Article
Computer Science, Information Systems
Mena S. Elmenshawy, Ahmed M. Massoud
Summary: This paper presents an Adaptive Extended Kalman Filter (AEKF) and Forgetting Factor Adaptive Extended Kalman Filter (FFAEKF) algorithm for short-term load forecasting in distribution networks. Compared to AEKF, FFAEKF improves forecasting performance by reducing Maximum Absolute Error (MaxAE) and Root Mean Square Error (RMSE).
Article
Automation & Control Systems
Chen Qian, Qingwei Chen, Yifei Wu, Jian Guo, Yang Gao
Summary: A novel M-estimation based sparse grid quadrature filter (MSGQF) is proposed to improve the robust performance of the nonlinear system. The MSGQF outperforms other filters when abnormal measurement values appear, providing significant performance improvement in the robustness of the nonlinear system.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Optics
Nianfeng Wang, Jun Ma, Hui Ding, Cong Wei, Xinyu Miao, Zhonghao Shen, Caojin Yuan
Summary: This Letter presents an iterative pseudo-phase inpainting algorithm (IPPI) to solve the segmented phase unwrapping problem. By using image inpainting, the IPPI can connect pseudo-phases and reduce error points. The proposed algorithm does not require any processing on the effective area of the wrapped phase, ensuring the authenticity of the result. It has high precision and can be applied to segmented phases with severe noise.
Article
Engineering, Electrical & Electronic
Elnaz Moradi, Reza Mohseni
Summary: This paper proposes the problem of linear frequency modulated (LFM) or chirp signal analysis and suggests solutions based on signal state-space model and different versions of the Kalman filter. Compared with traditional methods, the proposed approaches have advantages in estimation performance and convergence, which are demonstrated through numerical simulations.
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Article
Automation & Control Systems
Andrea Tuveri, Fernando Perez-Garcia, Pedro A. Lira-Parada, Lars Imsland, Nadav Bar
Summary: In this study, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) were implemented to estimate key process variables in a fed-batch bacterial cultivation process. The results demonstrate precise estimation of biomass and substrate consumption, particularly when adapting the process covariance matrix to account for model inaccuracies during the feeding phase.
JOURNAL OF PROCESS CONTROL
(2021)
Article
Engineering, Aerospace
Junghyun Kim, Kyuman Lee
Summary: This study aims to compare the performance of different machine learning models for wind prediction, select the most suitable model, combine it with an unscented Kalman filter to improve accuracy, and conduct Monte Carlo simulations to quantify uncertainties in the modeling process.
Article
Computer Science, Information Systems
Zhifang Liao, Haihui Pan, Xiaoping Fan, Yan Zhang, Li Kuang
Summary: This study proposes a multiple wavelet convolutional neural network (MWCNN) for load forecasting, considering the actual deployability of a model based on prediction performance, robustness, dependence on external data, and storage size. The MWCNN shows good performance and robustness on two public data sets while using only load data and requiring a small storage size of 497 kB.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Engineering, Marine
Fei Deng, Carlos Levi, Hongdong Yin, Menglan Duan
Summary: The study proposes an optimized UKF algorithm to improve the estimation precision of hydrodynamic coefficients for an AUV, in combination with three KF algorithms for verification. The research enhances the adaptability and prediction performance of the identification approach and demonstrates the superior accuracy of OUKF compared to EKF and UKF in the presence of ARMA noisy model.
Article
Automation & Control Systems
Assia Daid, Eric Busvelle, Mohamed Aidene
Summary: This paper presents a convergence analysis of the modified unscented Kalman filter (UKF) as an observer for a class of nonlinear deterministic continuous time systems, comparing it with the extended Kalman filter (EKF) and highlighting the differences in convergence behavior. The study shows that the UKF is not an exponentially converging observer like the EKF, and proposes the unscented Kalman observer as a better candidate for observation. This work serves as a first step towards proving the global convergence of the high-gain version of the UKF observer.
EUROPEAN JOURNAL OF CONTROL
(2021)
Article
Automation & Control Systems
Jozsef Kuti, Imre J. Rudas, Huijun Gao, Peter Galambos
Summary: This article introduces a generic computational relaxation method for optimizing the filtering operation in Unscented Kalman filter-based applications, which improves the performance of advanced robotics and autonomous vehicles. The practical merit of the proposed method is demonstrated through real-world examples, showing significant advantages and reduced computational demand.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Energy & Fuels
Yuqi Jiang, Tianlu Gao, Yuxin Dai, Ruiqi Si, Jun Hao, Jun Zhang, David Wenzhong Gao
Summary: Very short-term load forecasting is crucial for residential microgrid systems. This paper proposes a Deep-Autoformer framework based on the Autoformer neural network for more efficient load forecasting. Experimental results demonstrate that the Deep-Autoformer achieves State-Of-The-Art results and two hypotheses are proposed to explain some unintuitive phenomena. Overall, the proposed Deep-Autoformer provides a feasible approach and a new baseline for VSTLF.
Article
Engineering, Electrical & Electronic
Jian Zheng, Duowen Yan, Ming Yan, Yun Li, Yabing Zhao
Summary: This paper proposes a method for online parameter identification of nonlinear ship motion systems using an unscented Kalman filter (UKF) combined with rolling wavelet denoising. The proposed navigation identification framework improves the accuracy and control effectiveness of ship motion system identification.
