Results 51 to 60 of about 15,255 (305)

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study

Sensors, 2018
Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error
Siavash Hosseinyalamdary
doaj   +1 more source

Dictionary‐based weak‐form training for noise‐robust series hybrid models with multiplicative unknowns

AIChE Journal, EarlyView.
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho, Akshay Kudva, Parth Shah, Dongheon Lee, Joseph Sang‐Il Kwon   +4 more
wiley   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Parallelized Kalman Filters for Mitigation of the Excess Phase Noise of Fast Tunable Lasers in Coherent Optical Communication Systems

IEEE Photonics Journal, 2018
Numerical and experimental investigations are carried out on the performance of parallelized Kalman filters applied for mitigation of the excess phase noise of fast tunable lasers.
Fan Liu, Yi Lin, Yonglin Yu, Liam P. Barry   +3 more
doaj   +1 more source

Analysis and Forecast of the Number of Deaths, Recovered Cases, and Confirmed Cases From COVID-19 for the Top Four Affected Countries Using Kalman Filter

Frontiers in Physics, 2021
COVID-19 is a virus that spread globally, causing severe health complications and substantial economic impact in various parts of the world. The COVID-19 forecast on infections is significant and crucial information that will help in executing policies ...
Abdullah Ali H. Ahmadini, Muhammad Naeem, Muhammad Naeem, Muhammad Aamir, Raimi Dewan, Raimi Dewan, Shokrya Saleh A. Alshqaq, Wali Khan Mashwani   +7 more
doaj   +1 more source

Differentially private Kalman filtering [PDF]

2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2012
9 pages.
Jerome Le Ny, George J. Pappas
openaire   +2 more sources

Autodifferentiable Ensemble Kalman Filters

SIAM Journal on Mathematics of Data Science, 2022
Data assimilation is concerned with sequentially estimating a temporally-evolving state. This task, which arises in a wide range of scientific and engineering applications, is particularly challenging when the state is high-dimensional and the state-space dynamics are unknown.
Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett   +2 more
openaire   +3 more sources

The Ensemble Kalman Filter and its Relations to Other Nonlinear Filters

, 2015
The Ensemble Kalman filter (EnKF) is a standard algorithm in oceanography and meteorology, where it has got thousands of citations. It is in these communities appreciated since it scales much better with state dimension n than the standard Kalman filter (
Fritsche, Carsten,, Fredrik Gustafison, Hendeby, Gustaf, Fritsche, Carsten, Carsten Fritsche, Gustaf Hendeby, Roth, Michael,, Michael Roth, Gustafsson, Fredrik,, Gustafsson, Fredrik, Hendeby, Gustaf,, Roth, Michael   +11 more
core   +1 more source

Extending Battery Usage Time of a Heavy‐Duty Mecanum‐Wheeled Autonomous Electric Vehicle Used in Iron–Steel Industry by Considering Wheel Slippage

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
It is a fact that slippage causes tracking errors in both longitudinal and lateral directions which results to have less travel distance in tracking a reference trajectory. Less travel distance means having energy loss of the battery and carrying loads less than planned.
Gokhan Bayar, Goktug Hambarci, Ali Sezai Demirel   +2 more
wiley   +1 more source

Implementation of unknown parameter estimation procedure for hybrid and discrete non‐linear systems

IET Radar, Sonar & Navigation
The application of the hybrid extended Kalman filter (HEKF), hybrid unscented Kalman filter (HUKF), hybrid particle filter (HPF), and hybrid extended Kalman particle filter (HEKPF) is discussed for hybrid non‐linear filter problems, when prediction ...
Mahdi Razm‐Pa
doaj   +1 more source