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Deep learning for parameter identification of nonlinear dynamical system driven by fractional Brownian motion

Nonlinear Dynamics 2025年 1-20页

作者： Hou, Wentao Ma, Shaojuan School of Mathematics and Information Science North Minzu University Yinchuan China Department of Mathematics and Information Technology Yuncheng University Yuncheng China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan China

Accurate parameters identification in nonlinear dynamical systems is crucial for understanding complex phenomena. Fractional Brownian motion is widely used in modeling nonlinear systems due to its long memory, but it also complicates parameter identification. Traditional methods typically rely on linear models or assume Gaussian white noise, which makes them less effective for handling such complex systems. Although data-driven methods have been proposed, most of them utilize simple network architectures and focus primarily on identifying the Hurst exponent or parameters of linear systems. This paper presents a deep learning-based parameter identifier capable of jointly identifying all parameters of a nonlinear dynamical system driven by fractional Brownian motion. Firstly, a new parameter identifier (Transformer-Bidirectional Long Short-Term Memory Neural Network) is constructed by combining the advantages of Transformer in dealing with long-term dependence and Bidirectional Long Short-Term Memory in extracting local features. We then incorporate fractional Brownian motion as the random effect in the nonlinear dynamical system and provide three examples to simulate the system. Finally, the proposed identifier is used to identify parameters in the three systems individually, and its performance is compared to that of the Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention Neural Network and the Parameter Estimation Neural Network to demonstrate its effectiveness. Taking Example 3 and using the mean absolute error to evaluate identification accuracy. The mean absolute error values for the Transformer-Bidirectional Long Short-Term Memory Neural Network are 0.0171, 0.0699, 0.0328 and 0.0851, which are significantly better than the Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention Neural Network, with values of 0.0277, 0.1054, 0.0494 and 0.1037, and the Parameter Estimation Neural Network, with values of 0.0419, 0.1265,

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A Study on the Performances and Parameter-Dependence of Water-Alternating-Gas Flooding for Conglomerate Reservoirs

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Fluid Dynamics & Materials processing 2025年第2期21卷 293-308页

作者： Haishui Han Jian Tan Junshi Li Changhong Zhao Ruoyu Liu Qun Zhang Zemin Ji Hao Kang CNPC Advisory Center CNPCBeijing100724China Fengcheng Oilfield Operation Area of PetroChina Xinjiang Oilfield CompanyPetroChinaKaramay834000China State Key Laboratory of Enhanced Oil Recovery PetroChinaBeijing100083China Research Institute of Petroleum Exploration and Development PetroChinaBeijing100083China Polytechnic Institute Hebei Normal UniversityShijiazhuang050000China Hebei Provincial Key Laboratory of Information Fusion and Intelligent Control Shijiazhuang050000China

To address the water sensitivity of conglomerate reservoirs,a series of core sensitivity tests were conducted to evaluate the effects of varying ionic *** findings serve as a foundation for improving reservoir fluid dynamics and optimizing the concentration of anti-swelling agents in water flooding *** experiments revealed a marked disparity in response between cores with differing *** Core No.5,characterized by low permeability,a 0.5% anti-swelling agent achieved only a modest 7.47% reduction in water ***,in the higher-permeability Core No.8,a 5%anti-swelling agent significantly reduced the water sensitivity index by 44.84% while enhancing ***,two displacement strategies-gas flooding following water flooding and water flooding after gas injection-were tested to assess the potential of CO_(2)water-alternating-gas(WAG)***_(2)injection after water flooding in Core No.5 increased oil recovery by 9.24%,though gas channeling,evidenced by a sharp rise in the gas-liquid ratio,emerged as a critical *** Core No.8,water flooding following gas injection failed to improve recovery,likely due to pronounced water sensitivity,reduced permeability,and the formation of dominant flow channels under high displacement pressure,which limited sweep efficiency.

关键词： Water sensitivity conglomerate reservoir water flooding WAG CO_(2)drive

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A Tetrahedral Spectral Element Method for Thermo-Mechanical Analysis of Electronic Devices

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Feng, Naixing Zhang, Shuai Huang, Zhixiang Liu, Qi Qiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Hefei China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou310018 China

For the coupled thermo-mechanical simulation of complex electronic devices, we propose a Tetrahedral Spectral Element Method (TSEM), which truly combines the high accuracy of spectral methods with the strong geometric adaptability of Finite Element Methods (FEMs). The TSEM employs a collapse transformation method and successfully converts the orthogonal Gauss-Lobatto-Legendre (GLL) basis functions from a hexahedron to a tetrahedral reference element. This ensures numerical accuracy and stability for arbitrary high-order interpolation. We also generate non-equidistant GLL spectral interpolation nodes for tetrahedral elements through Warp&Blend transformation, enabling direct nodal numerical integration calculation through Vandermonde matrices. This approach replaces the classical Gaussian quadrature in the FEM, not only significantly enhancing the computational efficiency of element matrix but also avoiding potential numerical dissipation and dispersion associated with Gaussian integration, thus achieving rapid convergence. In the TSEM thermo-mechanical coupling, we further construct a separate solver based on the implicit Newmark-β method, which realizes unconditional stability for temporal integration for arbitrary time step increment. Moreover, it supports independent inversion of system matrices for temperature and stress fields, without increasing computational resource consumption due to the coupling of multi-physics equations. By quantitatively comparing with the COMSOL Multiphysics software, several typical case studies are used to demonstrate the significant advantages of the TSEM in numerical accuracy, efficiency, and stability. © 2025 IEEE. All rights reserved.

