Using real-time correction technology for typhoons, this paper discusses real-time correction for forecasting the track of four typhoons during 2009 and 2010 in Japan, Beijing, Guangzhou, and Shanghai. It was determin...Using real-time correction technology for typhoons, this paper discusses real-time correction for forecasting the track of four typhoons during 2009 and 2010 in Japan, Beijing, Guangzhou, and Shanghai. It was determined that the short-time forecast effect was better than the original objective mode. By selecting four types of integration schemes after multiple mode path integration for those four objective modes, the forecast effect of the multi-mode path integration is better, on average, than any single model. Moreover, multi-mode ensemble forecasting has obvious advantages during the initial 36 h.展开更多
The process of optimized placement of long-term health monitoring sensors for large bridges generally begins with finite element models, but there will arise great discrepancies between theoretically-calculated result...The process of optimized placement of long-term health monitoring sensors for large bridges generally begins with finite element models, but there will arise great discrepancies between theoretically-calculated results and actual measurements.Therefore, rectified finite element models need to be rectified by virtue of model rectifying technology. Firstly, the result of construction monitoring and finished state load test is used to real-time modification of finite element model. Subsequently, an accurate finite element model is established. Secondly, the optimizing the layout of sensor with following orthogonality guarantees orthogonal property and linear independence for the measured data. Lastly, the effectiveness and feasibility of method in the paper is tested by real-time modifying finite element model and optimizing the layout of sensor for Nujiang Bridge.展开更多
In recent years,the global installed capacity of wind power has grown rapidly,making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy.Curre...In recent years,the global installed capacity of wind power has grown rapidly,making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy.Current research on ultra-short-term wind power prediction often overlooks load characteristics,resulting in an inability to adequately address grid connection requirements and load dispatching demands across different time periods.To address this limitation,this study proposes a novel approach to ultra-short-term wind power prediction error correction that incorporates load peak-valley characteristics.The methodology involves three key steps:first,deriving interannual prediction error characteristics from ultra-short-term prediction results of wind farm clusters;second,establishing error correction intervals for load peak and valley periods,calculating corresponding correction coefficients,and analyzing the impact of varying correction radii on the final results;third,validating the proposed method through empirical analysis of wind farm clusters in three northeastern provinces.The results demonstrate that this approach not only improves wind power prediction accuracy but also significantly reduces the occurrence of harmful error days,thereby better meeting the operational requirements of power system dispatch.展开更多
Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dua...Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.展开更多
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.展开更多
Aerial surveys are dynamic and continuous processes,and there are different height distributions of the ground in the measurement area,which leads to problems such as overlapping measurement areas and inaccurate altit...Aerial surveys are dynamic and continuous processes,and there are different height distributions of the ground in the measurement area,which leads to problems such as overlapping measurement areas and inaccurate altitude correction during the survey process.Commonly used terrain correction methods are based on the concept of finite elementization of ground surface radioactive sources,using GPS coordinates,radar altitude,and ground elevation distribution information from aerial surveys,combined with the sourceless efficiency calibration method to construct a response matrix,which is then inverted for surface nuclide content.However,most of the sourceless efficiency calibration methods used are numerical calculations that consider the body detector as a point detector and do not consider the changes in intrinsic detection efficiency under different incident directions of gamma rays.Therefore,when the altitude of the measurement area varies significantly or the flight altitude of the aerial survey is relatively low,such sourceless efficiency calibration method calculations tend to have a large bias,which affects the accuracy of the terrain correction.To address the above problems,this study employs a novel sourceless efficiency calibration method based on the Boolean operation of the ray deposition process and simplifies the traditional body source measurement model to a surface source measurement model to achieve fast and accurate efficiency calibration.Then,through the discretization of the measurement process,the static measurement process is superposed as equivalent to the dynamic measurement process,and the dynamic measurement response matrix is built and optimized based on the calibration method.Finally,the PSO-MLEM algorithm was used to solve the dynamic measurement response matrix to achieve dynamic terrain correction of aerial survey data.Analysis of the Baiyun'ebo test area revealed that,after applying dynamic terrain correction,the inverted anomalies in uranium(eU),thorium(eTh),and potassium(K)concentrations were closer to ground measurements(within 5.72%-30.79%)and exhibited clearer anomaly boundaries compared to traditional height-based corrections.However,owing to the inherent statistical fluctuations and characteristics of matrix inversion,higher measurement values tend to absorb lower ones,potentially enlarging the anomalous regions.Nevertheless,the highanomaly regions after inversion largely coincided with the ground truth validation,demonstrating that the proposed method can effectively correct airborne gamma spectrometry data.展开更多
