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Digital twin intersection based on roadside multi-sensor data fusion 认领 引用
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作者 Yuhang Wang Yanzhan Chen Liang Zheng 《Digital Transportation and Safety》 2025年第4期242-250,共9页
Digital twin technology is pivotal in the advancement of smart cities and autonomous driving due to its unique capabilities in virtual-reality integration,interactive control,and predictive analysis.The primary enable... Digital twin technology is pivotal in the advancement of smart cities and autonomous driving due to its unique capabilities in virtual-reality integration,interactive control,and predictive analysis.The primary enabler for achieving advanced transportation digital twins lies in enhancing environmental sensing capabilities,with multi-sensor data fusion emerging as a widely adopted strategy to improve sensing performance.However,existing research has predominantly focused on onboard systems,leaving roadside sensor deployment and roadside multi-sensor data fusion strategies insufficiently explored.Recognizing the potential advantages of roadside sensor systems,such as broader sensory field coverage and reduced occlusion.This study investigates the integration of roadside multi-sensor data fusion with digital twin technology in the transportation domain.Consequently,this paper introduces an innovative intersection digital twin system developed through a simulation-based approach,leveraging roadside multi-sensor data late fusion.The Car Learning to Act-Simulation of Urban Mobility(CARLA-SUMO)co-simulator acts as a data generation platform,synchronously producing RGB images and Light Detection and Ranging(LiDAR)point clouds with spatiotemporal consistency.For object detection,we employ the You Only Look Once version 5(YOLOv5)and PointPillars algorithms.Then,a decision-level fusion strategy is proposed to integrate these heterogeneous sensor outputs into a cohesive roadside digital twin system.Experimental results demonstrate that YOLOv5 and PointPillars achieve a mean Average Precision(mAP)of approximately 90%and 60%,respectively.Moreover,the detection frequency of both detectors is well-suited to the dynamic nature of intersection traffic,and the fusion strategy synergistically exploits the complementary advantages of heterogeneous sensors to enhance overall system performance.This research contributes to the field by facilitating low-cost autonomous driving simulation tests and enabling the reconstruction of intersections using roadside digital twin technology,with significant implications for vehicle-road coordination and traffic management. 展开更多
关键词 Digital twin CARLA-SUMO co-simulator Multi-sensor data fusion YOLOv5 PointPillars
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Indoor Vehicle Positioning Based on Multi-Sensor Data Fusion 认领 引用
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作者 王明阳 时良仁 李元龙 《Journal of Shanghai Jiaotong university(Science)》 EI 2023年第1期77-85,共9页
This study proposes a Kalman filter-based indoor vehicle positioning method for cases in which the steering angle and rotation speed of the vehicle’s wheels are unknown.By fusing the position and velocity data from t... This study proposes a Kalman filter-based indoor vehicle positioning method for cases in which the steering angle and rotation speed of the vehicle’s wheels are unknown.By fusing the position and velocity data from the ultra-wideband sensors and acceleration and orientation data from the inertial measurement unit,we developed two algorithms to estimate the real-time position of the vehicle based on a linear Kalman filter and extended Kalman filter,respectively.We then conducted simulations and experiments to examine the performances of the algorithms.In the experiment,the Kalman filtering hyperparameters are configured,and we then ran the two algorithms to determine the positioning precision and accuracy with the ground truth produced via LiDAR.We verified that our method can improve precision and accuracy compared with the raw positioning data and can achieve desirable effects for indoor vehicle positioning when vehicles travel at low speeds. 展开更多
关键词 indoor vehicle positioning multi-sensor data fusion ultra-wideband linear Kalman filter extended Kalman filter
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Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function 认领 引用 被引量：8
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作者 BIN Guangfu JIANG Zhinong +1 位作者 LI Xuejun DHILLON B S 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2011年第5期899-904,共6页
As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery... As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement. 展开更多
关键词 vibration signal multi-sensor data level fusion correlation function weighted value
