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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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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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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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An NDVI-constrained multi-source remote sensing data fusion network for detecting the level of larch caterpillar(Dendrolimus superans)infestation 认领 引用
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作者 WU Linlin WANG Mingchang +1 位作者 WANG Fengyan MAO Dehua 《Journal of Mountain Science》 SCIE CSCD 2026年第7期3170-3191,共22页
Outbreaks of the larch caterpillar(Dendrolimus superans)cause severe ecological and economic damage to boreal forests,underscoring the urgent need for effective monitoring and early warning systems.However,the utility... Outbreaks of the larch caterpillar(Dendrolimus superans)cause severe ecological and economic damage to boreal forests,underscoring the urgent need for effective monitoring and early warning systems.However,the utility of space-borne multispectral imagery(MSI)for this purpose is often constrained by either coarse spatial resolution or insufficient spectral bands,limiting the accurate classification of pest occurrence levels.To address this challenge,we developed an NDVI-constrained Dynamic Ridge Polynomial Neural Network(NDRPNN)to fuse Sentinel-2 MSI data with Gaofen-2(GF-2)panchromatic imagery,thereby enhancing spatial detail while preserving spectral integrity.Timeseries spectral,textural,and polarimetric features derived from Sentinel-1/2 imagery were subsequently integrated,and correlation analysis was applied to identify the most sensitive indicators.Four classification models—Random Forest,Light Gradient Boosting Machine,Stacking Ensemble,and Soft Voting Ensemble(SVE)—were evaluated for detecting infestation levels,with Shapley(SHAP)analysis employed to interpret feature contributions.The NDRPNN exhibited robust fusion performance in forested landscapes.Ensemble methods outperformed single classifiers,with the SVE model achieving the highest accuracy(overall accuracy=87.6%,Kappa=0.83).SHAP analysis identified the mean and maximum Normalized Difference Vegetation Index(NDVI),minimum Anthocyanin Reflectance Index(ARI),minimum Normalized Burn Ratio(NBR),and seasonal amplitude of Enhanced Vegetation Index(EVI)as key contributing features,highlighting the critical role of time-series vegetation indices and textural metrics in early pest detection.This study demonstrates that the integration of high-quality Sentinel-2 and GF-2 imagery with ensemble learning enables rapid and precise assessment of pest occurrence,offering a robust foundation for the early warning and scientific management of forest pests in mountain regions. 展开更多
关键词 Larch caterpillar infestation Multisource data fusion Pansharpening Ensemble learning Early detecting
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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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Monitoring of agricultural drought based on multi-source remote sensing data in Heilongjiang Province,China 认领 引用
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作者 Chenfa Jiang Changhui Ma +4 位作者 Sibo Duan Xiaoxiao Min Youzhi Zhang Dandan Li Xia Zhang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第4期1716-1730,共15页
Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Curre... Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Currently,in-situ drought monitoring based on weather stations and based on remote sensing data has limitations,including infrequent updates,limited coverage,and low accuracy.This study leverages multi-source remote sensing data to monitor agricultural drought in Heilongjiang Province,China.We developed multi-source composite drought indices(MCDIs)at various timescales(3,6,9,and 12 months)by integrating precipitation,land surface temperature,soil moisture,and vegetation indices.Utilizing remote sensing data from various sources,we calculated a series of single drought indices,which are the precipitation condition index,soil moisture condition index,vegetation condition index,and temperature condition index.These are then integrated into MCDIs using a multivariable linear regression approach.The analysis reveals that MCDIs correlate more with standardized precipitation evapotranspiration index(SPEI)than single drought indices.When examining the correlation between different MCDIs and the affected area of crops and major grain production,MCDI-9 showed the highest correlation with the affected area of crops,while MCDI-12 showed the highest correlation with grain production.This suggests that these two MCDIs at different timescales are better indicators of agricultural drought.The spatio-temporal analysis of MCDI indicates that drought in Heilongjiang Province primarily occurs in early spring,gradually spreading from the Greater Khingan Mountains region to the southeastern plains.The drought gradually alleviates during the summer,ending by the autumn harvest period.Therefore,the MCDIs constructed in this study can serve as effective methods and indicators for drought monitoring in Heilongjiang Province and similar regions. 展开更多
关键词 agricultural drought spatio-temporal monitoring multi-source remote sensing data SPEI Heilongjiang Province
