| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A bias-variance perspective of data-driven control
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Colin, Kévin
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58 |
15 |
p. 85-90
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artikel
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| 2 |
A comparison of indirect and direct filter designs from data for LTI systems: the effect of unknown noise covariance matrices
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Mazzoleni, M.
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58 |
15 |
p. 133-138
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artikel
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| 3 |
A global approach to estimate continuous-time LPV models for wastewater nitrification ⁎ ⁎ This work benefits from the financial support of the research program MOCOPÉE (an acronym of the French words ”MOdélisation, Contrôle et Optimisation des Procédés d’Épuration des Eaux”) and of the Cadi Ayyad University.
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Boutourda, F.Z.
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58 |
15 |
p. 61-66
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artikel
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| 4 |
A kernel-based PEM estimator for forward models
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Fattore, Giulio
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58 |
15 |
p. 31-36
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artikel
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| 5 |
A latent representation of brain networks based on EEG ⁎ ⁎ This work was partially supported by Fondazione CARIPARO (Borse di Dottorato CARIPARO 2020) and the Italian Ministry of University and Research under the grant “Dipartimenti di Eccellenza 2023-2027” of the Department of Informatics, Systems and Communication of the University of Milano-Bicocca, Italy. G. Cisotto also acknowledges the financial support of PON ”Green and Innovation” 2014-2020 action IV.6 funded by the Italian Ministry of University and Research to the University of Milano-Bicocca (Milan, Italy).
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Falconi, Lucia
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58 |
15 |
p. 414-419
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artikel
|
| 6 |
A Local Gaussian Process Regression Approach to Frequency Response Function Estimation ⁎ ⁎ This work was funded by NSFC under contract No. 62273287, Shenzhen Science and Technology Innovation Commission under contract No. JCYJ20220530143418040, and the Thousand Youth Talents Plan funded by the central government of China.
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Fang, Xiaozhu
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58 |
15 |
p. 115-120
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artikel
|
| 7 |
An Efficient Implementation for Regularized Frequency Response Function and Transient Estimation
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Xu, Yu
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58 |
15 |
p. 127-132
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artikel
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| 8 |
An information theory approach for recursive LPV-ARX model identification via LS-SVM
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Corrini, F.
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58 |
15 |
p. 486-491
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artikel
|
| 9 |
A practical approach for transfer function estimation using short, transient process data ⁎ ⁎ This work was supported by a grant of the Romanian Ministry of Research, Innovation and Digitization, PNRR-III- C9-2022–I8, grant number 760068/23.05.2023 and by a grant of the Romanian Ministry of Education and Research, CNCS- UEFISCDI, PN-III-P1-1.1-PD-2021-0204.
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De Keyser, Robin
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58 |
15 |
p. 426-431
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artikel
|
| 10 |
ARMA Identification of Kronecker graphical models
|
Zorzi, Mattia
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58 |
15 |
p. 396-401
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artikel
|
| 11 |
A Sample Based Algorithm for Constructing Guaranteed Confidence Ellipsoids for Linear Regression Models with Deterministic Regressor
|
Wang, Xiaopuwen
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58 |
15 |
p. 193-198
|
artikel
|
| 12 |
A Weighted Least-Squares Method for Non-Asymptotic Identification of Markov Parameters from Multiple Trajectories
|
He, Jiabao
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58 |
15 |
p. 169-174
|
artikel
|
| 13 |
Baseline Results for Selected Nonlinear System Identification Benchmarks
|
Champneys, Max
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58 |
15 |
p. 474-479
|
artikel
|
| 14 |
Bayesian System Identification of a River
|
Jibran, Muhammad
|
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58 |
15 |
p. 7-12
|
artikel
|
| 15 |
Calendering Process MPC using recursive DMDc
|
Hamdan, Taleb Bou
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58 |
15 |
p. 450-455
|
artikel
|
| 16 |
Characterization of minimal network structures modeling stochastic processes
|
Biparva, Darya
|
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|
58 |
15 |
p. 390-395
|
artikel
|
| 17 |
Contents
|
|
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|
58 |
15 |
p. i-vii
|
artikel
|
| 18 |
Continuous-time identification of grey-box and black-box models of an industrial oven
|
Previtali, Davide
|
|
|
58 |
15 |
p. 175-180
|
artikel
|
| 19 |
Control oriented modeling for particle size distributions in a spray drying process
|
Lepsien, A.
