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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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3D Geological Modeling with Multi-source Data Integration in Polymetallic Region:A Case Study of Luanchuan,Henan Province,China 认领 引用 被引量：1
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作者 Gongwen Wang Shouting Zhang +2 位作者 Changhai Yan Yaowu Song Limei Wang 《地学前缘》 CAS 北大核心 2009年第S1期166-167,共2页
The development of 3D geological models involves the integration of large amounts of geological data,as well as additional accessible proprietary lithological,structural,geochemical,geophysical,and borehole data.Luanc... The development of 3D geological models involves the integration of large amounts of geological data,as well as additional accessible proprietary lithological,structural,geochemical,geophysical,and borehole data.Luanchuan,the case study area,southwestern Henan Province,is an important molybdenum-tungsten-lead-zinc polymetallic belt in China. 展开更多
关键词 3D geological modeling multi-source data mineral exploration metallogenic model virtual geological section Luanchuan polymetallic region
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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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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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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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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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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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m6ATEpre:Predicting YTHDF1-mediated mRNA Translation Efficiency Regulated by m6A Sites via Multi-omics Data Integration 认领 引用
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作者 ZHANG Teng ZHANG Ming +1 位作者 ZHANG Shao-Wu LIU Lian 《生物化学与生物物理进展》 SCIE CAS CSCD 北大核心 2026年第4期1087-1102,共16页
Objective The most prevalent mRNA modification,N6-methyladenosine(m6A)plays an important role in various RNA metabolism,including gene expression and translation.By recruiting different“reader”proteins and their ... Objective The most prevalent mRNA modification,N6-methyladenosine(m6A)plays an important role in various RNA metabolism,including gene expression and translation.By recruiting different“reader”proteins and their cofactors,m6A modification can affect messenger RNA(mRNA)degradation,splicing,nuclear export and translation.However,the selective mechanism by which m6A sites regulate mRNA translation through m6A reader YTHDF1 binding remains poorly understood,due to a lack of computational methods for identifying context-specific m6A sites that regulate translation.To address this,we developed a novel computational framework named m6ATEpre,the first tool designed to predict cell-specific m6A sites that regulate translation efficiency.Methods m6ATEpre integrates multi-omics data,introduces a novel feature representation strategy for m6A site sequences,and employs an autoencoder to effectively capture embedded feature representations.Specifically,m6ATEpre first integrated MeRIP-seq data and PAR-CLIP data through overlapping m6A sites with YTHDF1 binding sites and identified YTHDF1-mediated m6A sites.Then,m6ATEpre detected the translation gene by analyzing the Ribo-seq data under YTHDF1 knockdown vs control condition.Genes whose translation is mediated by YTHDF1 in an m6A-dependent manner were identified by a significant decrease in translation efficiency upon YTHDF1 knockdown.Next,we proposed a binary vector indicating the presence or absence of YTHDF1 binding motifs to characterize each m6A site sequence.This represents a novel feature representation strategy for m6A sites.m6ATEpre utilized the autoencoder to extract the potentially important feature representations and constructed a multilayer perceptron neural networks model to predict potential m6A sites that regulating translation efficiency.Results A comprehensive evaluation of m6ATEpre was conducted through a series of experiments.We compared its performance against that of a similar prediction task model,as well as other classifiers.The results indicate that m6ATEpre achieved the best prediction performance.In addition,we analyzed different feature representation strategies and performed ablation experiments to validate the rationality of the model design.The results demonstrate that our proposed feature representation strategy has a greater advantage in improving prediction performance.In the HeLa cell line,bioinformatic analysis of the metagene distribution and sequence minimum free energy of m6A sites regulating translation efficiency(m6A-reg-TE sites)revealed their specific properties in translation regulation.Functional enrichment analysis indicated that m6A-reg-TE genes are associated with specific biological processes and KEGG pathways.By integrating the binding sites of YTHDF1 co-factors with m6A-reg-TE sites,we revealed that YTHDF1-mediated and m6A-dependent translation