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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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In-situ monitoring of layer-wise process quality and signal analysis for laser powder bed fusion using multi-source optical signal 认领 引用
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作者 Di Wang Tao Tang +10 位作者 Tingyi Wang Renwu Jiang Xiaoqiang Zheng Long Zhou Laizhu Chen Wenlong Chen Pan Wang Zhiguang Zhou Ying Ma Yongqiang Yang Linqing Liu 《Additive Manufacturing Frontiers》 CAS CSCD 2026年第2期59-77,共19页
Laser powder bed fusion is a key metal additive manufacturing technology capable of fabricating geometrically complex parts,yet its reliable industrial adoption is hindered by the inherent complexity and stochastic de... Laser powder bed fusion is a key metal additive manufacturing technology capable of fabricating geometrically complex parts,yet its reliable industrial adoption is hindered by the inherent complexity and stochastic defect formation of the process.Current quality assessment is constrained by the inherent latency of offline methods and the diagnostic limitations of single-sensor monitoring.To address these challenges,this study developed a multi-source optical signal monitoring system integrating coaxial photodiodes and an off-axis industrial camera to achieve simultaneous powder spreading detection and radiation signal monitoring during LPBF layer-wise process quality monitoring.Based on the successful identification and analysis of typical detectable features,the YOLOv5s deep learning model was employed to achieve rapid and accurate detection of lack-of-powder defects during the printing process.The training results indicated that the model exhibited good performance metrics.The relationships between process parameters,typical defects,and multi-channel monitoring data were also investigated.The monitoring system achieved a spatial resolution of 300μm for in-process monitoring and demonstrated high accuracy in detecting various defect types,including lack of powder,pores,warping,stitching seams,and printing failures.Furthermore,the algorithm-detected signal anomalies exhibited good spatial correlation with the actual surface defects.Simultaneously,wavelet time-frequency analysis was employed to evaluate molten pool dynamic stability under different process parameters and to analyze energy distribution for different defects.Furthermore,3D model reconstruction from signals enabled effective correlation with actual part defects.Based on the signal-driven process optimization,complex conformal cooling molds were successfully fabricated with a grafting accuracy error of less than 0.12 mm on high-performance substrates,demonstrating the practical efficacy of the developed monitoring methodology.This study provides both a technological and a theoretical foundation for intelligent quality control in LPBF and its practical implementation in industry. 展开更多
关键词 Laser powder bed fusion Multi-source optical signal Signal analysis Layer-wise quality monitoring
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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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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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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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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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MILOF-TCN:A Hierarchical Edge-Fog Framework for Monitoring Abnormal and Missing Patterns in Electric Vehicle Charging Data 认领 引用
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作者 Hwa-Young Jeong 《Computers, Materials & Continua》 SCIE EI 2026年第9期2004-2024,共21页
The rapid growth of electric vehicle(EV)charging infrastructures has introduced new challenges in monitoring abnormal load behaviors under strict latency and resource constraints.Conventional anomaly detection approac... The rapid growth of electric vehicle(EV)charging infrastructures has introduced new challenges in monitoring abnormal load behaviors under strict latency and resource constraints.Conventional anomaly detection approaches either rely on centralized processing or incur excessive false alarms,limiting their practical applicability in large-scale deployments.This paper proposes a hierarchical edge-fog anomaly detection framework that integrates lightweight edge-level filtering with a fog-level Temporal Convolutional Network(TCN)detector.The edge component suppresses non-informative patterns,while the fog layer performs temporal modeling on selectively forwarded data.This design enables controllable reduction of fog-level processing load.Under corrected end-to-end evaluation on real-world EV charging load data,the hierarchical pipeline should be interpreted as a system operating point rather than a uniformly superior detector.Relative to fog-only TCN-AE inference,the selected routing policy reduces fog workload by 34.9%and shortens average detection delay from 93.6 to 75.6 h,but increases false alarms per day from 0.88 to 7.29 and lowers F1 from 0.547 to 0.455.Sensitivity experiments over routing thresholds reveal a consistent trade-off among fog workload,alert burden,detection delay,and retained anomaly evidence.Additional routing diagnostics show that the primary source of performance degradation is information loss induced by filtering,rather than weakness of the fog detector on the forwarded subset.These findings suggest that hierarchical edge intelligence is a practical but calibration-sensitive direction for scalable anomaly monitoring in EV charging infrastructures. 展开更多
