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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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Initial Alignment Method for SINS of UAV Based on Digital Filter Pretreatment

Initial Alignment Method for SINS of UAV Based on Digital Fi...

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2021 China Automation Congress, CAC 2021

作者： Yang, Wenbo Xu, Jie Li, Shaoyuan Wang, Jianjun Wang, Zhifeng Department of Automation Shanghai Polytechnic University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9781665426473

The two-stage method, which is divided into coarse alignment and fine alignment, is a traditional initial alignment method for SINS (Strapdown inertial navigation system, SINS). First, the coarse alignment is carried out based on the transformation relationship between the geographic information of the earth and the output of inertial measurement unit, and then the fine alignment is carried out based on optimal estimation. However, this method can mainly solve the alignment problem under angular interference. Aiming at in-motion alignment for SINS of UAVs (Unmanned aerial vehicle, UAV) under linear interference, this paper introduced the idea of digital filter and designed the digital filter by analyzing the frequency characteristic of useful signal. Then the reference velocity information is filtered to realize isolation of linear interference at specific frequency. Thus, the in-motion alignment problem under the combined action of angular interference and linear interference is effectively solved. Finally, numerical simulation results show the effectiveness of the above method. © 2021 IEEE

关键词： Digital filters

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DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds

arXiv

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arXiv 2023年

作者： Peng, Chensheng Wang, Guangming Lo, Xian Wan Wu, Xinrui Xu, Chenfeng Tomizuka, Masayoshi Zhan, Wei Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University China Mechanical Systems Control Laboratory University of California Berkeley United States

Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits feature fusion from both modalities for point-wise scene flow estimation. Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling, KNN, and ball query algorithms for local feature aggregation. To mitigate these issues in scene flow learning, we regularize raw points to a dense format by storing 3D coordinates in 2D grids. Unlike the sampling operation commonly used in existing works, the dense 2D representation 1) preserves most points in the given scene, 2) brings in a significant boost of efficiency, and 3) eliminates the density gap between points and pixels, allowing us to perform effective feature fusion. We also present a novel warping projection technique to alleviate the information loss problem resulting from the fact that multiple points could be mapped into one grid during projection when computing cost volume. Sufficient experiments demonstrate the efficiency and effectiveness of our method, outperforming the prior-arts on the FlyingThings3D and KITTI dataset. Our source codes will be released on https://***/IRMVLab/DELFlow. © 2023, CC BY.

关键词： Efficiency

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Fixed-time extended state observer-based trajectory tracking control for autonomous underwater vehicles

Fixed-time extended state observer-based trajectory tracking...

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作者： Zheng, Jiaqi Song, Lei Liu, Lingya Yu, Wenbin Zhu, Shanying Wang, Yiyin Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

In this paper, we investigate the trajectory tracking control problem for autonomous underwater vehicles (AUVs) with time-varying external disturbances of currents and waves in the sea. To solve the problem, a fixed-time extended state observer (FTESO)-based tracking controller is proposed. Firstly, by using the extended state observer, the FTESO is proposed for estimating the external disturbances and the velocities of the AUV within the fixed convergence time. Based on the FTESO, the fixed-time controller is proposed for reducing the tracking errors and improving robustness performance. Theoretical analysis is given, which shows that the unknown external disturbances can be compensated and tracking error can converge to zero within the fixed time. Simulation results show that the proposed FTESO controller not only guarantees the fixed-time convergence but also reduces the trajectory tracking errors of the AUV. © 2021 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd.

关键词： Autonomous underwater vehicles

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Distributed Short-Term Load Forecasting for Interconnecting Power systems

Distributed Short-Term Load Forecasting for Interconnecting ...

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2021 China Automation Congress, CAC 2021

作者： Guan, Yaonan Li, Dewei Xi, Yugeng Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education of China China

ISBN: (纸本)9781665426473

The social factors which exert an important influence on the load variations are difficult to adopt in centralized load forecasting models. A distributed short-term load forecasting method is proposed in this paper to compensate for the lack of social factors in load forecasting of the interconnecting power system network. In this method, we focus on the influence of social factors rather than the elements themselves. First, a classification method is proposed to arrange systems into several functional groups according to their load curve feature. Second, the interactions between systems are introduced into the model to represent social factors' influence on systems. Finally, experiments are conducted to verify the advantage of the proposed model. © 2021 IEEE

关键词： Data mining

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High-Accuracy Pose Estimation for 3D Reconstruction Considering the Manipulator Motion Constraints with an Eye-in-Hand RGB-D Camera 42

High-Accuracy Pose Estimation for 3D Reconstruction Consider...

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42nd Chinese control Conference, CCC 2023

作者： Zhou, Yanling Zhang, Xuebo Wang, Runhua Zhang, Shiyong Yuan, Mingxing Yuan, Jing Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300350 China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education China

ISBN: (纸本)9789887581543

Three-dimensional(3D) environmental reconstruction of the manipulator workspace is a prerequisite to enable autonomous collision-free path planning and subsequent task execution, wherein the positioning accuracy of the equipped sensor is one key issue to determine the performance of 3D reconstruction. In this paper, an eye-in-hand RGB-D camera is used to complete the manipulator workspace scanning and a novel high-accuracy pose estimation method for 3D reconstruction is proposed, which not only considers the geometric and photometric constraints constructed from the visual information used in traditional methods, but also considers the manipulator motion constraints since the positioning accuracy of the manipulator is usually high. Based on above three constraints, three error functions are constructed respectively and fused with different weights to form an integrated objective function. Then, the problem is converted into solving the least squares problem by the Gaussian Newton nonlinear iterative method, which involves the computation of Jacobian matrices and residuals of the three error functions. To this end, the mathematical derivation of their formulas considering the manipulator motion constraints are given in detail. By qualitative and quantitative experimental comparison with traditional methods, the proposed method can greatly improve the accuracy of pose estimation and 3D reconstruction, which always maintains the error within 3 cm, and show good robustness even in the challenging environments with weak texture or geometric features. (Supplemented video link: https://***/FkFTxssnWms) © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Manipulators