efficiency regulation requires the cooperation of multiple translation-regulatory RNA-binding proteins among its co-factors in the HeLa cell line.Furthermore,we extended our predictions to the dataset of the HEK293T cell line.Similarly,bioinformatic analysis of the metagene distribution and functional enrichment revealed the cell-specific characteristic of these predicted m6A-reg-TE sites in HEK293T cells.Likewise,integrated analysis of multiple YTHDF1 co-factors and m6A-reg-TE sites predicted in the HEK293T cell line reveals their m6A-dependent cooperation in regulating translation efficiency.Conclusion m6ATEpre is a timely tool that will advance our understanding of the mechanisms of m6A regulation in translation efficiency.The source code and datasets used in this work can be downloaded from http://gffzz6591ccda58324448sc06f0vuwfxo06nvf.ffgz.tsg.suse.edu.cn/s/bAZZFr. 展开更多
关键词 m6A modification YTHDF1-mediated translation efficiency multi-omics data integration feature representation
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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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Multi-source data integration and multi-scale modeling framework for progressive prediction of complex geological interfaces in tunneling 认领 引用 被引量：10
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作者 Jingxiao Wang Peinan Li +3 位作者 Xiaoying Zhuang Xiaojun Li Xi Jiang Jun Wu 《Underground Space》 SCIE EI CSCD 2024年第2期1-25,共25页
A reliable geological model plays a fundamental role in the efficiency and safety of mountain tunnel construction.However,regional models based on limited survey data represent macroscopic geological environments but ... A reliable geological model plays a fundamental role in the efficiency and safety of mountain tunnel construction.However,regional models based on limited survey data represent macroscopic geological environments but not detailed internal geological characteristics,especially at tunnel portals with complex geological conditions.This paper presents a comprehensive methodological framework for refined modeling of the tunnel surrounding rock and subsequent mechanics analysis,with a particular focus on natural space distortion of hard-soft rock interfaces at tunnel portals.The progressive prediction of geological structures is developed considering multi-source data derived from the tunnel survey and excavation stages.To improve the accuracy of the models,a novel modeling method is proposed to integrate multi-source and multi-scale data based on data extraction and potential field interpolation.Finally,a regional-scale model and an engineering-scale model are built,providing a clear insight into geological phenomena and supporting numerical calculation.In addition,the proposed framework is applied to a case study,the Long-tou mountain tunnel project in Guangzhou,China,where the dominant rock type is granite.The results show that the data integration and modeling methods effectively improve model structure refinement.The improved model’s calculation deviation is reduced by about 10%to 20%in the mechanical analysis.This study contributes to revealing the complex geological environment with singular interfaces and promoting the safety and performance of mountain tunneling. 展开更多
关键词 Mountain tunnel Geological modeling Multi-source data Progressive prediction Tunnel portals
Discussion on the Application of Geotechnical Survey Data Integration Technology in Geotechnical Engineering 认领 引用
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作者 XIN Nan 《外文科技期刊数据库（文摘版）工程技术》 2026年第3期011-015,共5页
Geotechnical engineering serves as an indispensable pillar in national economic development, imposing stringent and meticulous requirements for project safety assurance and construction quality. Traditional methods of... Geotechnical engineering serves as an indispensable pillar in national economic development, imposing stringent and meticulous requirements for project safety assurance and construction quality. Traditional methods of geotechnical survey data collection and management have proven outdated, with fragmented data sources and isolated information silos that hinder comprehensive analysis and utilization. To address these challenges, a specialized data integration technology has been developed to resolve practical issues in geotechnical projects, ensuring systematic organization and rational allocation of data for collaborative use. The study thoroughly examines critical issues arising during data formatting, management, and transmission, proposing a technical solution centered on data standardization and information integration. This approach emphasizes unified data formats and standardized processing procedures, significantly enhancing seamless information exchange across platforms and departments. A detailed management framework covers the entire workflow—from data collection and preliminary processing to centralized storage—ensuring orderly execution at every stage. Practical applications demonstrate remarkable effectiveness: the integrated technology improves data management efficiency, reduces error rates, and provides reliable decision-support data for field operations. Research confirms its exceptional performance in geotechnical projects, effectively supporting quality control and risk mitigation while driving advancements in engineering informatization. This study significantly enriches the theoretical framework for geotechnical engineering data management. In practical applications, it demonstrates substantial potential for widespread adoption, effectively enhancing overall project quality while markedly improving construction safety—delivering profound significance and value. 展开更多
