Article
Automation & Control Systems
Cheung-Chieh Ku, Wen-Jer Chang, Tsung-Chun Lee
Summary: This paper discusses the Event-Triggered (ET) control of a nonlinear system with time-varying parameters. A polynomial Takagi-Sugeno (T-S) fuzzy model is used to represent the nonlinearity. The time-dependent parameter is modeled using a Linear Parameter Variation (LPV) model by combining the weighting function and linear systems. The polynomial T-S fuzzy model is then constructed to interpret the considered system. A fuzzy controller is built using polynomial gains with time-varying parameters for the stabilization problem. Additionally, an ET scheme is applied to save control cost. A criterion is proposed and the Sum of Squares (SOS) algorithm is used to find solutions for the stabilization problem. Finally, simulations are provided to demonstrate the applicability of the proposed approach.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Yi Qin, Rui Yang, Haiyang Shi, Biao He, Yongfang Mao
Summary: This article proposes an adaptive fast chirplet transform (AFCT) method for the fault diagnosis of bearings with time-varying speed. The method optimizes the search band of frequency modulation (FM) parameters using the modulation operator of synchrosqueezed transform, overcoming the low computational efficiency problem of chirplet transform (CT) and its variants. Simulation results show that the proposed method has good time-frequency concentration performance and low computational complexity. It has been successfully applied to estimate rotation frequencies from fault vibration signals and outperforms existing classical TFA methods in ridge extraction accuracy and computational efficiency.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Civil
Lei Tang, Xu-Qiang Shang, Tian-Li Huang, Ning-Bo Wang, Wei-Xin Ren
Summary: In this study, an improved local maximum synchrosqueezing transform (LMSST) with adaptive window width (ALMSST) is proposed to overcome the difficulty in selecting the appropriate window width. ALMSST can provide a highly concentrated time-frequency representation for all mono-component signals. Simulation and experimental results demonstrate that ALMSST performs well in identifying the instantaneous frequencies (IFs) of time-varying structures.
ENGINEERING STRUCTURES
(2023)
Article
Computer Science, Artificial Intelligence
Zuolin Liu, Jian Xu, Hongbin Fang
Summary: This article proposes a local linear neuro-fuzzy networks based approach to identify time-varying systems. The approach utilizes fuzzy neural networks to expand the unknown time-varying parameters and incorporates a local linear model tree optimization algorithm to optimize the network architecture. Experimental results demonstrate that the approach can simultaneously extract parsimonious model structures and identify time-varying parameters without adding additional procedures or increasing computation burden.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Laura Menini, Corrado Possieri, Antonio Tornambe
Summary: The technical communique proposes a locally convergent continuous-time model for the Durand-Kerner method to determine all roots of a time-varying polynomial simultaneously. The effectiveness of this method is demonstrated through its application to a benchmark example.
Article
Automation & Control Systems
Shuai Mao, Ziwei Dong, Wei Du, Yu-Chu Tian, Chen Liang, Yang Tang
Summary: This article focuses on the nonconvex optimization problems in industrial smart manufacturing and proposes a distributed solution based on event-triggered strategy. The convergence of the algorithm is theoretically established under assumptions on local objective functions, gradients, and step sizes, and the convergence rate is determined. The effectiveness of the proposed algorithm is validated through examples of industrial systems.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Environmental Studies
Yongjian Lyu, Heling Yi, Yingyi Hu, Mo Yang
Summary: This study analyzes the impact of economic policy uncertainty shocks on China's commodity futures returns using a time-varying parameter vector autoregressive framework. The research finds negative effects of EPU shocks on commodity futures returns, with varying effects over time and different reactions for different sectors. Additionally, the study reveals that money policy uncertainty shocks have relatively large effects on commodity futures returns compared to other types of policy uncertainty shocks.
