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第41届中国控制会议

作者： Wei Quan Chen Chen Zhuang Shao Kai Meng School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

When arriving at the airport,flight needs to be served by special *** at the dynamic time window scheduling problem of airport refueling vehicles,this paper establishes a vehicle routing problem model with time window to minimize the operating ***,a multi-strategy genetic algorithm is designed to gain the solve time window scheduling problem,which employs the crossover based on particle swarm optimization to accelerate the early search capability,and the local search method based on simulated annealing to increase the local optimization *** aiming at dynamically adjusting vehicle routes on the basis of static scheduling,a local replanning strategy based on a dynamic time window is introduced,which uses the original route matching and rescheduling *** results show that the multi-strategy hybrid algorithm can effectively reduce the number of routes and vehicles the airport *** different scales' dynamic changes of time windows,the algorithm could enable vehicles to still meet time constraints and effectively minimize the change of routes.

关键词： vehicle scheduling multi-strategy genetic algorithm dynamic scheduling local replanning strategy

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Heterogeneity in the volatility spillover of cryptocurrencies and exchanges

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Financial Innovation 2024年第1期10卷 1558-1603页

作者： Meiyu Wu Li Wang Haijun Yang School of Economics and Management Beihang UniversityNo.37 Xueyuan RoadHaidian DistrictBeijing100191China Key Laboratory of Complex System Analysis Management and DecisionBeihang UniversityMinistry of EducationBeijingChina

This study examines the volatility spillovers in four representative exchanges and for six liquid *** the high-frequency trading data of exchanges,the heterogeneity of exchanges in terms of volatility spillover can be examined dynamically in the time and frequency *** find that Ripple is a net receiver on Coinbase but acts as a net contributor on other *** and Binance have different net spillover effects on the six cryptocurrency ***,we identify the determinants of total connectedness in two types of volatility spillover,which can explain cryptocurrency or exchange interlinkage.

关键词： Cryptocurrency Cryptocurrency exchanges Volatility spillover Heterogeneity of volatility spillover

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Dehazing Network Based on U-Net Structure and Residual Block 41

Dehazing Network Based on U-Net Structure and Residual Block

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第41届中国控制会议

作者： Wenbo Zhang Haoze Li Han Sun Yongqiang Bai School of Automation Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of deep learning technology,the dehazing method based on convolutional neural network has also developed ***,it still faces some problems such as incomplete dehazing and difficulty in detail *** at the problems,we propose a dehazing network based on U-Net structure and residual *** the down sampling process,we introduce the residual block of attention mechanism,which effectively improves the feature extraction and expression ability of the *** the middle part of the network,the smooth dilation convolution residual block is used to expand the receptive field of the network and improve the restoration effect of small *** the same time,we introduce a feature fusion mechanism based on attention mechanism,which can reduce the loss of information and weight the down-sampling features into intermediate *** the process of up sampling,the method of adding elements instead of feature stitching is used to reduce the number of parameters and realize the function of jump ***,dense convolution residual blocks are added to the jump connection between input and output to further improve the *** experimental results show that the PSNR of this method can reach 32.893,which is better than the existing methods.

关键词： Image Dehazing Residual Network Attention mechanism

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Model Reconstruction Based on Physical-Informed Polynomial Neural ODEs 13

Model Reconstruction Based on Physical-Informed Polynomial N...

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13th IEEE Data Driven Control and Learning systems Conference, DDCLS 2024

作者： Chen, Zhang Zhu, Wei Li, Jingsui Bian, Hanlin Pei, Chao School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing400065 China

ISBN: (纸本)9798350361674

Neural networks are capable function approximators for data, but their lack of interpretability poses challenges. Physical-informed Polynomial Neural Ordinary Differential Equations (PIPNODEs) are proposed. They combine the constraints of prior knowledge of the data with interpretable Polynomial Neural Ordinary Differential Equations (PolyNODEs) to discover or approximate the governing equations. In this work, Neural Ordinary Differential Equations (NODEs) are categorized into two layers: a physical-informed term, which represents the priori knowledge of the data, and a discrepancy term, which is derived from the approximation of PolyNODEs. Pruning strategies are also used to remove redundant terms with coefficients lower than a certain threshold. We demonstrate the effectiveness and flexibility of this framework in a series of numerical algorithms with varying levels of prior knowledge. We found that having correct but incomplete physical information accelerates training and approaching thresholds compared to having incorrect physical information. © 2024 IEEE.