|
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58 |
15 |
p. 438-443
|
artikel
|
| 20 |
Control-Relevant Input Signal Design For Integrating Processes: Application to a Microalgae Raceway Reactor ⁎ ⁎ This work has been financed by the following projects: PID2020–112709RB-C21 project financed by the Spanish Ministry of Science and the Horizon Europe – the Framework Programme for Research and Innovation (2021–2027) under the agreement of grant no. 101060991 REALM.
|
Banerjee, Sarasij
|
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58 |
15 |
p. 360-365
|
artikel
|
| 21 |
Covariance Analysis of the Estimated Markov Parameters in a Subspace Identification Method ⁎ ⁎ This work was supported by JSPS KAKENHI Grant Number 21K04124.
|
Ikeda, Kenji
|
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58 |
15 |
p. 408-413
|
artikel
|
| 22 |
Data-Augmented Numerical Integration in State Prediction: Rule Selection
|
Duník, J.
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58 |
15 |
p. 139-144
|
artikel
|
| 23 |
Data-Driven Control of Highly Interactive Systems using 3DoF Model-On-Demand MPC: Application to a MIMO CSTR ⁎ ⁎ Support for this research has been provided by NIH grants R01LM013107 and R01CA244777 and NSF grant CBET 2200161.
|
Banerjee, Sarasij
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58 |
15 |
p. 420-425
|
artikel
|
| 24 |
Data-driven control of input saturated systems: a LMI-based approach
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Porcari, F.
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58 |
15 |
p. 205-210
|
artikel
|
| 25 |
Data-driven explicit predictive control with limited resources: an exploration-based strategy ⁎ ⁎ This project was partially supported by the Italian Ministry of University and Research under the PRIN’17 project “Data-driven learning of constrained control systems”, contract no. 2017J89ARP.
|
Sassella, Andrea
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58 |
15 |
p. 348-353
|
artikel
|
| 26 |
Data-driven identification of quadrotor dynamics: a tutorial
|
Wi, Yejin
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58 |
15 |
p. 229-234
|
artikel
|
| 27 |
Data Driven Positive Subspace System Identification
|
Wang, Yueyang
|
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58 |
15 |
p. 372-377
|
artikel
|
| 28 |
Data-Driven Predictive Control and MPC: Do we achieve optimality?
|
Anand, A.S.
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58 |
15 |
p. 73-78
|
artikel
|
| 29 |
Deep Learning of Dynamic Systems using System Identification Toolbox™
|
Dai, Tianyu
|
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58 |
15 |
p. 580-585
|
artikel
|
| 30 |
Deep learning of vehicle dynamics ⁎ ⁎ This project has received funding from the European Defence Fund programme under grant agreement number No 101103386 and has also been supported by the Air Force Office of Scientific Research under award number FA8655-23-1-7061. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them.
|
Szécsi, M.
|
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58 |
15 |
p. 283-288
|
artikel
|
| 31 |
Differentiable multi-ridge regression for system identification
|
Maroni, Gabriele
|
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58 |
15 |
p. 187-192
|
artikel
|
| 32 |
Dynamics Modeling of Robot Joints with Asymmetric Load-Dependent Friction ⁎ ⁎ This work was supported by Innovation Fund Denmark [ref no. 1044-00187B] and Universal Robots A/S.
|
Graabæk, Søren
|
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58 |
15 |
p. 43-48
|
artikel
|
| 33 |
Efficient tuning for motion control in diverse systems: a Bayesian framework
|
Catenaro, E.
|
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58 |
15 |
p. 354-359
|
artikel
|
| 34 |
Experiment Design Taking Nonasymptotic Properties of the Model into Consideration ⁎ ⁎ This work was supported by JST SPRING, Grant Number JP-MJSP2110 and JSPS KAKENHI, Grant Number JP22K04816. In addition, M.O. was supported by a graduate exchange fellowship from JGP, Kyoto University.
