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Drive-by spatial offset detection for high-speed railway bridges based on fusion analysis of multi-source data from comprehensive inspection train 认领 引用
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作者 Chuang Wang Jiawang Zhan +4 位作者 Nan Zhang Yujie Wang Xinxiang Xu Zhihang Wang Zhen Ni 《Railway Engineering Science》 EI 2026年第1期128-148,共21页
The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR ... The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges. 展开更多
关键词 High-speed railway bridge Drive-by inspection Spatial offset Multi-source data fusion Deep learning
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Optimizing Energy Efficiency in Tunnel Boring Machine Rock Breaking via Multi-source Data Fusion 认领 引用 被引量：1
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作者 Xiaojun Yan Wencong Qi +4 位作者 Chuan Qu Minghui Ma Shanglin Liu Qian Zhang Xuesong Cheng 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2026年第4期481-493,共13页
The era of big data has profoundly transformed mechanics research,with data-driven approaches playing a vital role in modeling and optimization.This study focuses on tunnel boring machine(TBM),where the thrust-torque ... The era of big data has profoundly transformed mechanics research,with data-driven approaches playing a vital role in modeling and optimization.This study focuses on tunnel boring machine(TBM),where the thrust-torque ratio is a key determinant of their tunneling energy efficiency.However,due to the complexity of experiments and the testing requirements,obtaining sufficient high-quality data under varying geological conditions remains a major challenge in optimizing the tunneling energy efficiency of TBM.To address this,multi-cutter rotary cutting machine experiments and numerical simulations were conducted on 22 different rock types.Comprehensive datasets of normal and rolling forces were systematically collected.Using specific energy(SE)as the rock-breaking efficiency metric,we integrated physical and numerical data through a CatBoost-based fusion framework.The predictive model was initially trained on simulation data to capture the relationships among penetration,uniaxial compressive strength,tensile strength,and SE,and was subsequently fine-tuned with experimental data to develop the final fused model.Compared to models trained solely on experimental or simulated data,the fused model reduced RMSE by 37.1%and 58.6%,respectively,and improved R2by 19.0%and 44.6%,thereby enhancing both prediction accuracy and generalization capability.Furthermore,Bayesian optimization was employed to minimize SE and identify the optimal penetration.The results indicate that as rock strength increases,the optimal penetration decreases,while the corresponding minimal SE increases.These findings provide theoretical and engineering insights for improving TBM energy efficiency and parameter optimization,while establishing a robust data fusion framework for mechanical data analysis. 展开更多
关键词 Multi-source data fusion CatBoost Tunnel boring machine Multi-cutter rock-breaking Energy efficiency
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CCDNN:A Novel Deep Learning Architecture for Multi-Source Data Fusion 认领 引用
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作者 Zhiwen Chen Siwen Mo +4 位作者 Haobin Ke Steven X.Ding Zhaohui Jiang Chunhua Yang Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期555-567,共13页
With the rapid development of Industrial 4.0 and Industrial Internet of Things,the data collection with multisource has significantly improved.How to effectively fuse these data for various engineering applications is... With the rapid development of Industrial 4.0 and Industrial Internet of Things,the data collection with multisource has significantly improved.How to effectively fuse these data for various engineering applications is still an open and challenge issue.To this end,we propose the canonical correlation guided deep neural network(CCDNN),a novel deep learning architecture,to learn a correlated representation for multi-source data fusion.Unlike the linear canonical correlation analysis(CCA),kernel CCA and deep CCA,in the proposed method,the optimization formulation is not restricted to maximize correlation,instead we make canonical correlation as a constraint,which preserves the correlated representation learning ability and focuses more on the engineering tasks endowed by optimization formulation,such as reconstruction,classification and prediction.Furthermore,to reduce the redundancy induced by correlation,a redundancy filter is designed.We illustrate its data fusion ability via correlated representation learning and superior performance on various engineering tasks.In experiments on MNIST dataset,the results show that CCDNN has better reconstruction performance in terms of mean squared error and mean absolute error than deep CCA and deep canonically correlated autoencoders(DCCAE).Also,we present the application of the proposed network to industrial fault diagnosis and remaining useful life cases for the classification and prediction tasks accordingly.The proposed method demonstrates approving performance in both tasks when compared to existing methods.Extension of CCDNN to much more deeper with the aid of residual connection is also presented in Appendix. 展开更多
关键词 Canonical correlation analysis(CCA) correlated representation learning deep learning fault diagnosis multi-source data fusion remaining useful life
