Article
Engineering, Marine
Sisi Wang, Lijun Wang, Namkyun Im, Weidong Zhang, Xijin Li
Summary: A real-time parameter identification method based on nonlinear Gaussian filtering algorithm and nonlinear ship response model is proposed to improve system identification accuracy and reduce computational complexity.
Article
Engineering, Electrical & Electronic
Junwei Wang, Zhen Ma, Xiyuan Chen
Summary: By introducing a new neuron growth-attenuation mechanism based on the fuzzy neural network model and incorporating the theory of strong tracking filter, a generalized dynamic fuzzy neural network model based on MSCKF (MSCKF-GDFNN) is proposed, which demonstrates improved generalization ability and prediction accuracy during GNSS signal loss.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
M. A. Gonzalez-Cagigal, J. A. Rosendo-Macias, A. Gomez-Exposito
Summary: This research presents a state estimation approach using Kalman filtering to identify the phase to which single-phase customers are connected in three-phase distribution grids. The study compares different nonlinear formulations of the Kalman filter and shows that the ensemble Kalman filter provides better estimation results as the system size increases. The accuracy, robustness, and limitations of the estimator are also tested with consideration of measurement errors.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Automation & Control Systems
Huan Wu, Yong-Ping Zhao, Hui-Jun Tan
Summary: This study proposes a method for monitoring the flow patterns of supersonic inlets using neural network technology, which integrates dynamic time warping and Kalman filter techniques to achieve better performance in feature extraction and classification.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Engineering, Marine
C. T. Liong, K. H. Chua
Summary: This paper presents a data assimilation framework for vessel motion prediction in real-time, combining artificial neural network (ANN) and Ensemble Kalman Filter (EnKF). The framework improves prediction accuracy by incorporating measured data with ANN predictions, especially for experimental measurements with higher uncertainties.
Article
Engineering, Mechanical
Chuang Yang, Zhe Gao, Yue Miao, Tao Kan
Summary: Two types of fractional-order cubature Kalman filters were designed to address the initial value influence problem in state estimation of nonlinear continuous-time fractional-order systems. The proposed methods effectively reduce the impact of initial value on the state estimation, as demonstrated in simulation examples.
NONLINEAR DYNAMICS
(2021)
Article
Automation & Control Systems
Juan-Carlos Santos-Leon, Ramon Orive, Daniel Acosta, Leopoldo Acosta
Summary: This paper revisits the construction and effectiveness of the Cubature Kalman Filter (CKF) and its extensions for higher precision, establishing stable cubature rules within a mathematical framework of numerical integration. By discretizing higher order partial derivatives, stable rules for degrees 5 and 7 are provided and tested for application in filter algorithms through various examples.
Article
Physics, Fluids & Plasmas
Christine M. Greve, Manoranjan Majji, Kentaro Hara
Summary: An extended Kalman filter is developed for estimating unobserved states and parameters in plasma dynamical systems, ensuring consistency between estimates and physical processes by adjusting noise covariances. The EKF demonstrates robustness with sparse measurement data and is successfully applied to investigate discharge current oscillations. It shows that electron temperature dynamics can be estimated using discharge current fluctuation as measurement data, with quantified uncertainties in the estimates.
PHYSICS OF PLASMAS
(2021)
Article
Automation & Control Systems
Hong-Sen Yan, Guo-Biao Wang
Summary: This article presents a tractable adaptive control scheme for stochastic nonlinear systems with time-varying delays. An adaptive embedded Cubature Kalman Filter is developed to realize robust state estimation. The proposed method utilizes the Multidimensional Taylor Network to evaluate dynamic performance and approximate the optimal policy. The effectiveness of the method is confirmed through numerical simulation.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Engineering, Biomedical
Peiyang Li, Cunbo Li, Joyce Chelangat Bore, Yajing Si, Fali Li, Zehong Cao, Yangsong Zhang, Gang Wang, Zhijun Zhang, Dezhong Yao, Peng Xu
Summary: This study proposes a novel dynamic network estimation method, called L1-ADTF, to address the issue of pseudo connections in brain-computer interfaces. Through comparisons in simulations and real experiments, it is found that L1-ADTF can accurately capture dynamic state transformation patterns and exhibits efficiency in handling complex noises. The significance of this research lies in its practical applications in brain-computer interfaces as well as its potential in solving other dynamic system problems.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Engineering, Mechanical
Dinghua Li, Jun Zhou, Yingying Liu
Summary: This paper proposes a method to estimate and compensate the random drift of MEMS gyroscopes in real time, combining UKF and RNN, and the effectiveness and superiorities of the proposed method are verified by experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Chemistry, Analytical
Yuxi Li, Gang Hao
Summary: This paper proposes an improved modified model predictive control algorithm by combining the Sage-Husa adaptive Kalman filter (SHAKF), the cubature Kalman filter (CKF), and the back-propagation neural network (BPNN) to mitigate the negative impacts of system noise on energy-optimal adaptive cruise control (EACC) and achieve further energy reduction.