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Inversion of Evaporation and Water Vapor Transport Using HY-2 Multi-Sensor Data 认领 引用
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作者 LIU Dong’ang SUN Jian GUAN Changlong 《Journal of Ocean University of China》 SCIE CAS CSCD 2020年第1期13-22,共10页
HY-2 satellite is the first marine dynamic environment satellite of China.In this study,global evaporation and water vapor transport of the global sea surface are calculated on the basis of HY-2 multi-sensor data from... HY-2 satellite is the first marine dynamic environment satellite of China.In this study,global evaporation and water vapor transport of the global sea surface are calculated on the basis of HY-2 multi-sensor data from April 1 to 30,2014.The algorithm of evaporation and water vapor transport is discussed in detail,and results are compared with other reanalysis data.The sea surface temperature of HY-2 is in good agreement with the ARGO buoy data.Two clusters are shown in the scatter plot of HY-2 and OAFlux evaporation due to the uneven global distribution of evaporation.To improve the calculation accuracy,we compared the different parameterization schemes and adopted the method of calibrating HY-2 precipitation data by SSM/I and Global Precipitation Climatology Project(GPCP)data.In calculating the water vapor transport,the adjustment scheme is proposed to match the balance of the water cycle for data in the low latitudes. 展开更多
关键词 HY-2 multi-sensor data inversion evaporation water vapor transport data calibration
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STUDY ON THE COAL-ROCK INTERFACE RECOGNITION METHOD BASED ON MULTI-SENSOR DATA FUSION TECHNIQUE 认领 引用 被引量：7
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作者 Ren FangYang ZhaojianXiong ShiboResearch Institute of Mechano-Electronic Engineering,Taiyuan University of Technology,Taiyuan 030024, China 《Chinese Journal of Mechanical Engineering》 EI CAS 2003年第3期321-324,共4页
The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data... The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones. 展开更多
关键词 Coal-rock interface recognition (CIR) Data fusion (DF) Multi-sensor
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APPLICATION OF MULTI-SENSOR DATA FUSION BASED ON FUZZY NEURAL NETWORK IN ROTA TING MECHANICAL FAILURE DIAGNOSIS 认领 引用 被引量：2
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作者 周洁敏 林刚 +1 位作者 宫淑丽 陶云刚 《Transactions of Nanjing University of Aeronautics and Astronautics》 2001年第1期91-96,共6页
At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sen... At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sensor fusion system, which is model-based and used for rotating mechanical failure diagnosis. In the data fusion process, the fuzzy neural network is selected and used for the data fusion at report level. By comparing the experimental results of fault diagnoses based on fusion data wi th that on original separate data,it is shown that the former is more accurate than the latter. 展开更多
关键词 multi-sensor data fus ion fuzzy neural network rotating mechanical fault diagnosis grade of members hip
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Multi-sensor Data Fusion by Improved Hough Transformation 认领 引用
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作者 张鸿宾 《High Technology Letters》 EI CAS 1995年第2期7-11,共5页
In this paper we present an evidence-gathering approach to slove the multi-sensor data fusion problem. It uses an improved Hough transformation method rather than the usual statistical or geometric approach to extract... In this paper we present an evidence-gathering approach to slove the multi-sensor data fusion problem. It uses an improved Hough transformation method rather than the usual statistical or geometric approach to extract the directions and positions of the walls in a room and update the location (orientation and position)of a mobile robot. The simulation results show that the proposed method is of practical importance since it is very simple and easy to implement. 展开更多
关键词 Multi-sensor data fusion Hough transformation Mobile robot
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Multi-Sensor Data Fusion Technologies for Blanket Jamming Localization 认领 引用 被引量：1
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作者 WANG Ju WU Si-liang ZENG Tao 《Journal of Beijing Institute of Technology》 EI CAS 2005年第1期22-26,共5页
