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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Li, Zhi Xin, Jinghao Li, Ning Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Autonomous exploration in unknown environments is a significant capability for mobile robots. In this paper, we present an end-To-end autonomous exploration model based on deep reinforcement learning (DRL), which takes the sensor data and a novel exploration map as inputs, and directly outputs the motion control commands of the robot. In contrast to the existing DRL-based exploration methods, the proposed model has no requirements to be combined with the traditional exploration or navigation algorithms, resulting in lower computational complexity. We directly transfer the DRL-based model trained in the training map to four test maps with different sizes and layouts, and the results show that the robot can rapidly adapt to unknown scenes. Besides, a comparison study with RRT-exploration algorithm indicates that the proposed model can reach a higher map exploration rate within less distance and time. Furthermore, we also conduct experiments on the real physical robot to demonstrate the transferability of learned policy from simulation to reality. A video of our experiments in the Gazebo simulator and real world can be found here1 © 2022 IEEE.

关键词： Reinforcement learning

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Boosting Unsupervised Monocular Depth Estimation with Auxiliary Semantic information

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China Communications 2021年第6期18卷 228-243页

作者： Hui Ren Nan Gao Jia Li State Key Laboratory of Media Convergence and Communication、Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism(Communication University of China)、Beijing Key Laboratory of Modern Entertainment Technology(Communication University of China)、School of Information and Communication EngineeringCommunication University of China.No.1Dingfuzhuang StreetChaoyang DistrictBeijing100024China

Learning-based multi-task models have been widely used in various scene understanding tasks,and complement each other,i.e.,they allow us to consider prior semantic information to better infer *** boost the unsupervised monocular depth estimation using semantic segmentation as an auxiliary *** address the lack of cross-domain datasets and catastrophic forgetting problems encountered in multi-task training,we utilize existing methodology to obtain redundant segmentation maps to build our cross-domain dataset,which not only provides a new way to conduct multi-task training,but also helps us to evaluate results compared with those of other *** addition,in order to comprehensively use the extracted features of the two tasks in the early perception stage,we use a strategy of sharing weights in the network to fuse cross-domain features,and introduce a novel multi-task loss function to further smooth the depth *** experiments on KITTI and Cityscapes datasets show that our method has achieved state-of-the-art performance in the depth estimation task,as well improved semantic segmentation.

关键词： unsupervised monocular depth estimation semantic segmentation multi-task model

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Helicopter Rotor Fault Diagnosis based on IPSO and RVM 14

Helicopter Rotor Fault Diagnosis based on IPSO and RVM

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Si, Xiaoyi Zheng, Liang Zheng, Guo Wang, Jinglin Zhao, Zhenping Guo, Ziang School of Electronic Information Hangzhou Dianzi University Hangzhou China AVIC United Technology Center for Basic Research on Aircraft System Fault Diagnosis and Health Management Shanghai Institute of Aerospace Control Engineering Shangai China Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management Shanghai Institute of Aerospace Control Engineering Shangai China

ISBN: (纸本)9798350301359

To address the issue of low accuracy in traditional rotor fault diagnosis during the helicopter rotor fault diagnosis process, this paper presents a fault diagnosis method based on the Improved Particle Swarm Optimization (IPSO) algorithm for Relevance Vector Machine (RVM). The fitness function of the IPSO algorithm is defined as the mean squared error between the predicted classification results obtained through cross-validation and the actual classification. The IPSO algorithm is used to search for the optimal width factor of the RVM kernel function and establish the fault diagnosis model. Through simulation experiments and comparisons with Particle Swarm Optimization (PSO) algorithm-based RVM and Improved Particle Swarm Optimization-based Support Vector Machine (SVM), it is demonstrated that this approach, compared to traditional rotor fault diagnosis methods, effectively improves the accuracy of fault recognition. © 2023 IEEE.

