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An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 298-322页

作者： Shuyan Shi Ding Liu Ruirui Ji Yuchao Han School of Automation and Information Engineering Xi‘an University of TechnologyXi‘an 710048P.R.China National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi‘an 710048P.R.China Xi’an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi‘an 710048P.R.China

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss ***,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,*** they fall into local *** other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the *** addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the ***,these problems have made PINN-based methods of limited use on practical *** this paper,we propose a novel physics-informed neural network,*** adaptive physics-informed neural network with a two-stage training *** algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal *** pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss *** order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical *** Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden *** results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction *** codes developed in this manuscript are publicy available at https://***/callmedrcom/ATPINN.

关键词： Physics informed neural networks partial differential equations two-stage learning scientific computing

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An efficient adaptive space partitioning algorithm for electromagnetic scattering calculation of complex 3D models

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Journal of systems engineering and Electronics 2021年第5期32卷 1071-1082页

作者： HUANG Minjie ZHOU Yaoming WANG Yongchao LIU Zhongtie School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China Key Laboratory of Complex Aviation System Simulation Beijing 100076China

The space partitioning algorithm based on the rounding and addressing operations has been proved to be an efficient space partitioning algorithm with the potential for real-time *** improvement on this kind of space partitioning algorithms for solving complex 3D models is *** examples show that the efficiency of the improved algorithm is better than that of the original *** the size of most target elements is smaller than the size of spatial grids,the efficiency of the improved method can be more than four times of that of the original *** adaptive method of space partitioning based on the improved algorithm is developed by taking the surface element density or the curvature as the threshold for deep partitioning and conducting the deep partitioning using the octree method.A computer program implementation for applying the method in some typical applications is discussed,and the performance in terms of the efficiency,reliability,and resource use is *** testing shows that the results of the adaptive spacing partitioning are more convenient for the follow-up use than that of the basic uniform space ***,when it is used to calculate the electromagnetic scattering of complex targets by the ray tracing(RT)method,the adaptive space partitioning algorithm can reduce the calculation time of the RT process by more than 40%compared with the uniform space segmentation algorithm.

关键词： adaptive space partitioning computer graphics binary space partitioning ray tracing(RT)method stealth technology

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Non-convex Optimization Based On Deep Koopman Operator 38

Non-convex Optimization Based On Deep Koopman Operator

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Guo, Jun Jia, Yubin Yong, Panxiao Zhou, Jun Liu, Zhimin Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350303636

Non-convex optimization problems are prevalent in various fields, and finding the global optimal solution for such problems remains challenging. This paper proposes an optimization method based on deep Koopman operator, which leverages the power of deep neural networks to provide a data-driven architecture for the Koopman function. The Koopman operator is used to linearly represent non-convex problems, and the proposed method utilizes model predictive control to optimize the resulting linear systems. Simulation results show that this method can solve the non-convex optimization problem effectively, quickly and accurately, while obtaining the global optimal solution. © 2023 IEEE.

关键词： Koopman operator model predictive control Non-convex

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Data-Driven Frequency Regulation Method for Multi-Terminal HVDC Grids 38

Data-Driven Frequency Regulation Method for Multi-Terminal H...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Yong, Panxiao Jia, Yubin Guo, Jun Zhou, Jun Liu, Zhimin Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350303636

High voltage direct current (HVDC) lines allow low-loss transmission over long distances. The multi-terminal HVDC network consists of multiple terminals connected by HVDC lines, and each area shares the frequency reserve. Through secondary frequency control, the frequency of AC and DC areas can be regulated back to the set point when the system frequency has deviated. In this paper, a data-driven model predictive control (MPC) strategy is proposed. The Kooman operator is used to obtain the high-dimensional linear model of a nonlinear system, and the Deep Learning method is used to approximate the infinite-dimensional Koopman operator. The nominal MPC is used for optimization and control, and the optimal problem is solved for the Koopman linearized system. The optimal control is applied to the original nonlinear system. Simulation results show the effectiveness of the proposed method. © 2023 IEEE.

关键词： data-driven Koopman operator model predictive control Multi-terminal HVDC

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Boosting Adversarial Training with Learnable Distribution

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Computers, Materials & Continua 2024年第3期78卷 3247-3265页

作者： Kai Chen Jinwei Wang James Msughter Adeke Guangjie Liu Yuewei Dai School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Intelligent Support Technology for Complex Environments Ministry of EducationNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing Center for Applied Mathematics Nanjing211135China

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial *** paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture *** distribution centroid is built to classify samples and constrain the distribution of the sample *** natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the *** proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial *** algorithm gradually increases the accuracy and robustness of the model by scaling ***,the proposed method outputs the predicted labels and the distance between the sample and the distribution *** distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model *** effectiveness of the proposed method is demonstrated through comprehensive experiments.

关键词： Adversarial training feature space learnable distribution distribution centroid

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Measurement-based Path Loss Prediction and Analysis in Water Scenarios

Measurement-based Path Loss Prediction and Analysis in Water...

