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Journal articles on the topic 'Estimation scalable de l'incertitude'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Perret, Christian, P. Marchand, Arnaud Belleville, et al. "La variabilité en fonction du temps des relations hauteur débit. Sa prise en compte dans l'estimation des incertitudes des données hydrométriques par une méthode tabulée." La Houille Blanche, no. 4 (August 2018): 65–72. http://dx.doi.org/10.1051/lhb/2018043.

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La démarche engagée depuis plusieurs années par la communauté des hydromètres pour quantifier les incertitudes associées au processus d'élaboration des données de débit mériterait de trouver des applications opérationnelles. La présente étude incite notamment les gestionnaires des stations hydrométriques à mieux valoriser les jaugeages effectués en systématisant la démarche de précision du modèle de courbe de tarage. Les auteurs proposent ensuite une démarche simplifiée de quantification de l'incertitude associée à une valeur de débit prédite par une courbe de tarage à partir du calcul de l'éc

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2

Jiang, Zhuqing, Likuo Wei, Ganmin Zeng, et al. "Bitrate Estimation for Spatial Scalable Videos." IEEE Transactions on Broadcasting 67, no. 2 (2021): 549–55. http://dx.doi.org/10.1109/tbc.2021.3064278.

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3

Wang, Xianglu. "Gaussian graphical model estimation with measurement error." JUSTC 53, no. 11 (2023): 1105. http://dx.doi.org/10.52396/justc-2022-0108.

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It is well known that regression methods designed for clean data will lead to erroneous results if directly applied to corrupted data. Despite the recent methodological and algorithmic advances in Gaussian graphical model estimation, how to achieve efficient and scalable estimation under contaminated covariates is unclear. Here a new methodology called convex conditioned innovative scalable efficient estimation (COCOISEE) for Gaussian graphical model under both additive and multiplicative measurement errors is developed. It combines the strengths of the innovative scalable efficient estimation

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4

Surya A, Kavya Sri Vaipava S, Ashika Deulin J, and Janani S. "Cognitive Brain Age Estimation." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 648–52. https://doi.org/10.47392/irjaeh.2025.0089.

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Cognitive brain age estimation combines computational techniques with health sciences to assess cognitive health. Traditional methods like neuroimaging and clinical evaluations are costly and not scalable. This research proposes a machine learning-based system for cognitive age estimation using non-invasive data, including speech patterns, behavioral metrics, and lifestyle factors. The system follows a modular architecture with data collection, preprocessing, feature extraction, and predictive modelling. By analyzing behavioral logs, speech characteristics, and lifestyle metrics, it generates

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Cicala, Marco, Egidio D’Amato, Immacolata Notaro, and Massimiliano Mattei. "Scalable Distributed State Estimation in UTM Context." Sensors 20, no. 9 (2020): 2682. http://dx.doi.org/10.3390/s20092682.

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This article proposes a novel approach to the Distributed State Estimation (DSE) problem for a set of co-operating UAVs equipped with heterogeneous on board sensors capable of exploiting certain characteristics typical of the UAS Traffic Management (UTM) context, such as high traffic density and the presence of limited range, Vehicle-to-Vehicle communication devices. The proposed algorithm is based on a scalable decentralized Kalman Filter derived from the Internodal Transformation Theory enhanced on the basis of the Consensus Theory. The general benefit of the proposed algorithm consists of,

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6

Li, Cheng, Sanvesh Srivastava, and David B. Dunson. "Simple, scalable and accurate posterior interval estimation." Biometrika 104, no. 3 (2017): 665–80. http://dx.doi.org/10.1093/biomet/asx033.

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Summary Standard posterior sampling algorithms, such as Markov chain Monte Carlo procedures, face major challenges in scaling up to massive datasets. We propose a simple and general posterior interval estimation algorithm to rapidly and accurately estimate quantiles of the posterior distributions for one-dimensional functionals. Our algorithm runs Markov chain Monte Carlo in parallel for subsets of the data, and then averages quantiles estimated from each subset. We provide strong theoretical guarantees and show that the credible intervals from our algorithm asymptotically approximate those fr

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7

Emerson, Joseph, Robert Alicki, and Karol Życzkowski. "Scalable noise estimation with random unitary operators." Journal of Optics B: Quantum and Semiclassical Optics 7, no. 10 (2005): S347—S352. http://dx.doi.org/10.1088/1464-4266/7/10/021.

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8

马, 跃. "Scalable Model Averaging Estimation with Missing Responses." Advances in Applied Mathematics 13, no. 05 (2024): 2520–29. http://dx.doi.org/10.12677/aam.2024.135240.

