbafrani, Mahsa, Shreya Kar, Sebastian Kaiser et Wil M. P. van der Aalst. « Remaining Time Prediction for Processes with Inter-case Dynamics ». Dans 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.
Texte intégralLiu, Wendi, Léan E. Garland, Jesus Ochoa et Michael J. Pyrcz. « A Geostatistical Heterogeneity Metric for Spatial Feature Engineering ». Dans 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.
Texte intégralFani Sani, Mohammadreza, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst et Wil M. P. van der Aalst. « Event Log Sampling for Predictive Monitoring ». Dans 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.
Texte intégralLee, Suhwan, Marco Comuzzi et Xixi Lu. « Continuous Performance Evaluation for Business Process Outcome Monitoring ». Dans 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.
Texte intégralWarmuth, Christian, et Henrik Leopold. « On the Potential of Textual Data for Explainable Predictive Process Monitoring ». Dans 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.
Texte intégralMontesinos López, Osval Antonio, Abelardo Montesinos López et Jose Crossa. « Linear Mixed Models ». Dans 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.
Texte intégralBautista-Hernández, Jorge, et María Ángeles Martín-Prats. « Monte Carlo Simulation Applicable for Predictive Algorithm Analysis in Aerospace ». Dans 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.
Texte intégralOnishi, Ryo, Joe Hirai, Dmitry Kolomenskiy et Yuki Yasuda. « Real-Time High-Resolution Prediction of Orographic Rainfall for Early Warning of Landslides ». Dans 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.
Texte intégralSpenrath, Yorick, Marwan Hassani et Boudewijn F. van Dongen. « Online Prediction of Aggregated Retailer Consumer Behaviour ». Dans 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.
Texte intégralActes de conférences sur le sujet "FOV PREDICTION"
Zhang, Zhihao, Haipeng Du, Shouqin Huang, Weizhan Zhang et Qinghua Zheng. « VRFormer : 360-Degree Video Streaming with FoV Combined Prediction and Super resolution ». Dans 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.
Texte intégralDeshwal, Aryan, Janardhan Rao Doppa et Dan Roth. « Learning and Inference for Structured Prediction : A Unifying Perspective ». Dans 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.
Texte intégralRohani, Muhammad Joehan Bin, Azam Bin A. Rahman, M. Syazwan Kamil Bin Abdullah, M. Nazmi Bin Ali, I. Wayan Eka Putra, Hazwani Binti Hidzir et Ehsan Amirian. « IMGESA (Integrated Meteorological and Geohazard System Advisory) as Predictive Analytics Tool for Managing Geohazard Impacts to Pipeline ». Dans ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211292-ms.
Texte intégralAssaf, Roy, et Anika Schumann. « Explainable Deep Neural Networks for Multivariate Time Series Predictions ». Dans 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.
Texte intégralColban, Will F., Karen A. Thole et David Bogard. « A Film-Cooling Correlation for Shaped Holes on a Flat-Plate Surface ». Dans ASME Turbo Expo 2008 : Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50121.
Texte intégralJakša, Rudolf, Martina Zeleňáková, Juraj Koščák et Helena Hlavatá. « Local Prediction of Precipitation Based on Neural Network ». Dans Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.079.
Texte intégralLu, Ziqi, Shixiao Fu, Mengmeng Zhang, Haojie Ren et Leijian Song. « A Non-Iterative Method for Vortex Induced Vibration Prediction of Marine Risers ». Dans 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.
Texte intégralLauerova, Dana, Vladislav Pistora, Milan Brumovsky et Milos Kytka. « Warm Pre-Stressing Tests for WWER 440 Reactor Pressure Vessel Material ». Dans ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77287.
Texte intégralDhakksinesh, A., Olivia R. Katherine et V. S. Pooja. « Crime Analysis and Prediction Based on Machine Learning Algorithm ». Dans International Research Conference on IOT, Cloud and Data Science. Switzerland : Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-y21866.
Texte intégralTong, Michael T. « A Machine-Learning Approach to Assess Aircraft Engine System Performance ». Dans ASME Turbo Expo 2020 : Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14661.
Texte intégralRapports d'organisations sur le sujet "FOV PREDICTION"
Roberson, Madeleine, Kathleen Inman, Ashley Carey, Isaac Howard et 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.), juin 2022. http://dx.doi.org/10.21079/11681/44483.
Texte intégralKumar, Kaushal, et Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, mars 2023. http://dx.doi.org/10.31979/mti.2023.2211.
Texte intégralZhu, Xian-Kui, Brian Leis et Tom McGaughy. PR-185-173600-R01 Reference Stress for Metal-loss Assessment of Pipelines. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), août 2018. http://dx.doi.org/10.55274/r0011516.
Texte intégralVecherin, Sergey, Stephen Ketcham, Aaron Meyer, Kyle Dunn, Jacob Desmond et Michael Parker. Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium. Engineer Research and Development Center (U.S.), septembre 2022. http://dx.doi.org/10.21079/11681/45300.
Texte intégralPeterson, Warren. PR-663-19600-Z01 Develop Guidance for Calculation of HCDP in Pipelines. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), mars 2020. http://dx.doi.org/10.55274/r0011659.
Texte intégralMartin, Marcus G., Edward J. Maginn, Robin D. Rogers, Greg Voth et 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, juillet 2008. http://dx.doi.org/10.21236/ada485626.
Texte intégralOliver, Amanda, Catherine Murphy, Edmund Howe et John Vest. Comparing methods for estimating water surface elevation between gages in the Lower Mississippi River. Engineer Research and Development Center (U.S.), avril 2023. http://dx.doi.org/10.21079/11681/46915.
Texte intégralBuchanan, Randy, Christina Rinaudo, George Gallarno et M. Lagarde. Early life-cycle prediction of reliability. Engineer Research and Development Center (U.S.), avril 2023. http://dx.doi.org/10.21079/11681/46919.
Texte intégralPanek, Krol et 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), février 2016. http://dx.doi.org/10.55274/r0010858.
Texte intégralWei, Dongmei, Yang Sun et 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, avril 2023. http://dx.doi.org/10.37766/inplasy2023.4.0014.
Texte intégral