关键词 geotechnical investigation data integration technology geotechnical engineering data standardization information sharing
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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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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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A Low-Code Orchestration Middleware for Secure and Transparent IoT-Blockchain Integration 认领 引用 被引量：1
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作者 Jesús Rosa-Bilbao 《Computers, Materials & Continua》 SCIE EI 2026年第7期1967-1979,共13页
The integration of Internet of Things(IoT)infrastructures with Distributed Ledger Technologies(DLT)remains challenging due to the reliance on complex,tightly coupled back-end systems or centralized oracle services tha... The integration of Internet of Things(IoT)infrastructures with Distributed Ledger Technologies(DLT)remains challenging due to the reliance on complex,tightly coupled back-end systems or centralized oracle services that hinder scalability,maintainability,and trust.This paper introduces a lightweight middleware architecture based on a Low-Code Development Platform(LCDP)that enables flexible and secure IoT-to-blockchain orchestration.We develop a custom workflow extension for the n8n platform that supports direct interaction with smart contracts,thereby removing the need for third-party oracle intermediaries.The proposed system was evaluated in a real-world deployment involving a network of Netatmo environmental sensors and the Alastria consortium blockchain.Experimental results show that the middleware can process 12 concurrent sensor data streams with an average end-to-end latency of 37.4 s,a delay dominated by the blockchain consensus time rather than middleware overhead,while ensuring the generation of immutable and verifiable audit trails.These findings demonstrate that low-code orchestration can deliver an effective,scalable,and fault-tolerant alternative for integrating IoT infrastructures with blockchain in Industry 4.0 environments. 展开更多
关键词 Low-code development platform IoT-blockchain integration distributed ledger technologies workflow orchestration smart-contract interaction data integrity
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Blockchain-Based Transparent Certificateless Data Integrity Auditing with Enhanced Tag Security 认领 引用
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作者 Chao Zhang Weidong Zhong +6 位作者 Xu An Wang Weiwei Jiang Ziteng Wang Miao Tian Jianhong Ling Hangjiang Du Yunhui Duan 《Computers, Materials & Continua》 SCIE EI 2026年第8期948-976,共29页
The integrity risks posed by data outsourcing in cloud storage have driven the development of remote data integrity auditing(RDIA)technologies.However,traditional schemes rely on trusted third-party auditors(TPAs),lea... The integrity risks posed by data outsourcing in cloud storage have driven the development of remote data integrity auditing(RDIA)technologies.However,traditional schemes rely on trusted third-party auditors(TPAs),leading to potential collusion and single-point failure vulnerabilities.The integration of blockchain alleviates these issues through decentralization and transparency,yet existing blockchain-based certificateless auditing schemes still suffer from security flaws in the tag generation phase.Addressing the tag forgery vulnerability in Miao et al.’s scheme,which stems from the absence of random parameters in the hash function input,this paper proposes a lightweight enhancement mechanism:incorporating a random factor into the hash input during tag generation to ensure dynamic unforgeability of tags.While retaining the efficiency advantages of the original framework,the improved scheme achieves resistance against tag forgery,proof forgery,and collusion attacks under the Computational Diffie-Hellman(CDH)and Discrete Logarithm(DL)hardness assumptions,validated through rigorous formal proofs.Experimental performance analysis demonstrates that the proposed enhanced scheme introduces negligible computational overhead,providing a secure,practical,and transparent auditing solution for multi-cloud storage environments. 展开更多
关键词 Blockchain tag security certificateless cryptography data integrity auditing cloud storage
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Integrating Multi-Source Web Records into Relational Database 认领 引用 被引量：1