关键词： Orthogonal functions

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Thermal constraint control of reversible solid oxide cell for optimal system efficiency

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International Journal of Hydrogen Energy 2025年 134卷 198-211页

作者： Xu, Yuanwu Yin, Jie Wang, Jingjing Wu, Xiao-long Peng, Jingxuan Tang, Ruifeng Zou, Suhang Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Wuhan University of Science and Technology Hubei Wuhan430081 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China School of Information Engineering Nanchang University Jiangxi Nanchang330031 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore China-Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

Reversible solid oxide cell (RSOC) demonstrates considerable promise in multimodal energy conversion architectures, particularly as a technology for hydrogen-based energy storage systems. Nevertheless, under dynamic operating regimes with time-varying current inputs, realizing time-efficient and reliable transitions to steady-state operation remains difficult. This difficulty arises from the intricate coupling of thermal, electrical, and gaseous processes within the RSOC system. This study aims to address these challenges by developing a 5 kW mathematical model of the RSOC system. The proposed model enables systematic investigation of thermal safety issues arising during current switching operations while introducing a thermal constraint control strategy to enhance both system efficiency and thermal safety performance. This efficiency-driven study employs the Grid Search algorithm to identify optimal steady-state operating points for the RSOC system, ensuring strict adherence to safety constraints across varying current inputs. Furthermore, by integrating the pre-identified optimal operating points as feedforward control with Model Predictive control (MPC)-based feedback correction, this hybrid strategy effectively mitigates stack temperature gradient overshoot while synchronously ensuring optimal system efficiency throughout current transition processes. Under simulated variable current inputs, the proposed RSOC system model and control strategy demonstrate the capability to constrain stack temperature gradient overshoot within 2.3K while enhancing transient efficiency by 5 %–8 %, achieving a peak system efficiency of 85 %. It provides a novel methodology for safe and efficient working with intermittent renewable energy. © 2025 Hydrogen Energy Publications LLC

关键词： Thermal variables control

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A Nonconformal SETI-DP Method for Transient Thermal Simulation

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Feng, Naixing Li, Hongyang Huang, Zhixiang Liu, Qi Qiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education The Information Materials and Intelligent Sensing Laboratory of Anhui Province The Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Hefei China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou310018 China

This paper proposes a novel nonconformal meshbased dual-primal spectral element tearing and interconnecting (SETI-DP) method designed for the efficient transient simulation of complex large-scale heat conduction problems utilizing a domain decomposition method (DDM). This method allows for the division of complex, large-scale problems into smaller subdomain problems. By applying Robin-type transmission conditions at the subdomain interfaces, rapid convergence of the global interface iterative solution is achieved for arbitrary non-conformal subdomain problems. This non-conformal DDM approach supports independent meshing of subdomains, eliminating the need to generate a globally continuous mesh for large-scale problems, thereby significantly enhancing meshing efficiency. In addition, this non-conformal meshing strategy enables high-contrast spatial discretization and mixed-order spectral interpolation between different subdomains. This significantly increases meshing flexibility, minimizes the number of degrees of freedom (DoF) in multi-scale problems, and reduces computational costs. In addition, by developing a subdomain-coupled time iteration scheme, the global corner problem of residual transfer is explicitly given, which greatly improves the versatility and flexibility of the algorithm. Finally, to demonstrate the accuracy, efficiency, and robustness of our algorithm, we conduct benchmark tests on several representative examples. © 2011-2012 IEEE.

关键词： Domain decomposition methods

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Throughput Improvement for RIS-Empowered Wireless Powered Anti-Jamming Communication Networks (WPAJCN)

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IEEE Transactions on information Forensics and Security 2025年 20卷 4622-4637页

作者： Chu, Zheng Chieng, David Kwong, Chiew Foong Jin, Huan Zhu, Zhengyu Huang, Chongwen Yuen, Chau University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China University of Nottingham Ningbo China Next Generation Internet of Everything Laboratory Ningbo315100 China University of Nottingham Ningbo China School of Computer Science Ningbo315100 China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an710071 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore