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Research on Rapid Update and Accuracy Improvement of DOM Based on Multi-source Aerial Data Fusion 认领 引用
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作者 GAN Yuting 《外文科技期刊数据库（文摘版）自然科学》 2026年第1期042-045,共4页
As a core geospatial data product, Digital Orthophoto Map (DOM) plays a key role in many fields such as urban planning, disaster assessment, engineering surveying and mapping, and topographic mapping. With the increas... As a core geospatial data product, Digital Orthophoto Map (DOM) plays a key role in many fields such as urban planning, disaster assessment, engineering surveying and mapping, and topographic mapping. With the increasing requirements of various industries for the timeliness and accuracy of DOM, the traditional single-data-source update method can hardly meet the actual needs. Aiming at this problem, this paper deeply studies the rapid update and accuracy improvement methods of DOM driven by multi-source aerial data fusion. The research shows that multi-source aerial data fusion technology can effectively improve the efficiency and accuracy of DOM update, providing reliable support for the efficient application of geospatial data. 展开更多
关键词 multi-source aerial data data fusion Digital Orthophoto Map (DOM) rapid update accuracy optimization
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High-precision classification of benthic habitat sediments in shallow waters of islands by multi-source data 认领 引用
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作者 Qiuhua TANG Ningning LI +4 位作者 Yujie ZHANG Zhipeng DONG Yongling ZHENG Jingjing BAO Jingyu ZHANG 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2026年第1期99-108,共10页
Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications... Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs. 展开更多
关键词 Wuzhizhou Island marine remote sensing coastal mapping multi-spectral remote sensing shallow water reef seabed sediment classification benthic habitat mapping multi-source data fusion random forest(RF)
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A Survey of Key Technologies for Multi-source Heterogeneous Data in Intelligent Manufacturing 认领 引用
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作者 Minghao Zhu Pengfei Yang +2 位作者 Bo Gao Xuehan Li Letian Wang 《Instrumentation》 2026年第1期26-39,共14页
Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large v... Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large volumes of multi-source heterogeneous data across all stages of the product life cycle.However,current data utilization methods remain simplistic,and the goal of leveraging multi-source heterogeneous data to drive manufacturing value has yet to be fully realized.To address this issue,this study first defines the concept and characteristics of multi-source heterogeneous data in intelligent manufacturing,based on an analysis of its relationship with industrial big data.Then,integrating principles from data science,a technological framework for multi-source heterogeneous data is proposed.The key technologies involved in each stage of data processing are investigated,and typical applications of such data in intelligent manufacturing are discussed.Finally,this paper analyzes the challenges and future development directions of multi-source heterogeneous data processing in intelligent manufacturing.The goal is to provide theoretical and technical support for integrating intelligent manufacturing with data science. 展开更多
关键词 intelligent manufacturing multi-source heterogeneous data feature fusion data system technological framework
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Evaluating Urban Housing Contradictions Through Multisource Data Fusion:a Case Study of Spatiotemporal Mismatch Analysis in Shenzhen with the HCEWI Model 认领 引用 被引量：1
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作者 JIANG Aiyi CHEN Guanzhou CAO Jinzhou 《Journal of Geodesy and Geoinformation Science》 CSCD 2025年第3期1-16,共16页
The rapid urbanization and structural imbalances in Chinese megacities have exacerbated the housing supplydemand mismatch,creating an urgent need for fine-scale diagnostic tools.This study addresses this critical gap ... The rapid urbanization and structural imbalances in Chinese megacities have exacerbated the housing supplydemand mismatch,creating an urgent need for fine-scale diagnostic tools.This study addresses this critical gap by developing the Housing Contradiction Evaluation Weighted Index(HCEWI)model,making three key contributions to high-resolution housing monitoring.First,we establish a tripartite theoretical framework integrating dynamic population pressure(PPI),housing supply potential(HSI),and functional diversity(HHI).The PPI innovatively combines mobile signaling data with principal component analysis to capture real-time commuting patterns,while the HSI introduces a novel dual-criteria system based on Local Climate Zones(LCZ),weighted by building density and residential function ratio.Second,we develop a spatiotemporal coupling architecture featuring an entropy-weighted dynamic integration mechanism with self-correcting modules,demonstrating robust performance against data noise.Third,our 25-month longitudinal analysis in Shenzhen reveals significant findings,including persistent bipolar clustering patterns,contrasting volatility between peripheral and core areas,and seasonal policy responsiveness.Methodologically,we advance urban diagnostics through 500-meter grid monthly monitoring and process-oriented temporal operators that reveal“tentacle-like”spatial restructuring along transit corridors.Our findings provide a replicable framework for precision housing governance and demonstrate the transformative potential of mobile signaling data in implementing China’s“city-specific policy”approach.We further propose targeted intervention strategies,including balance regulation for high-contradiction zones,Transit-Oriented Development(TOD)activation for low-contradiction clusters,and dynamic land conversion mechanisms for transitional areas. 展开更多