Article
Engineering, Civil
Tianli Guo, Songbai Song, Yating Yan
Summary: This study introduces a new time-varying autoregressive (TVAR) model for nonstationary groundwater depth prediction. The focus of the study is on the parameter estimation of the TVAR model and its performance in predicting groundwater depth. The TVAR model is transformed into a time-invariance regression problem and the parameters are estimated using a fading memory recursive least squares algorithm. The results show that the TVAR model exhibits better prediction performance, lower model complexity, and more straightforward application compared to other models.
JOURNAL OF HYDROLOGY
(2022)
Article
Automation & Control Systems
Mikhail Ivanov Krastanov, Margarita Nikolaeva Nikolova
Summary: This paper investigates the local properties of a class of polynomial control systems using a general differential-geometrical approach based on the Campbell-Baker-Hausdorff (C-B-H) formula. The main contribution is a new sufficient condition for small-time local controllability. Two four-dimensional examples are provided to illustrate the effectiveness of the proposed approach.
SYSTEMS & CONTROL LETTERS
(2023)
Article
Automation & Control Systems
Dinh Cong Huong, Hieu Trinh
Summary: In this paper, a robust dynamic event-triggered state observer is proposed to address the problem of event-triggered state estimation for recurrent neural networks with unknown time-varying delays. The new dynamic ETM helps reduce unnecessary transmissions and provides practicality and potential saving in network bandwidth. The proposed method is validated through numerical examples and simulation results.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Physics, Multidisciplinary
Irena Barjasic, Nino Antulov-Fantulin
Summary: The article analyzes the time series of minute price returns on the Bitcoin market using statistical models of the GARCH family. By incorporating external information signals such as Bitcoin-related tweets, trade volume, and bid-ask spread, improvements in volatility prediction are tested. The results indicate that GARCH(1,1) and cGARCH(1,1) models react the best to the addition of external signals.
FRONTIERS IN PHYSICS
(2021)
Article
Economics
Francisco Blasques, Siem Jan Koopman, Marc Nientker
Summary: This paper proposes a time-varying parameter model to describe the formation and burst of bubbles in financial and economic time series. The model can predict the emergence of bubbles and extract the unobserved bubble process from observed data through parameter estimation and an implied filter. The finite-sample properties of the estimator are examined through Monte Carlo simulations, and the model's superiority is demonstrated in a financial application involving the Bitcoin/US dollar exchange rate.
JOURNAL OF ECONOMETRICS
(2022)
Article
Automation & Control Systems
Huaying Li, Na Lin, Ronghu Chi
Summary: In this paper, an event-triggered P-type iterative learning control (ILC) scheme is developed, with control input update only occurring in the event-triggered iterations. The proposed algorithms ensure convergence and stability by using pre-defined event-triggering conditions, as shown in simulation results that the control input update frequency can be reduced to save resources.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Automation & Control Systems
Zhiguang Feng, Yang Yang, Zhengyi Jiang, Yuxin Zhao, Xin Yuan
Summary: This paper proposes a method for admissibility analysis and polynomial fuzzy controller design for singular polynomial fuzzy systems with time-varying delay. By utilizing Lyapunov stability theory and SOS method, a sufficient condition for the closed-loop system is obtained, and a controller is designed, showing better performance compared to existing research.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2021)
Article
Engineering, Mechanical
Zhihong Liang, Sanbo Ding, Lei Zhang, Xiangpeng Xie
Summary: In this paper, the synchronization control problem for a class of discrete-time complex dynamical networks (CDNs) under an event-triggered mechanism (ETM) is studied. A discrete-time distributed periodic ETM is developed based on communication measurement. Under this mechanism, the signal is sampled in a periodic manner, but whether the control signal is updated or not depends on the pre-designed triggering condition. The effectiveness and accuracy of the conclusion are verified through a simulation example.
NONLINEAR DYNAMICS
(2023)
Article
Neurosciences
Linling Li, Xin Di, Huijuan Zhang, Gan Huang, Li Zhang, Zhen Liang, Zhiguo Zhang
Summary: This study found that the patterns of brain interactions induced by nociceptive pain are significantly different from those of other sensory modalities, and pain is more likely to modulate the overall functional connectivity of the brain, providing new insights into the neural mechanisms of pain processing.
