Results 41 to 50 of about 12,338 (264)

Measurement of ship's magnetization parameters based on Kalman filtering method

Zhongguo Jianchuan Yanjiu, 2022
ObjectivesThis study proposes a measurement scheme based on the Kalman filtering method for improving efficiency and lowering complexity in measuring the magnetization parameters of shipboard three-component geomagnetic field measurement systems ...
Hui YAN, Guohua ZHOU
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

Considering spatiotemporal evolutionary information in dynamic multi‐objective optimisation

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Preserving population diversity and providing knowledge, which are two core tasks in the dynamic multi‐objective optimisation (DMO), are challenging since the sampling space is time‐ and space‐varying. Therefore, the spatiotemporal property of evolutionary information needs to be considered in the DMO.
Qinqin Fan, Min Jiang, Wentao Huang, Qingchao Jiang   +3 more
wiley   +1 more source

Distributed Kalman Filtering

, 2013
Publication in the conference proceedings of EUSIPCO, Marrakech, Morocco ...
Subhro Das, José M. F. Moura
openaire   +3 more sources

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

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

Boundary Value Problems Arising in Kalman Filtering

Boundary Value Problems, 2009
The classic Kalman filtering equations for independent and correlated white noises are ordinary differential equations (deterministic or stochastic) with the respective initial conditions.
Sinem Ertürk, Zeka Mazhar, Agamirza Bashirov   +2 more
doaj   +1 more source

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

Lidar‐Based Object Tracking of Traffic Participants with Sensor Nodes in Existing Urban Infrastructure

Advanced Intelligent Systems, EarlyView.
This paper presents a lidar‐based sensor node design and a rule‐based state observer for edge‐based traffic participant tracking. Unlike other state‐of‐the‐art methods, this state observer enables real‐time, CPU‐only edge processing without relying on machine learning approaches.
Simon Schäfer, Bassam Alrifaee, Ehsan Hashemi   +2 more
wiley   +1 more source

Application of Strong Tracking Modified SRCKF Algorithm in Single Observer Passive Tracking [PDF]

Jisuanji gongcheng, 2016
In order to improve the performance of Square Root Cubature Kalman Filtering(STSRCKF) algorithm to track maneuvering target in single observer passive tracking,a Strong Tracking Modified SRCKF(ST-MSRCKF) algorithm is presented.Target state variables and ...
ZHANG Zhuoran,YE Guangqiang,ZHAO Xiaolin
doaj   +1 more source