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2024 China Automation Congress, CAC 2024

作者： Wang, Hancheng Li, Peng Ai, Mingxi Wu, Jiande Yang, Chuangyan Xu, Qingmin School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350368604

In complex industrial process monitoring, prevalent challenges include high dimensionality, extensive redundancies, and dynamic characteristics. In this paper, we propose an Improved Dynamic Latent Variable-Neighborhood Preserving Embedding (DLV-NPE) algorithm specifically designed to tackle these issues. The methodology begins with the implementation of a variance-covariance feature selection method to minimize redundancies among monitoring variables. This is followed using a dynamic latent variable algorithm to extract dynamic regression properties from the selected variables. Subsequently, the NPE algorithm is employed to preserve the residuals of high-dimensional dynamic latent variables and the high-dimensional residuals of selected variables, thereby preserving the integrity of the low-dimensional manifold structure. Finally, Bayesian inference is applied to develop comprehensive monitoring indicators. The efficacy of the DLV-NPE algorithm is validated through its application to the Tennessee Eastman (TE) process, where it demonstrates superior fault detection capabilities compared to other algorithms. © 2024 IEEE.

关键词： Embeddings

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Graph Neural Network with Heterogeneous Attributes for Node Classification 4

Graph Neural Network with Heterogeneous Attributes for Node ...

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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Jiang, Bohou Wang, Jiahui Yue, Kun Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming China

ISBN: (纸本)9798350394375

Node classification on heterogeneous graphs with multiple node types and relationships has been widely used in commodity classification, friend recommendation, and so on. Most of the existing models need to define the meta paths manually with incomplete attribute information, leading to the low accuracy of classification. To improve the accuracy of node classification task, we propose a novel graph neural network with heterogeneous attributes (GNNHA) by introducing the heterogeneous attributes of nodes and edges without defining meta paths. First, we design a spatial mapping for heterogeneous edges by mapping and fusing the source and target nodes to a hidden layer space. Then, we make a unique linear projection for each node to obtain the embeddings containing the attributes of nodes and edges by aggregating the information of neighbor nodes. Finally, we provide an improved loss function by leveraging the information loss of mapping and node class imbalance. Our model can capture more complete heterogeneous graph attributes and obtain more objective and accurate classification results. Experimental results show that our proposed model outperforms state-of-the-art ones. © 2024 IEEE.

关键词： Classification (of information)

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A Domain Decomposition Method for Nonconforming Finite Element Approximations of Eigenvalue Problems

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Communications on Applied Mathematics and Computation 2025年第2期7卷 606-636页

作者： Qigang Liang Wei Wang Xuejun Xu School of Mathematical Science Tongji UniversityShanghai200092China Key Laboratory of Intelligent Computing and Applications(Tongji University) Ministry of EducationShanghai200092China LSEC Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China

Since the nonconforming finite elements(NFEs)play a significant role in approximating PDE eigenvalues from below,this paper develops a new and parallel two-level preconditioned Jacobi-Davidson(PJD)method for solving the large scale discrete eigenvalue problems resulting from NFE discretization of 2mth(m=1.2)order elliptic eigenvalue *** a spectral projection on the coarse space and an overlapping domain decomposition(DD),a parallel preconditioned system can be solved in each iteration.A rigorous analysis reveals that the convergence rate of our two-level PJD method is optimal and *** results supporting our theory are given.

关键词： PDE eigenvalue problems Nonconforming finite elements(NFEs) Preconditioned Jacobi-Davidson(PJD)method Overlapping domain decomposition(DD)

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Of Rodents and Primates: Time-Variant Gain in Drift–Diffusion Decision Models

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Computational Brain and Behavior 2024年第2期7卷 195-206页

作者： Asadpour, Abdoreza Tan, Hui Lenfesty, Brendan Wong-Lin, KongFatt Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Magee Campus Northern Ireland Derry~Londonderry United Kingdom Département Electronique et Technologies Numériques Polytech Nantes Nantes Université Nantes France

Sequential sampling models of decision-making involve evidence accumulation over time and have been successful in capturing choice behaviour. A popular model is the drift–diffusion model (DDM). To capture the finer aspects of choice reaction times (RTs), time-variant gain features representing urgency signals have been implemented in DDM that can exhibit slower error RTs than correct RTs. However, time-variant gain is often implemented on both DDM’s signal and noise features, with the assumption that increasing gain on the drift rate (due to urgency) is similar to DDM with collapsing decision bounds. Hence, it is unclear whether gain effects on just the signal or noise feature can lead to a different choice behaviour. This work presents an alternative DDM variant, focusing on the implications of time-variant gain mechanisms, constrained by model parsimony. Specifically, using computational modelling of choice behaviour of rats, monkeys, and humans, we systematically showed that time-variant gain only on the DDM’s noise was sufficient to produce slower error RTs, as in monkeys, while time-variant gain only on drift rate leads to faster error RTs, as in rodents. We also found minimal effects of time-variant gain in humans. By highlighting these patterns, this study underscores the utility of group-level modelling in capturing general trends and effects consistent across species. Thus, time-variant gain on DDM’s different components can lead to different choice behaviours, shed light on the underlying time-variant gain mechanisms for different species, and can be used for systematic data fitting. © 2024, Crown.