The localization of the blanket jamming is studied and a new method of solving the localization ambiguity is proposed.Radars only can acquire angle information without range information when encountering the blanket j... The localization of the blanket jamming is studied and a new method of solving the localization ambiguity is proposed.Radars only can acquire angle information without range information when encountering the blanket jamming.Netted radars could get position information of the blanket jamming by make use of radars'relative position and the angle information,when there is one blanket jamming.In the presence of error,the localization method and the accuracy analysis of one blanket jamming are given.However,if there are more than one blanket jamming,and the two blanket jamming and two radars are coplanar,the localization of jamming could be error due to localization ambiguity.To solve this confusion,the Kalman filter model is established for all intersections,and through the initiation and association algorithm of multi-target,the false intersection can be eliminated.Simulations show that the presented method is valid. 展开更多
关键词 data fusion blanket jamming localization Kalman filter
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Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques 认领 引用 被引量：1
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作者 Kashif Iqbal Sagheer Abbas +4 位作者 Muhammad Adnan Khan Atifa Ather Muhammad Saleem Khan Areej Fatima Gulzar Ahmad 《Computers, Materials & Continua》 SCIE EI 2021年第2期1595-1612,共18页
The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved d... The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved due to the development of deep learning algorithms.Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or events.The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection.In this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking images.We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces.Image augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through video-camera.The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported methodologies.Moreover,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection.The results are verified using Python,TensorFlow,OpenCV computer simulation frameworks. 展开更多
关键词 Smart parking-lot detection deep convolutional neural network data augmentation region-of-interest object detection
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A New Multi-sensor Data Fusion Algorithm Based on EMD-MMSE 认领 引用 被引量：2
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作者 张琦 阙沛文 +1 位作者 陈天璐 黄晶 《Journal of Shanghai Jiaotong university(Science)》 EI 2007年第2期153-158,共6页
A new multi-sensor data fusion algorithm based on EMD-MMSE was proposed.Empirical mode decomposition(EMD)is used to extract the noise of every time series for estimating the variance of the noise.Then minimum mean squ... A new multi-sensor data fusion algorithm based on EMD-MMSE was proposed.Empirical mode decomposition(EMD)is used to extract the noise of every time series for estimating the variance of the noise.Then minimum mean square error(MMSE)estimator is used to calculate the weights of the corresponding series.Finally,the fused signal is the weighted addition of all these series.The experiments in lab testified the efficiency of this method.In addition,the comparison in fusion time and fusion results with existing fusion method based on wavelet and average technique shows the advantage of this method greatly. 展开更多
关键词 data fusion empirical mode decomposition （EMD） minimum mean square error （MMSE） multisensor system
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Multi-sensor data merging of sea ice concentration and thickness 认领 引用
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作者 Keguang WANG Thomas LAVERGNE Frode DINESSEN 《Advances in Polar Science》 CSCD 2020年第1期1-13,共13页
With the rapid change in the Arctic sea ice,a large number of sea ice observations have been collected in recent years,and it is expected that an even larger number of such observations will emerge in the coming years... With the rapid change in the Arctic sea ice,a large number of sea ice observations have been collected in recent years,and it is expected that an even larger number of such observations will emerge in the coming years.To make the best use of these observations,in this paper we develop a multi-sensor optimal data merging(MODM)method to merge any number of different sea ice observations.Since such merged data are independent on model forecast,they are valid for model initialization and model validation.Based on the maximum likelihood estimation theory,we prove that any model assimilated with the merged data is equivalent to assimilating the original multi-sensor data.This greatly facilitates sea ice data assimilation,particularly for operational forecast with limited computational resources.We apply the MODM method to merge sea ice concentration(SIC)and sea ice thickness(SIT),respectively,in the Arctic.For SIC merging,the Special Sensor Microwave Imager/Sounder(SSMIS)and Advanced Microwave Scanning Radiometer 2(AMSR2)data are merged together with the Norwegian Ice Service ice chart.This substantially reduces the uncertainties at the ice edge and in the coastal areas.For SIT merging,the daily Soil Moisture and Ocean Salinity(SMOS)data is merged with the weekly-mean merged CryoSat-2 and SMOS(CS2SMOS)data.This generates a new daily CS2SMOS SIT data with better spatial coverage for the whole Arctic. 展开更多