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Temporal and spatial variations in evapotranspiration on the northern slope of the Kunlun Mountains based on multi-source datasets 认领 引用
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作者 YuanYuan Zhang MingJun Zhang +2 位作者 ShiQin Xu CunWei Che QinQin Du 《Research in Cold and Arid Regions》 CAS CSCD 2026年第1期59-70,共12页
By combining eight types of evapotranspiration datasets,the spatial and temporal variations in the evapotranspiration(ET)on the northern slope of the Kunlun Mountains were analyzed in uninhabited areas that lack obser... By combining eight types of evapotranspiration datasets,the spatial and temporal variations in the evapotranspiration(ET)on the northern slope of the Kunlun Mountains were analyzed in uninhabited areas that lack observational data.The order of the average annual ET was ERA5_Land(312.32 mm/a)>CR(239.80 mm/a)>MOD16STM(211.87 mm/a)>GLADS(119.02 mm/a)>ETM(111.88 mm/a)>EB-ET(109.90 mm/a)>GLEAM(100.84 mm/a)>MERRA-2(100.81 mm/a).The ET value from the ERA5_Land dataset was three times higher than that of the other five datasets.The ET values of the CR and MOD16STM datasets were twice that of the other five datasets.In terms of time,the correlation coefficient between the GLEAM and MERRA-2 datasets was the highest(R?0.82).In terms of space,GLDAS and MERRA-2 had the highest multi-year average ET correlation coefficient(R?0.80).The reduction in spatial scale resulted in clear differences in the multi-year average ET correlations among different products in the same region.In terms of time,the average annual ET of the basins on the northern slope of the Kunlun Mountains exhibited an overall increasing trend for all data sources,and the overall annual average change in the study area estimated by the eight datasets was 1.09 mm/a.The most rapid rates of increase were obtained from GLDAS(1.38 mm/a)and GLEAM(1.38 mm/a).In the CR,ERA5_Land,GLEAM,GLDAS,MERRA-2,ETM,MOD16STM,and EB-ET datasets,46.46%,41.47%,87.30%,40.30%,49.10%,47.13%,57.16%,and 45.12%of the watersheds,respectively,showed a significantly increasing trend.The ET value of the Yarkand River Basin showed a significantly increasing trend for all eight data sources.The results of this study provide a scientific reference for the allocation of water resources on the northern slope of the Kunlun Mountains. 展开更多
关键词 North slope basin of Kunlun Mountains Evapotranspiration Multi-source data Climate change
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Short-term Ozone Exposure and Its Impact on Mortality Risk from Circulatory System Diseases:A Comparative Analysis Based on Multi-source Data 认领 引用
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作者 Chaodong Long Shunshun Zhang +4 位作者 Wenjing Su Mike ZHe Qinghua Sun Cheng Liu Tiantian Li 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2026年第5期501-511,共11页
Objective Ozone pollution significantly impacts public health;however,inconsistent exposure assessment data introduce uncertainty to health risk evaluations.The accurate assessment of health risks and disease burden i... Objective Ozone pollution significantly impacts public health;however,inconsistent exposure assessment data introduce uncertainty to health risk evaluations.The accurate assessment of health risks and disease burden is essential to protecting public health and formulating effective control strategies.Methods This study used a generalized linear model to compare health risks and disease burdens assessed using three ozone datasets(CNEMC,TAP,and USTC)based on circulatory system disease mortality data from 199 Chinese counties(2014–2018).Results The impact of ozone exposure on the risk of death from circulatory system diseases was most significant at lag03.In the CNEMC dataset,a 10μg/m3increase in O3-MAD8 was associated with a 0.14%(95%CI:0.01%—0.26%)increase in the risk of death.In contrast,the risk estimates for TAP and USTC were 0.26%(95%CI:0.10%—0.42%)and 0.23%(95%CI:0.09%—0.37%),respectively,indicating a difference of up to 46%.The estimated annual attributable deaths by TAP and USTC were 1.96 and 1.85 times higher than those in the CNEMC dataset,respectively.Conclusion Ozone exposure was associated with increased circulatory system disease mortality.Both risk estimates and attributable mortality burdens varied substantially across different datasets,thus highlighting that exposure data selection can materially influence health risk evaluation. 展开更多
关键词 Circulatory system diseases Mortality Ozone Monitoring data Simulated data
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An SSA-RF-based Lunar Surface Rock Abundance Retrieval Method Using Multi-source Remote Sensing Data 认领 引用
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作者 Zhiyuan Guo Zhaobo Song +2 位作者 Chenya Li Gaofeng Shu Ning Li 《Research in Astronomy and Astrophysics》 SCIE CAS CSCD 2026年第2期224-239,共16页
