Results 31 to 40 of about 113 (109)

BP Neural Network–Based Kalman Filtering Method Under Multiple Cyberattacks

IET Signal Processing, Volume 2026, Issue 1, 2026.
This paper proposes a Kalman‐gain‐driven neural Kalman filtering (KF) defense framework, termed KFDBP, for secure state estimation in cyber–physical systems (CPSs) under denial‐of‐service (DoS), spoofing, and replay attacks. Unlike end‐to‐end neural filtering approaches such as KalmanNet that directly learn state estimators or implicitly approximate ...
Zijing Li, Keting Huang, Gang Wang, Zhuowei Liang, Yun Zhou, Desheng Zheng, Yaoxin Duan, Wenling Li   +7 more
wiley   +1 more source

Student’s t‐Kernel‐Based Graph Signals Maximum Correntropy Unscented Kalman Filter Under Hybrid Cyberattacks

IET Signal Processing, Volume 2026, Issue 1, 2026.
The implementation of Kalman filter (KF) in tracking high‐dimensional, strongly correlated graph structured data is often complex and unstable. Meanwhile, in practical applications, the system may be subject to interference from non‐Gaussian noise and various cyberattacks.
Bingyu Yin, Xinmin Song, Wenling Li
wiley   +1 more source

Intelligent Vehicle Sideslip Angle Observation Strategy Based on Steady‐State Model and SCKF With Nonlinear Compensation

Journal of Engineering, Volume 2026, Issue 1, 2026.
Vehicle sideslip angle is one of the irreplaceable variable indicators for evaluating vehicle stability. However, it is difficult to directly measure vehicle sideslip angle with onboard sensors. In order to obtain precise vehicle sideslip angle using onboard sensors, a novel observation strategy based on fusion of steady‐state model method and square ...
Zhendong Zhu, Qiangqiang Yao, Yiheng Shi, Abdullah A. Kendoush   +3 more
wiley   +1 more source

A Novel Weighted Unscented Kalman Filter for Dynamic Load Identification

Structural Control and Health Monitoring, Volume 2026, Issue 1, 2026.
To address the limitations of traditional unscented Kalman filter (UKF)‐based algorithms—which typically require either additional displacement measurements or iterative optimization for load identification—this study proposes a fast and convenient load excitation identification algorithm.
Yanzhe Zhang, Bowen Zheng, Xiaoming Sun, Lina Guo, Yoshiki Ikeda   +4 more
wiley   +1 more source

A probabilistic diagnostic for Laplace approximations: Introduction and experimentation

Canadian Journal of Statistics, Volume 53, Issue 4, December 2025.
Abstract Many models require integrals of high‐dimensional functions: for instance, to obtain marginal likelihoods. Such integrals may be intractable, or too expensive to compute numerically. Instead, we can use the Laplace approximation (LA). The LA is exact if the function is proportional to a normal density; its effectiveness therefore depends on ...
Shaun McDonald, Dave Campbell
wiley   +1 more source

Joint Estimation of Lithium‐Ion Battery Health Status and Remaining Service Life by Transfer Learning Based on PatchTST and Dynamic Weighted MSE Loss Function

Energy Science &Engineering, Volume 13, Issue 9, Page 4371-4386, September 2025.
Develops a joint SOH‐RUL estimation model suitable for LIBs. This method leverages the PatchTST model and novel dynamic weighted kernel MSE (DWKMSE) loss function, employing transfer learning techniques to estimate SOH and RUL across different batteries. ABSTRACT This study proposes a transfer learning estimation method based on dynamic weighted kernel
Kaiyi Zhang, Xingzhu Wang
wiley   +1 more source

Putatively Optimal Projective Spherical Designs With Little Apparent Symmetry

Journal of Combinatorial Designs, Volume 33, Issue 6, Page 222-234, June 2025.
ABSTRACT We give some new explicit examples of putatively optimal projective spherical designs, that is, ones for which there is numerical evidence that they are of minimal size. These form continuous families, and so have little apparent symmetry in general, which requires the introduction of new techniques for their construction.
Alex Elzenaar, Shayne Waldron
wiley   +1 more source

Quantization‐Based Latin Hypercube Sampling for Dependent Inputs With an Application to Sensitivity Analysis of Environmental Models

Applied Stochastic Models in Business and Industry, Volume 41, Issue 3, May/June 2025.
ABSTRACT Numerical models are essential for comprehending intricate physical phenomena in different domains. To handle their complexity, sensitivity analysis, particularly screening is crucial for identifying influential input parameters. Kernel‐based methods, such as the Hilbert‐Schmidt Independence Criterion (HSIC), are valuable for analyzing ...
Guerlain Lambert, Céline Helbert, Claire Lauvernet   +2 more
wiley   +1 more source

Maximal point‐polyserial correlation for non‐normal random distributions

British Journal of Mathematical and Statistical Psychology, Volume 78, Issue 1, Page 341-377, February 2025.
Abstract We consider the problem of determining the maximum value of the point‐polyserial correlation between a random variable with an assigned continuous distribution and an ordinal random variable with k$$ k $$ categories, which are assigned the first k$$ k $$ natural values 1,2,…,k$$ 1,2,\dots, k $$, and arbitrary probabilities pi$$ {p}_i $$.
Alessandro Barbiero
wiley   +1 more source

l1‐ATSXKF‐based state and bias estimation for non‐linear systems with non‐Gaussian process noise

IET Control Theory &Applications, Volume 19, Issue 1, January/December 2025.
To solve the problem of state and bias estimation for the nonlinear system with non‐Gaussian noise terms, combining the l1 norm and adaptive factors, a series of exogenous Kalman filters (XKF) based state and bias estimation algorithms are proposed. The simulation results show that the proposed algorithms can reduce the influence of the non‐Gaussian ...
Boyu Yang, Xueqin Chen, Fan Wu, Ming Liu
wiley   +1 more source