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Deep learning based water leakage detection for shield tunnel lining

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of embedded systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

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Journal of Electronic Science and Technology 2024年第2期22卷 48-68页

作者： Gracieth Cavalcanti Batista Johnny Oberg Osamu Saotome Haroldo F.de Campos Velho Elcio Hideiti Shiguemori Ingemar Soderquist Division of Electronic and embedded systems KTH Royal Institute of TechnologyStockholm 16440Sweden Electronic Engineering Division Aeronautics Institute of TechnologySao Josédos Campos SP 12228-900Brazil laboratory of Applied Computing and Mathematics National Institute for Space ResearchSao Josédos Campos SP 12227-900Brazil Department of C4ISR Institute of Advanced StudiesSao Josédos Campos SP 12228-001Brazil Saab AB Linkoping 58188Sweden

Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,*** key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)***,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or *** alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced *** a high effort is required for image edge *** a support vector regression(SVR)model is proposed to reduce this computational load and processing *** dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable *** show that the implementation in hardware is 68 times faster than that in *** architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without *** is also the lowest power consumption in current machine learning hardware ***,the circuitry area is 41 times *** with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning *** system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.

关键词： Dynamic partial reconfiguration(DPR) Field programmable gate array(FPGA)implementation Image edge detection Support vector regression(SVR) Unmanned aerial vehicle(UAV) pose estimation

Evaluation of UAV spraying quality based on 1D-CNN model and wireless multi-sensors system

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Information Processing in Agriculture 2024年第1期11卷 65-79页

作者： Ziyuan Hao Minzan Li Wei Yang Xinze Li Key laboratory of Smart Agriculture systems Ministry of EducationChina Agricultural UniversityBeijing 100083PR China

The droplet deposition is a key index to evaluate the quality of unmanned aerial vehicle(UAV)*** detection of the droplet deposition is time-consuming and costly,therefore,it is difficult to achieve large-scale and rapid acquisition in the *** solve the above problems,a droplet deposition acquisition system(DDAS)was *** was composed of the multiple sensors,processing units,remote server database and Android-based software.A droplet deposition prediction model based on field experimental data was established by using a one-dimensional convolutional neural network(1D-CNN)algorithm,and the effects of different inputs on the prediction ability of the model were *** results showed that adding temperature and humidity data to the inputs can achieve higher prediction accuracy than only using UAV spraying operation parameters and wind speed data as the inputs to the *** addition,the prediction accuracy of the 1D-CNN model was the highest when compared with other models such as back propagation neural network,multiple correlation vector machine,and multiple linear *** 1D-CNN model was embedded into the DDAS,and the evaluation experiments were carried out in the *** correlation analysis was conducted between two datasets of the droplet deposition obtained by the DDAS and water sensitive paper(WSP),*** R2 was 0.924,and the RMSE was 0.026μL/*** is proved that the droplet deposition values obtained by the DDAS and WSP have high consistency,and the DDAS developed can provide an auxiliary solution for the intelligent evaluation of UAV spraying quality.

关键词： UAV Spraying quality Droplet deposition 1D-CNN model Sensor

Significant annealing-induced hardening effect in nanolaminate d-nanotwinne d(CrCoNi)_(97.4)Al_(0.8)Ti_(1.8)me dium-entropy alloy by severe cold rolling

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Journal of Materials Science & Technology 2024年第3期170卷 156-166页

作者： Shuqing Yuan Hui Fu Lei Qian Chi Fai Cheung Xu-Sheng Yang State Key laboratory of Ultra-precision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Hong Kong Polytechnic University Shenzhen research Institute Shenzhen 518060China