The spatial distribution and abundance of surface rocks on the Moon serve as critical determinants in landing site selection, mission planning, and scientific investigations. Due to the lack of optical and thermal inf... The spatial distribution and abundance of surface rocks on the Moon serve as critical determinants in landing site selection, mission planning, and scientific investigations. Due to the lack of optical and thermal infrared observations in permanently shadowed region, conventional remote sensing techniques encounter substantial challenges in directly retrieving rock-related information from these areas. Synthetic aperture radar(SAR)penetrates the lunar regolith, providing a robust and reliable means of acquiring subsurface information,particularly under rugged terrain or low-illumination conditions. This study proposes a rock abundance retrieval method by integrating Mini-RF SAR and Lunar Orbiter Laser Altimeter digital elevation model(DEM) data with a machine learning framework. Feature parameters are extracted from the SAR and the DEM data, and a random forest model optimized by the sparrow search algorithm is constructed to estimate rock abundance in lunar maria regions. Model training and validation in representative lunar maria areas demonstrate high predictive accuracy,with a coefficient of determination(R~2) of 0.77 and a root mean square error of 0.004. The predicted rock abundance shows strong agreement with measurements from the Diviner thermal radiometer, supporting the model's reliability. Furthermore, the trained model is applied to selected permanently shadowed regions near the lunar south pole, where optical remote sensing is unavailable due to the lack of sunlight. In these challenging environments, SAR and DEM data provide essential observational support for rock abundance estimation. This approach represents a viable pathway for investigating rock distribution in polar regions, and provides essential data and methodological insights for future lunar exploration, particularly regarding landing site safety and in-situ resource evaluation. 展开更多
关键词 Moon-methods data analysis-planets and satellites surfaces-techniques image processing
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An Optimized Ensemble Learning Framework for Energy Efficiency Assessment in Low-Voltage Distribution Networks Using Multi-Source Data Integration 认领 引用
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作者 Yujie Shi Guoxing Wu +2 位作者 Qingwei Wang Xieli Fu Wenfeng Yang 《Energy Engineering》 EI 2026年第9期350-375,共26页
This study proposes an optimized ensemble learning framework for energy-efficiency assessment in low-voltage distribution networks by integrating multiple data sources.The framework integrates heterogeneous data from ... This study proposes an optimized ensemble learning framework for energy-efficiency assessment in low-voltage distribution networks by integrating multiple data sources.The framework integrates heterogeneous data from smart meters,SCADA systems,meteorological stations,and network topology databases,employing advanced feature engineering to extract 89 essential predictors from 147 initial features.Three gradient boosting algorithms-Random Forest,XGBoost,and LightGBM-are combined through an elastic net stacking strategy with Bayesian hyperparameter optimization.The stacking ensemble achieved superior performance with an MAE of 118.4 kWh,an RMSE of 164.2 kWh,an MAPE of 3.98%,and an R2of 0.952,representing 16.8%improvement over individual models.SHAP analysis provided model interpretability,identifying temperature,historical consumption,and temporal features as the primary drivers of efficiency.The framework demonstrated robust performance under data quality degradation and successfully generalized across diverse network configurations.Field implementation yielded an 8.3%reduction in distribution losses(95%CI:7.2%-9.4%,p<0.0001),34%decrease in transformer failure rates(95%CI:28%-40%,p=0.003),and 12%-15%operational cost reduction.The framework's ability to provide accurate predictions from 15 min to 24 h ahead while maintaining computational efficiency enables proactive distribution network management,supporting the transition toward efficient and sustainable power systems. 展开更多
关键词 Ensemble learning energy efficiency assessment low-voltage distribution networks multi-source data integration SHAP analysis
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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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Monitoring track irregularities using multi-source on-board measurement data 认领 引用 被引量：1
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作者 Qinglin Xie Fei Peng +4 位作者 Gongquan Tao Yu Ren Fangbo Liu Jizhong Yang Zefeng Wen 《Railway Engineering Science》 EI 2025年第4期746-765,共20页
Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on co... Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models. 展开更多
关键词 Track irregularities Vehicle accelerations On-board monitoring Multi-source data Deep learning
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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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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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Multi-source errors evaluation of machine tools:from research gaps to methodologies and applications 认领 引用 被引量：1
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作者 Jingang Sun Yanbin Zhang +14 位作者 Xiao Ma Benkai Li Min Yang Liandi Xu Haiyuan Xin Qinglong An Lida Zhu Qingfeng Bie Xianxin Yin Shouhai Chen Guanqun Li Yusuf Suleiman Dambatta Rui Xue Zhenwei Yu Changhe Li 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第2期44-96,共53页
