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Neural-LAFC: A Neural Learning Adaptive Flight controller for Tailless Flapping-Wing Robots Under Uncertain Wind Disturbance Conditions

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Guo, Qingcheng Zhang, Yichen Mou, Jiawang Liu, Wu Wu, Xiaosheng Lu, Jun-Guo Shanghai Jiao Tong University Department of Automation China Key Laboratory of System Control and Information Processing Ministry of Education of China China National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai200240 China Shanghai Jiao Tong University National Key Laboratory of Advanced Micro and Nano Manufacture Technology China Shanghai Jiao Tong University Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai200240 China Designing Institute of Hubei Space Technology Academy Wuhan China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China

Enabling bio-inspired flapping-wing robots (FWRs) to maintain stable flight in windy environments, similar to real flying creatures, is of significant importance. In this work, a neural learning-based adaptive flight controller (Neural-LAFC) is proposed for the attitude and position control of a tailless FWR under uncertain wind conditions. The key approach involves employing a deep neural network (DNN) that incorporates not only attitude information but also instantaneous flapping angles of the wings to learn the aerodynamic torques that cannot be accurately modeled. The training data are obtained from flight experiments conducted with the FWR in a wind tunnel environment. Subsequently, a composite adaptive law is presented to update a set of linear coefficients. These linear coefficients are utilized to blend the pre-trained basis functions to represent the aerodynamic torques. The tracking errors are shown to exponentially converge to a bounded region, as guaranteed by a Lyapunov function. Based on the learning-based attitude controller, we also develop an adaptive controller for position control. Notably, apart from the training phase, the FWR operates without prior knowledge of wind speed. Experimental results demonstrate that the Neural-LAFC successfully achieves the desired attitude stabilization, target point tracking, and path tracking of the FWR in windy conditions. The proposed control scheme also outperforms baseline controllers in wind disturbance suppression, reducing the root mean square position errors by more than 40% when the FWR is subjected to a horizontal wind within the speed of 180 {cm s{-1}}. © 1965-2011 IEEE.

关键词： Wind tunnels

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Mechanical properties and microstructure evolution of T2 copper in multimodal ultrasonic vibration assisted micro-compression

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材料科学技术（英文版） 2025年第5期208卷 152-163页

作者： Weiqiang Wan Zidong Yin Guangchao Han Ming Yang Jitao Hu Fuchu Liu Linhong Xu Wei Bai Hui Chen School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Graduate School of System Design Tokyo Metropolitan UniversityTokyo 191-0065Japan Graduate School of System Design Tokyo Metropolitan UniversityTokyo 191-0065Japan School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Shenzhen Research Institute China University of GeosciencesShenzhen 518057China State Key Laboratory of Solidification Processing Northwestern Polytechnical UniversityXi'an 710072China School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Shenzhen Research Institute China University of GeosciencesShenzhen 518057China School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Shenzhen Research Institute China University of GeosciencesShenzhen 518057China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518000China State Key Laboratory of Solidification Processing Northwestern Polytechnical UniversityXi'an 710072China

Multimodal ultrasonic vibration(UV)assisted micro-forming has been widely investigated for its advan-tages of further reducing forming loads and improving forming ***,the influence mecha-nism of different UV modes on microstructure evolution and mechanical properties was still ***-timodal UV assisted micro-compression tests on T2 copper with different grains and sample sizes were conducted in this *** microstructure evolution for different UV modes was observed by *** results showed that the true stress reduction caused by UV was increased sequentially with tool ultrasonic vibration(TV),mold ultrasonic vibration(MV)and compound ultrasonic vibration(CV).The region of grain deformation was shifted along the direction of UV,and the MV promoted the uniform distribution of deformation *** grain refinement,fiber streamline density,grain deformation and rotation degree were further enhanced under CV,due to the synergistic effect of TV and ***,a coupled theoretical model considering both acoustic softening effect and size effect was proposed for describing the mechanical *** a physical model of dislocation motion in different UV modes was developed for describing the microstructure *** maximum error between the theoretical and experimental results was only 2.39％.This study provides a theoretical basis for the optimization of UV assisted micro-forming process.

关键词： Multimodal ultrasonic vibration Microstructure evolution Acoustic softening Size effect Coupling mechanism

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Edge-Assisted Flexible Platooning of Connected and Automated Vehicles Under Traffic Anomaly

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IEEE Transactions on Vehicular Technology 2025年

作者： Gao, Fengkun Yang, Bo Chen, Cailian Guan, Xinping Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China

