Littérature scientifique sur le sujet « Horizons de prédiction variable »
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Articles de revues sur le sujet "Horizons de prédiction variable"
Le Losq, Charles, et Matthieu Micoulaut. « Simuler le verre ». Reflets de la physique, no 74 (décembre 2022) : 34–38. http://dx.doi.org/10.1051/refdp/202274034.
Texte intégralAbdelkader, Bougara, Ezziane Karim et Kadri Abdelkader. « Prédiction des résistances du ciment au laitier durcissant sous une temperature variable ». Canadian Journal of Civil Engineering 28, no 4 (1 août 2001) : 555–61. http://dx.doi.org/10.1139/l01-017.
Texte intégralChachuat, B., N. Roche et M. A. Latifi. « Réduction du modèle ASM 1 pour la commande optimale des petites stations d'épuration à boues activées ». Revue des sciences de l'eau 16, no 1 (12 avril 2005) : 5–26. http://dx.doi.org/10.7202/705496ar.
Texte intégralFinkelhor3, David, Anne Shattuck, Heather Turner et Sherry Hamby. « La polyvictimisation comme facteur de risque de revictimisation sexuelle12 ». Criminologie 47, no 1 (25 mars 2014) : 41–58. http://dx.doi.org/10.7202/1024006ar.
Texte intégralCarter, R. E., et L. E. Lowe. « Lateral variability of forest floor properties under second-growth Douglas-fir stands and the usefulness of composite sampling techniques ». Canadian Journal of Forest Research 16, no 5 (1 octobre 1986) : 1128–32. http://dx.doi.org/10.1139/x86-197.
Texte intégralCamus, F., M. Gabsi et B. Multon. « Prédiction des vibrations du stator d'une machine à réluctance variable en fonction du courant absorbé ». Journal de Physique III 7, no 2 (février 1997) : 387–404. http://dx.doi.org/10.1051/jp3:1997129.
Texte intégralAcevedo-Sandoval, Otilio A., Francisco Prieto-García, Judith Prieto-Méndez, Yolanda Marmolejo-Santillán et Claudia Romo-Gómez. « Chemical weathering in hardened volcanic horizons (tepetates) of the State of Mexico ». Revista Mexicana de Ciencias Geológicas 39, no 2 (26 juillet 2022) : 116–27. http://dx.doi.org/10.22201/cgeo.20072902e.2022.2.1644.
Texte intégralGoldstein, Benjamin A., Michael J. Pencina, Maria E. Montez-Rath et Wolfgang C. Winkelmayer. « Predicting mortality over different time horizons : which data elements are needed ? » Journal of the American Medical Informatics Association 24, no 1 (29 juin 2016) : 176–81. http://dx.doi.org/10.1093/jamia/ocw057.
Texte intégralJONG, E. DE, D. F. ACTON et H. B. STONEHOUSE. « ESTIMATING THE ATTERBERG LIMITS OF SOUTHERN SASKATCHEWAN SOILS FROM TEXTURE AND CARBON CONTENTS ». Canadian Journal of Soil Science 70, no 4 (1 novembre 1990) : 543–54. http://dx.doi.org/10.4141/cjss90-057.
Texte intégralOrlova, L. A., et V. A. Panychev. « The Reliability of Radiocarbon Dating Buried Soils ». Radiocarbon 35, no 3 (1993) : 369–77. http://dx.doi.org/10.1017/s0033822200060379.
Texte intégralThèses sur le sujet "Horizons de prédiction variable"
Amor, Yasmine. « Ιntelligent apprοach fοr trafic cοngestiοn predictiοn ». Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMR129.