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9

Chen, Dong, Hua You Su, Wen Mei, Li Xuan Wang, and Chun Yuan Zhang. "Scalable Parallel Motion Estimation on Muti-GPU System." Applied Mechanics and Materials 347-350 (August 2013): 3708–14. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3708.

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With NVIDIA’s parallel computing architecture CUDA, using GPU to speed up compute-intensive applications has become a research focus in recent years. In this paper, we proposed a scalable method for multi-GPU system to accelerate motion estimation algorithm, which is the most time consuming process in video encoding. Based on the analysis of data dependency and multi-GPU architecture, a parallel computing model and a communication model are designed. We tested our parallel algorithm and analyzed the performance with 10 standard video sequences in different resolutions using 4 NVIDIA GTX460 GPU

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10

Galante, Franco, Giovanni Neglia, and Emilio Leonardi. "Scalable Decentralized Algorithms for Online Personalized Mean Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 16699–707. https://doi.org/10.1609/aaai.v39i16.33835.

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In numerous settings, agents lack sufficient data to learn a model directly. Collaborating with other agents may help, but introduces a bias-variance trade-off when local data distributions differ. A key challenge is for each agent to identify clients with similar distributions while learning the model, a problem that remains largely unresolved. This study focuses on a particular instance of the overarching problem, where each agent collects samples from a real-valued distribution over time to estimate its mean. Existing algorithms face impractical per-agent space and time complexities (linear

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11

Hassan, Beenish, Sobia Baig, and Saad Aslam. "On Scalability of FDD-Based Cell-Free Massive MIMO Framework." Sensors 23, no. 15 (2023): 6991. http://dx.doi.org/10.3390/s23156991.

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Cell-free massive multiple-input multiple-output (MIMO) systems have the potential of providing joint services, including joint initial access, efficient clustering of access points (APs), and pilot allocation to user equipment (UEs) over large coverage areas with reduced interference. In cell-free massive MIMO, a large coverage area corresponds to the provision and maintenance of the scalable quality of service requirements for an infinitely large number of UEs. The research in cell-free massive MIMO is mostly focused on time division duplex mode due to the availability of channel reciprocity

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12

Ju, Cheng, Susan Gruber, Samuel D. Lendle, et al. "Scalable collaborative targeted learning for high-dimensional data." Statistical Methods in Medical Research 28, no. 2 (2017): 532–54. http://dx.doi.org/10.1177/0962280217729845.

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Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typically, only one of the latter is optimized for the sake of constructing a well-behaved estimator of the low-dimensional parameter of interest. Optimizing more than one of them for the sake of achieving a better bias-variance trade-off in the estimation of the parameter of interest is the core idea driving the general template of the collaborative targeted minimum loss-based estimation procedure. The original instant

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13

ADACHI, Ryosuke, Yuh YAMASHITA, and Koichi KOBAYASHI. "Distributed Estimation over Delayed Sensor Network with Scalable Communication." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E102.A, no. 5 (2019): 712–20. http://dx.doi.org/10.1587/transfun.e102.a.712.

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14

Chun-Su Park, Seung-Jin Baek, Seung-Won Jung, Hye-Soo Kim, and Sung-Jea Ko. "Estimation-Based Interlayer Intra Prediction for Scalable Video Coding." IEEE Transactions on Circuits and Systems for Video Technology 19, no. 12 (2009): 1902–7. http://dx.doi.org/10.1109/tcsvt.2009.2026945.

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15

Fröhlich, Fabian, Barbara Kaltenbacher, Fabian J. Theis, and Jan Hasenauer. "Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks." PLOS Computational Biology 13, no. 1 (2017): e1005331. http://dx.doi.org/10.1371/journal.pcbi.1005331.

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16

Molino, A., F. Vacca, G. Masera, and T. Q. Nguyen. "Scalable phase extraction methods for phase plane motion estimation." IEE Proceedings - Vision, Image, and Signal Processing 153, no. 6 (2006): 860. http://dx.doi.org/10.1049/ip-vis:20060011.

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17

Choi, Jinha. "Motion vector memory reduction scheme for scalable motion estimation." Optical Engineering 48, no. 9 (2009): 090502. http://dx.doi.org/10.1117/1.3212689.

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18

Jingning Han, Vinay Melkote, and Kenneth Rose. "An Estimation-Theoretic Framework for Spatially Scalable Video Coding." IEEE Transactions on Image Processing 23, no. 8 (2014): 3684–97. http://dx.doi.org/10.1109/tip.2014.2331761.