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Robust Adatpive Sliding Mode Tracking control for an AUV

Robust Adatpive Sliding Mode Tracking Control for an AUV

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2021 China Automation Congress, CAC 2021

作者： Zheng, Jiaqi Yu, Wenbin Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665426473

In our work, the research on trajectory tracking with time-varying disturbances is investigated via robust adaptive sliding mode control (RASMC). We first design a nonsingular sliding mode surface for tracking error system and show that the sliding mode surface is continuous. Then, since the underwater environment is very complex and the AUV's mathematical model is strongly nonlinear and highly coupled with uncertainties, we propose the robust adaptive law to ensure the superior robust performance of AUV system. The obtained results suggest that the proposed controller can reduce chattering and tracking error by designing a nonsingular sliding mode surface. © 2021 IEEE

关键词： Environmental regulations

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Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement Learning

Synthesis of Temporally-Robust Policies for Signal Temporal ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Siqi Wang Shaoyuan Li Li Yin Xiang Yin Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Institute of Systems Engineering Macau University of Science and Technology Taipa China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper investigates the problem of designing control policies that satisfy high-level specifications described by signal temporal logic (STL) in unknown, stochastic environments. While many existing works concentrate on optimizing the spatial robustness of a system, our work takes a step further by also considering temporal robustness as a critical metric to quantify the tolerance of time uncertainty in STL. To this end, we formulate two relevant control objectives to enhance the temporal robustness of the synthesized policies. The first objective is to maximize the probability of being temporally robust for a given threshold. The second objective is to maximize the worst-case spatial robustness value within a bounded time shift. We use reinforcement learning to solve both control synthesis problems for unknown systems. Specifically, we approximate both control objectives in a way that enables us to apply the standard Q-learning algorithm. Theoretical bounds in terms of the approximations are also derived. We present case studies to demonstrate the feasibility of our approach.

关键词： Measurement Uncertainty Q-learning Approximation algorithms Robustness Logic Task analysis

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Comprehensive study of the ultrafast photoexcited carrier dynamics in Sb_(2)Te_(3)–GeTe superlattices

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Chinese Physics B 2024年第7期33卷 381-387页

作者：叶之江金钻明蒋叶昕卢琦贾梦辉钱冬黄夏敏李舟彭滟朱亦鸣 Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation CenterShanghai Key Laboratory of Modern Optical SystemUniversity of Shanghai for Science and TechnologyShanghai 200093China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 200092China Key Laboratory of Artificial Structures and Quantum Control(Ministry of Education) Shenyang National Laboratory for Materials ScienceSchool of Physics and AstronomyShanghai Jiao Tong UniversityShanghai 200240China Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Precision Spectroscopy East China Normal UniversityShanghai 200062China GBA Branch of Aerospace Information Research Institute Chinese Academy of SciencesGuangzhou 510700China Guangdong Provincial Key Laboratory of Terahertz Quantum Electromagnetics Guangzhou 510700China University of Chinese Academy of Sciences Beijing 100039China

Chalcogenide superlattices Sb_(2)Te_(3)-GeTe is a candidate for interfacial phase-change memory(iPCM) data storage *** employing terahertz emission spectroscopy and the transient reflectance spectroscopy together,we investigate the ultrafast photoexcited carrier dynamics and current transients in Sb_(2)Te_(3)-GeTe *** orientation and excitation polarization dependences of the THz emission confirm that ultrafast thermo-electric,shift and injection currents contribute to the THz generation in Sb_(2)Te_(3)-GeTe *** decreasing the thickness and increasing the number of GeTe and Sb_(2)Te_(3) layer,the interlayer coupling can be enhanced,which significantly reduces the contribution from circular photo-galvanic effect(CPGE).A photo-induced bleaching in the transient reflectance spectroscopy probed in the range of~1100 nm to~1400 nm further demonstrates a gapped state resulting from the interlayer *** demonstrates play an important role in the development of iPCM-based high-speed optoelectronic devices.

关键词： Sb_(2)Te_(3)/GeTe superlattices ultrafast carrier dynamics interfacial phase change memory THz emission spectroscopy transient reflectance spectroscopy

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Velocity-Varying Target Tracking of Mobile Sensor Network Based on Flocking control

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Journal of Shanghai Jiaotong university(Science) 2021年第4期26卷 446-453页

作者： ZHANG Lalu Xiangxiang Dong YAO Lixiu CAI Yunze Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai200240China Department of Automation Shanghai Jiao Tong UniversityShanghai200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong UniversityShanghai200240China

Existing coupled distributed estimation and motion control strategies of mobile sensor networks present limitations in velocity-varying target tracking. Therefore, a velocity-varying target tracking algorithm based on flocking control is proposed herein. The Kalman-consensus filter is utilized to estimate the position, velocity and acceleration of a target. The flocking control algorithm with a velocity-varying virtual leader enables the position of the center of the mobile sensor network to converge to that of the target. By applying an effective cascading Lyapunov method, stability analysis is performed. Simulation results are provided to validate the feasibility of the proposed algorithm.

关键词： mobile sensor network velocity-varying target tracking flocking control Kalman-consensus filtering

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