Texte intégralTraffic congestion presents a critical challenge to urban areas, as the volume of vehicles continues to grow faster than the system’s overall capacity. This growth impacts economic activity, environmental sustainability, and overall quality of life. Although strategies for mitigating traffic congestion have seen improvements over the past few decades, many cities still struggle to manage it effectively. While various models have been developed to tackle this issue, existing approaches often fall short in providing real-time, localized predictions that can adapt to complex and dynamic traffic conditions. Most rely on fixed prediction horizons and lack the intelligent infrastructure needed for flexibility. This thesis addresses these gaps by proposing an intelligent, decentralized, infrastructure-based approach for traffic congestion estimation and prediction.We start by studying Traffic Estimation. We examine the possible congestion measures and data sources required for different contexts that may be studied. We establish a three-dimensional relationship between these axes. A rule-based system is developed to assist researchers and traffic operators in recommending the most appropriate congestion measures based on the specific context under study. We then proceed to Traffic Prediction, introducing our DECentralized COngestion esTimation and pRediction model using Intelligent Variable Message Signs (DECOTRIVMS). This infrastructure-based model employs intelligent Variable Message Signs (VMSs) to collect real-time traffic data and provide short-term congestion predictions with variable prediction horizons.We use Graph Attention Networks (GATs) due to their ability to capture complex relationships and handle graph-structured data. They are well-suited for modeling interactions between different road segments. In addition to GATs, we employ online learning methods, specifically, Stochastic Gradient Descent (SGD) and ADAptive GRAdient Descent (ADAGRAD). While these methods have been successfully used in various other domains, their application in traffic congestion prediction remains under-explored. In our thesis, we aim to bridge that gap by exploring their effectiveness within the context of real-time traffic congestion forecasting.Finally, we validate our model’s effectiveness through two case studies conducted in Muscat, Oman, and Rouen, France. A comprehensive comparative analysis is performed, evaluating various prediction techniques, including GATs, Graph Convolutional Networks (GCNs), SGD and ADAGRAD. The achieved results underscore the potential of DECOTRIVMS, demonstrating its potential for accurate and effective traffic congestion prediction across diverse urban contexts
Parrang, Sylvain. « Prédiction du niveau de bruit aéroacoustique d'une machine haute vitesse à reluctance variable ». Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLN044/document.
Texte intégralDue to its simple construction and robustness, Switched Reluctance Machine (SRM) is well suited for high rotation rates. SRM applications are however quite rare mainly because of the high level of noise this machine produces. First, this work aims to describe the noise emmitted by the studied SRM at high rotation rates. In accordance with the common understanding, it was proven that noise emitted by high rotation rates SRM is dominated by aeroacoustic noise. The aeroacoustic noise consists of the whole soundemission comig out of aerodynamic phenoma located in the air gap of the machine. Chapter two is concerned with the implementation of an estimation method for aeroacoustic noise level dedicated to the studied SRM. Aeroacoustic noise for electrical machines has not been quantitatively studied yet. Conversely, studies about aeroacoustic noise of rotating machinery (turboreactor, fan, ...) is quite abundant in the litterature. Consequently, this study focuses onrotating machinery to build an aeroacoustic noise estimation method for SRM. This estimationtool is based on a Computational Fluid Dynamics (CFD) calculation of the turbulent ow in theair gap. Estimated noise levels are then compared with experimental data. Emitted noise level is estimated and measured for two distinct rotor geometries over a wide range of rotation rates. Calculation assumptions are validated by the consistency between experimental and numerical results. Asexpected, the 2D CFD simulation brings an over estimation of noise level. Finally, the fourth chapter deals with the use of the aeroacoustic noise estimation tool to study the influence of geometrical parameters of a SRM on its noise emission level
Hmamouche, Youssef. « Prédiction des séries temporelles larges ». Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0480.
Texte intégralNowadays, storage and data processing systems are supposed to store and process large time series. As the number of variables observed increases very rapidly, their prediction becomes more and more complicated, and the use of all the variables poses problems for classical prediction models.Univariate prediction models are among the first models of prediction. To improve these models, the use of multiple variables has become common. Thus, multivariate models and become more and more used because they consider more information.With the increase of data related to each other, the application of multivariate models is also questionable. Because the use of all existing information does not necessarily lead to the best predictions. Therefore, the challenge in this situation is to find the most relevant factors among all available data relative to a target variable.In this thesis, we study this problem by presenting a detailed analysis of the proposed approaches in the literature. We address the problem of prediction and size reduction of massive data. We also discuss these approaches in the context of Big Data.The proposed approaches show promising and very competitive results compared to well-known algorithms, and lead to an improvement in the accuracy of the predictions on the data used.Then, we present our contributions, and propose a complete methodology for the prediction of wide time series. We also extend this methodology to big data via distributed computing and parallelism with an implementation of the prediction process proposed in the Hadoop / Spark environment
Hmamouche, Youssef. « Prédiction des séries temporelles larges ». Electronic Thesis or Diss., Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0480.