Article
Energy & Fuels
Jihen Loukil, Ferdaous Masmoudi, Nabil Derbel
Summary: This paper discusses the online identification of battery parameters and state of charge, aiming to establish a link between the accuracy and robustness of different estimation techniques used, while considering the low complexity of implementation for these methods.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Computer Science, Information Systems
Sakshi Verma, Vishal K. K. Singh
Summary: This paper proposes a novel approach to smartphone-based object tracking using IMU multi-sensor fusion with Kalman filter and rotation vector. It addresses challenges such as GPS signal, canyon effect, and orientation errors, and employs geohash filtering to display track paths on maps within the application. The mathematical analysis and comparison with existing algorithms prove the effectiveness and advancement of the proposed scheme.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Mechanical
Yunshi Zhao, Longjiang Shen, Zhongcheng Jiang, Bo Zhang, Guoyun Liu, Yao Shu, Bo Peng
Summary: The friction coefficient at the wheel-rail interface plays a crucial role in the traction, braking, and guidance of railway vehicles. This paper proposes an indirect measurement method using an unscented Kalman filter and develops a re-adhesion controller. The method is assessed in a Simpack-Simulink co-simulation environment and proves to accurately estimate the friction coefficient and improve vehicle braking performance while reducing wheel-rail damage.
VEHICLE SYSTEM DYNAMICS
(2023)
Article
Automation & Control Systems
Yingqi Zhang, Peng Shi, Michael Basin
Summary: This article presents the analysis and design problems of event-based finite-time H-infinity filtering for discrete-time singular Markov jump network systems based on separation of matrix inequality variables. Sufficient conditions of singular stochastic FT boundedness are obtained for the augmented SMNS model by introducing slack matrix variables. The co-designed EBFT H-infinity filter gain matrices and triggered ones ensure that the augmented SMNSs are singularly stochastic FT bounded with a prescribed performance index.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Alejandro L. Anderson, Pablo Abuin, Antonio Ferramosca, Esteban A. Hernandez-Vargas, Alejandro H. Gonzalez
Summary: The main contribution of this article is to provide the key concept of cyclic control equilibria and explain why typical permanence regions are not effective control targets for switched systems under waiting-time constraints. The theoretical results and proposed algorithm are tested through illustrative examples and simulation results.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Zongyu Zuo, Jiawei Song, Bailing Tian, Michael Basin
Summary: This article addresses the robust fixed-time stabilization control problem for generic linear systems with both matched and mismatched disturbances. A new observer-based fixed-time control technique is proposed to solve this robust stabilization problem, provided that the system matrix pair (A, B) is controllable. The ultimate boundedness of the closed-loop system in the presence of mismatched disturbances is proven. An upper bound of the convergence time is provided, which is irrelevant to initial conditions. Finally, a simulation example is presented to show the efficiency of the proposed control design method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Guangliang Liu, Michael Basin, Hongjing Liang, Qi Zhou
Summary: This article addresses the problem of bipartite tracking control in distributed nonlinear multiagent systems with input quantization, external disturbances, and actuator faults. The use of radial basis function neural networks is proposed to model unknown nonlinearities. A compensation term is introduced in the intermediate control law to eliminate the effects of disturbances and faults, and a novel smooth function is incorporated to reduce the impact of quantization on the virtual controller. The proposed distributed controller not only achieves bipartite tracking control but also ensures bounded signals in the closed-loop systems and convergence of follower outputs to the leader output.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Josephine N. A. Tetteh, Sorin Olaru, Hans Crauel, Esteban A. A. Hernandez-Vargas
Summary: Drug resistant pathogens pose a global public health threat and their control is a challenging task. A new health paradigm called sequential use of drugs has been proposed, where resistance to one drug leads to sensitivity to another drug, known as collateral sensitivity. Tailoring the order and time of drug cycling to the pathogen population in the host is crucial. Through abstracting mutation networks of collateral sensitivity based on switched systems, this study explores the control theoretical aspects and implications of collateral sensitivity on the eradication of drug-resistant pathogens. Numerical simulations demonstrate the potential of this approach to mitigate drug resistance or even eradicate pathogenic populations.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Nain de la Cruz, Michael Basin
Summary: This paper proposes a novel continuous predefined-time convergent control algorithm for higher-order systems. The algorithm can handle incompletely and completely measured states, as well as deterministic disturbances and stochastic noises. The efficiency of the algorithm is demonstrated through numerical simulations on a 4D permanent-magnet synchronous motor system. This is the first attempt to design a predefined-time convergent continuous control law for higher-order systems under such conditions.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Automation & Control Systems
Alison Garza-Alonso, Michael Basin, Pablo Rodriguez-Ramirez
Summary: This paper discusses the drive-response synchronization problem of competitive neural networks within a predefined time interval. The study focuses on the response system under deterministic disturbances satisfying Lipschitz conditions and both stochastic white noises and deterministic disturbances satisfying Lipschitz conditions. By designing a linear time-varying continuous control input, the effect of deterministic disturbances and stochastic noises is suppressed and the synchronization errors are driven to the origin within a predefined time, independent of initial conditions, deterministic disturbances, and stochastic noises. Numerical simulations are conducted to demonstrate the validity of the obtained theoretical results, which show that the proposed synchronization technique outperforms other predefined-time convergent synchronization algorithms.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2023)
Article
Biology
Rodolfo Blanco-Rodriguez, Fernanda Ordonez-Jimenez, Alexis Erich S. Almocera, Gustavo Chinney-Herrera, Esteban Hernandez-Vargas
Summary: The COVID-19 pandemic poses a significant public health threat, and questions remain regarding the role of the immune system in determining the severity of the disease. Using antibody kinetic data, topological data analysis (TDA) reveals that severity levels are not binary. Differences in the shape of antibody responses allow for classification of COVID-19 patients into non-severe, severe, and intermediate cases. Mathematical models were developed based on the TDA results to represent the dynamics between severity groups, with the best model identified using the Akaike Information Criterion. These findings suggest that different immune mechanisms contribute to the variations in severity, highlighting the importance of considering various components of the immune system in combating COVID-19.