关键词 index terms-housing contradiction assessment multi-source data fusion spatiotemporal heterogeneity job-housing spatial mismatch high-resolution urban diagnostics
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Utilizing Multi-source Data Fusion to Identify the Layout Patterns of the Catering Industry and Urban Spatial Structure in Shanghai,China 认领 引用
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作者 TIAN Chuang LUAN Weixin 《Chinese Geographical Science》 SCIE CSCD 2025年第5期1045-1058,共14页
Multi-source data fusion provides high-precision spatial situational awareness essential for analyzing granular urban social activities.This study used Shanghai’s catering industry as a case study,leveraging electron... Multi-source data fusion provides high-precision spatial situational awareness essential for analyzing granular urban social activities.This study used Shanghai’s catering industry as a case study,leveraging electronic reviews and consumer data sourced from third-party restaurant platforms collected in 2021.By performing weighted processing on two-dimensional point-of-interest(POI)data,clustering hotspots of high-dimensional restaurant data were identified.A hierarchical network of restaurant hotspots was constructed following the Central Place Theory(CPT)framework,while the Geo-Informatic Tupu method was employed to resolve the challenges posed by network deformation in multi-scale processes.These findings suggest the necessity of enhancing the spatial balance of Shanghai’s urban centers by moderately increasing the number and service capacity of suburban centers at the urban periphery.Such measures would contribute to a more optimized urban structure and facilitate the outward dispersion of comfort-oriented facilities such as the restaurant industry.At a finer spatial scale,the distribution of restaurant hotspots demonstrates a polycentric and symmetric spatial pattern,with a developmental trend radiating outward along the city’s ring roads.This trend can be attributed to the efforts of restaurants to establish connections with other urban functional spaces,leading to the reconfiguration of urban spaces,expansion of restaurant-dedicated land use,and the reorganization of associated commercial activities.The results validate the existence of a polycentric urban structure in Shanghai but also highlight the instability of the restaurant hotspot network during cross-scale transitions. 展开更多
关键词 multi-source data fusion urban spatial structure multi-center catering industry Shanghai,China
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Evaluation of Bird-watching Spatial Suitability Under Multi-source Data Fusion: A Case Study of Beijing Ming Tombs Forest Farm 认领 引用
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作者 YANG Xin YUE Wenyu +1 位作者 HE Yuhao MA Xin 《Journal of Landscape Research》 2025年第3期59-64,共6页
Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from... Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from the Global Biodiversity Information Facility(GBIF),population distribution data from the Oak Ridge National Laboratory(ORNL)in the United States,as well as information on the composition of tree species in suitable forest areas for birds and the forest geographical information of the Ming Tombs Forest Farm,which is based on literature research and field investigations.By using GIS technology,spatial processing was carried out on bird observation points and population distribution data to identify suitable bird-watching areas in different seasons.Then,according to the suitability value range,these areas were classified into different grades(from unsuitable to highly suitable).The research findings indicated that there was significant spatial heterogeneity in the bird-watching suitability of the Ming Tombs Forest Farm.The north side of the reservoir was generally a core area with high suitability in all seasons.The deep-aged broad-leaved mixed forests supported the overlapping co-existence of the ecological niches of various bird species,such as the Zosterops simplex and Urocissa erythrorhyncha.In contrast,the shallow forest-edge coniferous pure forests and mixed forests were more suitable for specialized species like Carduelis sinica.The southern urban area and the core area of the mausoleums had relatively low suitability due to ecological fragmentation or human interference.Based on these results,this paper proposed a three-level protection framework of“core area conservation—buffer zone management—isolation zone construction”and a spatio-temporal coordinated human-bird co-existence strategy.It was also suggested that the human-bird co-existence space could be optimized through measures such as constructing sound and light buffer interfaces,restoring ecological corridors,and integrating cultural heritage elements.This research provided an operational technical approach and decision-making support for the scientific planning of bird-watching sites and the coordination of ecological protection and tourism development. 展开更多
