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Nonreciprocity of energy transfer in a nonlinear asymmetric oscillator system with various vibration states

Applied Mathematics and Mechanics(English Edition) 2023年第5期44卷 727-744页

作者： Jian’en CHEN Jianling LI Minghui YAO Jun LIU Jianhua ZHANG Min SUN Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin 300384China School of Aeronautics and Astronautics Tiangong UniversityTianjin 300387China Agricultural Information Institute of Chinese Academy of Agricultural Sciences Key Laboratory of Agricultural Big DataMinistry of Agriculture and Rural AffairsBeijing 100081China School of Science Tianjin Chengjian UniversityTianjin 300384China

The nonreciprocity of energy transfer is constructed in a nonlinear asymmetric oscillator system that comprises two nonlinear oscillators with different parameters placed between two identical linear *** slow-flow equation of the system is derived by the complexification-averaging *** semi-analytical solutions to this equation are obtained by the least squares method,which are compared with the numerical solutions obtained by the Runge-Kutta *** distribution of the average energy in the system is studied under periodic and chaotic vibration states,and the energy transfer along two opposite directions is *** effect of the excitation amplitude on the nonreciprocity of the system producing the periodic responses is analyzed,where a three-stage energy transfer phenomenon is *** the first stage,the energy transfer along the two opposite directions is approximately equal,whereas in the second stage,the asymmetric energy transfer is *** energy transfer is also asymmetric in the third stage,but the direction is reversed compared with the second ***,the excitation amplitude for exciting the bifurcation also shows an asymmetric *** vibrations are generated around the resonant frequency,irrespective of which linear oscillator is *** excitation threshold of these chaotic vibrations is dependent on the linear oscillator that is being *** addition,the difference between the energy transfer in the two opposite directions is used to further analyze the nonreciprocity in the *** results show that the nonreciprocity significantly depends on the excitation frequency and the excitation amplitude.

关键词： nonreciprocity strong nonlinearity energy transfer chaotic vibration higher branch of response

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Combining Transformer and Convolutional Neural Network for Smoke Detection 9th

Combining Transformer and Convolutional Neural Network for...

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9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Gong, Yafei Lian, Xinkang Ma, Xuanchao Xia, Zhifang Zhou, Chengxu Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China National Information Center Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

Smoke detection plays a crucial role in the safety production of petrochemical enterprises and fire prevention. Image-based machine learning and deep learning methods have been widely studied. Recently, many works have applied the transformer to solve problems faced by computer vision tasks (such as classification and object detection). To our knowledge, there are few studies using the transformer structure to detect smoke. In order to research the application potential and improve the performance of the transformer in the smoke detection field, we propose a model consisting of two transformer encoders and a convolutional neural network (CNN) module. The first transformer encoder can be used to establish the global relationship of an image, and the CNN structure can provide additional local information to the transformer. The fusion of global information and local information is conducive to the second transfer encoder to make better decisions. Experiments results on large-size dataset for industrial smoke detection illustrate the effectiveness of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smoke

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Evolutionary Dynamics of Cooperation on Coupled Networks

Evolutionary Dynamics of Cooperation on Coupled Networks

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control Theory and Applications (ICoCTA), International Conference on

作者： Zhenyu Shi Qi Su Department of Automation Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331506155

ISBN: (纸本)9798331506162

Network reciprocity has been recognized as a crucial mechanism for promoting the evolution of cooperation. However, prior studies on network reciprocity have predominantly focused on isolated networks, neglecting the interconnected nature of real-world systems. In reality, many large-scale systems are closely coupled, where the interaction patterns between systems differ significantly from those within individual networks. Motivated by this observation, we construct a strategy evolution model on coupled networks to explore the effects of such interconnections. Our findings reveal that coupled networks foster cooperation more effectively than isolated networks. This advantage persists across various network structures, suggesting the universality of this phenomenon. Interestingly, we discover that the strength of the coupling plays a pivotal role: weaker coupling between networks is more conducive to the emergence and stability of cooperation compared to stronger coupling. These results high-light the importance of considering cross-system interactions in understanding and designing mechanisms to enhance cooperative behavior.

关键词： Couplings Stability analysis Large-scale systems control theory Game theory

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End-to-end Automatic Targetless Calibration between Low-resolution LiDARs and Cameras based on Cross-modal Attention

End-to-end Automatic Targetless Calibration between Low-reso...

