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arXiv 2024年

作者： Wang, Yanbo Zhao, Wentao Cao, Chuan Deng, Tianchen Wang, Jingchuan Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

Although LiDAR semantic segmentation advances rapidly, state-of-the-art methods often incorporate specifically designed inductive bias derived from benchmarks originating from mechanical spinning LiDAR. This can limit model generalizability to other kinds of LiDAR technologies and make hyperparameter tuning more complex. To tackle these issues, we propose a generalized framework to accommodate various types of LiDAR prevalent in the market by replacing window-attention with our sparse focal point modulation. Our SFPNet is capable of extracting multi-level contexts and dynamically aggregating them using a gate mechanism. By implementing a channel-wise information query, features that incorporate both local and global contexts are encoded. We also introduce a novel large-scale hybrid-solid LiDAR semantic segmentation dataset for robotic applications. SFPNet demonstrates competitive performance on conventional benchmarks derived from mechanical spinning LiDAR, while achieving state-of-the-art results on benchmark derived from solid-state LiDAR. Additionally, it outperforms existing methods on our novel dataset sourced from hybrid-solid LiDAR. Code and dataset are available at https://***/Cavendish518/SFPNet and https://***. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A Probabilistic Topology Inference Method for Networked Dynamical system via Single Excitation

A Probabilistic Topology Inference Method for Networked Dyna...

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IEEE Conference on Decision and control

作者： Qing Jiao Yushan Li Jianping He Dept. of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

A growing number of works have investigated inferring the topology of networked dynamical systems from observations, such as to better understand the system behaviour. Despite the tremendous advances, most of them require the observations to be abundant. This paper focuses on inferring the topology by injecting single excitation on a node and collecting several steps of noisy observations. The problem is challenging because the noises cannot be depressed in several observations and are mixed with the injected excitation, making it hard to directly reveal the topology. To practice, we develop a probabilistic method based on the hypothesis test framework. First, we infer the neighbors that are within h-hop of the excited node and derive the accuracy guarantees. Then, we extend the method to infer the exact h-hop neighbors. A computable lower bound for the accuracy probability is established to provide confidence support in the inference procedures. Furthermore, we give the conditions of excitation input to ensure a desired inference probability, which provides guidance for the input design. Numerical simulations are conducted to verify the effectiveness of the proposed method.

关键词： Lower bound Accuracy Network topology Noise Probabilistic logic Numerical simulation Topology Noise measurement Reliability Dynamical systems

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Topology-Preserving Second-Order Consensus: A Strategic Compensation Approach

Topology-Preserving Second-Order Consensus: A Strategic Comp...

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IEEE Conference on Decision and control

作者： Zitong Wang Yushan Li Xiaoming Duan Jianping He Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

The interaction topology plays a significant role in the collaboration of multiagent systems. How to preserve the topology against inference attacks has become an imperative task for security concerns. In this paper, we propose a distributed topology-preserving algorithm for second-order multi-agent systems by adding noisy inputs. The major novelty is that we develop a strategic compensation approach to overcome the noise accumulation issue in the second-order dynamic process while ensuring the exact second-order consensus. Specifically, we design two distributed compensation strategies that make the topology more invulnerable against inference attacks. Furthermore, we derive the relationship between the inference error and the number of observations by taking the ordinary least squares estimator as a benchmark. Extensive simulations are conducted to verify the topology-preserving performance of the proposed algorithm.

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Differential Game with Mixed Strategies: A Weak Approximation Approach

Differential Game with Mixed Strategies: A Weak Approximatio...

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IEEE Conference on Decision and control

作者： Tao Xu Wang Xi Jianping He Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

This paper utilizes the weak approximation method to analyze differential games that involve mixed strategies. Mixed strategies have the potential to produce unique strategic behaviors, whereas traditional models and tools in pure strategy games cannot be directly applied. Based on the stochastic processes with independent increments, we define the mixed strategy without assuming the knowledge of the opponents' strategy and system state. However, this general mixed strategy poses challenges in evaluating game payoff and game value. To overcome these challenges, we utilize the weak approximation method to employ a stochastic differential game to characterize the dynamics of the mixed strategy game. We demonstrate that the game's payoff function can be precisely approximated with an error of the same scale as the step size. Furthermore, we estimate the upper and lower value functions of the weak approximated game to analyze the existence of game value. Finally, we provide numerical examples to illustrate and elaborate on our findings.

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Robust Stability of Linear Time-Invariant systems with Hybrid Uncertainties

Robust Stability of Linear Time-Invariant Systems with Hybri...

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Automation, control and Electronics Engineering (CACEE), International Conference on

作者： Yangxuan Li Junguo Lu Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai People’s Republic of China

This paper presents a study on the robust stability analysis of linear time-invariant systems with parameter uncertainties and norm-bounded uncertainties. By utilizing the structured singular value, necessary and sufficient conditions for robust stability are derived. Based on the stability condition, the stability margin of the uncertain system is obtained from the skewed structured singular value. Additionally, numerical simulation results are provided to validate the effectiveness of the proposed methods.

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Adaptive Multi-weight Infrared and Visible Image Fusion via Multi-scale Transformation

Adaptive Multi-weight Infrared and Visible Image Fusion via ...