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19

Dimitrov, D., and E. Atanassov. "Scalable system with accelerators for financial option prices estimation." International Journal of Data Science 1, no. 4 (2016): 305. http://dx.doi.org/10.1504/ijds.2016.081367.

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20

Ghosal, S., and P. Vanek. "A fast scalable algorithm for discontinuous optical flow estimation." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 2 (1996): 181–94. http://dx.doi.org/10.1109/34.481542.

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21

Mietens, S., P. H. N. de With, and C. Hentschel. "Computational-complexity scalable motion estimation for mobile MPEG encoding." IEEE Transactions on Consumer Electronics 50, no. 1 (2004): 281–91. http://dx.doi.org/10.1109/tce.2004.1277875.

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22

Warrington, Stephen, Wai-Yip Chan, and Subramania Sudharsanan. "Scalable high-throughput variable block size motion estimation architecture." Microprocessors and Microsystems 33, no. 4 (2009): 319–25. http://dx.doi.org/10.1016/j.micpro.2009.02.011.

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23

Watts, Stephen C., Scott C. Ritchie, Michael Inouye, and Kathryn E. Holt. "FastSpar: rapid and scalable correlation estimation for compositional data." Bioinformatics 35, no. 6 (2018): 1064–66. http://dx.doi.org/10.1093/bioinformatics/bty734.

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24

ABBAS, GHULAM. "Bandwidth Price Estimation for Scalable and Responsive Rate Control." Journal of Interconnection Networks 16, no. 03n04 (2016): 1650005. http://dx.doi.org/10.1142/s0219265916500055.

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This paper concerns the analysis of important algorithmic attributes, namely, the rate of convergence and scalability, and their impact on Network Utility Maximization (NUM). The contribution of the paper is a novel distributed rate control mechanism with strong convergence and scalability properties. The proposed algorithm employs a distinctive distributed framework, where rate control is derived as a Sequential Quadratic Programming (SQP) mechanism incorporated with interior-point and trust-region methods. The NUM problem is solved by a barrier method that penalizes any violation of constrai

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25

Chen, Yian, and Mihai Anitescu. "Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices." SIAM/ASA Journal on Uncertainty Quantification 11, no. 2 (2023): 682–725. http://dx.doi.org/10.1137/21m1458880.

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26

Ila, Viorela, Lukas Polok, Marek Solony, and Pavel Svoboda. "SLAM++-A highly efficient and temporally scalable incremental SLAM framework." International Journal of Robotics Research 36, no. 2 (2017): 210–30. http://dx.doi.org/10.1177/0278364917691110.

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The most common way to deal with the uncertainty present in noisy sensorial perception and action is to model the problem with a probabilistic framework. Maximum likelihood estimation is a well-known estimation method used in many robotic and computer vision applications. Under Gaussian assumption, the maximum likelihood estimation converts to a nonlinear least squares problem. Efficient solutions to nonlinear least squares exist and they are based on iteratively solving sparse linear systems until convergence. In general, the existing solutions provide only an estimation of the mean state vec

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27

Eschle, Jonas, Albert Navarro Puig, Rafael Silva Coutinho, and Nicola Serra. "zfit: scalable pythonic fitting." EPJ Web of Conferences 245 (2020): 06025. http://dx.doi.org/10.1051/epjconf/202024506025.

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Statistical modeling and fitting is a key element in most HEP analyses. This task is usually performed in the C++ based framework ROOT/RooFit. Recently the HEP community started shifting more to the Python language, which the tools above are only loose integrated into, and a lack of stable, native Python based toolkits became clear. We presented zfit, a project that aims at building a fitting ecosystem by providing a carefully designed, stable API and a workflow for libraries to communicate together with an implementation fully integrated into the Python ecosystem. It is built on top of one of

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28

Starr, Jeffrey, Scott Moses, and Le Gruenwald. "A scalable approach for estimation of resource availability using bitfields." Integrated Computer-Aided Engineering 11, no. 4 (2004): 349–58. http://dx.doi.org/10.3233/ica-2004-11405.

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29

Cândido, Paulo Gustavo Lopes, Jonathan Andrade Silva, Elaine Ribeiro Faria, and Murilo Coelho Naldi. "Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation." Applied Sciences 12, no. 13 (2022): 6464. http://dx.doi.org/10.3390/app12136464.

