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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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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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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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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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Early Forest Fire Recognition Method Based on C-GhostNet Network 5

Early Forest Fire Recognition Method Based on C-GhostNet Net...

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5th International Symposium on Autonomous systems, ISAS 2022

作者： Li, Mengna Zhang, Youmin Mu, Lingxia Xin, Jing Yu, Ziquan Jiao, Shangbin Liu, Han Xie, Guo Yi, Yingmin Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Shaanxi Xi'an710048 China Concordia University Industrial and Aerospace Engineering Department of Mechanical MontrealQCH3G 1M8 Canada Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing211106 China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an Shaanxi 710048 China

ISBN: (纸本)9781665487085

Early forest fire image recognition plays an important role in timely fire fighting. This paper proposes an early forest fire recognition method based on C-GhostNet network. The C-GhostNet network is an improved version of GhostNet, which mainly consists of a series of c-ghost bottlenecks, each c-ghost bottleneck mainly consists of two c-ghost modules. Through the concat operation and channel shuffle, the c-ghost module establishes the relationship between the input data and the output features generated by the original ghost module, so that GhostNet network has the ability to capture contextual features. The experimental results show that the C-GhostNet network achieves better recognition performance than GhostNet without introducing additional computational load, and the running speed reaches about 20FPS, which basically realizes real-time recognition. In addition, like GhostN et, the C-GhostNet network is also a lightweight network. © 2022 IEEE.

关键词： Image recognition

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

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Proportional-Integral Observer-Based PID control of Positive Switched systems

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Journal of systems Science & Complexity 2025年第3期 1047-1071页

作者： XING Wei ZHOU Xiaoyue ZHANG Junfeng JIA Xuan School of Information and Communication Engineering Hainan University Key Laboratory of System Control and Information Processing Ministry of Education School of Automation Hangzhou Dianzi University

This paper presents a new proportional-integral-derivative control approach for positive switched systems based on a positive proportional-integral observer. First, a positive proportional-integral observer is constructed. By using the observer state, the proportional-integral-derivative control and the corresponding integral part are designed, respectively. Using 1-norm inequality, two dynamic event-triggering conditions are established for the proportional-integral observer and proportional-integral-derivative controller, respectively. A dynamic event-triggered proportional-integral observer-based dynamic event-triggered proportional-integral-derivative controller is proposed by combining the sample state and the integral of the weighted sample output estimation error. Under the designed event-triggering conditions, an interval system with upper and lower bounds is introduced. The positivity and ?1-gain stability are achieved by realizing the according properties of the lower and upper bound systems in terms of multiple copositive Lyapunov function, respectively. All gain matrices are designed by a matrix decomposition approach and the corresponding conditions are solved by linear programming. Finally, two examples are provided to illustrate the validity of the results.

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