HUMAN BRAIN MAPPING
(2022)
Article
Biochemical Research Methods
Xingyi Jin, Zhiguo Zhang, Li Zhang, Linling Li, Gan Huang
Summary: This study used extensive numerical simulations to investigate the dynamics of endogenous alpha oscillations and their modulation by exogenous stimuli. The study found that the dynamics of endogenous alpha oscillations are relatively simple, exhibiting fixed-point attractor or limit-cycle attractor behavior. The study also explained the dynamic mechanism of phase-locked visual feedback for amplitude and frequency modulation of the alpha rhythm, as well as the influence of parameter settings in the modulation.
JOURNAL OF NEUROSCIENCE METHODS
(2022)
Article
Neurosciences
Wanrou Hu, Zhiguo Zhang, Li Zhang, Gan Huang, Linling Li, Zhen Liang
Summary: Electroencephalography (EEG) microstate analysis is a powerful tool to study human brain activity. Current research mainly focuses on rest-state EEG, but there are limitations in microstate analysis for EEG signals recorded during naturalistic tasks. This study used natural and dynamic music videos as stimulation to explore the effects of different topographical clustering strategies for task-state EEG microstate analysis. The results showed that a single-trial-based bottom-up topographical clustering strategy achieves comparable results, suggesting that it can be a good choice for microstate analysis and neural activity study on naturalistic EEG data.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Psychiatry
Diyang Qu, Yanni Wang, Zhiguo Zhang, Linlin Meng, Feng Zhu, Tiansheng Zheng, Kongliang He, Yue Zhou, Chuanxiao Li, He Bu, Yongjie Zhou
Summary: The present study adapted the Functional Assessment of Self-Mutilation (FASM) into Chinese and examined its reliability and validity. The adapted Chinese version of the FASM (C-FASM) showed good content, structural validity, and reliability, making it a helpful tool in assessing non-suicidal self-injury behaviors among Chinese adolescents.
FRONTIERS IN PSYCHIATRY
(2022)
Article
Neurosciences
Wanrou Hu, Zhiguo Zhang, Huilin Zhao, Li Zhang, Linling Li, Gan Huang, Zhen Liang
Summary: This study investigates the relationship between EEG microstates and emotion dynamics under a video-watching task. The results reveal the patterns of microstates related to emotion dynamics and provide insights into the neural representation under emotion dynamics modulation.
Article
Neurosciences
Xin Di, Marie Woelfer, Simone Kuehn, Zhiguo Zhang, Bharat B. Biswal
Summary: The influences of environmental factors such as weather on the human brain are still largely unknown. In this study, machine learning regression was used to predict weather and environmental parameters using resting-state functional MRI (fMRI) data. The results showed that daylight length and air temperatures can be reliably predicted using resting-state parameters, but similar accuracies can also be achieved using EPI or anatomical images.
HUMAN BRAIN MAPPING
(2022)
Article
Clinical Neurology
Linling Li, Xue Han, Erni Ji, Xiangrong Tao, Manjun Shen, Dongjian Zhu, Li Zhang, Lingjiang Li, Haichen Yang, Zhiguo Zhang
Summary: This study analyzed functional MRI data of bipolar disorder (BD) patients and healthy controls during a face-matching task and found widely distributed aberrant task-modulated functional connectivity (FC) patterns in BD. The fronto-parietal network was identified as the primary network demonstrating changes in both FC strength and local efficiency in BD.
JOURNAL OF AFFECTIVE DISORDERS
(2022)
Article
Computer Science, Artificial Intelligence
Li Zhang, Gan Huang, Zhen Liang, Linling Li, Zhiguo Zhang
Summary: This paper proposes a new method (GSTR) to infer scale-free dynamic effective connectivity (dEC) networks from functional magnetic resonance imaging (fMRI). The method utilizes group-wise penalty, spatial sparsity, and temporal smoothness regularizations to accurately infer scale-free dEC patterns.
