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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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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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MFGB: Molecular Properties Prediction leveraging Self-supervised Morgan Fingerprint Representation Learning

MFGB: Molecular Properties Prediction leveraging Self-superv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Fang, Shuyan Liu, Yu Liu, Song Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Prov. Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Wuhan University of Science and Technology Hubei Provincial Key Laboratory of Occupational Hazard Identification and Control Hubei Province Wuhan China

ISBN: (纸本)9798350337488

Simple molecular graphs or molecular line notations are insufficient for molecular representation learning models that automatically learn molecule representations to acquire deep semantic features about chemistry. Therefore, we proposed the Morgan Fingerprint Graph, a novel molecular graph model constructed with the nodes of the Morgan Fingerprint Identifier, whose nodes incorporate various properties of the atom neighborhood. Based on our Morgan Fingerprint Graph, we trained a BERT-based molecular representation model MFGB which employs a local rather than global attention module to adapt the model to the Morgan Fingerprint Graph. The attention module of MFGB regulates the exchange of information in the attention layer based on the connection information of the atoms in the molecules. To verify the effectiveness of MFGB, we selected several mainstream datasets for evaluation. The results demonstrated that our method improve the performance of molecular properties prediction. In addition, we analyzed MFGB from both the over-smoothing and the attention perspectives. The analysis indicates that the node vector representations learned from the Morgan Fingerprint Graph are generally less smoothing, and the attention distributions of MFGB match the partial charge density distribution of the molecules. © 2023 IEEE.

关键词： Molecular property prediction Molecular representation learning Morgan Fingerprint Self-supervised learning

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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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Integrating Regularization and PnP Priors for SAR Image Reconstruction Using Multi-agent Consensus Equilibrium

Integrating Regularization and PnP Priors for SAR Image Reco...

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2024 International Workshop on the Theory of Computational Sensing and its Applications to Radar, Multimodal Sensing and Imaging, CoSeRa 2024

作者： Fan, Yizhe Liu, Mingqian Shao, Mingxiao Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering China Aeronautical Remote Sensing Center Chinese Academy of Sciences China

ISBN: (纸本)9798350365504

Multi-agent consensus equilibrium mechanism is a generalization of popular used PnP-ADMM method and composite regularization in computational sensing. We propose a novel SAR image sparse reconstruction method based on MACE mechanism. High-quality SAR images necessitate low ambiguity, high SNR, and low coherent speckle. We therefore integrate regularization priors and PnP priors for azimuth ambiguity suppression, sparsity inducing and despeckling. The proposed method's performance is evaluated through experiments on both simulated and QILU-1 satellite SAR data. © 2024 IEEE.

关键词： Image reconstruction

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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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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Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-control for Intelligent Vehicles

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1658-1672页

作者： Hong Mo Yinghui Meng Fei-Yue Wang Dongrui Wu the College of Electric and Information Engineering Changsha University of Science and TechnologyChangsha 410114China the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Qingdao Academy of Intelligent Industries Qingda 266109 the Center of China Economic and Social Security University of Chinese Academy of SciencesBeijing 100190China the Ministry of Education Key Laboratory on Image Information Processing and Intelligent Control the School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

Intelligent vehicles can effectively improve traffic congestion and road traffic *** cruise followingcontrol(ACFC)is a vital part of intelligent *** this paper,a new hierarchical vehicle-following control strategy is presented by synthesizing the variable time headway model,type-2 fuzzy control,feedforward+fuzzy proportion integration(PI)feedback(F+FPIF)control,and inverse longitudinal dynamics model of ***,a traditional variable time headway model is improved considering the acceleration of the lead ***,an interval type-2 fuzzy logic controller(IT2 FLC)is designed for the upper structure of the ACFC system to simulate the driver's operating *** reduce the nonlinear influence and improve the tracking accuracy for the desired acceleration,the control strategy of F+FPIF is given for the lower control ***,the lower control method proposed in this paper is compared with the fuzzy PI control and the traditional method(no lower controller for tracking desired acceleration)***,the proportion integration differentiation(PID),linear quadratic regulator(LQR),subsection function control(SFC)and type-1 fuzzy logic control(T1 FLC)are respectively compared with the IT2 FLC in control performance under different ***,the simulation results show the effectiveness of IT2 FLC for the upper structure and F+FPIF control for the lower structure.

关键词： Adaptive cruise following-control(ACFC) feedforward+fuzzy proportion integration(PI)feedback(F+FPIF) type-2 fuzzy control variable time headway

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SAR Tomographic Inversion of Urban Areas via Morphology Regularization 15

SAR Tomographic Inversion of Urban Areas via Morphology Regu...

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15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Li, Jie Li, Zhiyuan Fan, Yizhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing101408 China

ISBN: (纸本)9783800762873

The current synthetic aperture radar (SAR) images with ultra high resolution provide the detailed structures of the urban areas, which are often utilized to retrieve 3D spatial information of the detailed structures based on tomographic synthetic aperture radar (TomoSAR). Compressive sensing (CS), as a favorable sparse reconstruction technique has been widely used in TomoSAR, however, the sparsity constraints by CS algorithms often lose detailed structures of the targets due to the pixel-wise processing. In this paper, we apply the morphology regularization as a prior term to form a novel approach based on the CS algorithm. The morphology regularization can shrink the concatenations caused by outliers in the iterative reconstruction, enhancing the detailed structural properties of reconstructed targets. And the alternating direction method of multipliers (ADMM) is applied, where the Bregman iteration is adopted for solving the sub-problem with the morphology regularization. Both simulation experiments and tests on real data show that the proposed method can suppress the outliers and preserve the detailed structures of tomographic reconstruction, leading to improved correctness and completeness. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Morphology

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Aβ-Aggregation-Generated Blue Autofluorescence Illuminates Senile Plaques as well as Complex Blood and Vascular Pathologies in Alzheimer’s Disease

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Neuroscience Bulletin 2024年第8期40卷 1115-1126页

作者： Hualin Fu Jilong Li Chunlei Zhang Peng Du Guo Gao Qiqi Ge Xinping Guan Daxiang Cui Institute of Nano Biomedicine and Engineering School of Sensing Science and EngineeringSchool of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China National Center for Translational Medicine Shanghai Jiao Tong UniversityShanghai 200240China Department of Colorectal Surgery School of MedicineXinhua HospitalShanghai Jiao Tong UniversityShanghai 200092China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China The Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China

Senile plaque blue autofluorescence was discovered around 40 years ago,however,its impact on Alzheimer’s disease(AD)pathology has not been fully *** analyzed senile plaques with immunohistochemistry and fluorescence imaging on AD brain sections and also Aβ aggregation in *** DAPI or Hoechst staining,the nuclear blue fluorescence could only be correctly assigned after subtracting the blue plaque *** flower-like structures wrapping dense-core blue fluorescence formed by cathepsin D staining could not be considered central-nucleated neurons with defective lysosomes since there was no nuclear staining in the plaque core when the blue autofluorescence was *** Aβ self-oligomers and Aβ/hemoglobin heterocomplexes generated blue *** Aβ amyloid blue autofluorescence not only labels senile plaques but also illustrates red cell aggregation,hemolysis,cerebral amyloid angiopathy,vascular plaques,vascular adhesions,and *** summary,we conclude that Aβ-aggregation-generated blue autofluorescence is an excellent multi-amyloidosis marker in Alzheimer’s disease.

关键词： Senile plaque Blue autofluorescence Aβ Amyloid aggregation Hemoglobin Cathepsin D.Microaneurysm Alzheimer's disease

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