During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study invest...During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study investigates the coupled thermo-mechanical behavior of granite during heating and cooling through a combination of laboratory tests and finite difference method analysis.Initial investigations involve X-ray diffraction,thermal expansion test,thermogravimetric analysis,and uniaxial compression test.Results show the significant variations of granite properties under different thermal conditions,attributed to temperature gradients,water evaporation,and mineral phase transitions.Subsequently,a model considering temperature-dependent parameters and real-time cooling rates was employed to simulate linear heating and nonlinear cooling processes.Simulation results indicate that the thermal cracking predominantly occurs during the heating stage,with tensile failure as the primary mode.Additionally,a faster real-time cooling rate at higher temperatures intensifies the thermal cracking behavior in granite.This study effectively elucidates the thermomechanical coupling behavior of granite during heating and cooling processes,providing insights into the mechanisms of mechanical property changes with rising or decreasing temperatures.展开更多
Correction to“Liu QQ,Li YD,Chen JX,Zhang LL,Guan RC,Zhao W,Meng LY.Prognostic value of preoperative fibrinogen,neutrophil-to-lymphocyte ratio,serum alpha-fetoprotein,and prealbumin for patients with primary liver can...Correction to“Liu QQ,Li YD,Chen JX,Zhang LL,Guan RC,Zhao W,Meng LY.Prognostic value of preoperative fibrinogen,neutrophil-to-lymphocyte ratio,serum alpha-fetoprotein,and prealbumin for patients with primary liver cancer undergoing transarterial chemoembolization.World J Gastrointest Oncol 2025;17(6):103198 PMID:40547171 DOI:10.4251/wjgo.v17.i6.103198”.The funding number listed in the"Supported by"section of this article needs to be corrected.展开更多
The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navi...The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.展开更多
Aerodynamic performances of axial compressors are significantly affected by variation of Reynolds number in aero-engines.In the design and analysis of compressors,previous correction methods for cascades and stages ha...Aerodynamic performances of axial compressors are significantly affected by variation of Reynolds number in aero-engines.In the design and analysis of compressors,previous correction methods for cascades and stages have difficulties in predicting comprehensively Reynolds number effects on airfoils,matching and characteristics curves.This study proposes Re-correction models for loss,deviation angle and endwall blockage based on classical theories and cascade tests,and loss and deviation models show good agreement in test data of NACA65 and C4 cascades.Throughflow method considering Reynolds number effects is developed by integrating the correction models into a verified Streamline Curvature(SLC)tool.A three-stage axial compressor is investigated through SLC and CFD methods from design Reynolds number(Red=2106)to low Re=4104,and the numerical methods are validated with test data of characteristic curves and spanwise distributions at Red.With Re reduction,SLC method with correction models well predicts variation in overall performances compared with CFD calculations and Wassell's model.Streamwise and spanwise matching such as total pressure and loss distributions in SLC predictions are basically consistent with those in CFD results at near-stall points under design and low Reynolds numbers.SLC and CFD methods share similar detections of stall risks in the third stage(Stg3),and their analyses of diffusion processes deviate to some extent due to different predictions in separated endwall flow.The correction models can be adopted to consider Reynolds number effects in through-flow design and analysis of axial compressors.展开更多
The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the ini...The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the initial field quality and the forecasting accuracy of the model. This study assimilated FY-4B AGRI data into the CMA-MESO model and analyzed the bias characteristics and correction methods. Analysis of the AGRI data revealed a clear diurnal variation in the bias, which was positively correlated with the solar elevation angle. However, the diurnal variation in the bias lagged behind the solar elevation angle, likely owing to temperature changes and delayed instrument responses resulting from solar radiation. To address this issue, we propose a correction method that utilizes the solar elevation angle after an optimal time shift. Using the time-shifted solar elevation angle as a predictor effectively reduces the diurnal variation in bias and significantly improves the correction effect. This approach provides theoretical support for the assimilation of FY-4B AGRI data into mesoscale numerical weather predictions, thereby enhancing the reliability of the assimilation results.展开更多
To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens...To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks.展开更多
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a...An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.展开更多
Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,i...Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.展开更多