Article
Neurosciences
Zhenxing Hu, Zhiguo Zhang, Zhen Liang, Li Zhang, Linling Li, Gan Huang
Summary: This study rigorously evaluated the test-retest reliability of ERPs in a multisensory and cognitive experiment involving 82 healthy adolescents. It found that a stronger group-level response in ERPs did not guarantee higher individual reliability. The consistency between group-level ERP responses and individual reliability was influenced by inter-subject latency jitter and inter-trial variability. These findings suggest the need to consider a neural oscillation perspective when assessing the reliability of ERPs.
Article
Neurosciences
Rushi Zou, Linling Li, Li Zhang, Gan Huang, Zhen Liang, Lizu Xiao, Zhiguo Zhang
Summary: Characterization and prediction of individual pain sensitivity are crucial in clinical practice. Previous studies have been limited by using one imaging modality or one type of metrics, failing to fully reveal the pain-related information in MRI and explore the associations among different imaging modalities and features. In this study, using multi-modal MRI and multi-features, we found that fusing fMRI-DTI and regional-connectivity features can more accurately predict an individual's pain sensitivity. These findings provide a comprehensive characterization of individual pain sensitivity from both structural and functional perspectives, and hold great potential for clinical pain management.
FRONTIERS IN MOLECULAR NEUROSCIENCE
(2022)
Article
Engineering, Biomedical
Shaorong Zhang, Zhibin Zhu, Benxin Zhang, Bao Feng, Tianyou Yu, Zhi Li, Zhiguo Zhang, Gan Huang, Zhen Liang
Summary: In this paper, a new ensemble learning algorithm framework is proposed to improve the decoding performance of common spatial pattern (CSP) in motor imagery-based brain-computer interface (BCI) system. The proposed framework comprehensively considers regularization, temporal-spatial-frequency joint optimization, and pair number of spatial filters for CSP. Experimental results on five motor imagery datasets show that the proposed method achieves a better classification effect with low computational cost, low model complexity, and high robustness.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Neurosciences
Jinwen Wei, Ziqing Yao, Gan Huang, Linling Li, Zhen Liang, Li Zhang, Zhiguo Zhang
Summary: This study used analyses of the general linear mixed model and event-related potentials to show that the prestimulus alpha power over the occipital area directly affected visual perception. Low-frequency frontal-occipital phase synchronization predicted the prestimulus alpha power over the occipital area.
COGNITIVE NEURODYNAMICS
(2023)
Article
Neurosciences
Li Zhang, Yiwen Pan, Gan Huang, Zhen Liang, Linling Li, Min Zhang, Zhiguo Zhang
Summary: This study conducted a brain-wide genome-wide association study (GWAS) to explore the genetic basis of pain sensitivity. The results suggest that the right insula and multiple candidate loci may be involved in pain sensitivity modulation, which provides guidance for the development of precision pain therapeutics.
Article
Neurosciences
Linling Li, Yutong Li, Zhaoxun Li, Gan Huang, Zhen Liang, Li Zhang, Feng Wan, Manjun Shen, Xue Han, Zhiguo Zhang
COGNITIVE NEURODYNAMICS
(2023)
Article
Neurosciences
Shuyue Xu, Zhiguo Zhang, Linling Li, Yongjie Zhou, Danyi Lin, Min Zhang, Li Zhang, Gan Huang, Xiqin Liu, Benjamin Becker, Zhen Liang
Summary: Determining and decoding emotional brain processes under ecologically valid conditions is challenging in affective neuroscience. This study used movie clips as ecologically valid emotion-evoking procedures to explore emotion-specific functional connectivity profiles. The results showed that emotion manifests as distributed representation of multiple networks and that the Visual Network and Default Mode Network have a strong contribution to emotion classification. Late stimulation contributes most to the classification, indicating that continuous exposure to emotional stimulation can lead to more intense emotions and enhance emotion-specific representations.
