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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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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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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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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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Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

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Tsinghua Science and Technology 2025年第1期30卷 68-86页

作者： Yahang Qin Zhenni Li Shengli Xie Bo Li Ming Liu Victor Kuzin School of Automation Guangdong University of Technologyand also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete ManufacturingGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Information Processing and System Integration of IoTMinistry of EducationGuangzhou 510006China Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologHong Kong 999077China Academician of Russian Engineering Academy Moscow Moscow 125009Russia

Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite system(BDS).However,most existing approaches to this issue involve supervised machine learning(ML)methods,and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal *** by an autoencoder with powerful unsupervised feature extraction,we propose a new deep learning(DL)model for BDS signal recognition that places a long short-term memory(LSTM)module in series with a convolutional sparse autoencoder to create a new autoencoder ***,we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time ***,we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data,which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series ***,we add an l_(1/2) regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition *** tested our proposed approach on a real urban canyon dataset,and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods(e.g.,11%better than a support vector machine)and two existing DL-based methods(e.g.,7.26%better than convolutional neural networks).

关键词： convolutional sparse autoencoder BeiDou Navigation Satellite system(BDS) long short-term memory(LSTM) multipath classification

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Provably Safe Deep Learning-Based Offset-Free Model Predictive control and its Application to Safety-Critical Autonomous Driving

Provably Safe Deep Learning-Based Offset-Free Model Predicti...

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Robotics, control and Automation Engineering (RCAE), International Conference on

Offset-free model predictive control (MPC) provides a useful means for controlling systems with uncertainties 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 its application in systems requiring high realtime requirements, such as autonomous driving system. To address this problem, we develop a provably safe deep learning-based offset-free MPC framework. 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 MPC solution to greatly reduce online computational time. Furthermore, a gauge map is used to guarantee the satisfaction of safe constraints. The proposed MPC is used in trajectory tracking control for smart autonomous driving. The simulation results show that the proposed MPC is an order of magnitude faster than the nominal offset-free MPC in safety-critical systems.

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A Novel FL-AoI-Based control and Event-Triggered Strategy Integrating Network Characteristics for ICPS

A Novel FL-AoI-Based Control and Event-Triggered Strategy In...

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Annual Conference of Industrial Electronics Society

作者： Xuanzhao Lu Qimin Xu Jinglong Zhang Meihan Lin Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China

The joint design of control and transmission has been demonstrated to be a successful technique for enhancing the performance of industrial cyber-physical systems (ICPS). In the majority of existing works, the control cost and the transmission cost are defined independently, followed by a weighted total calculation. This approach suffers from a dimension consistency issue, leading the results to diverge from the system's actual optimal performance. Hence, it is necessary to consider the overall information of the loop and characterize the coupling relationship between control and transmission to construct the overall system performance function. This paper proposes a full loop age of information (FL-AoI) based control and transmission joint design architecture for multi-subsystem ICPS integrating multi-hop network. The state delay, input delay, and event trigger are all taken into consideration by FL-AoI to more fully portray the freshness of the information. We provide a novel control performance based on FL-AoI where the network characteristics are incorporated into the control cost, which could tackle the dimensionality mismatch brought by the form of weighted summation of the control and transmission cost. We also provide the FL-AoI-based strategy for the controller and event-triggering mechanism and derive the cost's boundary. The evaluation results demonstrate that, in comparison to the conventional joint design strategy, our solution increases the stability of the control system while decreasing the network burden.

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Aggregating Correlation Filter with Multiple Kernels Learning for Robust Visual Object Tracking 22

Aggregating Correlation Filter with Multiple Kernels Learnin...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Wei, Yanxia Xiao, Hairong Feng, Deying Mu, Jian Yang, Jie School of Mechanical and Automotive Engineering Liaocheng University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Liaocheng University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781450396110

Recently, Discriminative Correlation Filter based trackers have increasingly become popular in the domain of visual object tracking, which is benefited by their effective and robustness in terms of tracking performance. However, the significant variations of target appearance, such as Occlusion or Deformation, always suppress the performance improvement. Although the multi-feature fusion strategy partly alleviates the above factors, the synthetic scheme also ambiguates the overall presentation ability of multiple features and results in degradation of discriminative power during classification filter learning. In this paper, we present a novel tracking framework based on the correlation filter with multiple kernel learning. Instead of directly integrating different features, we decompose a tracking process into several module, including low-dimension modules and high-dimension modules. The handcraft features extracted from low-dimension modules are robust to the small variation of target while the semantic convolutional features from the high-dimension modules can distinguish the target object from the relative complex tracking environment. In addition, we also design a reliability detection mechanism based on the confidence map to accurately measure the important degree of each kernel learning and precisely estimate the target state in the subsequent frames. For verifying our proposed tracker, we conduct extensive experiments on several standard sequence benchmark. Experiment results show that our proposed trackers perform favorably against other state-of-the-art trackers. © 2022 ACM.

关键词： Semantics

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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