关键词 Edge computing fog computing anomaly detection electric vehicle charging time-series monitoring temporal convolutional networks missing data patterns hierarchical architecture
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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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Data Calibration and Quality Control for Grid-based Micro Air Monitoring Stations 认领 引用
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作者 LI Miao 《外文科技期刊数据库（文摘版）自然科学》 2026年第1期061-065,共5页
In recent years, grid-based micro-air quality monitoring stations have been widely adopted for urban air quality monitoring due to their low cost and high density, significantly addressing the issue of low spatial res... In recent years, grid-based micro-air quality monitoring stations have been widely adopted for urban air quality monitoring due to their low cost and high density, significantly addressing the issue of low spatial resolution associated with traditional national monitoring networks. However, the sensor technologies employed in these micro-stations exhibit several systemic limitations during prolonged continuous operation. Typically, data collected by these low-cost sensors are less reliable than those from conventional reference instruments, being highly susceptible to external environmental factors such as temperature and humidity, as well as other atmospheric components, leading to cumulative measurement errors. Studies also indicate that after prolonged installation, sensors develop significant calibration instability: changes in humidity alter the slope of calibration curves, affecting particulate matter detection accuracy;most micro-stations experience baseline drift—electrochemical sensors suffer from zero-point and span drift due to electrode aging, while optical particle counters experience reduced sensitivity from dust accumulation on lenses. Environmentally, high humidity can cause aerosol particles to absorb moisture and expand, resulting in artificially elevated photometric readings and decreased sensor sensitivity at low temperatures, leading to underestimated concentrations. Additionally, there are cross-interference effects: NO₂ sensors demonstrate cross-sensitivity to O₃, while certain metal oxide sensors exhibit broad response ranges to various volatile organic compounds, rendering measured values unreliable indicators of actual pollutant concentrations. 展开更多
关键词 Grid-based monitoring Micro-environment air quality monitoring stations Data verification Quality control management Artificial intelligence
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Whole-Process Quality Assurance and Quality Control Strategies for Standardized Environmental Monitoring Data Acquisition 认领 引用
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作者 LIU Changqing 《外文科技期刊数据库（文摘版）自然科学》 2026年第2期036-040,共5页
High-quality environmental monitoring data act as the fundamental basis for ecological environment supervision, pollution source regulation, environmental policy formulation and ecological risk assessment. Qualified m... High-quality environmental monitoring data act as the fundamental basis for ecological environment supervision, pollution source regulation, environmental policy formulation and ecological risk assessment. Qualified monitoring data must satisfy five core indicators: representativeness, accuracy, precision, integrity and comparability. To eliminate systematic and random errors generated in monitoring workflows, comprehensive quality assurance (QA) and quality control (QC) mechanisms must be implemented throughout the entire monitoring chain, covering on-site sampling, sample transportation & preservation, and laboratory quantitative analysis. This paper systematically sorts out operable QA/QC technical measures applicable to each monitoring link, classifies blank control samples, parallel samples, certified reference materials, calibration verification and recovery testing methods, and elaborates the judgment criteria for each QC indicator. Combined with domestic and international mainstream environmental monitoring specifications, this study analyzes the indicative function of QC test results on data reliability. It further proposes a whole-process linkage QA/QC supervision mode combining field sampling and laboratory testing, which can effectively locate pollution interference, equipment deviation, operational errors and matrix effects. The research conclusions provide standardized technical references for environmental monitoring institutions to optimize quality management systems, reduce data invalidation rates and guarantee the authenticity and credibility of environmental monitoring datasets. 展开更多
