Autonomous Tracking and State Estimation With Generalized Group Lasso

Gao, Rui; Särkkä, Simo; Claveria-Vega, Rubén; Godsill, Simon

doi:10.1109/tcyb.2021.3085426

Cited by 6 publications

(3 citation statements)

References 70 publications

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“…Variable splitting methods have recently been introduced in the arena of sparsity-type problems, where it has become increasingly important due to splitting property [40], [41]. The methods, for example ADMM [42], [43], are efficient methods that can tackle this kind of sparsity problem.…”

Section: B Eeg Signal Classification Methodsmentioning

confidence: 99%

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification

Wang

Gao

Zheng

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification accuracy. To do so, we propose a weighted neighborhood field graph (WNFG) to represent EEG signals. The WNFG reduces redundant edges between graph nodes and has lower graph generation time and memory usage than the baseline solution. The sequential graph convolutional network is further developed from a WNFG by combining sparse weight pruning and the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art method, our method has the same classification accuracy on the Bonn public dataset and the spikes and slow waves (SSW) clinical real dataset when the connection rate is ten times smaller.

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Section: B Eeg Signal Classification Methodsmentioning

confidence: 99%

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification

Wang

Gao

Zheng

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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“…While the convergence from any initial guess to the solution point cannot be rigorously proven, this method has achieved wide success in the optimization of power systems and many other areas. Just to name a few among many, power system state estimation with nonlinear measurement models using the WLS estimator [37], [58] and the least absolute value (LAV) estimator [37], [59], model reduction of induction machines using the nonlinear LASSO [60], autonomous tracking and state estimation using the generalized group LASSO [61], etc. As has been shown extensively in existing literature, this method has satisfactory performance for a wide variety of nonlinear programming problems in practice.…”

Section: Convergence Of the Solution Algorithmmentioning

confidence: 99%

Model-Measurement Data Integrity Attacks

Lin

Yan

Zhao

et al. 2023

IEEE Trans. Smart Grid

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The vulnerabilities of information and communication technology (ICT) infrastructures leave room for cyber attacks threatening the reliable operations of power systems. Based on the real-world evidence of the Ukraine power grid attack and the popular technical discussion that cyber attacks could be launched at the control-center level, this paper reveals a new attack strategy: model-measurement data integrity (MMI) attack. Instead of compromising measurements only, we investigate the possibility where network parameters are coordinately manipulated when constructing false data injection attack (FDIA) vectors. Furthermore, we model cyber adversaries' possible behavior of co-planning the manipulated measurement channels and parameter attack vectors prior to the launch of FDIAs. The revealed MMI attack strategy allows a drastic reduction of measurement channels to compromise in run-time for keeping the stealth property. Simulations in the IEEE 14-bus test system and the IEEE 118-bus test system demonstrate the feasibility of the revealed MMI attack strategy.

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“…Hao et al investigated an asynchronous information fusion issue for a camera and radar in an intelligent driving system [7]. In order to improve the tracking and estimation accuracy, Gao et al proposed the method of Generalized Group Lasso [8]. Galanido et al estimated the hydrogen fuel filling time for hydrogen-powered fuel cell electric vehicles at different initial conditions through a dynamic simulation by using Aspen Dynamics v.11 with the Peng-Robinson equation of state for creating a thermodynamic model [9].…”

Section: Introductionmentioning

confidence: 99%

Vehicle state and parameter estimation based on improved extend Kalman filter

Liu,

Cui,

Peng

2023

J. meas. eng.

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In order to reduce the influence of historical measurement data errors in the process of vehicle state estimation and improve the accuracy of the vehicle state estimation, a limited memory random weighted extended Kalman filter (LMRWEKF) algorithm is proposed. Firstly, a 3-DOF nonlinear vehicle dynamics model is established. Secondly, the limited memory extended Kalman filter is formed by fusing the limited memory filter and the extended Kalman filter. Then, according to the random weighting theory, the weighting coefficients that obey Dirichlet distribution are introduced to further improve the filtering estimation accuracy. Finally, a virtual test based on the ADAMS/CAR is used for the experimental verification. The results show that the error in the longitudinal velocity and the yaw rate is small, especially in the mean value of estimation error of side slip angle which is different in just 0.015 degrees between the virtual test and the simulation result. And also, the results compared with traditional methods indicate that the proposed LMRWEKF algorithm can solve the problem of vehicle state estimation with the performance of noise fluctuation suppression and higher estimation accuracy. The mean absolute error (MAE) and root mean square error (RMSE) are considered to verify the estimation accuracy of the proposed algorithm. And the comparison results indicate that the estimation accuracy of the LMRWEKF algorithm is significantly higher than those of the EKF and DEKF methods.

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Autonomous Tracking and State Estimation With Generalized Group Lasso

Cited by 6 publications

References 70 publications

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification

Model-Measurement Data Integrity Attacks

Vehicle state and parameter estimation based on improved extend Kalman filter

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