Texte intégralNowadays, storage and data processing systems are supposed to store and process large time series. As the number of variables observed increases very rapidly, their prediction becomes more and more complicated, and the use of all the variables poses problems for classical prediction models.Univariate prediction models are among the first models of prediction. To improve these models, the use of multiple variables has become common. Thus, multivariate models and become more and more used because they consider more information.With the increase of data related to each other, the application of multivariate models is also questionable. Because the use of all existing information does not necessarily lead to the best predictions. Therefore, the challenge in this situation is to find the most relevant factors among all available data relative to a target variable.In this thesis, we study this problem by presenting a detailed analysis of the proposed approaches in the literature. We address the problem of prediction and size reduction of massive data. We also discuss these approaches in the context of Big Data.The proposed approaches show promising and very competitive results compared to well-known algorithms, and lead to an improvement in the accuracy of the predictions on the data used.Then, we present our contributions, and propose a complete methodology for the prediction of wide time series. We also extend this methodology to big data via distributed computing and parallelism with an implementation of the prediction process proposed in the Hadoop / Spark environment
Chagneau, Pierrette. « Modélisation bayésienne hiérarchique pour la prédiction multivariée de processus spatiaux non gaussiens et processus ponctuels hétérogènes d'intensité liée à une variable prédite : application à la prédiction de la régénération en forêt tropicale humide ». Montpellier 2, 2009. http://www.theses.fr/2009MON20157.
Texte intégralOne of the weak points of forest dynamics models is the recruitment. Classically, ecologists make the assumption that recruitment mainly depends on both spatial pattern of mature trees and environment. A detailed inventory of the stand and the environmental conditions enabled them to show the effects of these two factors on the local density of seedlings. In practice, such information is not available: only a part of seedlings is sampled and the environment is partially observed. The aim of the paper is to propose an approach in order to predict the spatial distribution and the seedlings genotype on the basis of a reasonable sampling of seedling, mature trees and environmental conditions. The spatial pattern of the seedlings is assumed to be a realization of a marked point process. The intensity of the process is not only related to the seed and pollen dispersal but also to the sapling survival. The sapling survival depends on the environment; so the environment must be predicted on the whole study area. The environment is characterized through spatial variables of different nature and predictions are obtained using a spatial hierarchical model. Unlike the existing models which assume the environmental covariables as exactly known, the recruitment model we propose takes into account the error related to the prediction of the environment. The prediction of seedling recruitment in tropical rainforest in French Guiana illustrates our approach
Shimagaki, Kai. « Advanced statistical modeling and variable selection for protein sequences ». Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS548.
Texte intégralOver the last few decades, protein sequencing techniques have been developed and continuous experiments have been done. Thanks to all of these efforts, nowadays, we have obtained more than two hundred million protein sequence data. In order to deal with such a huge amount of biological data, now, we need theories and technologies to extract information that we can understand and interpret.The key idea to resolve this problem is statistical physics and the state of the art of machine learning (ML). Statistical physics is a field of physics that can successfully describe many complex systems by extracting or reducing variables to be interpretable variables based on simple principles. ML, on the other hand, can represent data (such as reconstruction and classification) without assuming how the data was generated, i.e. physical phenomenon behind of data. In this dissertation, we report studies of protein sequence generative modeling and protein-residue contact predictions using statistical physics-inspired modeling and ML-oriented methods. In the first part, we review the general background of biology and genomics. Then we discuss statistical modelings for protein sequence. In particular, we review Direct Coupling Analysis (DCA), which is the core technology of our research. We also discuss the effects of higher-order statistics contained in protein sequences and introduces deep learning-based generative models as a model that can go beyond pairwise interaction
Naud, Hélène. « Prédire le comportement suicidaire des détenus avec le Suicide Probability Scale et des variables actuarielles ». Thèse, Université de Sherbrooke, 2008. http://savoirs.usherbrooke.ca/handle/11143/2787.
Texte intégralBrunot, Mathieu. « Identification of rigid industrial robots - A system identification perspective ». Phd thesis, Toulouse, INPT, 2017. http://oatao.univ-toulouse.fr/20776/1/BRUNOT_Mathieu_20776.pdf.
Texte intégralYoussfi, Younès. « Exploring Risk Factors and Prediction Models for Sudden Cardiac Death with Machine Learning ». Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG006.