|
Oshima, Masanori
|
|
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58 |
15 |
p. 550-555
|
artikel
|
| 35 |
Explainable AI: motivations and connections with system identification
|
Warnick, Sean
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|
|
58 |
15 |
p. 502-507
|
artikel
|
| 36 |
Explaining complex systems: a tutorial on transparency and interpretability in machine learning models (part I)
|
Materassi, Donatello
|
|
|
58 |
15 |
p. 492-496
|
artikel
|
| 37 |
Explaining complex systems: a tutorial on transparency and interpretability in machine learning models (part II)
|
Materassi, Donatello
|
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|
58 |
15 |
p. 497-501
|
artikel
|
| 38 |
Fast dynamic analysis of damaged 1D periodic waveguides
|
Rojas, Alvaro Gavilán
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|
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58 |
15 |
p. 325-329
|
artikel
|
| 39 |
Fault detection and diagnosis using the dynamic network framework ⁎ ⁎ Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.
|
Shi, Yibo
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58 |
15 |
p. 384-389
|
artikel
|
| 40 |
Frequency-Domain Identification of Discrete-Time Systems using Sum-of-Rational Optimization ⁎ ⁎ This work was partially supported by the Swiss National Science Foundation under NCCR Automation, grant agreement 51NF40 180545.
|
Abdalmoaty, Mohamed
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58 |
15 |
p. 121-126
|
artikel
|
| 41 |
Gaussian Sum Filtering for Wiener State-Space Models with a Class of Non-Monotonic Piecewise Nonlinearities ⁎ ⁎ This work was partially funded by FONDECYT through projects No 3240181 and 1211630, the Advanced Center for Electrical and Electronic Engineering (AC3E) Base Project FB0008, and by the research program VIDI 15698, which is partially funded by the Netherlands Organization for Scientific Research (NWO).
|
Cedeño, Angel L.
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|
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58 |
15 |
p. 25-30
|
artikel
|
| 42 |
Generalized performance criteria for identified models
|
Vau, Bernard
|
|
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58 |
15 |
p. 181-186
|
artikel
|
| 43 |
Grey-box modelling and identification of the industrial oven of a shrink tunnel
|
Previtali, Davide
|
|
|
58 |
15 |
p. 55-60
|
artikel
|
| 44 |
Identification of a population balance model for Streptococcus thermophilus
|
Holtorf, L.
|
|
|
58 |
15 |
p. 444-449
|
artikel
|
| 45 |
Identification of Deformable Linear Object Dynamics from Input-output Measurements in 3D Space ⁎ ⁎ This research was supported by FWO-Vlaanderen through SBO project ELYSA for cobot applications (S001821N).
|
Floren, Merijn
|
|
|
58 |
15 |
p. 468-473
|
artikel
|
| 46 |
Identification of Hammerstein-Wiener models using Hamiltonian Monte Carlo
|
Holdsworth, James R.Z.
|
|
|
58 |
15 |
p. 456-461
|
artikel
|
| 47 |
Identification of Low Order Systems in a Loewner Framework 1 1 A. Honarpisheh and M. Sznaier were partially supported by NSF grants IIS–1814631 and CNS-2038493, AFOSR grant FA9550-19-1-0005, ONR grant N00014-21-1-2431, and the Sentry DHS Center of Excellence under Award 22STESE00001-03-03
|
Honarpisheh, Arya
|
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|
58 |
15 |
p. 199-204
|
artikel
|
| 48 |
Identification of Process Networks with Recycle Streams
|
Pinnamaraju, Vivek S.
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|
|
58 |
15 |
p. 432-437
|
artikel
|
| 49 |
Identification of the friction model of a single elastic robot actuator from video
|
Corrêa do Lago, Antonio Weiller
|
|
|
58 |
15 |
p. 514-519
|
artikel
|
| 50 |
Identifying the dynamics of interacting objects with applications to scene understanding and video temporal manipulation 1 1 This work was partially supported by NSF grants IIS-1814631 and CNS-2038493, AFOSR grant FA9550-19-1-0005, ONR grant N00014-21-1-2431, and the Sentry DHS Center of Excellence under Award 22STESE00001-03-02.
|
Comas, Armand
|
|
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58 |
15 |
p. 301-306
|
artikel
|
| 51 |
In-context learning of state estimators
|
Busetto, R.