MATHEMATICAL BIOSCIENCES
(2023)
Article
Automation & Control Systems
Nain de la Cruz, Michael Basin
Summary: This paper presents a predefined-time convergent robust controller design for a brushed DC motor system affected by matched and unmatched deterministic disturbances and stochastic noises. It considers both fully measurable and incompletely measurable states. The control algorithm allows the control designer to set the convergence time independently of initial conditions and disturbances. Numerical simulations demonstrate the efficiency of the designed control algorithm in countering disturbances and mitigating their influence.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2023)
Article
Automation & Control Systems
Jun Cheng, Jiangming Xu, Ju H. H. Park, Michael V. V. Basin
Summary: This article focuses on load frequency control for interconnected multiarea power systems (IMAPSs) with nonhomogeneous sojourn probabilities (NSPs) and cyber-attacks. A generalized framework of NSPs is formulated to describe the dynamic behavior of IMAPSs. To govern variations of sojourn probabilities, a deterministic switching signal is introduced using the average dwell-time technique. An improved event-triggered protocol relevant to the dynamic quantizer parameter is presented to increase triggering intervals. Both denial-of-service and deception attacks, following Bernoulli distributions, are considered during information transmission. The mean-square exponential stability of the considered systems is established using the Lyapunov theory. The effectiveness of the obtained results is verified through a numerical example.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Yao Zou, Ziyang Meng, Michael V. Basin
Summary: This article investigates a noncooperative game involving multiple clusters of double-integrator agents subject to set constraints. A hierarchical structure is used to seek the generalized Nash equilibrium (GNE) by decomposing the cost function of each cluster and allocating them to respective agents. Two distributed seeking strategies are developed based on intracluster and inter-cluster interactions, with or without messenger information. The effectiveness of the strategies is validated through an application.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Mara Perez, Alejandro Anderson, Pablo Abuin, Esteban A. Hernandez-Vargas, Alejandro H. Gonzalez, Marcelo Actis
Summary: This letter investigates how waiting-time constraints can be incorporated into optimization-based control formulations for switched systems. By introducing hard constraints, the stability analysis is affected and concepts such as equilibrium and invariance sets are modified. The paper explores general regions in the state space where switched system trajectories under waiting-time constraints can remain indefinitely, introducing the concept of permanence as a replacement for invariance. The study provides explicit algorithms for computing these regions and applies them to the glucose regulation problem for Type 1 Diabetes Mellitus patients as an example to illustrate its main properties.
IEEE CONTROL SYSTEMS LETTERS
(2023)
Article
Automation & Control Systems
Yukang Cui, Lingbo Cao, Xin Gong, Michael V. Basin, Jun Shen, Tingwen Huang
Summary: This article explores the distributed resilient output containment control of heterogeneous multiagent systems against composite attacks. Inspired by digital twin technology, the problem is decoupled into defense protocols against DoS attacks on a twin layer (TL) and defense protocols against actuation attacks on the cyber-physical layer (CPL). The proposed control schemes achieve uniformly ultimately bounded (UUB) convergence and effectively resist unbounded actuation attacks.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Yipeng Yang, Xinghu Yu, Zhan Li, Michael V. Basin
Summary: This article proposes a new multirotor flight platform called Quad3DV, which uses a biaxial-tilt actuation unit (BTAU) and has the capability of antidisturbance 6-degree-of-freedom (6-DoF) trajectory tracking. Quad3DV has a simpler structure compared to other multirotors with BTAUs and can hover at 90 degrees pitch with higher flight efficiency.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Chaohai Yu, Jie Ma, Huihui Pan, Michael V. Basin
Summary: An adaptive iterative learning constrained control method for a linear-motor-driven gantry stage is proposed in this article. It successfully addresses the practical state and input constraints of the gantry stage and avoids the difficulties of accurate modeling. Through iterative learning and backstepping collaborative design, the proposed method achieves system stability without prior knowledge of the system dynamic model and parameters. Experimental results on a linear-motor-driven gantry stage demonstrate the efficacy of the method.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.