关键词： Cognitive computational modelling Drift–diffusion model Perceptual decision-making Time-variant gain Urgency signal

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EEG Signal-Based Autism Spectrum Disorder Detection Through Normalized Mutual Information and Convolutional Neural Network 2nd

EEG Signal-Based Autism Spectrum Disorder Detection Through ...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Peya, Zahrul Jannat Maria, Mahfuza Akter Akhand, M.A.H. Siddique, Nazmul Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna9203 Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland Londonderry United Kingdom

ISBN: (纸本)9789819989362

Autism Spectrum Disorder (ASD) is a diverse neurological problem with several contributing factors involving both genetic and environmental variables. The diagnosis of ASD based on neural activity analysis of various signals from the brain, especially electroencephalography (EEG), has drawn attention. A technique for capturing an electrogram of the brain's spontaneous electrical activity is called EEG which is simple to use and non-intrusive. The aim of this study is to classify people with ASD and controls using the most efficient band by examining different sub-bands (such as alpha, beta, and gamma) of the EEG data. Normalized Mutual Information (NMI) is used to create Connectivity Feature Maps (CFMs). Classification is performed through Convolutional Neural Network (CNN). Gamma band is found effective for ASD detection with an accuracy of 96%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electroencephalography

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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An Integrated Approach for Fault Detection in Industrial Processes Using Approximate Entropy and Slow Feature Analysis 43

An Integrated Approach for Fault Detection in Industrial Pro...

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43rd Chinese Control Conference, CCC 2024

作者： Yang, Chuangyan Li, Peng Wu, Jiande Lang, Xun Ai, Mingxi Yunnan University School of Information Science and Engineering Yunnan Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing China

ISBN: (纸本)9789887581581

Multivariate statistical process monitoring has been widely used in industry process. However, traditional algorithms often ignore the complexity dynamic features of actual industry process. This study proposes a novel algorithm based on Approximate Entropy and Slow Feature Analysis (AE-SFA), which has completed the full-condition monitoring of a dynamic complexity system and quantitative divided static components and dynamic components. The algorithm achieves the optimal detection rate under T2 and SPE control limits. To enrich the experiment, we used the Tennessee Eastman process dataset to demonstrate the superiority of our algorithm. From the comprehensive score of the optimal detection rate, the accuracy of the proposed method is improved by 3.24% and 10.50% of T2 and SPE statistics, respectively. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Statistical process control

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An improved variational mode decomposition with adaptive initial center frequency for bearing fault feature extraction

An improved variational mode decomposition with adaptive ini...

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2024 China Automation Congress, CAC 2024

作者： Wang, Ying Lang, Xun Wu, Jiande Li, Yuzhen Li, Peng Zhang, Yufeng Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350368604

Variational mode decomposition (VMD) is extensively utilized in the field of industrial signal processing due to its superior anti-mixing and denoising capabilities. However, in practice, the performance of VMD is significantly influenced by the initial center frequency. An improper setting of can significantly reduce the stability of decomposition, resulting in the loss or masking of feature information by noise. To address the above issue, an improved VMD algorithm is proposed in this work. Our algorithm redefines based on the narrowband characteristics of intrinsic mode function (IMF). Under the new definition, can be adaptively adjusted based on the signal spectrum processed by Wiener filtering. This adjustment significantly enhances the flexibility and anti-mixing performance of the original VMD. To validate our proposed method, comparative experiments were conducted using two sets of fault data from Case Western Reserve University (CWRU). The results demonstrate that our method is more effective and superior in extracting fault features. © 2024 IEEE.

关键词： Variational mode decomposition

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Optimal Tracking Performance of Networked Control systems Under DoS and Its Applications

Optimal Tracking Performance of Networked Control Systems Un...

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2024 China Automation Congress, CAC 2024

作者： Ren, Xinyu Li, Bo Zhao, Feng Chen, Xiangyong Zhang, Xianhe Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9798350368604

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output (MIMO) discrete networked control systems (NCSs) under the influence of Denial of Service (DoS) attack, channel noise, and bandwidth constraints. The channel noise is assumed to be additive Gaussian white noise (AWGN). Due to the randomness of DoS attack, a Bernoulli distribution is used for DoS modeling. Utilizing frequency domain analysis methods, coprime factorization, all-pass decomposition, and Youla parameterization, an explicit expression for the optimal tracking performance (OTP) of the system under the two-degree-of-freedom (TDOF) controller is derived. The analysis indicates that non-minimum phase (NMP) zeros, unstable poles (UPs), DoS attack, and network constraints affect the OTP of NCSs. Finally, the theoretical results are validated through simulations of MIMO NCS and vehicle control system. © 2024 IEEE.

关键词： White noise

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Simplified prescribed performance tracking control of uncertain nonlinear systems

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Science China(Information Sciences) 2022年第8期65卷 291-292页

作者： Quan-Yong FAN Shuoheng XU Bin XU Jianlong QIU School of Automation Northwestern Polytechnical University School of Automation and Electrical Engineering Linyi University Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong Linyi University

Dear editor,In practical engineering, some certain transient and steady performance, such as overshoot, adjustment time, and steady-state error, need to meet specific requirements. In order to ensure that the system s...

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