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Chinese control and Decision Conference, CCDC

作者： Tianyang Huang Jun-Guo Lu Ming Wei Qing-Hao Zhang Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

LiDARs and cameras, commonly used sensors on autonomous vehicles and robots, require precise calibration for accurate joint environmental perception. Current calibration methods, capable of adjusting both intrinsic and extrinsic parameters, often depend on physical targets or human intervention, making them unsuitable for automatic online calibration. Moreover, these methods are primarily optimized for dense point clouds, as they are often tuned on open datasets featuring high-quality point cloud data. However, low-resolution LiDARs, preferred for their cost-effectiveness, are prevalent in practical applications. To automate the calibration process while maintaining high precision on sparse point clouds, multi-modal image registration techniques are leveraged to develop an end-to-end, cross-attention-based neural network for automatic calibration of low-resolution LiDARs and Cameras. Specifically, given an initial perturbation on point clouds, our network is capable of recovering the true extrinsic parameters without manual intervention. Additionally, specialized input preprocessing and training strategies are incorporated to enhance performance with sparse point clouds. Promising results are achieved on both open datasets and custom sensor acquisitions, highlighting the effectiveness and adaptability of our network to diverse scenarios.

关键词： Point cloud compression Training Image registration Visualization Laser radar Perturbation methods Neural networks

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Streaming-Data-Driven Distributionally Robust Joint Operation of Multi-Microgrids and Off-Site Hydrogen Refueling Stations under Uncertainties 11

Streaming-Data-Driven Distributionally Robust Joint Operatio...

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11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Li, Longyan Ning, Chao Qiu, Haifeng Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China School of Electrical & Electronic Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798350399660

This paper proposes a novel online-learning-enabled hierarchical distributionally robust energy management framework for multi-microgrids (MMGs) with off-site hydrogen refueling stations (HRSs). The proposed framework integrates day-ahead scheduling, intra-day rescheduling and real-time control. The day-ahead scheduling is implemented via a central energy management system for optimal joint scheduling of the MMG systems and off-site HRSs, then the intra-day rescheduling is carried out to optimize the real-time operation and to generate the reference for local controllers. To adequately characterize the statistical properties of renewable and load uncertainties from data streams, we leverage a Dirichlet process mixture model to construct an online-update ambiguity set, which encodes multimodality and local moment information. Based upon this ambiguity set, an online-learning distributionally robust model predictive control (OL-DRMPC) is developed to enhance the tracking performance of energy storage references and to reduce conservatism while guaranteeing probabilistic operational constraints. Its effectiveness is verified in a case study. © 2022 IEEE.

关键词： Real time control

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Cluster Consensus Problem on Time-varying Higher-Order Interaction Networks

Cluster Consensus Problem on Time-varying Higher-Order Inter...

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IEEE Conference on Decision and control

作者： Haoyu Wei Lulu Pan Haibin Shao Dewei Li Wenbin Yu Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper examines the cluster consensus problem of higher-order interaction networks on switching networks. The matrix-valued inter-agent coupling mechanism is employed to capture the higher-order interactions amongst neighboring agents. Specifically, for a higher-order interaction network with both positive definite and positive semi-definite coupling matrices, we examine the scenario in which the nullspace of positive semi-definite coupling matrices is constructed by a set of orthogonal basis vectors. From the perspective of the lower-order component graph -the projection of network structure onto the direction of each orthogonal basis–results on the explicit characterization of cluster consensus of higher-order interaction networks are provided. Moreover, for time-varying higher-order interaction networks, necessary and sufficient conditions are provided to ensure that a time-varying network shares the same steady-state values as the integral time-invariant network. Simulation results are provided to demonstrate the theory.

关键词： Couplings Sufficient conditions Network topology Simulation Switches Vectors Topology Steady-state Matrix decomposition Convergence

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SoftNeRF: A Self-Modeling Soft Robot Plugin for Various Tasks

SoftNeRF: A Self-Modeling Soft Robot Plugin for Various Task...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Jiwei Shan Yirui Li Qiyu Feng Ditao Li Lijun Han Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Building a self-model for robots, enabling them to simulate their physical selves and predict future states without direct interaction with the physical world, is crucial for robot motion planning and control. Existing self-modeling methods primarily focus on rigid robots and typically require significant time, effort, and resources to gather training data. In this study, we introduce SoftNeRF, a self-supervised visual self-model designed for soft robots. We use a hybrid neural shape representation based on the Signed Distance Function (SDF) to capture both the geometry and complex nonlinear motion of soft robots. By leveraging differentiable rendering, our method learns a self-model from readily available RGB images, similar to how humans understand their physical state through reflection. To improve training efficiency and model accuracy, we propose an error-guided adaptive sampling strategy. SoftNeRF can serve as a plug-in for various downstream tasks, even when trained with data unrelated to those tasks. We demonstrate SoftNeRF’s ability to support shape prediction and motion planning for robots in both simulated and real-world environments. Furthermore, SoftNeRF excels in detecting and recovering from damage, thereby enhancing machine resilience. Code is available at: https://***/irmvlab/soft-nerf.