The platooning of connected and automated vehicles (CAVs) has the great potential to significantly improve travel experience in terms of safety, comfortableness, and energy efficiency. However, constrained by sensing range and prediction accuracy, the performance of CAV platoon can not be guaranteed in the scenario of mixed traffic consisting of CAVs and human-driven vehicles (HDVs). This is not only due to the uncertainty of HDVs but also related to the occurrence of various traffic anomalies, such as emergency braking and accidents. In this paper, we aim to investigate an efficient control strategy to guarantee the operation of the platoon in mixed traffic, especially in the case of traffic anomalies. To tackle the problem, an edge-assisted platoon control strategy, called E-CACC, is proposed. First, an error-aware flexible tracking policy is designed to adapt to HDV uncertainties and speed prediction errors. Based on the tracking policy, a zone model predictive control (ZMPC) method is then designed to guarantee travel safety and avoid undesired acceleration. Further, a coordination strategy of CAV platoon and roadside unit (RSU) is proposed to prevent the platoon from potential collision in the case of traffic anomalies, where an event-based communication trigger is designed to determine whether and when the platoon establishes communication links with RSU to acquire exogenous sensing data of anomaly segment. Finally, the evaluation results illustrate the effectiveness of the proposed method in improving travel safety and comfortableness. © 1967-2012 IEEE.

关键词： Braking performance

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Analysis of the SEIR mean-field model in dynamic networks under intervention

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Infectious Disease Modelling 2025年第3期10卷 850-874页

作者： Li, Jiangmin Jin, Zhen Tang, Ming Complex System Research Center Shanxi University Shanxi Taiyuan 030006 China Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi University Shanxi Taiyuan 030006 China Shanxi Key Laboratory for Mathematical Technology in Complex Systems Shanxi University Shanxi Taiyuan 030006 China School of Physics and Electronic Science East China Normal University Shanghai 200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai 200241 China

For emerging respiratory infectious diseases like COVID-19, non-pharmaceutical interventions such as isolation are crucial for controlling the spread. From the perspective of network transmission, non-pharmaceutical interventions like isolation alter the degree distribution and other topological structures of the network, thereby controlling the spread of the infectious disease. In this paper, we establish a SEIR mean-field propagation dynamics model for the synchronous evolution of dynamic networks caused by propagation and tracing isolation. We employ the reducing-dimension method to convert the mean-field model in networks into an equivalent and simpler low-dimension model, and then calculate the exact expression of the final size. In addition, we get the differential equations of the degree distribution over time in dynamic networks under tracing isolation and the relationships between the first and second moment of the dynamic network. While the degree of a node remains constant regardless of its state in many previous studies, this paper takes into account that the degree of each node changes over time whatever its state under the disease spread and intervention measures. © 2025 The Authors

关键词： Degree distribution Dynamic network The final size Tracing isolation

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React Agent-Based Question Answering Method for Biology in National College Entrance Examination 24

React Agent-Based Question Answering Method for Biology in N...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Chen, Yulin Luo, Jing Tu, Xinhui College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan China College of Computer Science Central China Normal University Hubei Wuhan China

ISBN: (纸本)9798400717826

Recent advancements in large language models have led to significant improvements in various natural language processing tasks, including automated question answering. However, these models still struggle with providing accurate responses to complex biology questions, such as those found in the National College Entrance Examination. To address this issue, researchers have explored retrieval-augmented generation techniques, which incorporate external knowledge sources to enhance answer accuracy. While these methods have shown promise in improving efficiency, they often lack the ability to perform in-depth analysis of complex questions. In response to these limitations, the paper proposes a novel ReAct agent-based question answering method. This method combines biology textbook document libraries with Google search engine capabilities, leveraging the strengths of large language models. By integrating these components, this method can engage in autonomous decision-making and multi-step reasoning, allowing for a more thorough analysis of complex biology questions. Experimental results demonstrate the effectiveness of this new approach. When compared to responses generated directly by ChatGPT and GPT-4o models, the method showed significant improvements in accuracy for biology questions from the National College Entrance Examination. Specifically, the new method achieved accuracy increases of 11.1% and 4.64% over ChatGPT and GPT-4o, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Question answering

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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DD-Net: Dynamic Network Architecture for Optimized Curve Segmentation and Reduce Computational Redundancy 27th

DD-Net: Dynamic Network Architecture for Optimized Curve Seg...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cao, Yunxiang Chen, Li Wang, Yubo Feng, Zhida Tian, Jing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Institute of Systems Science National University of Singapore Singapore Singapore

ISBN: (纸本)9783031783821

Curvilinear Structure segmentation has numerous applications in various fields, including providing a better understanding of defects such as cracks on roads or walls, thereby assessing the structural safety of buildings. Numerous curve segmentation methods based on Convolutional Neural Networks(CNN) have been developed in recent years. However, preserving feature integrity remains a challenge. To address this issue, this paper introduces a double dynamic network, called DD-Net, which consists of a dynamic structure for training and dynamic masking for inference. Firstly, to enhance the nonlinearity of the model, a paradigm to auto-adjust the CNN structure via a trainable module is formed for better fine-grained feature extraction. This dynamic structure (DS) paradigm enables a trade-off to extract or discard the feature data in the model training. Due to the data heterogeneity between the training and inference data, models may contain some useless nodes that are less effective during inference. The dynamic masking (DM) is proposed to omit the useless nodes based on the score difference between the train and inference feature statistics, thereby reducing redundant computations. To further improve the model’s performance on thin-curve segmentation and to preserve feature integrity, we introduce a non-curve suppression (NCS) module. This module focuses on background information while considering foreground prediction to address noisy conditions. The experimental results show that our DD-Net achieves promising results on three benchmark datasets and outperforms state-of-the-art curve segmentation models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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UW-DNeRF: Deformable Soft Tissue Reconstruction with Uncertainty-Guided Depth Supervision and Local information Integration