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58 |
15 |
p. 145-150
|
artikel
|
| 52 |
Iterative Feedback Tuning with automated reference model selection ⁎ ⁎ This paper is partially supported by FAIR (Future Artificial Intelligence Research) project, funded by the NextGenerationEU program within the PNRR-PE-AI scheme (M4C2, Investment 1.3, Line on Artificial Intelligence), by the Italian Ministry of Enterprises and Made in Italy in the framework of the project 4DDS (4D Drone Swarms) under grant no. F/310097/01-04/X56 and by the PRIN PNRR project P2022NB77E “A data-driven cooperative framework for the management of distributed energy and water resources” (CUP: D53D23016100001), funded by the NextGeneration EU program.
|
Ickenroth, Tjeerd
|
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58 |
15 |
p. 211-216
|
artikel
|
| 53 |
Kernel-Based Particle Filtering for Scalable Inference in Partially Observed Boolean Dynamical Systems ⁎ ⁎ The authors acknowledge the support of the National Institute of Health award 1R21EB032480-01, National Science Foundation awards IIS-2311969 and IIS-2202395, ARMY Research Laboratory award W911NF2320179, ARMY Research Office award W911NF2110299, and Office of Naval Research award N00014-23-1-2850.
|
Alali, Mohammad
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58 |
15 |
p. 1-6
|
artikel
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| 54 |
Lasso-based state estimation for cyber-physical systems under sensor attacks
|
Cerone, V.
|
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|
58 |
15 |
p. 163-168
|
artikel
|
| 55 |
Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems ⁎ ⁎ This work has been supported by Sioux Technologies B.V., The MathWorks Inc., and by the European Union within the framework of the National Laboratory for Autonomous Systems (RRF-2.3.1-21-2022-00002). Opinions, findings, conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of Sioux Technologies B.V., the MathWorks Inc., or the European Union.
|
Koelewijn, Patrick J.W.
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58 |
15 |
p. 265-270
|
artikel
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| 56 |
Least Squares Projection Onto the Behavior for SISO LTI Models ⁎ ⁎ This work was supported in part by the KU Leuven Research Fund under Project iBOF/23/064 and Project C16/15/059, and several LRD bilateral industrial projects; in part by the Flemish Government agencies: FWO through the EOS Project under Grant G0F6718N (SeLMA) and EWI through the Flanders AI Research Program; and in part by the European Commission (ERC Adv. Grant) under Grant 885682. The work of Sibren Lagauw was supported by the FWO Fundamental Research Fellowship under Grant 11K5623N.
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Lagauw, S.
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58 |
15 |
p. 336-341
|
artikel
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| 57 |
Li-ion cell impedance identification in the time domain as an alternative to identification in the frequency domain
|
Arahbi, Omar
|
|
|
58 |
15 |
p. 151-156
|
artikel
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| 58 |
Neural Data–Enabled Predictive Control
|
Lazar, Mircea
|
|
|
58 |
15 |
p. 91-96
|
artikel
|
| 59 |
Noise Covariances Identification by MDM: Weighting, Recursion, and Implementation
|
Kost, Oliver
|
|
|
58 |
15 |
p. 342-347
|
artikel
|
| 60 |
Nonparametric Frequency-Domain Identification of Magnetic Bearings: An Experimental Study
|
Schietecat, Mathias
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|
|
58 |
15 |
p. 103-108
|
artikel
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| 61 |
One-shot backpropagation for multi-step prediction in physics-based system identification
|
Donati, Cesare
|
|
|
58 |
15 |
p. 271-276
|
artikel
|
| 62 |
On identifying the non-linear dynamics of a hovercraft using an end-to-end deep learning approach ⁎ ⁎ This research was supported by the Swiss National Science Foundation under the NCCR Automation (grant agreement 51NF40 180545).
|
Schwan, R.