Article
Chemistry, Analytical
Ojonugwa Adukwu, Darci Odloak, Amir Muhammed Saad, Fuad Kassab Junior
Summary: The focus of this work is to extend nonlinear state estimation methods to gas-lifted systems. The study compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) in estimating the nonlinear states. It was found that UKF provided slightly better estimates than EKF, while PF performed the worst. The gas-lifted system exhibited casing heading instability, and the results showed that either EKF or UKF could be used for nonlinear state estimation, with UKF being preferred if computational cost is not considered.
Article
Construction & Building Technology
Jae-Seung Hwang, Dae-Kun Kwon, Jungtae Noh, Ahsan Kareem
Summary: This study proposes a frequency domain framework to decompose the overall acrosswind load into motion-induced, buffeting, and Strouhal components based on wind tunnel experiments or full-scale measurements. The total acrosswind load is identified using the Kalman filter technique, and then each load component is modeled and estimated using the unscented Kalman filter in frequency domain. The framework effectively decomposes the total load and sheds light on the underlying mechanism of the load and structural response.
WIND AND STRUCTURES
(2023)
Article
Engineering, Electrical & Electronic
Runlong Xiao, Gang Wang, Lijun Fu, Fan Ma, Chun Li, Renji Huang, Xiaoliang Hao
Summary: An adaptive estimation method is proposed in this paper to ensure the accuracy of estimation and reliability of the algorithm by adaptively adjusting the estimation method according to changes in the system operating conditions, providing a solution to the electromagnetic transient issue caused by the periodic pulse load power changes in the medium-voltage DC integrated power system. The method is proven to be superior to existing methods in terms of estimative effect through experiments and is also validated for its reliability through consistency tests on the filter.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
L. Michel, P. Van Hentenryck
Article
Engineering, Electrical & Electronic
Xiaorong Sun, Peter B. Luh, Kwok W. Cheung, Wei Guan, Laurent D. Michel, S. S. Venkata, Melanie T. Miller
IEEE TRANSACTIONS ON POWER SYSTEMS
(2016)
Article
Materials Science, Composites
Frederic Lachaud, Christine Espinosa, Laurent Michel, Pierre Rahme, Robert Piquet
APPLIED COMPOSITE MATERIALS
(2015)
Article
Engineering, Electrical & Electronic
Che Guan, Peter B. Luh, Laurent D. Michel, Yuting Wang, Peter B. Friedland
IEEE TRANSACTIONS ON POWER SYSTEMS
(2013)
Article
Computer Science, Theory & Methods
Fanghui Liu, Waldemar Cruz, Laurent Michel
Summary: Tolerant Algebraic Side-Channel Attack (TASCA) exploits an algebraic formulation of a cipher and side-channel information to recover secret keys. Constraint Programming (CP), as an optimization technology, provides high-level and expressive models for solving cryptanalysis challenges. TASCA-CP significantly improves the speed of key recovery compared to the original TASCA method when noisy side-channel measurements are available.
JOURNAL OF CRYPTOGRAPHIC ENGINEERING
(2022)
Article
Computer Science, Software Engineering
L. Michel, P. Schaus, P. Van Hentenryck
Summary: MiniCP is a lightweight, open-source solver for constraint programming, designed to provide students with a core implementation that supports extensibility and flexibility. Although MiniCP does not support all constraint programming features and techniques, they could be implemented as future extensions or exploratory projects.
MATHEMATICAL PROGRAMMING COMPUTATION
(2021)
Proceedings Paper
Computer Science, Information Systems
Timothy Curry, Devon Callahan, Benjamin Fuller, Laurent Michel
INFORMATION SECURITY AND PRIVACY, ACISP 2019
(2019)
Proceedings Paper
Engineering, Multidisciplinary
Wei Yan, Daniel Fontaine, John A. Chandy, Laurent Michel
2017 12TH SYSTEM OF SYSTEMS ENGINEERING CONFERENCE (SOSE)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Daniel Fontaine, Laurent Michel, Pascal Van Hentenryck
INTEGRATION OF AI AND OR TECHNIQUES IN CONSTRAINT PROGRAMMING, CPAIOR 2016
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Pascal Van Hentenryck, Laurent Michel
PRINCIPLES AND PRACTICE OF CONSTRAINT PROGRAMMING, CP 2014
(2014)
Proceedings Paper
Computer Science, Artificial Intelligence
Daniel Fontaine, Laurent Michel, Pascal Van Hentenryck
PRINCIPLES AND PRACTICE OF CONSTRAINT PROGRAMMING, CP 2014
(2014)
Proceedings Paper
Energy & Fuels
Wei Guan, Kenneth Chung, Kwok W. Cheung, Xiaorong Sun, Peter B. Luh, Laurent D. Michel, Stephen Corbo
2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES)
(2013)
Proceedings Paper
Computer Science, Theory & Methods
Pascal Van Hentenryck, Laurent Michel
PRINCIPLES AND PRACTICE OF CONSTRAINT PROGRAMMING, CP 2013
(2013)
Proceedings Paper
Computer Science, Theory & Methods
Daniel Fontaine, Laurent Michel, Pascal Van Hentenryck
PRINCIPLES AND PRACTICE OF CONSTRAINT PROGRAMMING, CP 2013
(2013)