关键词 sea ice concentration sea ice thickness data merging remote sensing Arctic
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Multi-sensor Data Fusion for Wheelchair Position Estimation with Unscented Kalman Filter 认领 引用 被引量：9
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作者 Derradji Nada Mounir Bousbia-Salah Maamar Bettayeb 《International Journal of Automation and computing》 CSCD 2018年第2期207-217,共11页
This paper investigates the problem of estimation of the wheelchair position in indoor environments with noisy mea- surements. The measuring system is based on two odometers placed on the axis of the wheels combined w... This paper investigates the problem of estimation of the wheelchair position in indoor environments with noisy mea- surements. The measuring system is based on two odometers placed on the axis of the wheels combined with a magnetic compass to determine the position and orientation. Determination of displacements is implemented by an accelerometer. Data coming from sensors are combined and used as inputs to unscented Kalman filter （UKF）. Two data fusion architectures： measurement fusion （MF） and state vector fusion （SVF） are proposed to merge the available measurements. Comparative studies of these two architectures show that the MF architecture provides states estimation with relatively less uncertainty compared to SVF. However, odometers measurements determine the position with relatively high uncertainty followed by the accelerometer measurements. Therefore, fusion in the navigation system is needed. The obtained simulation results show the effectiveness of proposed architectures. 展开更多
关键词 Data fusion unscented Kalman filter （UKF） measurement fusion （MF） navigation state vector fusion （SVF） wheelchair.
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A Study of Multi-sensor Data Fusion System Based on MAS for Nutrient Solution Measurement 认领 引用
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作者 Feng Chen Dafu Yang +1 位作者 Bing Wang Xianhu Tan 《稀有金属材料与工程》 SCIE EI CAS 北大核心 2006年第A3期264-267,共4页
For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system u... For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF. 展开更多
关键词 multi-sensor data fusion multi-agent system nutrient solution reliability diagnosis.
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A Novel Multi-sensor Data Fusion Algorithm and Its Application to Diagnostics 认领 引用 被引量：2
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作者 Li Xiong Xu Zongchang Dong Zhiming 《仪器仪表学报》 EI CAS 北大核心 2005年第Z1期788-790,共3页
To Meet the requirements of multi-sensor data fusion in diagnosis for complex equipment systems,a novel, fuzzy similarity-based data fusion algorithm is given. Based on fuzzy set theory, it calculates the fuzzy simila... To Meet the requirements of multi-sensor data fusion in diagnosis for complex equipment systems,a novel, fuzzy similarity-based data fusion algorithm is given. Based on fuzzy set theory, it calculates the fuzzy similarity among a certain sensor's measurement values and the multiple sensor's objective prediction values to determine the importance weigh of each sensor,and realizes the multi-sensor diagnosis parameter data fusion.According to the principle, its application software is also designed. The applied example proves that the algorithm can give priority to the high-stability and high -reliability sensors and it is laconic ,feasible and efficient to real-time circumstance measure and data processing in engine diagnosis. 展开更多
关键词 Diagnostics Multi-sensor Data fusion Algorithm Engine
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Multi-sensor missile-borne LiDAR point cloud data augmentation based on Monte Carlo distortion simulation 认领 引用 被引量：1
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作者 Luda Zhao Yihua Hu +4 位作者 Fei Han Zhenglei Dou Shanshan Li Yan Zhang Qilong Wu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第1期300-316,共17页
Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmenta... Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms. 展开更多
关键词 data augmentation LiDAR missile-borne imaging Monte Carlo simulation point cloud
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tsRNADisease:a manually curated database of tsRNAs associated with human disease 认领 引用 被引量：1
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作者 Hui Yang Shaoying Zhu +5 位作者 Huijun Wei Wei Huang Qi Chen Yungang He Kun Lv Zhen Yang 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2026年第3期537-543,共7页
tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years f... tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years from accumulating studies.However,repositories for cataloging the detailed information on tsRNA–disease associations are scarce.In this study,we provide a tsRNADisease database by integrating experimentally and computationally supported tsRNA–disease associations from manual curation of literatures and other related resources.tsRNADisease contains 5571 manually curated associations between 4759 tsRNAs and 166 diseases with experimental evidence from 346 studies.In addition,it also contains 5013 predicted associations between 1297 tsRNAs and 111 diseases.tsRNADisease provides a user-friendly interface to browse,retrieve,and download data conveniently.This database can improve our understanding of tsRNA deregulation in diseases and serve as a valuable resource for investigating the mechanism of disease-related tsRNAs.tsRNADisease is freely available at http://gffzz9c504e06f78b4edahf00bpowvqbvu66wq.ffgz.tsg.suse.edu.cn. 展开更多
关键词 tsRNA Disease Cancer Data integration Database
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Heavy-haul rail surface defect identification based onmulti-sensor data fusion 认领 引用
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作者 Xiyun Wang Yongkui Sun +3 位作者 Yuan Cao Shuai Su Feng Wang Yuntong An 《Transportation Safety and Environment》 EI CSCD 2025年第4期137-145,共9页
Heavy-haul railways play a vital role in freight transportation,and the health of the rails directly impacts the safety and efficiency of railway operations.The heavy axle loads and long train compositions of heavy-ha... Heavy-haul railways play a vital role in freight transportation,and the health of the rails directly impacts the safety and efficiency of railway operations.The heavy axle loads and long train compositions of heavy-haul trains make the rail surface susceptible to damage such as rail corrugation,spalling and abrasion,threatening operational safety.To address the issue,this paper proposes a multi-source data fusion method for identifying rail surface defects on heavy-haul railways.First,complete ensemble empirical mode decomposition with adaptive noise is used to decompose vibration signals and extract multi-dimensional vibration features.Next,dynamic time warping is applied to align rail profile data and extract key geometric features.Then,the vibration features and profile features are fused using Relief-F to select the most discriminative features.Finally,a support vector machine is utilized for defect identification.Experiment results show that the proposed method achieves high accuracy in identifying rail surface defects,with an accuracy of 96.4%. 展开更多
关键词 heavy-haul railways rail surface defect identification vibration signal profile data data fusion
Research on data assimilation for turbulence model constants via airfoil wind tunnel experiments 认领 引用 被引量：1
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作者 Junwei Yang Lingting Meng +1 位作者 Xiangjun Wang Hua Yang 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第2期42-56,共15页
Data assimilation algorithms have been demonstrated to increase the accuracy of predictions in airfoil flow fields.However,slight changes in airfoil geometry and Reynolds number(Re)variations could lead to differences... Data assimilation algorithms have been demonstrated to increase the accuracy of predictions in airfoil flow fields.However,slight changes in airfoil geometry and Reynolds number(Re)variations could lead to differences in aerodynamic characteristics and stall behavior,consequently affecting assimilation outcomes.Hence,this research uses the ensemble Kalman filter(EnKF)algorithm.The aerodynamic characteristics of two wind turbine airfoils obtained through wind tunnel experiments were investigated under varying degrees of stall by recalibrating the constants in the(S-A)model.The impacts of the airfoil thickness,Re variation,and Gurney flap installation on the assimilation results were subsequently examined.Verifying the applicability of the constants obtained via data assimilation under varying conditions might offer opportunities to reduce the demand for computational resources.The assimilation results indicate that at a Re on the order of magnitude of 105,the original model tends to delay flow separation as the Re increases.Consequently,the recalibrated constant Cb1 generally decreases with increasing Re.Despite belonging to the same airfoil family,discrepancies in the flow separation behavior predicted by the original model resulted in variations in the recalibrated constants.The constants derived from the thinner airfoil induce premature flow separation in the thicker YA-30 airfoil under stall conditions.When assimilated constants are applied to flow field calculations under analogous stall conditions,constants from another condition may demonstrate an optimization effect and substitute the self-assimilated constants,provided that simulations using default constants for both conditions consistently exhibit an experimental separation trend.However,practical implementation requires caution due to the risk of overadjustment. 展开更多