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2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024

作者： Li, Hanpeng Chen, Xiaomin Li, Hangang Ma, Jian Ye, Xuchao Song, Lixin Zhang, Taotao Zhong, Weizhi Zhu, Qiuming Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Aviation Key Laboratory of Science and Technology On Aero Electromechanical System Integration Nanjing211106 China State Key Lab. of Complex Electromagnetic Environment Effects On Electronics and Information System Luoyang471003 China

ISBN: (纸本)9798350383317

Unmanned aerial vehicles (UAVs) have enabled the unmanned management of water areas. Path loss (PL) serves as a critical factor that affects the UAV communication quality. In this work, a PL model for UAV channel in water scenarios is proposed, aiming to explore the multidimensional relationship between transceiver distance, carrier frequency, UAV height, and PL by constructing an Artificial Neural Network (ANN). Moreover, channel measurements are conducted in a river area, and the obtained data are analyzed using the proposed ANN model as well as the close-in (CI) model. The analysis results demonstrates that the PL increases initially and then declines as the UAV height increases. The proposed ANN model has higher prediction accuracy than the classic empirical model, and can achieve precise prediction of the PL in water scenarios. © 2024 IEEE.

关键词： Neural networks

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Formal Verification Based Synthesis for Behavior Trees 9th

Formal Verification Based Synthesis for Behavior Trees

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9th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2023

作者： Hong, Weijiang Chen, Zhenbang Li, Minglong Li, Yuhan Huang, Peishan Wang, Ji College of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

ISBN: (纸本)9789819986637

Behavior trees (BTs) have been extensively applied in the area of both computer games and robotics, as the control architectures. However, the construction of BTs is labor-expensive, time-consuming, and even impossible as the complexity of task increases. In this work, we propose a formal verification based synthesis method to automatically construct BTs whose behaviors satisfy the given Linear Temporal Logic (LTL) specifications. Our method first explores candidate BTs by a grammar-based Monte Carlo Tree Search (MCTS), then the explored BTs are transformed into Communicating Sequential Processes (CSP) models. After that, we invoke the verifier to check the models’ correctness w.r.t. specifications, and provide feedback based on the verification result for guiding the search process. The application of our method on several representative robotic missions indicates its promising. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Behavior Trees CSP MCTS Synthesis

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Forming Circle Formations of Multi-agent systems Under Actuator Faults 5th

Forming Circle Formations of Multi-agent Systems Under Actu...

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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Liu, Jiajun Ru, Xinfeng Xia, Weiguo Wang, Chen China National Aeronautical Radio Electronic Research Institute and National Key Laboratory of Science and Technology on Avionics System Integration Shanghai China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian China National Engineering Research Center of Software Engineering and the State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

ISBN: (纸本)9789811939976

A distributed fault-tolerant formation control law is designed in this paper for multi-agent systems under external disturbances and actuator faults including time-varying loss of effectiveness faults. The initial position of each agent is already on a circle and the control purpose is to form a desired formation on this circle. Based on this local information, a distributed adaptive formation control law is designed by employing the congelation of variables method to deal with time-varying actuator faults. Finally, a simulation example is given to verify the effectiveness of the adaptive fault-tolerant control design method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Challenges of Using Pre-trained Models: The Practitioners' Perspective 31

Challenges of Using Pre-trained Models: The Practitioners' P...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Tan, Xin Li, Taichuan Chen, Ruohe Liu, Fang Zhang, Li School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Economics and Management Beihang University Beijing China

ISBN: (纸本)9798350330663

The challenges associated with using pre-trained models (PTMs) have not been specifically investigated, which hampers their effective utilization. To address this knowledge gap, we collected and analyzed a dataset of 5,896 PTM-related questions on Stack Overflow. We first analyze the popularity and difficulty trends of PTM-related questions. We find that PTM-related questions are becoming more and more popular over time. However, it is noteworthy that PTM-related questions not only have a lower response rate but also exhibit a longer response time compared to many well-researched topics in software engineering. This observation emphasizes the significant difficulty and complexity associated with the practical application of PTMs. To delve into the specific challenges, we manually annotate 430 PTM-related questions, categorizing them into a hierarchical taxonomy of 42 codes (i.e., leaf nodes) and three categories. This taxonomy encompasses many PTM prominent challenges such as fine-tuning, output understanding, and prompt customization, which reflects the gaps between current techniques and practical needs. We discuss the implications of our study for PTM practitioners, vendors, and educators, and suggest possible directions and solutions for future research. © 2024 IEEE.

关键词： Taxonomies

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An Approach for Upper Limb Movement Intention Recognition Using EEG and sEMG Fusion based on the MCPSA-CIIM 5

An Approach for Upper Limb Movement Intention Recognition Us...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhang, Weiming Zhang, Xiaodong Xu, Cheng Zhou, Guchuan Zhang, Teng Wang, Yu Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robot School of Mechanical Engineering Shaanxi Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China

ISBN: (纸本)9798350307320

The upper limb motion intention recognition method based on electroencephalography (EEG) and surface electromyography (sEMG) fusion has achieved significant results in fields such as prosthetic control. However, most existing fusion methods use manual means to select features, which cannot capture temporal and spatial features at different scales, and ignore the correlation features between the two types of signals. To address these issues, this article proposes a fusion recognition method for upper limb motion intention EEG and sEMG based on Multi-scale Convolution, Polarized Self-Attention, and Cross Intelligence Integration Module. This article extracts multidimensional temporal and spatial features of EEG and sEMG through multi-scale convolution, and introduce polarized self-attention mechanism to filter the extracted multi-scale features. Simultaneously using cross enhancement strategy to extract correlation features between EEG and sEMG. Finally, the features are input into the classification network for recognition. This method was validated on the Jeong database, and the results showed that compared with CNN-LSTM and EEGNet, the recognition accuracy of this method increased by 2.63% and 3.15%, respectively. © 2023 IEEE.

关键词： Electroencephalography

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