In this paper, we propose a reconfigurable intelligent surface (RIS)-aided wireless powered anti-jamming communication network (WPAJCN), where the RIS is utilized to participate in downlink wireless power transfer (WPT), as well as uplink anti-jamming wireless information transfer (AJ-WIT). To evaluate the network anti-jamming performance, we maximize a sum anti-jamming throughput, with the constraints of downlink WPT and uplink AJ-WIT time scheduling, and unit-modulus RIS phase shifts. The formulated problem is not convex in terms of these two types of coupled variables, which cannot be directly solved. To address this problem, the Lagrange dual method and Karush-Kuhn-Tucker conditions are presented to transform its sum-of-logarithmic objective function into the logarithmically fractional counterpart, which reformulate the original problem into that with respect to RIS phase shift vectors and WPT time scheduling. Next, we propose to apply the Dinkelback algorithm to solve a non-linear fractional programming with respect to the downlink WPT and uplink AJ-WIT RIS phase shifts in an alternating fashion, each of which is derived into a semi-closed solution by utilizing the Riemannian Manifold Optimization (RMO). In addition, the optimal WPT time scheduling is obtained by numerical search. Finally, the numerical results are demonstrated to confirm the improved performance of the proposed approach compared to the benchmark counterparts, which highlights the that RIS can effectively enhance the uplink anti-jamming WIT capability as well as the downlink WPT efficiency. © 2005-2012 IEEE.

关键词： Lagrange multipliers

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical Images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training Image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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Stability Enhancement of Inverter-Current Feedback Grid-Following Inverters Using a Passivity-Based Voltage controller

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IEEE Transactions on Energy Conversion 2025年

作者： Huang, Yukai Xu, Jinbang Ye, Jie Huang, Songtao Ren, Ziao Shen, Anwen Hu, Yihua Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China King's College London Department of Engineering LondonWC2R 2LS United Kingdom

Grid-following inverters remain the primary interface for modern renewable energy systems due to their strong power output performance. As the global demand for clean and efficient energy conversion increases, optimizing the stability of inverter systems is crucial for integrating renewable sources into the power grid. In certain energy conversion scenarios, current sensors are placed on the inverter side due to equipment constraints and space limitations, which can impact the stability of the inverter. Although the stability of inverter-side current feedback (ICF) configurations has been studied, methods to further enhance their stability remain underexplored. In this paper, the stability of ICF grid-following inverters is analyzed using a passivity-based design approach, focusing on a point of common coupling voltage controller. The proposed controller is thoroughly analyzed, and a detailed design strategy is presented to ensure stability under varying grid impedance conditions. Experimental results validate the theoretical analysis, demonstrating the effectiveness of the proposed design in enhancing the stability of ICF grid-following inverters. These advancements contribute to improving energy conversion efficiency and reliability. © 2025 IEEE.

关键词： Energy conversion efficiency

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Online Voltage Regulation of Distribution Networks in Energy Sharing Markets: a Feedback-Based Method With Parameter Estimation

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IEEE Transactions on Smart Grid 2025年

作者： Wang, Wenyue Liu, Yiming Wang, Zhaojian Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

With the number of prosumers rising quickly, energy sharing markets in distribution networks are emerging. The trading of active power influences the power flow in the distribution network, which will cause voltage fluctuation when the line resistance is not trivial. In this paper, we investigate the voltage regulation problem of distribution networks with the consideration of electricity markets. We first formulate the problem in the form of a non-cooperative game. The uniqueness of generalized Nash Equilibrium (GNE) is proved. Then, a feedback-based GNE seeking algorithm is proposed with online parameter estimation without knowing accurate values of resistance and reactance. The estimation error is proved to be accurate in expectation. The convergence of the algorithm is also guaranteed theoretically. Finally, numerical results in the IEEE 33-bus system validate the effectiveness of the algorithm. © 2010-2012 IEEE.

关键词： Power distribution networks

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Identify influential nodes in directed networks: A neighborhood entropy-based method

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Chaos, Solitons and Fractals 2025年 197卷

作者： Dong, Ang Feng, Ru Qiu, Lipeng Wu, Yali Ren, Yuanguang Zhou, Aoran Department of Information and Control Engineering Xi'an University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an710016 China

The identification of influential nodes in complex networks is prominent and actively researched topic, with applications across various fields. Despite the emergence of numerous methods, many of these are inadequate for directed networks due to the asymmetry of information flow. To address this challenge, we propose a Neighborhood Entropy-based Method (NEM) for identifying key nodes in directed networks. Inspired by Shannon entropy, the k-orders in-degree entropy and k-orders out-degree entropy of nodes are defined to capture the neighborhood information of the node. A tunable parameter α is introduced to balance the contribution of in-neighbors and out-neighbors on neighbor influence ability, while a decay factor φk is set to adjust the influence of different order neighbors on the node. The importance of a node can be eventually evaluated by the influence capability of its in-neighbors and itself. To assess the effectiveness of NEM, we benchmark it against six existing methods on six real network datasets. The results demonstrate the high correctness of NEM, with its low time complexity enabling efficient application to large-scale directed networks. © 2025 Elsevier Ltd

关键词： k-nearest neighbors

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