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Multi-scale network for single image deblurring based on ensemble learning module

Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Insulator Defect Detection Based on YOLOv4-tiny with Improved Feature Fusion 12

Insulator Defect Detection Based on YOLOv4-tiny with Improve...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Zhong, Yuning Hu, Rong Li, Zuoyong Cai, Yuanzheng Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Insulator exposed to a wild environment for long-term running usually suffers from the damage of natural calamities that makes them become one of the most fragile components in the whole power system. In this case, even a slight fault will seriously affect the power supply safety of the power grid. Therefore, it is critical to find and identify the faulty insulator efficiently and accurately. However, an insulator is quite a small component in comparison with a huge power system which makes it hard to visible with naked eyes. This characteristic leads to poor recognition accuracy for existing detection methods. In order to solve the above problems, we propose an insulator defect detection algorithm based on an improved YOLOv4-tiny model. It differs from existing insulator detection models, our algorithm applies the effective channel attention network to the feature extraction network of YOLOv4-tiny, which significantly enhances the feature extraction ability of the backbone network. These two-way features of the pyramid can be fully fused in the two-way feature fusion stages seamlessly. Experimental results show that compared with, our proposed algorithm outperforms Faster RCNN and YOLOv3 in average classification accuracy, missed detection, and false detection. © 2022 IEEE.

关键词： Feature extraction

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Hmt: Hybrid Mechanism Transformer for Bio-Fabrication Prediction Under Complex Environmental Conditions

SSRN

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SSRN 2023年

作者： Song, Yichen Xu, Hu Li, Changdi He, Qunshan Tian, Zijian Liu, Xinggao State Key Laboratory of Industry Control Technology College of Control Science & Engineering Zhejiang University Hangzhou310027 China

Hybrid models that integrate mechanism-based and data-driven approaches have not gained widespread adoption in the field of bio-fabrication, primarily due to the inherent complexities of the fabrication process and the limited quality of available data for bio-fabrication. Addressing these limitations, this study presents a novel hybrid model based on the mechanism feature network and the Transformer-based data-driven model (HMT) that effectively overcomes these challenges. The HMT combines mechanism modeling with data-driven techniques to accurately predict the target sequence variables in the bio-fabrication process. Initially, the HMT explores the feature relationships between the time domain and the space domain of the target sequence variables through a mechanism model, facilitating the fusion of these variables across both domains. Subsequently, the fused target variables are modeled and predicted using the Transformer-based model. To achieve high-precision predictions, HMT incorporates feature dimension enhancement, attention weighting, and feature disassembly consecutively. Comparative analysis with various data-driven models, particularly those employing artificial neural network method in the field of bio-fabrication, reveals that the hybrid model outperforms others in terms of mean square error, mean absolute error, mean absolute percentage error, robustness performance, and Theil's U statistic. Moreover,The validity of the feature network based on the mechanism model and the feature fusion method in HMT are proved. The state-of-the-art performance of the HMT in long sequence prediction, periodic prediction, and short sequence prediction confirms its feasibility and potential for industrial applications. © 2023, The Authors. All rights reserved.

关键词： Fabrication

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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Path Preview Tracking for Autonomous Vehicles Based on Model Predictive control

Path Preview Tracking for Autonomous Vehicles Based on Model...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Lu, Yanrong Zhu, Jia Wang, Zhiwen Du, Nan Zhang, Jindou College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China Lanzhou University of Technology Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou China Lanzhou University of Technology National Demonstration Center for Experimental Electrical and Control Engineering Education Lanzhou China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

For the autonomous vehicles path tracking problem, a preview tracking strategy is proposed based on model predictive control (MPC) in this paper. First, under the scenario that the road ahead information is available, an augmented error system is constructed based on the prediction tracking error, state difference and preview information. By introducing an appropriate objective function, the original path preview tracking problem is transformed into an optimization problem of the unconstrained predictive control. Second, the optimal prediction control sequence is obtained according to the minimum value principle, and its first component, which contains the non-causal tracking error, state feedback and preview feedforward, is utilized to perform the receding-horizon control. Furthermore, the optimization problem of the constrained predictive control is also considered. Finally, the platform of CarSim and Simulink is adopted to verify the effectiveness of the path preview tracking. © 2022 IEEE.