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作者 HUANG Jianbin JI Hongbing SUN Heli 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第5期1177-1181,共5页
How to integrate heterogeneous semi-structured Web records into relational database is an important and challengeable research topic. An improved model of conditional random fields was presented to combine the learnin... How to integrate heterogeneous semi-structured Web records into relational database is an important and challengeable research topic. An improved model of conditional random fields was presented to combine the learning of labeled samples and unlabeled database records in order to reduce the dependence on tediously hand-labeled training data. The pro- posed model was used to solve the problem of schema matching between data source schema and database schema. Experimental results using a large number of Web pages from diverse domains show the novel approach＇s effectiveness. 展开更多
关键词 Web data integration schema matching conditional random fields
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Understory terrain estimation using multi-source remote sensing data under different forest-type conditions 认领 引用
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作者 HUANG Jia-Peng FAN Qing-Nan ZHANG Yue 《红外与毫米波学报》 SCIE EI CAS CSCD 北大核心 2025年第6期919-932,共14页
Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneit... Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneity when traditional forest topographic inversion methods consider the entire forest as the inversion unit,this study pro⁃poses a differentiated modeling approach to forest types based on refined land cover classification.Taking Puerto Ri⁃co and Maryland as study areas,a multi-dimensional feature system is constructed by integrating multi-source re⁃mote sensing data:ICESat-2 spaceborne LiDAR is used to obtain benchmark values for understory terrain,topo⁃graphic factors such as slope and aspect are extracted based on SRTM data,and vegetation cover characteristics are analyzed using Landsat-8 multispectral imagery.This study incorporates forest type as a classification modeling con⁃dition and applies the random forest algorithm to build differentiated topographic inversion models.Experimental re⁃sults indicate that,compared to traditional whole-area modeling methods(RMSE=5.06 m),forest type-based classi⁃fication modeling significantly improves the accuracy of understory terrain estimation(RMSE=2.94 m),validating the effectiveness of spatial heterogeneity modeling.Further sensitivity analysis reveals that canopy structure parame⁃ters(with RMSE variation reaching 4.11 m)exert a stronger regulatory effect on estimation accuracy compared to forest cover,providing important theoretical support for optimizing remote sensing models of forest topography. 展开更多
关键词 understory terrain forest type multi-source remote sensing data random forest model
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Energy data space ensuring reliable interaction of virtuality and reality in integrated energy system:A survey 认领 引用
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作者 Haoran Li Chenghui Zhang +2 位作者 Peide Liu Bo Sun Haoran Zhao 《Cyber-Physical Energy Systems》 2026年第1期1-12,共12页
With the deep integration of energy and information networks,the low-carbon,efficient,and economical oper-ation and management of integrated energy systems are increasingly driven by digital twins and large artificial... With the deep integration of energy and information networks,the low-carbon,efficient,and economical oper-ation and management of integrated energy systems are increasingly driven by digital twins and large artificial intelligence models,which rely heavily on the robust support of the Internet of Things and big data.However,the data interaction process within energy systems faces issues such as varying privacy protection demands and conflicts of interest among participating entities,leading to development bottlenecks like prominent data silos,a lack of secure sharing mechanisms,and low collaborative efficiency.As a novel infrastructure for data resource circulation,the energy data space integrates various software and hardware encryption methods to construct a trusted execution environment,providing solutions for the secure sharing and value realization of energy data re-sources.Based on a decentralized yet adjustable underlying architecture,the energy data space can be adaptively designed according to the coupling characteristics of energy and information flows,meeting the real-time,effi-ciency,and scalability requirements of cross-entity systems and large-scale businesses.This paper investigates,analyzes,and summarizes technical routes through which the energy data space can facilitate the digital and intelligent transformation of integrated energy systems.It also elaborates on the supporting role of the energy data space in the conceptual architecture,technical support,application scenarios,and value creation of energy industry upgrading. 展开更多
关键词 Energy data resources Energy data space Integrated energy system Digital energy network Reliable interaction
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