Multi-source errors,as critical obstacles limiting the accuracy retention and machining performance of machine tools,hold fundamental and strategic significance for achieving high-precision,high-efficiency,and high-re... Multi-source errors,as critical obstacles limiting the accuracy retention and machining performance of machine tools,hold fundamental and strategic significance for achieving high-precision,high-efficiency,and high-reliability machining in modern manufacturing systems.However,these errors typically exhibit complex characteristics such as strong coupling,time-variance,and nonlinearity,which challenge traditional methods of error identification,modeling,and compensation in terms of adaptability,real-time capability,and integration.Therefore,it is imperative to establish a systematic and intelligent multi-source error control framework.Firstly,this work systematically reviews typical error sources and their evolution mechanisms,evaluates multi-scale detection technologies including laser interferometry,double ball-bar systems,multi-sensor fusion,and vision-based systems,and constructs an intelligent error identification and evaluation framework.Next,it reviews classical modeling methods such as homogeneous transformation matrices,screw theory,thermal equilibrium models,finite element analysis,and modal analysis,compares physical modeling,data-driven,and hybrid modeling strategies,and develops an integrated multi-source error modeling architecture centered on digital twin technology and artificial intelligence.Furthermore,key technologies,including geometric error mapping and real-time compensation,online thermal error prediction and active temperature control,dynamic error suppression,and adaptive control,are summarized.A multi-level integrated error compensation architecture is proposed by combining physical models,data models,and cyber-physical synchronization.This architecture encompasses core processes such as error traceability and decoupling,dynamic prediction,real-time compensation,and closed-loop optimization,emphasizing engineering implementation mechanisms based on cyber-physical collaboration,multi-physics coupling,and multi-scale fusion,thereby effectively enhancing accuracy stability and control robustness under complex operating conditions.Finally,frontier challenges such as constructing high-fidelity coupled models from heterogeneous multi-source data,edge-cloud collaborative control,and cross-platform interoperability are discussed.The application prospects of multi-source error evaluation are also envisioned,providing theoretical foundations and technical support for the precise management and optimization of the entire lifecycle accuracy of machine tools. 展开更多
关键词 multi-source error evaluation error coupling integrated error modeling traceability and decoupling hybrid prediction closed-loop error compensation
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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://gffzz9c504e06f78b4edah9xppp96bo6qo6wox.ffgz.tsg.suse.edu.cn. 展开更多
关键词 tsRNA Disease Cancer Data integration Database
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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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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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Hybrid Deep Learning for Hydraulic Cylinder Fault Diagnosis under Complex Conditions via Multi-Source Signal Fusion 认领 引用 被引量：1
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作者 Chen Yang Jianwen Yan +2 位作者 Yixiong Feng Lei Li Jianrong Tan 《Instrumentation》 2026年第1期40-56,共17页
Hydraulic presses are indispensable in automotive and aerospace manufacturing,with hydraulic cylinders serving as key components for operational safety and product quality.Internal leakage faults in hydraulic cylinder... Hydraulic presses are indispensable in automotive and aerospace manufacturing,with hydraulic cylinders serving as key components for operational safety and product quality.Internal leakage faults in hydraulic cylinders are difficult to diagnose due to the scarcity of labeled data,the complexity of fault mechanisms,and the limited representation capability of single-signal methods under variable operating conditions.To address these issues,a hybrid deep learning feature fusion model based on displacement error and pressure signal,including convolutional autoencoder,multi-head attention mechanism,residual network and bidirectional long short time series neural network(CAEMRAB),is proposed for the diagnosis and classification of leakage faults in hydraulic cylinders.A hydraulic cylinder test system simulates heavy load,variable speed,and nonlinear motion under actual operating conditions.Through the all-round deep feature decoupling of the proposed model,the multi-source signal representation ability in complex and multi-noise environments is enhanced,effectively extracting the local and global features of displacement error and pressure signal fault data and achieving efficient classification.Experimental results indicate that the proposed model achieves at least a 3.95%improvement in diagnostic accuracy compared with ablation models.In addition,it exhibits high diagnostic stability across other models,single-signal diagnosis,varying sample sizes,and complex noise conditions.These experiments fully validate the superior performance of the proposed method in terms of diagnostic accuracy,reliability,and robustness. 展开更多
关键词 hydraulic cylinder feature fusion multi-source signal hybrid deep learning deep feature decoupling
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