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SF_(6)decomposition and insulation condition monitoring of GIE:A review

High Voltage 2021年第6期6卷 955-966页

作者： Fuping Zeng Haotian Li Hongtu Cheng Ju Tang Yilu Liu School of Electrical Engineering and Automation Wuhan UniversityWuhanChina Department of Electrical Engineering and Computer Science USA Oak Ridge National Laboratory KnoxvilleTennesseeUSA

The statistics of the Conference International des Grands Reseaux Electriques(CIGRE)indicate that the operational reliability of SF6 gas‐insulated equipment(GIE)is very high;however,the failure rate of the GIE in operation is much higher than that of the IEC standard,and the fault occurs frequently in the GIE at a high voltage *** reason is due to the complex and strong on‐site electromagnetic interference environment and fully enclosed structure of *** key method and technology for effective on‐line monitoring and fault diagnosis of GIE are still *** the partial strong electromagnetic energy and high temperature induced by early latent insulation faults in the equipment,SF6 gas insulation presents different degrees of *** decomposition products mainly include SO_(2)F_(2),SOF_(2),SO_(2),HF,and *** decomposition characteristics of SF6 are closely related to the property of insulation *** present,this area is attracting attention from the power industry and research *** study summarises the current research on SF_(6)decomposition component analysis(DCA).The content mainly includes the latest progress of SF_(6)decomposition characteristics and mechanism under fault conditions,and fault diagnosis methods based on decomposition components.

关键词： insulation fault diagnosis

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Physics-informed neural networks for modeling water flows in a river channel

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1001-1015页

作者： Nazari, Luis Fernando Camponogara, Eduardo Seman, Laio Oriel Department of Mathematics Federal Institute of Education Science and Technology Catarinense Rio do Sul89160-202 Brazil Department of Automation and Systems Engineering Federal University of Santa Catarina Florianopolis88040-900 Brazil Program in Applied Computer Science Universidade Do Vale Do Itajai Itajai88302-901 Brazil

The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained fromconceptual or data-drivenmethods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-Time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. Impact Statement-The research seeks to contribute to the hydrological modeling area with a surrogate model based on physicsinformed neural networks (PINNs) to water flow in a watershed. In practice, thesemodels use conceptual or *** models to reach the precision provided by themethodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system *** introduce PINNs as a viable solution for

关键词： Floods

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Bayes-Newton methods for approximate Bayesian inference with PSD guarantees

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The Journal of Machine Learning Research 2023年第1期24卷 3712-3761页

作者： William J. Wilkinson Simo Särkkä Arno Solin Department of Computer Science Aalto University Finland Department of Electrical Engineering and Automation Aalto University Finland

We formulate natural gradient variational inference (VI), expectation propagation (EP), and posterior linearisation (PL) as generalisations of Newton's method for optimising the parameters of a Bayesian posterior distribution. This viewpoint explicitly casts inference algorithms under the framework of numerical optimisation. We show that common approximations to Newton's method from the optimisation literature, namely Gauss-Newton and quasi-Newton methods (e.g., the BFGS algorithm), are still valid under this 'Bayes-Newton' framework. This leads to a suite of novel algorithms which are guaranteed to result in positive semi-definite (PSD) covariance matrices, unlike standard VI and EP. Our unifying viewpoint provides new insights into the connections between various inference schemes. All the presented methods apply to any model with a Gaussian prior and nonconjugate likelihood, which we demonstrate with (sparse) Gaussian processes and state space models.

关键词： approximate Bayesian inference optimisation variational inference expectation propagation Gaussian processes

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Machine learning applied to property prediction of metal additive manufacturing products with textural features extraction

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International Journal of Advanced Manufacturing Technology 2024年第1-2期132卷 83-98页

作者： Chang, Lien-Kai Chen, Ri-Sheng Tsai, Mi-Ching Lee, Rong-Mao Lin, Ching-Chih Huang, Jhih-Cheng Chang, Tsung-Wei Horng, Ming-Huwi Electrical Motor Technology Research Center National Cheng Kung University Tainan700 Taiwan Department of Mechanical Engineering National Cheng Kung University Tainan700 Taiwan Department of Intelligent Automation Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan700 Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan700 Taiwan