关键词 Multi-source data fusion GIS heat map Kernel density analysis bird-watching spot planning Habitat suitability
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Multi-Source Heterogeneous Data Fusion Analysis Platform for Thermal Power Plants 认领 引用
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作者 Jianqiu Wang Jianting Wen +1 位作者 Hui Gao Chenchen Kang 《Journal of Architectural Research and Development》 2025年第6期24-28,共5页
With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heter... With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%. 展开更多
关键词 Thermal power plant Multi-source heterogeneous data Data fusion analysis platform Edge computing
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Evapotranspiration inversion using a two-sources model coupling multiscale data fusion and interpolation methods 认领 引用
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作者 Shuo Lun TingXi Liu +6 位作者 LiNa Hao LiMin Duan Xin Tong YongZhi Bao YiXuan Wang SiMin Zhang YiRan Zhang 《Research in Cold and Arid Regions》 CAS CSCD 2026年第1期85-100,共16页
Continuous monitoring of high spatiotemporal resolution evapotranspiration(ET)is crucial for accurately assessing water resource management and irrigation efficiency at both global and regional scales.However,constrai... Continuous monitoring of high spatiotemporal resolution evapotranspiration(ET)is crucial for accurately assessing water resource management and irrigation efficiency at both global and regional scales.However,constraints such as satellite image transit time and cloud contamination can inhibit a single satellite to provide fine spatial resolution and continuous daily sequences of remote sensing data.In this study,we employed a coupled multi-scale fusion and interpolation method evapotranspiration model(CFIEM)framework integrated the remote sensing ET models,data fusion models,and the HANTS-GEE interpolation method to generate highresolution surface feature parameters and hydrological variables.Landsat remote sensing parameters and MODIS-Landsat fusion data were adopted to simulate daily regional ET,and the simulated results were subsequently validated.The results revealed the strong consistency between the simulated CFIEM-ET and Landsat-ET values and measured data,with the R2 values of 0.83 and 0.73.Among the simulations across the sand dunes,grasslands,rice,and maize ecosystems,maize exhibited the most accurate simulation(R2?0.84),while the sand dunes demonstrated certain deviation(R2?0.72).The geographical detector analysis identified the net radiation(Rn)as the primary driver of multi-year ET variation,followed by the land surface temperature(LST)and leaf area index(LAI).This study sheds light on the interannual ET variation and its driving mechanisms in arid regions,laying a theoretical foundation and offering technical support for regional water resource management and desertification control. 展开更多
关键词 Evapotranspiration Data fusion methods Data interpolation methods Spatiotemporal variation Driving factors
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Research on Data Fusion of Adaptive Weighted Multi-Source Sensor 认领 引用 被引量：6
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作者 Donghui Li Cong Shen +5 位作者 Xiaopeng Dai Xinghui Zhu Jian Luo Xueting Li Haiwen Chen Zhiyao Liang 《Computers, Materials & Continua》 SCIE EI 2019年第9期1217-1231,共15页
Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data mu... Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality. 展开更多
关键词 Adaptive weighting multi-source sensor data fusion loss of data processing grubbs elimination
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A heuristic cabin-type component alignment method based on multi-source data fusion 认领 引用 被引量：1
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作者 Hao YU Fuzhou DU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2020年第8期2242-2256,共15页
In cabin-type component alignment, digital measurement technology is usually adopted to provide guidance for assembly. Depending on the system of measurement, the alignment process can be divided into measurement-assi... In cabin-type component alignment, digital measurement technology is usually adopted to provide guidance for assembly. Depending on the system of measurement, the alignment process can be divided into measurement-assisted assembly(MAA) and force-driven assembly. In MAA,relative pose between components is directly measured to guide assembly, while in force-driven assembly, only contact state can be recognized according to measured six-dimensional force and torque(6 D F/T) and the process is completed based on preset assembly strategy. Aiming to improve the efficiency of force-driven cabin-type component alignment, this paper proposed a heuristic alignment method based on multi-source data fusion. In this method, measured 6 D F/T, pose data and geometric information of components are fused to calculate the relative pose between components and guide the movement of pose adjustment platform. Among these data types, pose data and measured 6 D F/T are combined as data set. To collect the data sets needed for data fusion, dynamic gravity compensation method and hybrid motion control method are designed. Then the relative pose calculation method is elaborated, which transforms collected data sets into discrete geometric elements and calculates the relative poses based on the geometric information of components.Finally, experiments are conducted in simulation environment and the results show that the proposed alignment method is feasible and effective. 展开更多