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第42届中国控制会议

作者： Tongyuehao Zhou Monan Zhang Yichen Li Wei Zhou Wenbin Yu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China PLA 91550

In image fusion,the desirable fused image is to obtain advantage information from different images of the same *** for the fusion of the infrared image and the visible image that have distinct features,this paper proposes an adaptive multiweight fusion based on multi-scale *** method designs different weight matrices according to the characteristics of the infrared image and the visible *** can also adaptively adjusts the weight size according to the *** on the difference of information entropy between infrared images and visible images,the method of this paper can keep the important information as much as *** results prove the method of this paper is fast and *** also has certain superiority compared with other methods.

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Semi-Supervised Learning for Low-Light Image Enhancement by Pseudo Low-Light Image

Semi-Supervised Learning for Low-Light Image Enhancement by ...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Shuo Xie Yingjie Ma Wei Xu Song Qiu Yifei Sun School of Communication and Electronic Engineering East China Normal University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai

Building paired datasets in low-light enhancement entails significant cost and time, making such datasets precious commodities. Many researchers focus on how to enable models to learn more information from limited datasets. A prevalent strategy involves employing semi-supervised learning techniques to enhance model performance through additional unpaired images. However, one of the main challenges faced is the scarcity of a vast number of unpaired images from the same domain as the original low-light images. Consequently, we introduce a semi-supervised image enhancement method using pseudo-low-light images. Initially, we generate pseudo low-light images with less noise compared with the source domain image by the Signal-to-Noise Ratio prior and diffusion models. We then employ the Mean-Teacher network and the feature constraints of the pseudo-low-light images to realize low-light image enhancement. Comprehensive experimental results validate the efficacy of our approach on real-world datasets.

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OGDM: An Observability Guaranteed Distributed Edge Sensing Method for Industrial Cyber-Physical systems

OGDM: An Observability Guaranteed Distributed Edge Sensing M...

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American control Conference (ACC)

作者： Shigeng Wang Zhiduo Ji Cailian Chen Department of Automation Ministry of Education of China Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Shanghai China

The new generation of industrial cyber-physical systems (ICPS) supported by the edge computing technology enables efficient distributed sensing under massive data volumes and frequent transmissions. Observability is essential to obtain good sensing performance, and most of existing sensing works directly assume that the system is observable. However, it is difficult to satisfy the assumption with the increasingly expanded network scale and dynamic scheduling of devices. To solve this problem, we propose an observability guaranteed distributed method (OGDM) for edge sensing with the cooperation of sensors and edge computing units (ECUs). We analyze the relationship between sensor scheduling and observability based on the network topology and graph signal processing (GSP) technology. In addition, we transform the observability condition into a convex form and take into account sensing error and energy consumption for optimization. Finally, our algorithm is applied to estimate the slab temperature in the hot rolling process. The effectiveness is verified by simulation results.

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SGDA: A Saliency-Guided Domain Adaptation Network for Nighttime Semantic Segmentation

SGDA: A Saliency-Guided Domain Adaptation Network for Nightt...

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Industrial Cyber-Physical systems (ICPS)

作者： Yijia Duan Jingzheng Tu Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China

Nighttime semantic segmentation has attracted considerable attention due to its crucial status in the smart city. However, it is challenging to handle poor illumination and indiscernible information. To tackle these problems, a saliency-guided domain adaptation network, SGDA, is proposed via adapting daytime models to nighttime scenes. Firstly, a saliency guidance branch is attached to the segmentation network to enrich the spatial features and guide the model to better perceive detail information. Secondly, to embed the saliency guidance to the segmentation network, a pyramid attention architecture is designed to fuse the features from the two branches. Thirdly, an illumination adaptation module is constructed to close the intensity distributions via adversarial learning, with an elaborately designed loss function to improve the performance. Extensive experiments on Dark Zurich dataset and Nighttime Driving dataset validate the effectiveness of SGDA, and indicate that our method improves the accuracy on small object categories,

关键词： Adaptation models Fuses Smart cities Semantic segmentation Semantics Lighting Cyber-physical systems

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Safe Deep Learning-Based Offset-Free MPC for Green Hydrogen Injected PEMFC systems

Safe Deep Learning-Based Offset-Free MPC for Green Hydrogen ...

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Chinese Automation Congress (CAC)

作者： Han Wang Longyan Li Chao Ning Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Safe and stable operation of a proton exchange membrane fuel cell (PEMFC) system requires stringent control of oxygen excess ratio (OER). However, the OER regulation in PEMFC is challenging due to frequent fluctuations of current, various modeling nonlinearities, constrained manipulated variable, and real-time requirements. Offset-free model predictive control (MPC) provides a useful means for controlling systems with disturbances and constraints, but suffers from the heavy computational burden of repeatedly solving an optimization problem in real time. Such computational issue precludes the possibility of meeting the real-time requirements of PEMFC. In this paper, a PEMFC cathode gas supply model is firstly established. Next, we develop a safe deep learning-based offset-free MPC algorithm. Based on the nominal offset-free MPC, the proposed MPC not only reserves the ability of disturbance rejection, but also leverages deep neural networks for approximating the explicit solution to the MPC problem to greatly reduce online computational time. Furthermore, a gauge map is used to guarantee the satisfaction of safe constraints regarding compressor voltage. The simulation results show that the proposed MPC is an order of magnitude faster than the nominal offset-free MPC.

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