关键词 environmental monitoring whole-process quality assurance quality control sampling management laboratory analysis data reliability
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Technical Security Vulnerabilities and Protection Strategies for Seismic Monitoring Data Transmission Networks 认领 引用
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作者 YANG Xiaofeng 《外文科技期刊数据库（文摘版）自然科学》 2026年第1期026-031,共6页
The seismic monitoring data transmission network is the core infrastructure for emergency management departments to carry out seismic monitoring, early warning and emergency response. Its safe and stable operation is ... The seismic monitoring data transmission network is the core infrastructure for emergency management departments to carry out seismic monitoring, early warning and emergency response. Its safe and stable operation is directly related to the safety of people's lives and property and regional social stability. Combined with the actual seismic monitoring work in Botou City, based on the local base station equipment configuration and network operation status, this paper systematically analyzes the existing technical security vulnerabilities in the transmission link, terminal equipment, network management and environmental adaptation of the current seismic monitoring data transmission network. In line with the requirements of the 14th Five-Year Plan for the upgrading of the seismic backbone network and industry security specifications, targeted and implementable protection strategies are proposed to strengthen the coordinated connection between technical and management protection, avoiding the listing of construction and project plans. It provides theoretical and practical support for the Emergency Management Bureau of Botou City to optimize the network security system and improve risk prevention and control capabilities, ensuring the real-time, accuracy and security of monitoring data. 展开更多
关键词 Botou City seismic monitoring data transmission network technical security vulnerabilities protection strategies emergency management
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Installation of the Automatic Noise Monitoring Network for Urban Functional Zones and Data Application 认领 引用
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作者 DUAN Lili 《外文科技期刊数据库（文摘版）自然科学》 2026年第1期066-070,共5页
Noise pollution is one of the major environmental pollution issues that harm people's quality of life. According to the "China Noise Pollution Prevention and Control Report (2024)" released by the Minist... Noise pollution is one of the major environmental pollution issues that harm people's quality of life. According to the "China Noise Pollution Prevention and Control Report (2024)" released by the Ministry of Ecology and Environment, in 2023, the number of public complaints about noise disturbances received by departments such as ecology and environment and public security in cities at or above the prefecture level nationwide (excluding municipalities directly under the central government, sub-provincial cities, and cities with independent planning status) reached approximately 5.7million cases, representing an increase of over 7%. To better address the increasingly severe noise pollution problem, the Ministry of Ecology and Environment issued the "Ten Measures for Sound Management" —the "14th Five-Year Plan for Noise Pollution Prevention and Control Action" —in 2023, explicitly proposing the comprehensive establishment of a regional automatic monitoring system for sound environmental quality: by January 1, 2025, all cities where provincial, autonomous region, or municipal governments are located, as well as cities with independent planning status, shall have established regional automatic monitoring stations for sound environmental quality connected to national and provincial platforms. After 2026, all cities at or above the prefecture level nationwide should participate in this automatic monitoring system. 展开更多
关键词 Functional area acoustic environment Automatic noise monitoring Network configuration Data utilization Disturbance level Voiceprint recognition
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Application and Data Correction of Portable XRF for Emergency Monitoring of Heavy Metals in Soils 认领 引用
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作者 ZHANG Meng TIAN Yanyan 《外文科技期刊数据库（文摘版）自然科学》 2026年第2期157-162,共6页
Soil heavymetal pollution emergencies caused by chemical leakage, industrial accidents, illegal discharge, and geological disasters require rapid on-site monitoring to support emergency response, pollution delimitatio... Soil heavymetal pollution emergencies caused by chemical leakage, industrial accidents, illegal discharge, and geological disasters require rapid on-site monitoring to support emergency response, pollution delimitation, and risk earlywarning. Portable X-ray fluorescence (pXRF) spectroscopy stands out among field analytical techniques for its non-destructive detection, multi-element simultaneous measurement, short testing time, and no complex chemical pre-treatment, and it has become a core screening tool in soil pollution emergency monitoring. Nevertheless, in complex emergency field environments, measurement accuracy is severely interfered with by matrix effects, soil moisture, particlesize