关键词 Alignment strategy Force-driven assembly Heuristic alignment method Multi-source data fusion Relative pose calculation
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Dynamic UAV data fusion and deep learning for improved maize phenological-stage tracking 认领 引用 被引量：2
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作者 Ziheng Feng Jiliang Zhao +8 位作者 Liunan Suo Heguang Sun Huiling Long Hao Yang Xiaoyu Song Haikuan Feng Bo Xu Guijun Yang Chunjiang Zhao 《The Crop Journal》 SCIE CAS CSCD 2025年第3期961-974,共14页
Near real-time maize phenology monitoring is crucial for field management,cropping system adjustments,and yield estimation.Most phenological monitoring methods are post-seasonal and heavily rely on high-frequency time... Near real-time maize phenology monitoring is crucial for field management,cropping system adjustments,and yield estimation.Most phenological monitoring methods are post-seasonal and heavily rely on high-frequency time-series data.These methods are not applicable on the unmanned aerial vehicle(UAV)platform due to the high cost of acquiring time-series UAV images and the shortage of UAV-based phenological monitoring methods.To address these challenges,we employed the Synthetic Minority Oversampling Technique(SMOTE)for sample augmentation,aiming to resolve the small sample modelling problem.Moreover,we utilized enhanced"separation"and"compactness"feature selection methods to identify input features from multiple data sources.In this process,we incorporated dynamic multi-source data fusion strategies,involving Vegetation index(VI),Color index(CI),and Texture features(TF).A two-stage neural network that combines Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)is proposed to identify maize phenological stages(including sowing,seedling,jointing,trumpet,tasseling,maturity,and harvesting)on UAV platforms.The results indicate that the dataset generated by SMOTE closely resembles the measured dataset.Among dynamic data fusion strategies,the VI-TF combination proves to be most effective,with CI-TF and VI-CI combinations following behind.Notably,as more data sources are integrated,the model's demand for input features experiences a significant decline.In particular,the CNN-LSTM model,based on the fusion of three data sources,exhibited remarkable reliability when validating the three datasets.For Dataset 1(Beijing Xiaotangshan,2023:Data from 12 UAV Flight Missions),the model achieved an overall accuracy(OA)of 86.53%.Additionally,its precision(Pre),recall(Rec),F1 score(F1),false acceptance rate(FAR),and false rejection rate(FRR)were 0.89,0.89,0.87,0.11,and 0.11,respectively.The model also showed strong generalizability in Dataset 2(Beijing Xiaotangshan,2023:Data from 6 UAV Flight Missions)and Dataset 3(Beijing Xiaotangshan,2022:Data from 4 UAV Flight Missions),with OAs of 89.4%and 85%,respectively.Meanwhile,the model has a low demand for input featu res,requiring only 54.55%(99 of all featu res).The findings of this study not only offer novel insights into near real-time crop phenology monitoring,but also provide technical support for agricultural field management and cropping system adaptation. 展开更多
关键词 Near real-time Maize phenology Deep learning UAV Multi-source data fusion
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Image Processing on Geological Data in Vector Format and Multi-Source Spatial Data Fusion 认领 引用
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作者 Liu Xing Hu Guangdao Qiu Yubao Faculty of Earth Resources, China University of Geosciences, Wuhan 430074 《Journal of China University of Geosciences》 2003年第3期278-282,共5页
The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper... The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper converts the vector data into 8 bit images according to their importance to mineralization each by programming. We can communicate the geological meaning with the raster images by this method. The paper also fuses geographical data and geochemical data with the programmed strata data. The result shows that image fusion can express different intensities effectively and visualize the structure characters in 2 dimensions. Furthermore, it also can produce optimized information from multi-source data and express them more directly. 展开更多
关键词 geological data GIS-based vector data conversion image processing multi-source data fusion
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Multi-relation spatiotemporal graph residual network model with multi-level feature attention:A novel approach for landslide displacement prediction 认领 引用
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作者 Ziqian Wang Xiangwei Fang +3 位作者 Wengang Zhang Xuanming Ding Luqi Wang Chao Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第7期4211-4226,共16页
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther... Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R2=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction. 展开更多