Due to the easy coarsening caused by poor thermal stability,the verified annealing-induced hardening in nanograined metals can only maintain at a relatively low-temperature *** this study,a nanolam-inated(CrCoNi)_(97.4)Al_(0.8)Ti_(1.8)medium-entropy alloy with an average lamellae thickness of∼20 nm embedded by thinner nanotwins was fabricated by severe cold rolling to achieve superior thermal *** with the conventional nanotwinned CrCoNi with nanotwins inside ultra-fined grains,the hier-archical nanolaminated-nanotwinned(CrCoNi)_(97.4)Al_(0.8)Ti_(1.8) exhibits a significant annealing-induced hard-ening effect,i.e.,hardness increasing from∼250 HV in the original specimen to∼500 HV in the cold-rolled status and finally∼630 HV after annealing at 600℃for 1 *** microstructure characterizations reveal that the reduced dislocation density and formation of L1_(2)ordered domain are mainly responsible for such hardening effect,which is facilitated by the effectively suppressed coarsening with annealing temperature,i.e.,slow detwinning process and well-retained low-angle nanolamellar *** coarsening mechanisms from the cold-rolled nanolamellae to the fully recrystallized micro-equiaxed structures under the annealing temperatures ranging from 400 to 800℃ were also elucidated by atomic observations.

关键词： Annealing-induced hardening Thermal stability Nanolaminated-nanotwinned structure CrCoNi Detwinning Grain boundary migration

A Temporary Frequency Response Strategy Using a Voltage Source-Based PermanentMagnet Synchronous Generator and Energy Storage systems

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Energy Engineering 2024年第2期121卷 541-555页

作者： Baogang Chen Fenglin Miao Jing Yang Chen Qi Wenyan Ji CSIC HaiZhuangWindpower Co. Ltd.Chongqing400000China State Key laboratory of Operation and Control of Renewable Energy&Storage systems China Electric Power Research InstituteBeijing100000China

Energy storage systems(ESS)and permanent magnet synchronous generators(PMSG)are speculated to be able to exhibit frequency regulation capabilities by adding differential and proportional control loops with different control *** available PMSG kinetic energy and charging/discharging capacities of the ESS were *** improve the inertia response and frequency control capability,we propose a short-term frequency support strategy for the ESS and *** this end,the weights were embedded in the control loops to adjust the participation of the differential and proportional controls based on the system frequency *** effectiveness of the proposed control strategy was verified using PSCAD/*** simulations revealed that the proposed strategy could improve the maximum rate of change of the frequency nadir and maximum frequency ***,it provides a promising solution of ancillary services for frequency regulation of PMSG and ESS.

关键词： Inertial control PMSG ESS wind power generation frequency support

Multi-Perspective Data Fusion Framework Based on Hierarchical BERT: Provide Visual Predictions of Business Processes

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Computers, Materials & Continua 2024年第1期78卷 1227-1252页

作者： Yongwang Yuan Xiangwei Liu Ke Lu School of Mathematics and Big Data Anhui University of Science and TechnologyHuainan232001China School of Economics and Management Anhui University of Science and TechnologyHuainan232001China The Key laboratory of embedded System and Service Computing Ministry of Education Tongji UniversityShanghai201804China

Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral *** present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process ***,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data ***,the first layer BERT network learns the correlations between different category attribute ***,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted ***,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual ***,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.

关键词： Business process prediction monitoring deep learning attention mechanism BERT multi-perspective

A Novel Method for Aging Prediction of Railway Catenary Based on Improved Kalman Filter

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Structural Durability & Health Monitoring 2024年第1期18卷 73-90页

作者： Jie Li Rongwen Wang Yongtao Hu Jinjun Li School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang453003China Technology Center Sichuan Injet Electric Co.Ltd.Deyang618000China embedded System research Institute Xinxiang Engineering Research Center for Intelligent Condition Monitoring of MachineryXinxiang453003China