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|
58 |
15 |
p. 289-294
|
artikel
|
| 63 |
Online Learning and Control for Data-Augmented Quadrotor Model
|
Šmíd, Matěj
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|
|
58 |
15 |
p. 223-228
|
artikel
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| 64 |
Online Lithium-ion Battery Modeling and State of Charge Estimation via Concurrent State and Parameter Estimation ⁎ ⁎ This work was supported by the National Natural Science Foundation of China (No. 62273167), the China Scholarship Council (No. 202206790087), the 111 Project (B23008) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX23 2435).
|
Li, Jimei
|
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|
58 |
15 |
p. 462-467
|
artikel
|
| 65 |
Online system identification of global lung heat transfers
|
Victor, Stéphane
|
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58 |
15 |
p. 319-324
|
artikel
|
| 66 |
On the adaptation of in-context learners for system identification
|
Piga, Dario
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58 |
15 |
p. 277-282
|
artikel
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| 67 |
Optimal experiment design for multivariable system identification using simultaneous excitation
|
Sigurdsson, Gunnar
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58 |
15 |
p. 544-549
|
artikel
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| 68 |
Output-Only Identification of Lur’e Systems with Prandtl-Ishlinskii Hysteresis Nonlinearities
|
Aljanaideh, Khaled F.
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|
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58 |
15 |
p. 366-371
|
artikel
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| 69 |
Parametric estimation of arbitrary fractional order models for battery impedances ⁎ ⁎ This research was financially supported by the Research Foundation Flanders (FWO-Vlaanderen, grant nr G.0052.18N) and by the Flemish Government (Methusalem Fund METH1).
|
Vandeputte, Freja
|
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58 |
15 |
p. 97-102
|
artikel
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| 70 |
Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks ⁎ ⁎ This work is funded by the European Union (ERC, COMPLETE, 101075836). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
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Liu, Yuhan
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58 |
15 |
p. 295-300
|
artikel
|
| 71 |
Physics-informed and black-box Identification of robotic actuator with a flexible joint
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Corrêa do Lago, Antonio Weiller
|
|
|
58 |
15 |
p. 259-264
|
artikel
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| 72 |
Physics-informed Neural Network for system identification of rotors ⁎ ⁎ This work was supported by the National Key Research and Development Program of China (No. 2019YFB1705403), the National Natural Science Foundation of China (No. 52175118), the Innovative Scientific Program of China National Nuclear Corporation (CNNC), the support of K.C. Wong Education Foundation, and the Fundamental Research Funds for the Central Universities. Corresponding authors: Wei Cheng, Wei Pan.
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Liu, Xue
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58 |
15 |
p. 307-312
|
artikel
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| 73 |
Physiology-Informed Deep Learning Modeling of Type 1 Diabetes Dynamics: Mapping Data to Virtual Subjects
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Crespo-Santiago,, Alvaro
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58 |
15 |
p. 235-240
|
artikel
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| 74 |
Power System Oscillation Monitoring and Damping Control Redesign under Ambient Conditions and Multiple Operating Points
|
Vanfretti, Luigi
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58 |
15 |
p. 526-531
|
artikel
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| 75 |
Rational Maps for System Identification
|
Singh, Rajiv
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58 |
15 |
p. 480-485
|
artikel
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| 76 |
Recursive identification with regularization and on-line hyperparameters estimation
|
Vau, Bernard
|
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58 |
15 |
p. 13-18
|
artikel
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| 77 |
Regularized Finite Impulse Response Models versus Laguerre Models: A Comparison
|
Illg, Christopher
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58 |
15 |
p. 67-72
|
artikel
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| 78 |
Regularized Iterative Weighted Total Least Squares for Vehicle Mass Estimation ⁎ ⁎ This work was supported by The Michelin Group.
|
Koide, Hugo
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58 |
15 |
p. 37-42
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artikel
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| 79 |
Set Membership identification for NMPC complexity reduction ⁎ ⁎ This work was supported by the NewControl project, within the Electronic Components and Systems For European Leadership Joint Undertaking (ESCEL JU) in collaboration with the European Union’s Horizon2020 Framework Programme and National Authorities, under grant agreement N° 826653-2.
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Boggio, Mattia
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58 |
15 |
p. 217-222
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artikel
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| 80 |
Set Membership State Estimation with Quantized Measurements and Optimal Threshold Selection
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Casini, Marco
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58 |
15 |
p. 157-162
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artikel
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| 81 |
SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study
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Ugolini, Aurelio Raffa
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58 |
15 |
p. 49-54
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artikel
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| 82 |
Space-Filling Input Design for Nonlinear State-Space Identification ⁎ ⁎ Funded by the European Union (ERC, COMPLETE, 101075836). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
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Kiss, Máté
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58 |
15 |
p. 562-567
|
artikel
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| 83 |
Split-Boost Neural Networks
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Cestari, Raffaele G.