关键词 Landslide displacement prediction Spatiotemporal fusion Dynamic graph Data feature enhancement Multi-level feature attention
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Recent trends of machine learning applied to multi-source data of medicinal plants 认领 引用 被引量：8
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作者 Yanying Zhang Yuanzhong Wang 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2023年第12期1388-1407,共20页
In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese... In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019(COVID-19)pandemic has attracted extensive attention globally.Medicinal plants have,therefore,become increasingly popular among the public.However,with increasing demand for and profit with medicinal plants,commercial fraudulent events such as adulteration or counterfeits sometimes occur,which poses a serious threat to the clinical outcomes and interests of consumers.With rapid advances in artificial intelligence,machine learning can be used to mine information on various medicinal plants to establish an ideal resource database.We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants.The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants.The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants. 展开更多
关键词 Machine learning Medicinal plant Multi-source data Data fusion Application
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Multidimensional Analysis of Urban Vitality Centers Using Multisource Data:A Case Study of Changchun,China 认领 引用
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作者 GAO He LIANG Shilong +2 位作者 ZHANG Peiqing JIANG Xue LIU Jibin 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第6期959-973,共15页
As a core city in the old industrial base of Northeast China,Changchun urgently needs to identify the core hubs and structural deficiencies of its urban vitality center system.This is essential for addressing spatial ... As a core city in the old industrial base of Northeast China,Changchun urgently needs to identify the core hubs and structural deficiencies of its urban vitality center system.This is essential for addressing spatial imbalance,enhancing urban efficiency,and revitalizing demographic vitality in a period of urban transition.By integrating multi-source data with social network analysis(SNA),this study examines the spatial structure and network characteristics of urban vitality centers in the central urban area of Changchun in 2024.The results reveal three main findings.First,67 vitality centers were identified and organized into a three-tier hierarchical system composed of core-level,sub-core-level,and node-level centers.Core-level centers are mainly dominated by commercial and consumption functions,whereas sub-core-level and node-level centers perform more differentiated and complementary service roles.Second,the spatial pattern of vitality exhibits strong central agglomeration and clear directional expansion.High-vitality areas are concentrated in the historical urban core,while secondary centers extend along major development corridors,forming a clear contrast between central concentration and peripheral weakness.Third,the vitality center network is characterized by low density but relatively high connectedness and efficiency.A limited number of core nodes dominate resource transmission,while peripheral nodes participate only weakly in the overall network.Blockmodel analysis further shows that cross-block linkages are more significant than intra-block cohesion,although weak internal cohesion in key intermediary blocks constrains the overall transmission efficiency of the network.Based on the integrated perspective of structure,function,and space,this study proposes hierarchical coordination,core-area quality enhancement,and peripheral service supplementation as key pathways for optimizing Changchun’s vitality center network and promoting more balanced and resilient urban development in old industrial cities undergoing transition. 展开更多
关键词 urban vitality centers vitality center network social network analysis(SNA) multi-source data fusion Changchun,China
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Enhanced Multi-Object Dwarf Mongoose Algorithm for Optimization Stochastic Data Fusion Wireless Sensor Network Deployment 认领 引用 被引量：3
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作者 Shumin Li Qifang Luo Yongquan Zhou 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第2期1955-1994,共40页
Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic ... Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained. 展开更多
关键词 Stochastic data fusion wireless sensor networks network deployment spatiotemporal coverage dwarf mongoose optimization algorithm multi-objective optimization
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Multi-scale intelligent fusion and dynamic validation for high-resolution seismic data processing in drilling 认领 引用 被引量：1
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作者 YUAN Sanyi XU Yanwu +2 位作者 XIE Renjun CHEN Shuai YUAN Junliang 《Petroleum Exploration and Development》 SCIE 2025年第3期680-691,共12页
During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resol... During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency. 展开更多
关键词 high-resolution seismic data processing while-drilling update while-drilling logging multi-source information fusion thin-bed weak reflection artificial intelligence modeling
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