关键词 Wind turbine airfoil Pressure distribution Data assimilation Spalart-Allmaras model Applicability
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MTSCFNet:a novel framework for improving tree species classification in a subtropical forest using RGB,LiDAR-derived,and GF-2 data 认领 引用 被引量：1
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作者 Linlong Wang Huaiqing Zhang +5 位作者 Rurao Fu Kexin Lei Yang Liu Tingdong Yang Jing Zhang Xiaoning Ge 《Journal of Forestry Research》 SCIE EI CAS CSCD 2026年第3期135-158,共24页
Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)... Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)often suffer from insufficient feature representation and noise interference,particularly in subtropical forests with high species diversity,leading to increased classification errors.To address these challenges,we proposed the Multi-source Tree Species Classification Fusion Network(MTSCFNet),a novel deep learning framework that integrates RGB imagery,LiDAR-derived feature maps,and GF-2 satellite data through a modified UNet backbone,which incorporates a three-branch encoder and a Triple Branch Feature Fusion(TBFF)module within a middle fusion strategy.We evaluated the MTSCFNet in Chinese-fir mixed forests located in the Shanxia Forest Farm,Jiangxi Province,China.The results showed that:(1)MTSCFNet outperformed four baseline models,achieving Macro F1(0.78±0.01),Micro F1(0.93±0.01),Weighted F1(0.93±0.01),a Matthews correlation coefficient(MCC)(0.89±0.01),Cohen’sĸ(0.89±0.01),and mIoU(0.69±0.01),with respective improvements of 4.05%in Macro F1,1.89%in Micro F1,0.09%in Weighted F1,1.67%in MCC,1.64%in Cohen’sĸ,and 5.92%in Mean IoU over the second best model,SwinUNet;(2)Compared to the best two-source combinations(R+S,R+L),MTSCFNet achieved up to 1.50%,3.28%,3.42%,6.72%,6.76%,and 3.51%higher Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,and up to 8.11%,2.63%,2.88%,5.01%,4.99%,and 11.48%improvements over single-source inputs,while also exhibiting the lowest variability,indicating strong robustness;(3)Under different fusion strategies,MTSCFNet with middle fusion surpassed early and late fusion by up to 15.31%,3.74%,3.99%,7.66%,7.76%,22.33%and 24.13%,5.76%,6.20%,11.48%,11.57%,32.96%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,respectively,validating the effectiveness of feature-level multi-modal integration;(4)In cross-region transfer experiments,MTSCFNet demonstrated strong spatial generalizability,achieving average scores of 0.78(Macro F1),0.87(Micro F1),0.86(Weighted F1),0.59(MCC),0.59(Cohen’sĸ),and 0.68(mIoU),and outperformed SwinUNet by up to 38.80%,9.40%,18.58%,22.48%,26.17%,and 33.00%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU across varying forest densities.Overall,MTSCFNet offers a robust,accurate,and transferable solution for tree species classification in complex subtropical forest environments. 展开更多
关键词 Tree species classification Deep learning Multi-source remote sensing data Subtropical forest
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Toward Secure and Auditable Data Sharing:A Cross-Chain CP-ABE Framework 认领 引用 被引量：1
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作者 Ye Tian Zhuokun Fan Yifeng Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第4期1509-1529,共21页
Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy a... Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy attribute-based encryption(CP-ABE)schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration.To address these issues,we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration.This scheme incorporates a multiauthority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption,effectively reducing both computational and communication overhead.It leverages the InterPlanetary File System(IPFS)for off-chain data storage and constructs a cross-chain regulatory framework—comprising a Hyperledger Fabric business chain and a FISCO BCOS regulatory chain—to record changes in decryption privileges and access behaviors in an auditable manner.Security analysis shows selective indistinguishability under chosen-plaintext attack(sIND-CPA)security under the decisional q-Parallel Bilinear Diffie-Hellman Exponent Assumption(q-PBDHE).In the performance and experimental evaluations,we compared the proposed scheme with several advanced schemes.The results show that,while preserving security,the proposed scheme achieves higher encryption/decryption efficiency and lower storage overhead for ciphertexts and keys. 展开更多
关键词 Data sharing blockchain attribute-based encryption dynamic permissions
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