关键词： Model predictive control

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controlled on-chip fabrication of large-scale perovskite single crystal arrays for high-performance laser and photodetector integration

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Light(science & Applications) 2023年第4期12卷 558-569页

作者： Zhangsheng Xu Xun Han Wenqiang Wu Fangtao Li Ru Wang Hui Lu Qiuchun Lu Binghui Ge Ningyan Cheng Xiaoyi Li Guangjie Yao Hao Hong Kaihui Liu Caofeng Pan CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 101400China School of Nanoscience and Technology University of Chinese Academy of SciencesBeijing 100049China College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060China Colllege of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060China Center on Nanoenergy Research School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China College of Materials Science and Engineering Ocean University of ChinaQingdao 266100China State Key Laboratory for Mesoscopic Physics Frontiers Science Centre for Nano-optoelectronicsSchool of PhysicsPeking UniversityBeijing 100871China Songshan Lake Materials Laboratory Dongguan 523808China

Metal halide perovskites possess intriguing optoelectronic properties,however,the lack of precise control of on-chip fabrication of the large-scale perovskite single crystal arrays restricts its application in integrated ***,we report a space confinement and antisolvent-assisted crystallization method for the homogeneous perovskite single crystal arrays spanning 100 square centimeter *** method enables precise control over the crystal arrays,including different array shapes and resolutions with less than 10%-pixel position variation,tunable pixel dimensions from 2 to 8 pm as well as the in-plane rotation of each *** crystal pixel could serve as a high-quality whispering gallery mode(WGM)microcavity with a quality factor of 2915 and a threshold of 4.14μJ cm^(-2).Through directly on-chip fabrication on the patterned electrodes,a vertical structured photodetector array is demonstrated with stable photoswitching behavior and the capability to image the input patterns,indicating the potential application in the integrated systems of this method.

关键词： arrays crystal precise

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Self-Learning Temporal-Spatial Graph Model for industrial Soft Sensing Application

Self-Learning Temporal-Spatial Graph Model for Industrial So...

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Data Driven control and Learning Systems (DDCLS)

作者： Chiye Zhang Le Yao Zhichao Chen Bingbing Shen Jiusun Zeng State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Mathematics Hangzhou Normal University Hangzhou China

In recent years, deep learning (DL) has been widely studied in soft sensing due to its advantages in feature extraction and nonlinear fitting. However, DL models are black-box models, which are difficult to explain. It is difficult to introduce the prior knowledge about the process into the model, and these shortcomings hinder its application in the actual industrial processes. In this paper, a Self-Learning Temporal-Spatial Graph (SLTSG) deep learning model is proposed and applied to the soft sensor modeling of complex dynamic industrial processes. It uses the graph attention network to construct the explicit nonlinear relationship between variables, and uses its ability to aggregate information to complete the feature extraction from the graph for each time step. Then, the spatio-temporal convolutional network is utilized to extract local nonlinear dynamic features for the prediction of quality variables. Since it is difficult and subjective to obtain graph structure by relying on prior knowledge such as process mechanism, a self-learning method of graph structure is adopted in this paper. To verify the effectiveness of the proposed method, the soft sensing case study of carbon monoxide content in the process of High-Low Transformer is presented. The experimental results show that the model not only has high accuracy, but also has certain knowledge discovery ability.

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Lycorine derivative effectively inhibits the replication of coronaviruses both in vitro and in vivo

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hLife 2024年第2期2卷 75-87页

作者： Liang Shen Jianzhong Zhao Ying Xia Junjie Lu Jiali Sun Jian Tang Hui Xing Lijuan Yin Yang Yang Chunhua Wang Department of Clinical Laboratory Xiangyang Central HospitalAffiliated Hospital of Hubei University of Arts and ScienceHubeiChina Shenzhen Key Laboratory of Pathogen and Immunity National Clinical Research Center for Infectious DiseaseState Key Discipline of Infectious DiseaseShenzhen Third People’s HospitalSecond Hospital Affiliated to Southern University of Science and TechnologyGuangdongChina Department of Clinical Laboratory Xiangyang No.1 People’s HospitalHubei University of MedicineHubeiChina State Key Laboratory of Food Nutrition and Safety Key Laboratory of Industrial MicrobiologyMinistry of EducationTianjin Key Laboratory of Industry MicrobiologyNational and Local United Engineering Lab of Metabolic Control Fermentation TechnologyChina International Science and Technology Cooperation Base of Food Nutrition/Safety and Medicinal ChemistryCollege of BiotechnologyTianjin University of Science&TechnologyTianjinChina

The established and ongoing prevalence of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)and seasonal human coronaviruses(HCoV)like HCoV-OC43,HCoV-NL63,and HCoV-229E,pose a continuous threat to public ***,it is urgently needed to explore antiviral drugs with broad-spectrum anti-coronavirus *** previous studies have revealed that lycorine is a potent broad-spectrum anti-coronavirus drug,a natural alkaloid extracted from Amaryllidaceae with various pharmacological and microbiological ***,it is unsafe to directly use lycorine as a clinical antiviral drug due to the cytotoxicity and induction of cell *** this study,a series of lycorine derivatives were designed and *** of them,named Ly-8,was found to effectively inhibit the replication of different coronavirus strains in vitro,including ***,Ly-8 was also shown to effectively inhibit HCoVOC43 replication in the central nervous system,and provide effective protection against HCoV-OC43 infection in mice with low drug ***,Ly-8-resistant mutants were not observed during the 30 times sequential passages in cell ***,these findings suggest that Ly-8 may be a potential candidate drug for the future development of broad-spectrum anti-coronavirus drugs.