The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified ***,in real-world scenarios,accurate predictions are challenging due to various *** paper addresses this challenge by proposing a novel method for predicting the aging of railway catenary based on an improved Kalman filter(KF).The proposed method focuses on modifying the priori state estimate covariance and measurement error covariance of the KF to enhance accuracy in complex *** comparing the optimal displacement value with the theoretically calculated value based on the thermal expansion effect of metals,it becomes possible to ascertain the aging status of the *** improve prediction accuracy,a railway catenary aging prediction model is constructed by integrating the Takagi-Sugeno(T-S)fuzzy neural network(FNN)and *** this model,an adaptive training method is introduced,allowing the FNN to use fewer fuzzy *** inputs of the model include time,temperature,and historical displacement,while the output is the predicted ***,the KF is enhanced by modifying its prior state estimate covariance and measurement error *** modifications contribute to more accurate ***,a low-power experimental platform based on FPGA is implemented to verify the effectiveness of the proposed *** test results demonstrate that the proposed method outperforms the compared method,showcasing its superior performance.

关键词： Railway catenary Takagi-Sugeno fuzzy neural network Kalman filter aging prediction

Sensing the heavy water concentration in an H_(2)O-D_(2)O mixture by solid-solid phononic crystals

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Chinese Physics B 2024年第4期33卷 493-498页

作者： Mohammadreza Rahimi Ali Bahrami Optoelectronic and Nanophotonics research laboratory(ONRL) Faculty of Electrical EngineeringSahand University of TechnologyTabrizIran

A new method based on phononic crystals is presented to detect the concentration of heavy water(D_(2)O)in an H_(2)O-D_(2)O *** have been obtained and analyzed in the concentration range of 0%-10%and 90%-100%D_(2)O.A proposed structure of tungsten scatterers in an aluminum host is *** order to detect the target material,a cavity region is considered as a sound wave resonator in which the target material with different concentrations of D_(2)O is *** changing the concentration of D_(2)O in the H_(2)O-D_(2)O mixture,the resonance frequency undergoes a frequency *** 1%change in D_(2)O concentration in the H_(2)O-D_(2)O mixture causes a frequency change of about 120 *** finite element method is used as the numerical method to calculate and analyze the natural frequencies and transmission spectra of the proposed *** performance evaluation index shows a high Q factor up to 1475758 and a high sensitivity up to 13075,which are acceptable values for sensing *** other figures of merit related to the detection performance also indicate high-quality performance of the designed sensor.

关键词： phononic crystals sensor H_(2)O-D_(2)O mixture cavity

Repeat-pass space-surface bistatic SAR tomography:accurate imaging and first experiment

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Science China(Information Sciences) 2024年第9期67卷 245-262页

作者： Zhiyang CHEN Yuanhao LI Cheng HU Shenglei WANG Xinpeng CHEN Mihai DATCU Andrea Virgilio MONTI-GUARNIERI Radar Technology research Institute School of Information and Electronics Beijing Institute of Technology Beijing Key laboratory of embedded Real-Time Information Processing Technology Beijing Institute of Technology Advanced Technology research Institute Beijing Institute of Technology CEOSpace Tech University Politehnica of Bucharest Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano

Space-surface bistatic synthetic aperture radar(SS-BiSAR) offers an additional observation angle for monostatic spaceborne SAR, making it a promising technology for high-accuracy deformation retrieval technology in local regions. Repeat-pass SS-BiSAR tomography can accurately estimate the surfaces of buildings and steep areas, effectively removing terrain phases during deformation retrieving. However, inaccuracies in the orbital ephemeris can lead to image geometry distortion, reducing image pair coherence,introducing interferometric phase errors, and consequently deteriorating tomographic precision. This paper precisely models the image geometry distortion and interferometric phase error caused by repeat-pass ephemeris error. We propose an ephemeris correction method based on the chirp-Z transform to address these issues. Furthermore, we introduce an accurate tomography model to improve 3D reconstruction *** first SS-BiSAR tomography experiment, conducted using the Chinese Lutan-1 satellite, demonstrates that the correlation coefficient is improved by 0.16 after ephemeris error correction. Moreover, the density and precision of the tomographic point cloud are improved by 13.7% and 12.1%, respectively.

关键词： space-surface SAR tomography orbit error image distortion Lutan-1