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Shan, Jiwei Zhang, Zixin Li, Hao Hsieh, Cheng-Tai Li, Yirui Wu, Wenhua Wang, Hesheng School of Electronic Information and Electrical Engineering of Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University School of Mechanical Engineering Shanghai200240 China Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China

Reconstructing deformable soft tissues from endoscopic videos is a critical yet challenging task. Leveraging depth priors, deformable implicit neural representations have seen significant advancements in this field. However, depth priors from pre-Trained depth estimation models are often coarse, and inaccurate depth supervision can severely impair the performance of these neural networks. Moreover, existing methods overlook local similarities in input sequences, which restricts their effectiveness in capturing local details and tissue deformations. In this paper, we introduce UW-DNeRF, a novel approach utilizing neural radiance fields for high-quality reconstruction of deformable tissues. We propose an uncertainty-guided depth supervision strategy to mitigate the impact of inaccurate depth information. This strategy relaxes hard depth constraints and unlocks the potential of implicit neural representations. In addition, we design a local window-based information sharing scheme. This scheme employs local window and keyframe deformation networks to construct deformations with local awareness and enhances the model's ability to capture fine details. We demonstrate the superiority of our method over state-of-The-Art approaches on synthetic and in-vivo endoscopic datasets. Code is available at: https://***/IRMVLab/UW-DNeRF. © 2025 IEEE.

关键词： Endoscopy

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Polar-segmented vector optical field and its anomalous focal properties

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Optics Express 2025年第10期33卷 21030-21039页

作者： Gao, Xu-Zhen Song, Xin-Yu Chen, Jing-Rui Wang, Dan Xu, Shi-Tong Niu, Mingsheng Cao, Hongzhong Ma, Rende Pan, Yue School of Physics and Physical Engineering Qufu Normal University Qufu 273165 China Shandong Provincial Key Laboratory of Laser Polarization and Information Technology Qufu Normal University Qufu 273165 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education College of Physics and Optoelectronics Taiyuan University of Technology Shanxi Taiyuan 030024 China

In recent years, study of spatially structured light has become an attractive and promising area, providing more ways to explore unusual and anomalous effects in optics. Here, we propose a kind of polar-segmented vector optical field (VOF), and demonstrate anomalous focal properties. Due to the appearance of the longitudinal component in tight focusing processes, the transverse energy flow and transverse spin angular momentum (SAM) are achieved, which can be applied in areas such as manipulating particles, as the transverse energy can transport the particles and the SAM can spin the particles in more degrees. Overall, the proposed polar-segmented VOF and the corresponding anomalous focal properties are crucial for advancing theories and technologies in optics, underscoring its importance in both fundamental research and practical applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Circular polarization Optical fields Optical properties Optical vortex beams Spiral phase Structured light

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Ultrahigh capacitive energy storage through dendritic nanopolar design

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Science (New York, N.Y.) 2025年第6743期388卷 211-216页

作者： Liu, Yajing Zhang, Yang Wang, Jing Yang, Chao Wang, Hongguang MacManus-Driscoll, Judith L. Yang, Hao van Aken, Peter A. Li, Weiwei Nan, Ce-Wen College of Physics MIIT Key Laboratory of Aerospace Information Materials and Physics State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and Astronautics Nanjing China College of Integrated Circuits MIIT Key Laboratory of Aerospace Integrated Circuits and Microsystem Nanjing University of Aeronautics and Astronautics Nanjing China College of Aerospace Engineering State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and Astronautics Nanjing China Max Planck Institute for Solid State Research Stuttgart Germany Department of Materials Science and Metallurgy University of Cambridge Cambridge United Kingdom State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing China

Electrostatic dielectric capacitors with ultrahigh power densities are sought after for advanced electronic and electrical systems owing to their ultrafast charge-discharge capability. However, low energy density resulting from low breakdown strength and suppressed polarization still remains a daunting challenge for practical applications. We propose a microstructural strategy with dendritic nanopolar (DNP) regions self-assembled into an insulator, which simultaneously enhances breakdown strength and high-field polarizability and minimizes energy loss and thus markedly improves energy storage performance and stability. For illustration, in this study, we achieved a high energy density of 215.8 joules per cubic centimeter with an efficiency of 80.7% at a high electric field of 7.4 megavolts per centimeter in a DNP structure-designed PbZr0.53Ti0.47O3-MgO film. The proposed strategy is generally applicable for development of high-performance dielectric microcapacitors.

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