关键词： data modeling discprepancy modeling interpretability Neural Ordinary Differential Equations physics-informed machine learning polynomial neural network

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Parameter Inversion of High-Dimensional Chaotic systems Using Neural Ordinary Differential Equations 13

Parameter Inversion of High-Dimensional Chaotic Systems Usin...

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13th IEEE Data Driven Control and Learning systems Conference, DDCLS 2024

作者： Bian, Hanlin Zhu, Wei Chen, Zhang Jingsui, L. Pei, Chao Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9798350361674

High-dimensional chaotic systems possess complex structural characteristics, which have potential research value in fields like encrypted communication and chaos control. The inverse problem of parameters in high-dimensional chaotic systems is of significant importance for a profound understanding of the dynamic properties of such systems. This article takes a departure from neural ordinary differential equations and embeds the structural dynamics of the dynamical systems to the parameter inversion of high-dimensional chaotic systems. Numerical experiments on different high-dimensional chaotic systems indicate that neural ordinary differential equations perform exceptionally well in solving these inversion problems, with low errors and strong robustness. © 2024 IEEE.

关键词： Inverse problems

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Deep Multimodal Learning and Fusion Based intelligent Fault Diagnosis Approach

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Journal of Beijing Institute of Technology 2021年第2期30卷 172-185页

作者： Huifang Li Jianghang Huang Jingwei Huang Senchun Chai Leilei Zhao Yuanqing Xia Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of TechnologyBeijing 100081China

Industrial Internet of Things(IoT)connecting society and industrial systems represents a tremendous and promising paradigm *** IoT,multimodal and heterogeneous data from industrial devices can be easily collected,and further analyzed to discover device maintenance and health related potential knowledge *** data-based fault diagnosis for industrial devices is very helpful to the sustainability and applicability of an IoT *** how to efficiently use and fuse this multimodal heterogeneous data to realize intelligent fault diagnosis is still a *** this paper,a novel Deep Multimodal Learning and Fusion(DMLF)based fault diagnosis method is proposed for addressing heterogeneous data from IoT environments where industrial devices ***,a DMLF model is designed by combining a Convolution Neural Network(CNN)and Stacked Denoising Autoencoder(SDAE)together to capture more comprehensive fault knowledge and extract features from different modal ***,these multimodal features are seamlessly integrated at a fusion layer and the resulting fused features are further used to train a classifier for recognizing potential ***,a two-stage training algorithm is proposed by combining supervised pre-training and fine-tuning to simplify the training process for deep structure models.A series of experiments are conducted over multimodal heterogeneous data from a gear device to verify our proposed fault diagnosis *** experimental results show that our method outperforms the benchmarking ones in fault diagnosis accuracy.

关键词： fault diagnosis deep learning multimodal heterogeneous data multimodal fused features

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Data-Driven Discovery of Nonlinear Dynamical systems from Noisy and Sparse Observations

Data-Driven Discovery of Nonlinear Dynamical Systems from No...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Zhu, Wei Pei, Chao Liang, Yulan Chen, Zhang Li, Jingsui School of Science Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798331517021

Sparse Identification of Nonlinear Dynamics (SINDy) is a data-driven algorithm for interpretability mod-eling. SINDy has a strong ability to learn dynamical systems from time series data. Sparse Identification of Nonlinear Dynamics (SINDy) can extract equations from data, but its performance will degrade when data has noise or sparse. In order to deal with this case, the idea of Monte Carlo Markov Chain (MCMC) is introduced, which is used to optimally select models. The SINDy with MCMC (MCMC-SINDy) can comprehensively discover model structures by employing random samples in data space. Simulation results show that MCMC-SINDy is more robust than SINDy with 1 % -15 % levels of noise or sparse 0.5. These results are promising for the discovery of governing equations for complex systems in neuroscience, power grids, epidemiology, finance or ecology, where governing equations have remained elusive. mportantly, compared to existing machine learning methods, the accuracy of modeling using MCMC SINDy is higher. © 2024 IEEE.

