Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-st...Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-stationarity,and short-term interruptions of SCB data under complex environments,this paper proposes an enhanced SCB prediction model combining Temporal Convolutional Networks(TCN)and Transformers.Experimental results indicate that,in a 24-h prediction task,the proposed model reduces root mean square error(RMSE)and range error(RE)by 95.6%,86.0%,and 61.3%,and93.7%,86.3%,and 58.8%,respectively,compared with LSTM,Transformer,and CNN-BiGRU-Attention models,while improving computational efficiency by 48.6%over the Transformer.Moreover,although the clock bias products generated by the proposed method result in slightly higher static PPP positioning errors than the International GNSS Service(IGS)rapid clock products,the error differences are generally at the millimeter level,demonstrating the feasibility of using predicted clock bias products to replace rapid clock products in the short term.This method addresses the PPP positioning issue during short-term network service interruptions from the perspective of time series prediction and provides potential solutions for engineering applications such as landslide,earthquake,and subsidence monitoring.展开更多
Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks,challenging conventional monitoring.This study probes the coupled acoustic-electrical ...Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks,challenging conventional monitoring.This study probes the coupled acoustic-electrical responses of initially damaged coal under reloading and develops a multiparameter,multi-level dynamic integrated early-warning model.Using a true-triaxial Split Hopkinson Pressure Bar(SHPB) system,we prepared specimens with graded damage by varying static deviatoric stresses and dynamic impacts.Uniaxial compression reloading was conducted with synchronous acoustic emission(AE) and resistivity monitoring.Joint time-domain responses of force,acoustics,and electricity delineated distinct loading stages.Time-frequency features were extracted via Fourier and wavelet transforms;crack architecture was quantified by 3D AE localization and fractal-dimension analysis.Initial damage markedly reduced load-bearing capacity.Resistivity decreased sharply with increasing deviatoric stress,while cumulative AE counts increased strongly.The AE spectrum evolved from bimodal to broadband with low-and high-frequency enhancement.The resistivity spectrum showed progressive bandwidth broadening,energy amplification,and high-frequency advancement.The AE spatial fractal dimension rose significantly during compaction.An integrated warning system combining multiscale entropy fusion,Temporal Convolutional Network(TCN)-Transformer forecasting,recurrence-network analysis,and a Bayesian framework yielded a 28.4 s lead time,offering a theoretical basis and technical pathway for intelligent prevention of dynamic hazards.展开更多
基金supported by the National Natural Science Foundation of China(42304050)Major Science and Technology Projects in Anhui Province,grant number(202103a05020026)+1 种基金Open Foundation of the Key Laboratory of Universities in Anhui Province for Prevention of Mine Geological Disasters(2022-MGDP-08)University Natural Science Research Project of Anhui Province(2023AH051190)。
文摘Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-stationarity,and short-term interruptions of SCB data under complex environments,this paper proposes an enhanced SCB prediction model combining Temporal Convolutional Networks(TCN)and Transformers.Experimental results indicate that,in a 24-h prediction task,the proposed model reduces root mean square error(RMSE)and range error(RE)by 95.6%,86.0%,and 61.3%,and93.7%,86.3%,and 58.8%,respectively,compared with LSTM,Transformer,and CNN-BiGRU-Attention models,while improving computational efficiency by 48.6%over the Transformer.Moreover,although the clock bias products generated by the proposed method result in slightly higher static PPP positioning errors than the International GNSS Service(IGS)rapid clock products,the error differences are generally at the millimeter level,demonstrating the feasibility of using predicted clock bias products to replace rapid clock products in the short term.This method addresses the PPP positioning issue during short-term network service interruptions from the perspective of time series prediction and provides potential solutions for engineering applications such as landslide,earthquake,and subsidence monitoring.
基金supported by the National Key Scientific Instruments and Equipment Development Projects of China (No.52227901)the National Key R&D Program of China (No.2022YFC3004705)+2 种基金the Graduate Innovation Program of China University of Mining and Technology (No.2024WLKXJ153)the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No.KYCX24_2926)the Special Funding for the Jiangsu Provincial Science and Technology Plan (No.BM2022013)。
文摘Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks,challenging conventional monitoring.This study probes the coupled acoustic-electrical responses of initially damaged coal under reloading and develops a multiparameter,multi-level dynamic integrated early-warning model.Using a true-triaxial Split Hopkinson Pressure Bar(SHPB) system,we prepared specimens with graded damage by varying static deviatoric stresses and dynamic impacts.Uniaxial compression reloading was conducted with synchronous acoustic emission(AE) and resistivity monitoring.Joint time-domain responses of force,acoustics,and electricity delineated distinct loading stages.Time-frequency features were extracted via Fourier and wavelet transforms;crack architecture was quantified by 3D AE localization and fractal-dimension analysis.Initial damage markedly reduced load-bearing capacity.Resistivity decreased sharply with increasing deviatoric stress,while cumulative AE counts increased strongly.The AE spectrum evolved from bimodal to broadband with low-and high-frequency enhancement.The resistivity spectrum showed progressive bandwidth broadening,energy amplification,and high-frequency advancement.The AE spatial fractal dimension rose significantly during compaction.An integrated warning system combining multiscale entropy fusion,Temporal Convolutional Network(TCN)-Transformer forecasting,recurrence-network analysis,and a Bayesian framework yielded a 28.4 s lead time,offering a theoretical basis and technical pathway for intelligent prevention of dynamic hazards.