Laser powder bed fusion (LPBF) is one of the common metal additive manufacturing technologies, which has been increasingly applied across various industries, including healthcare, manufacturing, and aerospace, owing to its advantages in customization and faster prototyping. However, acquiring accurate product properties necessitates repetitive and time-consuming measurements, which risk damaging the product. Thus, there is a pressing need to develop an automated method for predicting product properties. In this study, to forecast these properties, we documented details related to metal additive manufacturing products, encompassing both the process parameters and textural features. These features were extracted from layer-by-layer images using the gray-level co-occurrence matrix (GLCM). Subsequently, we employed machine learning (ML) models, such as support vector regression (SVR), XGBoost, and LightGBM, to predict product properties and compare their performance. The experimental results reveal stronger correlations between process parameters and texture features of three-dimensional co-occurrence matrices of the product images, compared to two-dimensional ones. Additionally, the models exhibit high predictive accuracy, especially XGBoost, and LightGBM, with R2 scores approaching 0.9 for all properties. These findings highlight the superiority and feasibility of the proposed approach. Moreover, this proposed approach holds promise in accurately predicting diverse product properties, meeting the demands of multiple application contexts. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： 3D printing

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Enhancing Sentiment Analysis on SEED-IV Dataset with Vision Transformers: A Comparative Study 11

Enhancing Sentiment Analysis on SEED-IV Dataset with Vision ...

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11th International Conference on Information Technology: IoT and Smart City, ICIT 2023

作者： Tibermacine, Imad Eddine Tibermacine, Ahmed Guettala, Walid Napoli, Christian Russo, Samuele Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy Department of Computer Science University of Biskra Biskra Algeria Institute for Systems Analysis and Computer Science Italian National Research Council Rome Italy Department of Psychology Sapienza University of Rome Rome Italy

ISBN: (纸本)9798400709043

This paper introduces a new approach to emotion classification utilising deep learning models, specifically the Vision Transformer (ViT) model, in the analysis of electroencephalogram (EEG) signals. A dual-feature extraction approach was implemented in our study, utilising Power Spectral Density and Differential Entropy, to analyse the SEED IV dataset. This methodology resulted in the detailed classification of four distinct emotional states. The ViT model, which was originally designed for image processing, has been successfully applied to EEG signal analysis. It demonstrated remarkable performance by attaining a test accuracy of 99.02% with little variance. Notably, it outperformed conventional models like GRUs, LSTMs, and CNNs in this context. The findings of our study indicate that the ViT model has a high level of effectiveness in accurately identifying complex patterns present in EEG data. Specifically, the precision and recall rates achieved by the model surpass 98%, while the F1 score is estimated to be about 98.9%. The results of this study not only demonstrate the efficacy of transformer-based models in analysing cognitive states, but also indicate their considerable potential in improving systems for sympathetic human-computer interaction. © 2023 Owner/Author.

关键词： Electroencephalography

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Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and FastAI-2

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PeerJ computer Science 2025年 11卷 1-22页

作者： Saxena, Pranshu Singh, Sanjay Kumar Rashid, Mamoon Alshamrani, Sultan S. Alnfiai, Mrim M. School of Computer Science Engineering and Technology Bennett University Greater Noida India University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India School of Information Communication and Technology Bahrain Polytechnic Isa Town Bahrain Department of Information Technology College of Computers and Information Technology Taif University Taif Saudi Arabia

Background: Lung cancer has the highest global fatality rate, with diagnosis primarily relying on histological tissue sample analysis. Accurate classification is critical for treatment planning and patient outcomes. Methods: This study develops a computer-assisted diagnosis system for non-small cell lung cancer histology classification, utilizing the FastAI-2 framework with a modified ResNet-34 architecture. The methodology includes stain normalization using LAB colour space for colour consistency, followed by deep learning-based classification. The proposed model is trained on the LC25000 dataset and compared with VGG11 and SqueezeNet1_1, demonstrating modified ResNet-34’s optimal balance between depth and performance. FastAI-2 enhances computational efficiency, enabling rapid convergence with minimal training time. Results: The proposed system achieved 99.78% accuracy, confirming the effectiveness of automated lung cancer histopathology classification. This study highlights the potential of artificial intelligence (AI)-driven diagnostic tools to assist pathologists by improving accuracy, reducing workload, and enhancing decision-making in clinical settings. Copyright 2025 Saxena et al.

关键词： Deep learning

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A Hierarchical Optimization Method of Industrial Rectifier Transformer Loss Considering Winding Impedance Matching 26

A Hierarchical Optimization Method of Industrial Rectifier T...