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58 |
15 |
p. 241-246
|
artikel
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| 84 |
Stable state estimation in a network context ⁎ ⁎ This work was performed while the author was SimTech Visiting Professor at the Institute of Systems Theory, University of Stuttgart, partially supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2120/1 - 390831618 - EXC 2075/1 - 390740016.
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Bitmead, Robert R.
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58 |
15 |
p. 402-407
|
artikel
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| 85 |
State Derivative Normalization for Continuous-Time Deep Neural Networks
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Weigand, Jonas
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58 |
15 |
p. 253-258
|
artikel
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| 86 |
Stochastic Data-Driven Predictive Control: Regularization, Estimation, and Constraint Tightening
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Yin, Mingzhou
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58 |
15 |
p. 79-84
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artikel
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| 87 |
Structured state-space models are deep Wiener models
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Bonassi, Fabio
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58 |
15 |
p. 247-252
|
artikel
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| 88 |
Subglottal Impedance-Based Model Parameter Estimation via System Identification ⁎ ⁎ This work has been supported by ANID (through grants Advanced Center for Electrical and Electronic Engineering FB0008, ECOS 210008, FONDECYT 1230623 and Doctorado Nacional 21202402 scholarship), Universidad Técnica Federico Santa María (through grant PIIC 015/2021), and the National Institute of Health and National Institute on Deafness and Other Communication Disorders (through grant NIH P50DC015446). VE would like to thank the support of FONDECYT 11200665. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
|
Fontanet, Javier G.
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58 |
15 |
p. 313-318
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artikel
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| 89 |
SYSDYNET - A MATLAB App and Toolbox for Dynamic Network Identification ⁎ ⁎ Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. , ⁎⁎ ⁎⁎ The App and Toolbox are available for download from the landing page www.sysdynet.net.
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Van den Hof, Paul M.J.
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p. 574-579
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System Identification of User Engagement in mHealth Behavioral Interventions ⁎ ⁎ This research was funded by National Institutes of Health, National Cancer Institute and Office of Behavior and Social Sciences, U01CA229445.
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El Mistiri, Mohamed
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p. 508-513
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| 91 |
System Identification Techniques for Soft Sensors and Multiphase Flow Metering
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Paulo, Pedro H.C.
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p. 538-543
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| 92 |
Tensor Train Discrete Grid-Based Filters: Breaking the Curse of Dimensionality
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Matoušek, J.
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p. 19-24
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| 93 |
Time-domain versus frequency-domain system identification of lithium-ion batteries using fractional models
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Adel, Abderrahmane
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p. 109-114
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| 94 |
Towards targeted exploration for non-stochastic disturbances ⁎ ⁎ F. Allgöwer is thankful that his work was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 - 390740016 and under grant 468094890. J. Köhler acknowledges the support of the Swiss National Science Foundation under the NCCR Automation (grant agreement 51NF40_180545) and an ETH Career Seed Award funded through the ETH Zurich Foundation. J. Venkatasubramanian thanks the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting her.
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Venkatasubramanian, Janani
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p. 556-561
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| 95 |
Understanding “Just-in-Time” States in Behavioral Interventions using System Identification and Data Science Methods ⁎ ⁎ This research was funded by the National Institutes of Health (NIH) grants R01LM013107 and R01CA244777.
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El Mistiri, Mohamed
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p. 568-573
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| 96 |
Unveiling Fringe Social Network Dynamics via Parameter Estimation with Hawkes Processes ⁎ ⁎ The authors’ research has been partially supported by the PRIN 2022KPHA24 - TECHIE.
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Grande, Davide
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p. 378-383
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Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment ⁎ ⁎ This research was funded in part by NYSERDA under agreement 137951 and the National Science Foundation, Grant No. 2231677.
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Bogodorova, Tetiana
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p. 520-525
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| 98 |
Vertical load estimation in tractors via in-wheel optical sensing
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Cestari, R.G.
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Weighted Least-Squares PARSIM
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