Texte intégralSudden cardiac death (SCD) is defined as a sudden natural death presumed to be of cardiac cause, heralded by abrupt loss of consciousness in the presence of witness, or in the absence of witness occurring within an hour after the onset of symptoms. Despite progress in clinical profiling and interventions, it remains a major public health problem, accounting for 10 to 20% of deaths in industrialised countries, with survival after SCD below 10%. The annual incidence is estimated 350,000 in Europe, and 300,000 in the United States. Efficient treatments for SCD management are available. One of the most effective options is the use of implantable cardioverter defibrillators (ICD). However, identifying the best candidates for ICD implantation remains a difficult challenge, with disappointing results so far. This thesis aims to address this problem, and to provide a better understanding of SCD in the general population, using statistical modeling. We analyze data from the Paris Sudden Death Expertise Center and the French National Healthcare System Database to develop three main works:- The first part of the thesis aims to identify new subgroups of SCD to improve current stratification guidelines, which are mainly based on cardiovascular variables. To this end, we use natural language processing methods and clustering analysis to build a meaningful representation of medical history of patients.- The second part aims to build a prediction model of SCD in order to propose a personalized and explainable risk score for each patient, and accurately identify very-high risk subjects in the general population. To this end, we train a supervised classification algorithm, combined with the SHapley Additive exPlanation method, to analyze all medical events that occurred up to 5 years prior to the event.- The last part of the thesis aims to identify the most relevant information to select in large medical history of patients. We propose a bi-level variable selection algorithm for generalized linear models, in order to identify both individual and group effects from predictors. Our algorithm is based on a Bayesian approach and uses a Sequential Monte Carlo method to estimate the posterior distribution of variables inclusion
Soret, Perrine. « Régression pénalisée de type Lasso pour l’analyse de données biologiques de grande dimension : application à la charge virale du VIH censurée par une limite de quantification et aux données compositionnelles du microbiote ». Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0254.
Texte intégralIn clinical studies and thanks to technological progress, the amount of information collected in the same patient continues to grow leading to situations where the number of explanatory variables is greater than the number of individuals. The Lasso method proved to be appropriate to circumvent over-adjustment problems in high-dimensional settings.This thesis is devoted to the application and development of Lasso-penalized regression for clinical data presenting particular structures.First, in patients with the human immunodeficiency virus, mutations in the virus's genetic structure may be related to the development of drug resistance. The prediction of the viral load from (potentially large) mutations allows guiding treatment choice.Below a threshold, the viral load is undetectable, data are left-censored. We propose two new Lasso approaches based on the Buckley-James algorithm, which imputes censored values by a conditional expectation. By reversing the response, we obtain a right-censored problem, for which non-parametric estimates of the conditional expectation have been proposed in survival analysis. Finally, we propose a parametric estimation based on a Gaussian hypothesis.Secondly, we are interested in the role of the microbiota in the deterioration of respiratory health. The microbiota data are presented as relative abundances (proportion of each species per individual, called compositional data) and they have a phylogenetic structure.We have established a state of the art methods of statistical analysis of microbiota data. Due to the novelty, few recommendations exist on the applicability and effectiveness of the proposed methods. A simulation study allowed us to compare the selection capacity of penalization methods proposed specifically for this type of data.Then we apply this research to the analysis of the association between bacteria / fungi and the decline of pulmonary function in patients with cystic fibrosis from the MucoFong project
Livres sur le sujet "Horizons de prédiction variable"
New Horizons In Timedomain Astronomy Proceedings Of The 285th Symposium Of The International Astronomical Union Held In Oxford United Kingdom September 1923 2011. Cambridge University Press, 2012.
Trouver le texte intégralChapitres de livres sur le sujet "Horizons de prédiction variable"
Maddox, R. Neil. « Behavioral Intentions as an Intervening Variable in Housing Decisions : A Longitudinal Study ». Dans Marketing Horizons : A 1980's Perspective, 28–32. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10966-4_7.
Texte intégralAuer, Emma. « Empathy : Is it the Missing Independent Dispositional Variable in the Study of Innovative Behavior ? » Dans Marketing Horizons : A 1980's Perspective, 64–67. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10966-4_15.
Texte intégralMacPherson, Ronnie, Amy Jersild, Dennis Bours et Caroline Holo. « Assessing the Evaluability of Adaptation-Focused Interventions : Lessons from the Adaptation Fund ». Dans Transformational Change for People and the Planet, 173–86. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-78853-7_12.
Texte intégralDanka J. « Probability of failure calculation of dikes based on Monte Carlo simulation ». Dans Geotechnical Engineering : New Horizons. IOS Press, 2011. https://doi.org/10.3233/978-1-60750-808-3-181.
Texte intégralEmerson, Robert M., et Blair Paley. « Organizational Horizons and Complaint-Filing ». Dans The Uses Of Discretion, 231–48. Oxford University PressOxford, 1993. http://dx.doi.org/10.1093/oso/9780198257622.003.0007.
Texte intégralRacinais J. et Plomteux C. « Design of slab-on-grades supported with soil reinforced by rigid inclusions ». Dans Geotechnical Engineering : New Horizons. IOS Press, 2011. https://doi.org/10.3233/978-1-60750-808-3-105.