heterogeneity, organicmatter content, spectral overlap, and surface unevenness, which produce systematic deviation between raw pXRF readings and laboratory reference values such as ICP-MS. Reliable data correction is therefore indispensable for transforming semi-quantitative field screening data into quantitative data that satisfies emergency decision-making requirements. This paper systematically reviews the working principle of portable XRF, its practical advantages, and existing bottlenecks under emergency monitoring scenarios, designs field-oriented correction workflows for emergency conditions, and evaluates multiple correction strategies, including Compton scattering internalstandard correction, multivariate regression correction, site-specific empirical correction, and spectral pre-processing denoising. Real contaminatedsite emergency samples were used to verify correction performance for typical target elements Pb, Zn, Cu, Cr, Ni, and As. After correction, the coefficient of determination (R^2) between pXRF results and ICP-MS reference values increased from 0.61–0.74 to 0.82–0.92; the average relative error dropped from 28.7%–41.3% down to 10.2%–18.6%. 展开更多
关键词 portable Xray fluorescence (pXRF) soil heavymetal emergency monitoring matrix effect data correction insitu rapid detection
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Digital twin‐based error motion monitoring and prediction method for aerostatic spindle 认领 引用
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作者 Guoda Chen Shenghao Tang +2 位作者 Yuting Jiang Dingxu Zhou Dapeng Tan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期57-73,共17页
The aerostatic spindle is a key component of ultra-precision machine tools,and its error motion is crucial to machining accuracy and reliability.Spindle error motion is unavoidable,and its online monitoring and predic... The aerostatic spindle is a key component of ultra-precision machine tools,and its error motion is crucial to machining accuracy and reliability.Spindle error motion is unavoidable,and its online monitoring and prediction are quite important.Currently,there are relatively few studies on the online monitoring and prediction methods for the aerostatic spindle,and the level of intelligence is relatively low.To address this problem,an error motion monitoring system based on digital twin(DT)technology was established for the aerostatic spindle.A spindle error motion prediction method based on a mechanism and data fusion model(MDFM)was proposed.Additionally,a highly available and interactive aerostatic spindle DT service platform was developed.Experimental results have verified the good performance of this platform.The platform facilitates interaction between the physical and virtual entities of the aerostatic spindle,enabling three-dimensional visualization,monitoring,prediction,and simulation of spindle error motion,and shows good potential for engineering applications. 展开更多
关键词 Aerostatic spindle Digital twin Mechanism and data fusion model Spindle error motion monitoring
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A spatiotemporal recurrent neural network for missing data imputation in tunnel monitoring 认领 引用 被引量：1
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作者 Junchen Ye Yuhao Mao +3 位作者 Ke Cheng Xuyan Tan Bowen Du Weizhong Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第8期4815-4826,共12页
Given the swift proliferation of structural health monitoring(SHM)technology within tunnel engineering,there is a demand on proficiently and precisely imputing the missing monitoring data to uphold the precision of di... Given the swift proliferation of structural health monitoring(SHM)technology within tunnel engineering,there is a demand on proficiently and precisely imputing the missing monitoring data to uphold the precision of disaster prediction.In contrast to other SHM datasets,the monitoring data specific to tunnel engineering exhibits pronounced spatiotemporal correlations.Nevertheless,most methodologies fail to adequately combine these types of correlations.Hence,the objective of this study is to develop spatiotemporal recurrent neural network(ST-RNN)model,which exploits spatiotemporal information to effectively impute missing data within tunnel monitoring systems.ST-RNN consists of two moduli:a temporal module employing recurrent neural network(RNN)to capture temporal dependencies,and a spatial module employing multilayer perceptron(MLP)to capture spatial correlations.To confirm the efficacy of the model,several commonly utilized methods are chosen as baselines for conducting comparative analyses.Furthermore,parametric validity experiments are conducted to illustrate the efficacy of the parameter selection process.The experimentation is conducted using original raw datasets wherein various degrees of continuous missing data are deliberately introduced.The experimental findings indicate that the ST-RNN model,incorporating both spatiotemporal modules,exhibits superior interpolation performance compared to other baseline methods across varying degrees of missing data.This affirms the reliability of the proposed model. 展开更多
关键词 Monitoring Tunnel Machine learning Interpolation Missing data
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