关键词： Training Visualization Adaptation models Accuracy Shape Training data Soft robotics Rendering (computer graphics) Sampling methods Planning

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Efficient Reinforcement Learning for 3D LiDAR Navigation of Mobile Robot 41

Efficient Reinforcement Learning for 3D LiDAR Navigation of ...

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第41届中国控制会议

作者： Yu Zhai Zhe Liu Yanzi Miao Hesheng Wang China University of Mining and Technology MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Developing an efficient automatic navigation system for mobile robots is challenging in the strange scenarios where robots can only observe the environment of the surrounding limited *** other distributed automatic navigation systems exist,they often require extracting semantic information to calculate navigation action,which requires extra modules to provide perceptual information and is not *** propose an end-to-end automatic navigation system based on the reinforcement learning *** particular,the raw 3 D LiDAR data is used to directly map an efficient navigation *** design a novel dense reward function to handle the reward sparsity issue and provide a graphical representation method to enable the efficient feature learning from the raw 3 D LiDAR data in our navigation *** addition,an imitation learning based policy initialization is introduced before the subsequent reinforcement learning,which increases the learning efficiency and,in the meantime,still encouraging the robot to explore all the potential states to achieve advanced performance than the imitated *** navigation model is trained in the Webots environment and the experimental results show that our model has efficient and flexible navigation performance in complex *** importantly,trained model can be easily extended to unfamiliar environments.

关键词： End-to-end Navigation Mobile robot Reinforcement Learning 3D LiDAR

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DDS-SLAM: Dense Semantic Neural SLAM for Deformable Endoscopic Scenes

DDS-SLAM: Dense Semantic Neural SLAM for Deformable Endoscop...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Jiwei Shan Yirui Li Lujia Yang Qiyu Feng Lijun Han Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Estimating camera motion and continuously reconstructing dense scenes in deformable environments presents a complex and open challenge. Many existing approaches tend to rely on assumptions about the scene’s topology or the nature of deformable motion. However, these assumptions do not hold true in medical endoscopy applications. To address these challenges, we introduce DDS-SLAM, a novel dense deformable semantic neural SLAM that achieves accurate camera tracking, continuous dense scene reconstruction, and high-quality image rendering in deformable scenes. First, we propose a novel hybrid neural scene representation method capable of capturing both natural and artificial deformations. Additionally, by leveraging the 2D semantic information of the scene, we introduce a semantic loss function based on semantic distance fields. This approach guides network optimization at a higher level, thereby enhancing system performance. Furthermore, we validate our method through a series of experiments conducted on several representative medical datasets, demonstrating its superiority over other state-of-the-art approaches. The code is available at: https://***/IRMVLab/DDS-SLAM.

关键词： Simultaneous localization and mapping Tracking system performance Semantics Cameras Rendering (computer graphics) Topology Image reconstruction Optimization Biomedical imaging

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Target-background feature blocks and aberrance repressed correlation filters for real-time UAV tracking

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Signal, Image and Video Processing 2025年第7期19卷 1-11页

作者： Jia, Yunwei Liu, Bo Liu, Zhenzhong Wang, Tianyang Yv, Kaiying Wang, Shuo Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

In recent years, Unmanned Aerial Vehicle (UAV) tracking technology has been widely applied in fields such as agriculture and aviation. However, due to limitations in UAV computational resources and battery life, deep learning-based tracking algorithms are difficult to deploy directly. Therefore, efficient and robust tracking methods based on Discriminative Correlation Filters (DCF) have become the mainstream for UAV tracking. The traditional DCF method updates the model with a fixed step size, making it prone to contamination, while the boundary effect further limits its tracking capability. To address the issue of model contamination, an appearance model update and masking mechanism was designed, introducing feature confidence and overall confidence thresholds to evaluate the quality of features and images. Features and images with poor quality are masked, and the learning rate is reduced to prevent model contamination. This paper proposes the Target-Background Feature Block and Aberrance Repressed Correlation Filter (TBFBARCF), which introduces target and background regularization terms into the spatial filter to mitigate boundary effects and incorporates an aberrance repression regularization term into the temporal aberrance repressed filter to reduce response map aberrance. The two filters mutually constrain each other and are jointly trained. Experiments conducted on three challenging datasets, OTB100, UAV123, and UAVDT, demonstrate that the TBFBARCF tracker outperforms 11 other DCF-based trackers. Its average precision and average success rate reach 0.756 and 0.62, respectively, and it operates at a speed of 36.6 frames per second (FPS) on a CPU platform. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Drones

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