关键词： Markov chains

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Interpretable Fourier Neural Ordinary Differential Equations

Interpretable Fourier Neural Ordinary Differential Equations

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Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Hanlin Bian Wei Zhu Zhang Chen Jingsui Li Chao Pei Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

Numerous interacting elements can form complex systems. However, describing complex systems is extremely challenging and lacks interpretability. The construction of models still heavily relies on scientists’ understanding of complex systems. This paper explores the use of neural ordinary differential equations in the field of complex system modeling. The proposed Fourier neural ordinary differential equations offer interpretability and a relatively simple structure. By building upon interpretable modeling, the scale is significantly reduced. It is possible to deduce the system’s expression from the model’s parameters. Finally, a real-world system is provided to illustrate the effectiveness of the proposed model, demonstrating its commendable robustness.

关键词： Training Atmospheric modeling Fitting Time series analysis Ordinary differential equations Predictive models Mathematical models

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Magnetic Microrobots Fabricated by Photopolymerization and Assembly

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Cyborg and Bionic systems 2023年第1期4卷 41-46页

作者： Xiyue Liang Yue Zhao Dan Liu Yan Deng Tatsuo Arai Masaru Kojima Xiaoming Liu Key Laboratory of Biomimetic Robots and Systems Ministry of EducationState Key Laboratory of Intelligent Control and Decision of Complex SystemBeijing Advanced Innovation Center for Intelligent Robots and Systemsand School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Center for Neuroscience and Biomedical Engineering The University of Electro-CommunicationsTokyo 182-8585Japan Department of Materials Engineering Science Osaka UniversityOsaka 560-8531Japan

Magnetic soft microrobots have great potential to access narrow spaces and conduct multiple tasks in the biomedical *** now,drug delivery,microsurgery,disease diagnosis,and dredging the blocked blood vessel have been realized by magnetic soft microrobots in vivo or in ***,as the tasks become more and more complex,more functional units have been embedded in the body of the developed magnetic *** magnetic soft microrobots with complex designed geometries,mechanisms,and magnetic orientation are now greatly challenging the fabrication of the magnetic *** this paper,we propose a new method combining photopolymerization and assembly for the fabrication of magnetic soft *** the micro-hand assembly system,magnetic modules with different shapes and materials are firstly arrayed with precise position and orientation ***,the developed photopolymerization system is employed to fix and link these modules with soft *** on the proposed fabrication method,3 kinds of soft magnetic microrobots were fabricated,and the fundamental locomotion was *** believe that the presented fabrication strategy could help accelerate the clinical application of magnetic microrobots.

关键词： Magnetic system precise

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Containment Control of Fractional-order Nonlinear Multi-agent systems with Time Delay

Containment Control of Fractional-order Nonlinear Multi-agen...

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Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Yanqiu Li Wei Zhu Bo Chen Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

Formation control of multi-agent systems is an extension of its consensus problem, which has been widely used in control science and engineering, system science, information and communication engineering and other disciplines. Compared with using integral-order calculus to describe the change of the state of the object, the fractional-order calculus can represent and reflect the real physical characteristics of the object better. In this paper, containment control problem of fractional-order nonlinear multi-agent systems with time delay is studied. In spite of the difficulties and challenges caused by the operator complex property and system delay in fractional-order calculus, this paper solves the containment control problem by constructing a quadratic Lyapunov function and using matrix analysis theory, graph theory and fractional Razumikhin method. The conditions of linear matrix inequalities for asymptotically containment control are given. Finally, numerical simulation results verify the effectiveness of the proposed control scheme.

关键词： Delay effects Control systems Numerical simulation Calculus Graph theory Formation control Linear matrix inequalities

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