关键词： broad-spectrum antiviral drug lycorine derivative Ly-8

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Carbendazim residue in plant-based foods in China:Consecutive surveys from 2011 to 2020

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Environmental science and Ecotechnology 2024年第1期17卷 109-118页

作者： Dou Wang Guiling Yang Xiao Yun Ting Luo Hao Guo Liying Pan Wei Du Yanhua Wang Qiang Wang Pu Wang Qinghua Zhang Yun Li Nan Lin State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products Laboratory(Hangzhou)for Risk Assessment of Agricultural Products of Ministry of AgricultureInstitute of Agro-product Safety and NutritionZhejiang Academy of Agricultural SciencesHangzhou310021ZhejiangChina College of Urban and Environmental Sciences Peking UniversityBeijing100871China Yunnan Provincial Key Laboratory of Soil Carbon Sequestration and Pollution Control Faculty of Environmental Science&EngineeringKunming University of Science&TechnologyKunming650500China Hubei Key Laboratory of Industrial Fume and Dust Pollution Control School of Environment and HealthJianghan UniversityWuhan430056China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing100085China Key Laboratory of Agro-Product Quality and Safety of Ministry of Agriculture Institute of Quality Standards and Testing Technology for Agro-ProductsChinese Academy of Agricultural SciencesBeijing100081China Department of Environmental Health School of Public HealthShanghai Jiao Tong UniversityShanghai200025China

Carbendazim,a widely used fungicide in China,has been found to have reproductive toxicity and mutagenic ***,information on the spatial-temporal variations of carbendazim residues in food in China is ***,we investigated the presence of carbendazim in China's plant-based foods from 2011 to 2020,evaluated the spatial-temporal characteristics,and assessed the associated exposure risks in the Chinese *** results revealed a high detection frequency of carbendazim in fruits(26.4%)and high concentrations in vegetables(~110 mg kg^(-1)),indicating widespread misuse of the *** acute risks of consuming certain vegetables and cereals exceeded the recommended limits by up to 12 and 5 times,*** there has been a decline in carbendazim residue levels in food since the implementation of the Chinese government's action plan for zero growth of pesticide use in 2015,some provinces still exhibited high levels of carbendazim in multiple food categories,which were positively correlated with annual pesticide *** highlight that carbendazim contamination reflects the broader issue of pesticide use in *** emphasizes the need for committed and targeted national policies to reduce carbendazim residues in food and suggests that such measures could also regulate the use of other pesticides,given that pesticide abuse in China is not limited to specific *** call for the re-evaluation of maximum residue limits of carbendazim,particularly in highly consumed foods such as cereals.

关键词： Carbendazim Plant-based foods China Spatial-temporal variations Exposure and risk assessment

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Position Errors and Interference Prediction-Based Trajectory Tracking for Snake Robots

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1810-1821页

作者： Dongfang Li Yilong Zhang Ping Li Rob Law Zhengrong Xiang Xin Xu Limin Zhu Edmond Q.Wu IEEE School of Electrical Engineering and Automation Fuzhou UniversityFuzhou 350108 the Sino-US Global Logistics Institute Shanghai Jiao Tong UniversityShanghai 200240 the Key Laboratory for Agricultural Machinery Intelligent Control and Manufacturing of Fujian Education Institutions Fuzhou 350108China School of Biological Science and Medical Engineering and the Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education Beihang UniversityBeijing 100191China University of Macao Macao 999078China School of Automation Nanjing University of Science and TechnologyNanjing 210094China College of Intelligence Science and Technology National University of Defence TechnologyChangsha 410073China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

This work presents a trajectory tracking control method for snake *** method eliminates the influence of time-varying interferences on the body and reduces the offset error of a robot with a predetermined *** optimized line-of-sight(LOS)guidance strategy drives the robot’s steering angle to maintain its anti-sideslip ability by predicting position errors and ***,the predictions of system parameters and viscous friction coefficients can compensate for the joint torque control *** compensation is adopted to enhance the compatibility of a robot within ever-changing *** and experimental outcomes show that our work can decrease the fluctuation peak of the tracking errors,reduce adjustment time,and improve accuracy.

关键词： Anti-sideslip compensation snake robot trajectory tracking

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