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The increasing volume and velocity of the continuously generated data (data stream) challenge machine learning algorithms, which must evolve to fit real-world problems. The data stream clustering algorithms face issues such as the rapidly increasing volume of the data, the variety of the number of clusters, and their shapes. The present work aims to improve the accuracy of sequential clustering batches of data streams for scenarios in which clusters evolve dynamically and continuously, automatically estimating their number. In order to achieve this goal, three evolutionary algorithms are prese

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30

Ullah, Mahbuba Sheba, Ahmed Tewfik, and Robert W. Heath. "Leveraging Waveform Structure to Develop a Power Scalable AoA Estimation." IEEE Open Journal of the Communications Society 2 (2021): 2739–59. http://dx.doi.org/10.1109/ojcoms.2021.3134812.

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31

Metta, Ravindra, Martin Becker, Prasad Bokil, Samarjit Chakraborty, and R. Venkatesh. "TIC: a scalable model checking based approach to WCET estimation." ACM SIGPLAN Notices 51, no. 5 (2016): 72–81. http://dx.doi.org/10.1145/2980930.2907961.

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32

Gong, Wei, Ivan Stojmenovic, Amiya Nayak, Kebin Liu, and Haoxiang Liu. "Fast and Scalable Counterfeits Estimation for Large-Scale RFID Systems." IEEE/ACM Transactions on Networking 24, no. 2 (2016): 1052–64. http://dx.doi.org/10.1109/tnet.2015.2406669.

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33

Bates, Stephen, and Robert Tibshirani. "Log‐ratio lasso: Scalable, sparse estimation for log‐ratio models." Biometrics 75, no. 2 (2019): 613–24. http://dx.doi.org/10.1111/biom.12995.

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34

Zhang, Jingru, and Wei Lin. "Scalable estimation and regularization for the logistic normal multinomial model." Biometrics 75, no. 4 (2019): 1098–108. http://dx.doi.org/10.1111/biom.13071.

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35

Mrak, M., N. Sprljan, and E. Izquierdo. "Motion estimation in temporal subbands for quality scalable motion coding." Electronics Letters 41, no. 19 (2005): 1050. http://dx.doi.org/10.1049/el:20052863.

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36

McLoughlin, Terence H., and Mark Campbell. "Scalable Sensing, Estimation, and Control Architecture for Large Spacecraft Formations." Journal of Guidance, Control, and Dynamics 30, no. 2 (2007): 289–300. http://dx.doi.org/10.2514/1.21322.

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37

Vincent, Robert J., David J. Ives, and Seb J. Savory. "Scalable Capacity Estimation for Nonlinear Elastic All-Optical Core Networks." Journal of Lightwave Technology 37, no. 21 (2019): 5380–91. http://dx.doi.org/10.1109/jlt.2019.2942710.

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38

Kucuk, Kerem, and Adnan Kavak. "Scalable location estimation using smart antennas in wireless sensor networks." Ad Hoc Networks 8, no. 8 (2010): 889–903. http://dx.doi.org/10.1016/j.adhoc.2010.04.005.

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39

Fan, Yingying, and Jinchi Lv. "Innovated scalable efficient estimation in ultra-large Gaussian graphical models." Annals of Statistics 44, no. 5 (2016): 2098–126. http://dx.doi.org/10.1214/15-aos1416.

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40

Wang, Gang, Georgios B. Giannakis, and Jie Chen. "Robust and Scalable Power System State Estimation via Composite Optimization." IEEE Transactions on Smart Grid 10, no. 6 (2019): 6137–47. http://dx.doi.org/10.1109/tsg.2019.2897100.

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41

Jin, Ming, Igor Molybog, Reza Mohammadi-Ghazi, and Javad Lavaei. "Scalable and Robust State Estimation From Abundant But Untrusted Data." IEEE Transactions on Smart Grid 11, no. 3 (2020): 1880–94. http://dx.doi.org/10.1109/tsg.2019.2944986.

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42

Benallal, Abdellah, Nawal Cheggaga, Amine Hebib, and Adrian Ilinca. "LSTM-Based State-of-Charge Estimation and User Interface Development for Lithium-Ion Battery Management." World Electric Vehicle Journal 16, no. 3 (2025): 168. https://doi.org/10.3390/wevj16030168.