关键词： Fault Diagnosis Helicopter Rotor Improved Particle Swarm Optimization Relevance Vector Machine

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Fault Detection Method for Insulators Using Improved YOLOv4

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第4期7卷 818-834页

作者： Zhao, Li-Quan Jiang, Zi-Cong Teng, Zi-Ming Jia, Yan-Fei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University 168 Changchun Road Jilin132012 China Graduate school of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan College of information and communication Jilin University Changchun130012 China School of Electrical and Information Engineering Beihua University Jilin132013 China

A defective insulator detection method based on improved You Only Look Once version 4 (YOLOv4) is proposed to improve the precision of defective insulator detection. This method designs a dual-branch attention block by simulating the attention process of the human eye, and introduces it into the backbone of YOLOv4 to extract richer information on defective insulator features. To make the output feature map of shallow layer network fuse more different scale features, a path aggregation network has been designed. To improve detection accuracy, the spatial pyramid pooling has been replaced by atrous spatial pyramid pooling. A dynamic non-maximum suppression method is proposed to improve defective insulator detection accuracy. It employs an adaptive threshold control strategy and weighted operation to full use of the predicted box. We compare our method with defective insulator detection methods based on YOLO and R-CNN. Our simulation results show that the proposed method is more accurate than defective insulator detection methods based on YOLO, and has a faster detection speed than methods based on R-CNN. Compared with the defective insulator detection method based on R-CNN that has the highest mean average precision, the mean average precision of the proposed method is lower, but its detection speed is faster. Compared with YOLOv4, its detection speed is slower, but the mean average precision is higher. The proposed method strikes a satisfactory tradeoff between detection speed and mean average precision. © 2022, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Object detection

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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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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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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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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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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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Chlorine-Substituent Regulation in Dopant-Free Small-Molecule Hole-Transport Materials Improves the Effi ciency and Stability of Inverted Perovskite Solar Cells

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Transactions of Tianjin University 2024年第4期30卷 314-323页

作者： Xinyi Liu Xiaoye Zhang Zhanfeng Li Jinbo Chen Yanting Tian Baoyou Liu Changfeng Si Gang Yue Hua Dong Zhaoxin Wu Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi ProvinceTaiyuan University of TechnologyTaiyuan 030024China School of Electrical Engineering University of South ChinaHengyang 421001China College of Physics and Optoelectronics Taiyuan University of TechnologyTaiyuan 030024China Ningxia Hui Autonomous Region Screen Display Organic Materials Engineering Technology Research Center Ningxia Sinostar Display Material Co.Ltd.Yinchuan 750003China Organic Semiconductor Centre EaStCHEM School of ChemistryUniversity of St AndrewsFife KY169STUK Key Laboratory of Photonics Technology for Information School of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China

Although doped hole-transport materials(HTMs)off er an effi ciency benefi t for perovskite solar cells(PSCs),they inevi-tably diminish the ***,we describe the use of various chlorinated small molecules,specifi cally fl uorenone-triphenylamine(FO-TPA)-x-Cl[x=para,meta,and ortho(p,m,and o)],with diff erent chlorine-substituent positions,as dopant-free HTMs for *** chlorinated molecules feature a symmetrical donor-acceptor-donor structure and ideal intramolecular charge transfer properties,allowing for self-doping and the establishment of built-in potentials for improving charge *** effi cient hole-transfer interfaces are constructed between perovskites and these HTMs by strategi-cally modifying the chlorine ***,the chlorinated HTM-derived inverted PSCs exhibited superior effi ciencies and air ***,the dopant-free HTM FO-TPA-o-Cl not only attains a power conversion effi ciency of 20.82% but also demonstrates exceptional stability,retaining 93.8%of its initial effi ciency even after a 30-day aging test conducted under ambient air conditions in PSCs without *** fi ndings underscore the critical role of chlorine-substituent regulation in HTMs in ensuring the formation and maintenance of effi cient and stable PSCs.

关键词： Hole-transport materials Inverted perovskite solar cells Chlorinated small molecules Donor–acceptor–donor structure

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