Marine forecasting is critical for navigation safety and disaster prevention.However,traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial fe...Marine forecasting is critical for navigation safety and disaster prevention.However,traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features.To address the limitations,the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism.The model treats target forecast values as internal variables,and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer.Using a self-attention structure,the model captures correlations between exogenous variables and target sequences,explores intrinsic multi-dimensional relationships,and subsequently corrects endogenous variables with the mined exogenous features.The model’s performance is evaluated using metrics including MSE(Mean Squared Error),MAE(Mean Absolute Error),RMSE(Root Mean Square Error),MAPE(Mean Absolute Percentage Error),MSPE(Mean Square Percentage Error),and computational time,with TimeXer and PatchTST models serving as benchmarks.Experiment results show that the proposed model achieves lower errors and higher correction accuracy for both one-day and seven-day forecasts.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and pow...Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and power consumption overhead in the analog-to-digital conversion.In this work,we propose an analog-domain image correction architecture based on a proposed small-scale UNet,which implements a compact noise correction network within a one-transistor-one-memristor(1T1R)array.The statistical non-idealities of the fabricated 1T1R array(e.g.,device variability)are rigorously incorporated into the network's training and inference simulations.This correction network architecture leverages memristors for conducting multiply-accumulate operations aimed at rectifying non-uniform noise,defective pixels(stuck-at-bright/dark),and exposure mismatch.Compared to systems without correction,the proposed architecture achieves up to 50.13%improvement in recognition accuracy while demonstrating robust tolerance to memristor device-level errors.The proposed system achieves a 2.13-fold latency reduction and three orders of magnitude higher energy efficiency compared to conventional architecture.This work establishes a new paradigm for advancing the development of low-power,low-latency,and high-precision image processing systems.展开更多
The errors in radar quantitative precipitation estimations consist not only of systematic biases caused by random noises but also spatially nonuniform biases in radar rainfall at individual rain-gauge stations. In thi...The errors in radar quantitative precipitation estimations consist not only of systematic biases caused by random noises but also spatially nonuniform biases in radar rainfall at individual rain-gauge stations. In this study, a real-time adjustment to the radar reflectivity rainfall rates （Z R） relationship scheme and the gauge-corrected, radar-based, estimation scheme with inverse distance weighting interpolation was devel- oped. Based on the characteristics of the two schemes, the two-step correction technique of radar quantitative precipitation estimation is proposed. To minimize the errors between radar quantitative precipitation es- timations and rain gauge observations, a real-time adjustment to the Z R relationship scheme is used to remove systematic bias on the time-domain. The gauge-corrected, radar-based, estimation scheme is then used to eliminate non-uniform errors in space. Based on radar data and rain gauge observations near the Huaihe River, the two-step correction technique was evaluated using two heavy-precipitation events. The results show that the proposed scheme improved not only in the underestimation of rainfall but also reduced the root-mean-square error and the mean relative error of radar-rain gauge pairs.展开更多
In real-time hybrid simulation（RTHS）, it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This pa...In real-time hybrid simulation（RTHS）, it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This paper proposes a new force correction method based on online discrete tangent stiffness estimation（online DTSE） to provide accurate online estimation of the instantaneous stiffness of the physical substructure. Following the discrete curve parameter recognition theory, the online DTSE method estimates the instantaneous stiffness mainly through adaptively building a fuzzy segment with the latest measurements, constructing several strict bounding lines of the segment and calculating the slope of the strict bounding lines, which significantly improves the calculation efficiency and accuracy for the instantaneous stiffness estimation. The results of both computational simulation and real-time hybrid simulation show that：（1） the online DTSE method has high calculation efficiency, of which the relatively short computation time will not interrupt RTHS; and（2） the online DTSE method provides better estimation for the instantaneous stiffness, compared with other existing estimation methods. Due to the quick and accurate estimation of instantaneous stiffness, the online DTSE method therefore provides a promising technique to correct restoring forces in RTHS.展开更多
基金National Natural Science Foundation of China(41475060,41275067,41405060)
文摘Using real-time correction technology for typhoons, this paper discusses real-time correction for forecasting the track of four typhoons during 2009 and 2010 in Japan, Beijing, Guangzhou, and Shanghai. It was determined that the short-time forecast effect was better than the original objective mode. By selecting four types of integration schemes after multiple mode path integration for those four objective modes, the forecast effect of the multi-mode path integration is better, on average, than any single model. Moreover, multi-mode ensemble forecasting has obvious advantages during the initial 36 h.