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State-of-charge (SOC) estimation is pivotal in optimizing lithium-ion battery management systems (BMSs), ensuring safety, performance, and longevity across various applications. This study introduces a novel SOC estimation framework that uniquely integrates Long Short-Term Memory (LSTM) neural networks with Hyperband-driven hyperparameter optimization, a combination not extensively explored in the literature. A comprehensive experimental dataset is created using data of LG 18650HG2 lithium-ion batteries subjected to diverse operational cycles and thermal conditions. The proposed framework demo

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43

Ambroise, B. "Génèse des débits dans les petits bassins versants ruraux en milieu tempéré : 2 - Modélisation systémique et dynamique." Revue des sciences de l'eau 12, no. 1 (2005): 125–53. http://dx.doi.org/10.7202/705346ar.

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La deuxième partie de cette synthèse bibliographique sur la genèse des débits montre comment les connaissances acquises sur le fonctionnement des petits bassins ruraux (cf. Partie 1) peuvent être utilisées pour les modéliser. Elle présente les différents types de modèles hydrologiques (empiriques globaux de type "boîte noire", conceptuels globaux ou semi-spatialisés, physiques spatialisés, physico-conceptuels semi-spatialisés) disponibles pour générer des chroniques événementielles ou continues, et déduit de l'analyse de leurs avantages et limites respectifs certaines recommandations pour leur

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44

Yu, Chen, Luo Haiyong, Zhao Fang, Wang Qu, and Shao Wenhua. "Adaptive Kalman filtering-based pedestrian navigation algorithm for smartphones." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142093093. http://dx.doi.org/10.1177/1729881420930934.

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Pedestrian navigation with daily smart devices has become a vital issue over the past few years and the accurate heading estimation plays an essential role in it. Compared to the pedestrian dead reckoning (PDR) based solutions, this article constructs a scalable error model based on the inertial navigation system and proposes an adaptive heading estimation algorithm with a novel method of relative static magnetic field detection. To mitigate the impact of magnetic fluctuation, the proposed algorithm applies a two-way Kalman filter process. Firstly, it achieves the historical states with the op

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45

Bijur, Gururaj, Ramakrishna Mundugar, Vinayak Mantoor, and Karunakar A Kotegar. "Estimation of Adaptation Parameters for Dynamic Video Adaptation in Wireless Network Using Experimental Method." Computers 10, no. 4 (2021): 39. http://dx.doi.org/10.3390/computers10040039.

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A wireless network gives flexibility to the user in terms of mobility that attracts the user to use wireless communication more. The video communication in the wireless network experiences Quality of Services (QoS) and Quality of Experience (QoE) issues due to network dynamics. The parameters, such as node mobility, routing protocols, and distance between the nodes, play a major role in the quality of video communication. Scalable Video Coding (SVC) is an extension to H.264 Advanced Video Coding (AVC), allows partial removal of layers, and generates a valid adapted bit-stream. This adaptation

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46

Zheng, Liang-Wei. "Computation Controllable Mode Decision and Motion Estimation for Scalable Video Coding." ETRI Journal 35, no. 3 (2013): 469–79. http://dx.doi.org/10.4218/etrij.13.0112.0421.

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47

HoangVan, Xiem. "Adaptive Quantization Parameter Estimation for HEVC Based Surveillance Scalable Video Coding." Electronics 9, no. 6 (2020): 915. http://dx.doi.org/10.3390/electronics9060915.

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Visual surveillance systems have been playing a vital role in human modern life with a large number of applications, ranging from remote home management, public security to traffic monitoring. The recent High Efficiency Video Coding (HEVC) scalable extension, namely SHVC, provides not only the compression efficiency but also the adaptive streaming capability. However, SHVC is originally designed for videos captured from generic scenes rather than from visual surveillance systems. In this paper, we propose a novel HEVC based surveillance scalable video coding (SSVC) framework. First, to achieve

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48

Na, Sangkwon, and Chong-Min Kyung. "Activity-Based Motion Estimation Scheme for H.264 Scalable Video Coding." IEEE Transactions on Circuits and Systems for Video Technology 20, no. 11 (2010): 1475–85. http://dx.doi.org/10.1109/tcsvt.2010.2077493.

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49

Lin, Weiyao, Krit Panusopone, David M. Baylon, and Ming-Ting Sun. "A Computation Control Motion Estimation Method for Complexity-Scalable Video Coding." IEEE Transactions on Circuits and Systems for Video Technology 20, no. 11 (2010): 1533–43. http://dx.doi.org/10.1109/tcsvt.2010.2077773.

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50

Abebe, Ameha Tsegaye, and Chung G. Kang. "Joint Channel Estimation and MUD for Scalable Grant-Free Random Access." IEEE Communications Letters 23, no. 12 (2019): 2229–33. http://dx.doi.org/10.1109/lcomm.2019.2945577.

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