Texte intégralStock, James H., et Mark W. Watson. « Variable Trends in Economic Time Series ». Dans Long-Run Economic Relationships, 17–50. Oxford University PressOxford, 1991. http://dx.doi.org/10.1093/oso/9780198283393.003.0002.
Texte intégralLee, Mo Yee, Siu-Man Ng, Pamela Pui Yu Leung et Cecilia Lai Wan Chan. « Spiritual Growth and Transformation : Expanding Life’s Horizons ». Dans Integrative Body–Mind–Spirit Social Work, 171–96. Oxford University PressNew York, NY, 2009. http://dx.doi.org/10.1093/oso/9780195301021.003.0008.
Texte intégralYu, T. R. « Introduction ». Dans Chemistry of Variable Charge Soils. Oxford University Press, 1997. http://dx.doi.org/10.1093/oso/9780195097450.003.0004.
Texte intégralSpera, S. J., et J. R. Kyle. « Preliminary Petrographic and Isotopic Investigation of the Main Pass 299 Cap Rock-Hosted Sulfur Deposit ». Dans Selected Mineral Deposits of the Gulf Coast and Southeastern United States, 85–96. Society of Economic Geologists, 1995. http://dx.doi.org/10.5382/gb.24.05.
Texte intégralActes de conférences sur le sujet "Horizons de prédiction variable"
Zidan, A., et E. F. El-Saadany. « Network reconfiguration in balanced distribution systems with variable load demand and variable renewable resources generation ». Dans 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6345160.
Texte intégralCrowley, Daniel, Bradford Robertson, Rebecca Douglas, Dimitri Mavris et Barry Hellman. « Aerodynamic Surrogate Modeling of Variable Geometry ». Dans 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-268.
Texte intégralPavleski, Aleksandar. « RE-THINKING SECURITY THROUGH THE PRISM OF THE SECURITY STUDIES APPROACH ». Dans SECURITY HORIZONS. Faculty of Security- Skopje, 2022. http://dx.doi.org/10.20544/icp.3.6.22.p14.
Texte intégralRoss, Michael, Greg Meess et Ephrahim Garcia. « Dynamically Variable Blade Geometry for Wind Energy ». Dans 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-835.
Texte intégralTong, Xiaoling, et E. Luke. « An Error Transport Equation in Primitive Variable Formulation ». Dans 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-1295.
Texte intégralWhitley, Ryan, et Cesar Ocampo. « Direct Multiple Shooting Optimization with Variable Problem Parameters ». Dans 47th AIAA Aerospace Sciences Meeting including The New Horizons Forum and Aerospace Exposition. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-803.
Texte intégralAntunes, Eduardo, Andre Silva et Jorge Barata. « Evaluation of Numerical Variable Density Approach to Cryogenic Jets ». Dans 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-1282.
Texte intégralRuisheng Diao, N. Samaan, Y. Makarov, R. Hafen et Jian Ma. « Planning for variable generation integration through balancing authorities consolidation ». Dans 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6345108.
Texte intégralEllis, A., R. Nelson, E. Von Engeln, J. MacDowell, L. Casey, E. Seymour, W. Peter et J. R. Williams. « Review of existing reactive power requirements for variable generation ». Dans 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6345555.
Texte intégralYamazaki, Wataru, et Dimitri Mavriplis. « Derivative-Enhanced Variable Fidelity Surrogate Modeling for Aerodynamic Functions ». Dans 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-1172.
Texte intégralRapports d'organisations sur le sujet "Horizons de prédiction variable"
Djamai, N., R. A. Fernandes, L. Sun, F. Canisius et G. Hong. Python version of Simplified Level 2 Prototype Processor for retrieving canopy biophysical variables from Sentinel-2 multispectral data. Natural Resources Canada/CMSS/Information Management, 2024. http://dx.doi.org/10.4095/p8stuehwyc.
Texte intégralBroto, Carmen, et Olivier Hubert. Desertification in Spain : Is there any impact on credit to firms ? Madrid : Banco de España, février 2025. https://doi.org/10.53479/39119.
Texte intégralCárdenas-Cárdenas, Julián Alonso, Deicy J. Cristiano-Botia et Nicolás Martínez-Cortés. Colombian inflation forecast using Long Short-Term Memory approach. Banco de la República, juin 2023. http://dx.doi.org/10.32468/be.1241.
Texte intégralHunter, Fraser, et Martin Carruthers. Iron Age Scotland. Society for Antiquaries of Scotland, septembre 2012. http://dx.doi.org/10.9750/scarf.09.2012.193.
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