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26th International Conference on Electrical Machines and Systems, ICEMS 2023

作者： Yang, Yi Liu, Fang Liu, Qianyi Li, Yong Qureshi, Hassaan Khalig Central South University School of Automation Changsha China Hunan University College of Electrical and Information Engineering Changsha China National University of Science and Technology School of Electrical Engineering and Computer Science H-12 Islamabad Pakistan

ISBN: (纸本)9798350317589

Rectifier transformer operating in high current environment suffers from poor power quality and high loss. In order to reduce transformer loss and improve optimization accuracy, this article proposes a hierarchical optimization method considering winding impedance matching for inductive filtering transformer structure design. Firstly, based on the winding arrangement of the inductive filtering transformer, the impedance matching condition and its optimization feasibility are analyzed. Then, combined with the Sobol algorithm, the sensitivity analysis on transformer design parameters is carried out, and the hierarchical optimization model is established. Experimental results show that the hierarchical optimization model successfully provides a global feasible solution by minimizing total loss while effectively improves the accuracy of optimization. © 2023 IEEE.

关键词： Sensitivity analysis

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Ultimately Bounded State Estimation for Discrete-Time Singularly Perturbed Complex Networks Under Bit Rate Constraints 28

Ultimately Bounded State Estimation for Discrete-Time Singul...

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28th International Conference on automation and Computing, ICAC 2023

作者： Guo, Yuru Wang, Zidong Wang, Yezheng Xu, Yong School of Automation Guangdong University of Technology Guangzhou510006 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350335859

In this paper, the ultimately bounded state estimation problem is investigated for discrete-time singularly perturbed complex networks (SPCNs) under bit rate constraints. The measurement outputs of the considered SPCNs are transmitted to the remote estimator through a bandwidth-limited wireless network where a coding-decoding mechanism is employed. A state estimator is developed by fully considering the effects of the constrained bit rate to achieve the desired estimation performance index. The boundedness of the estimation error dynamics is analyzed with the help of the constructed Lyapunov-like functional candidate. Finally, a simulation example is used to verify the correctness of the obtained theoretical results. © 2023 IEEE.

关键词： Complex networks

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A Tensor-based t-SVD-LSTM Remaining Useful Life Prediction Model for Industrial Intelligence

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IEEE Transactions on Industrial Informatics 2022年 1-12页

作者： Wang, Xiaokang Yang, Laurence T. Cao, Enyuan Guo, Luyang Ren, Lei Deen, M. Jamal School of Computer Science and Technology Hainan University Haikou China School of Artificial Intelligence University of the Chinese Academy of Sciences Beijing China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Electrical and Computer Engineering McMaster University Hamilton Canada

The Industrial Internet of Things (IIoT) integrates smart sensors and actuators for the widespread digitization and enhancement of industrial and manufacturing processes. Smart equipment is used to improve the industrial intelligence and make industrial production more flexible, safer and more efficient. For complex equipment, product life-cycle management (PLM) including remaining useful life (RUL) is one of the essential issues for industrial intelligence. In this paper, a tensor-based remaining useful life prediction model is proposed to facilitate the life-cycle management, which combines features from time domain and frequency domain. For the characteristics of continuous generation of industrial data streaming, tensor singular value decomposition (t-SVD) is combined with long shortterm memory network (LSTM) method to predict the RUL of devices from high-order and high-noise time series data. Finally, experiments are carried out on three different data sets including the battery charge and discharge data set, the bearing acceleration life cycle data set, and the turbofan data set to measure the performance of the proposed model. IEEE

关键词： Time domain analysis

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Recent Developments in Event-Triggered Control of Nonlinear Systems:An Overview

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无人系统（英文） 2023年第1期11卷 27-56页

作者： Pengpeng Zhang Tengfei Liu Jie Chen Zhong-Ping Jiang Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

Event-triggered control is a most popular paradigm for transferring feedback information in an economical"as needed"*** study of event-triggered control can be traced back to the *** significant advances on the topic of control over networks and the topic of nonlinear control systems over the last two decades,event-triggered control has quickly emerged as a major theoretical subject in control *** of event-triggered control are wide-spread ranging from embedded control systems and industrial control processes to unmanned systems and cyber-physical transportation *** this paper,we first review developments in the synthesis of event-triggered sampling *** event triggering mechanisms,such as static event trigger,dynamic event trigger,time-regularized event trigger,and event trigger with positive threshold offsets,are systematically ***,we study how to design a stabilizing controller that is robust with respect to the sampling ***,we review some recent results in the directions of self-triggered control,event-triggered tracking control and cooperative control,and event-triggered control of stochastic systems and partial differential equation *** applications of event-triggered control are also discussed.

关键词： Event-triggered control event-triggered sampling mechanism event-triggered controller

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