基金Funded by the Special Found of the Ministry of Education for Doctor Station Subject(No.20115522110001)
文摘The process of optimized placement of long-term health monitoring sensors for large bridges generally begins with finite element models, but there will arise great discrepancies between theoretically-calculated results and actual measurements.Therefore, rectified finite element models need to be rectified by virtue of model rectifying technology. Firstly, the result of construction monitoring and finished state load test is used to real-time modification of finite element model. Subsequently, an accurate finite element model is established. Secondly, the optimizing the layout of sensor with following orthogonality guarantees orthogonal property and linear independence for the measured data. Lastly, the effectiveness and feasibility of method in the paper is tested by real-time modifying finite element model and optimizing the layout of sensor for Nujiang Bridge.
基金supported by the National Key R&D Program of China(Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption(2018YFB0904200).
文摘In recent years,the global installed capacity of wind power has grown rapidly,making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy.Current research on ultra-short-term wind power prediction often overlooks load characteristics,resulting in an inability to adequately address grid connection requirements and load dispatching demands across different time periods.To address this limitation,this study proposes a novel approach to ultra-short-term wind power prediction error correction that incorporates load peak-valley characteristics.The methodology involves three key steps:first,deriving interannual prediction error characteristics from ultra-short-term prediction results of wind farm clusters;second,establishing error correction intervals for load peak and valley periods,calculating corresponding correction coefficients,and analyzing the impact of varying correction radii on the final results;third,validating the proposed method through empirical analysis of wind farm clusters in three northeastern provinces.The results demonstrate that this approach not only improves wind power prediction accuracy but also significantly reduces the occurrence of harmful error days,thereby better meeting the operational requirements of power system dispatch.
基金supported by the National Institute of Health(R37CA240806,U01CA288351,and R50CA283816)support from UCI Chao Family Comprehensive Cancer Center(P30CA062203).
文摘Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.
基金supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
基金supported by the National Key Research and Development Program(No.2022YFC2807400)the National Natural Science Foundation of China(Nos.12265003 and 12205044)。
文摘Aerial surveys are dynamic and continuous processes,and there are different height distributions of the ground in the measurement area,which leads to problems such as overlapping measurement areas and inaccurate altitude correction during the survey process.Commonly used terrain correction methods are based on the concept of finite elementization of ground surface radioactive sources,using GPS coordinates,radar altitude,and ground elevation distribution information from aerial surveys,combined with the sourceless efficiency calibration method to construct a response matrix,which is then inverted for surface nuclide content.However,most of the sourceless efficiency calibration methods used are numerical calculations that consider the body detector as a point detector and do not consider the changes in intrinsic detection efficiency under different incident directions of gamma rays.Therefore,when the altitude of the measurement area varies significantly or the flight altitude of the aerial survey is relatively low,such sourceless efficiency calibration method calculations tend to have a large bias,which affects the accuracy of the terrain correction.To address the above problems,this study employs a novel sourceless efficiency calibration method based on the Boolean operation of the ray deposition process and simplifies the traditional body source measurement model to a surface source measurement model to achieve fast and accurate efficiency calibration.Then,through the discretization of the measurement process,the static measurement process is superposed as equivalent to the dynamic measurement process,and the dynamic measurement response matrix is built and optimized based on the calibration method.Finally,the PSO-MLEM algorithm was used to solve the dynamic measurement response matrix to achieve dynamic terrain correction of aerial survey data.Analysis of the Baiyun'ebo test area revealed that,after applying dynamic terrain correction,the inverted anomalies in uranium(eU),thorium(eTh),and potassium(K)concentrations were closer to ground measurements(within 5.72%-30.79%)and exhibited clearer anomaly boundaries compared to traditional height-based corrections.However,owing to the inherent statistical fluctuations and characteristics of matrix inversion,higher measurement values tend to absorb lower ones,potentially enlarging the anomalous regions.Nevertheless,the highanomaly regions after inversion largely coincided with the ground truth validation,demonstrating that the proposed method can effectively correct airborne gamma spectrometry data.
基金National Natural Science Foundation of China,Grant/Award Number:52104120Hunan Provincial Key Laboratory of Key Technology on Hydropower Development,Grant/Award Number:PKLHD202303。
文摘During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study investigates the coupled thermo-mechanical behavior of granite during heating and cooling through a combination of laboratory tests and finite difference method analysis.Initial investigations involve X-ray diffraction,thermal expansion test,thermogravimetric analysis,and uniaxial compression test.Results show the significant variations of granite properties under different thermal conditions,attributed to temperature gradients,water evaporation,and mineral phase transitions.Subsequently,a model considering temperature-dependent parameters and real-time cooling rates was employed to simulate linear heating and nonlinear cooling processes.Simulation results indicate that the thermal cracking predominantly occurs during the heating stage,with tensile failure as the primary mode.Additionally,a faster real-time cooling rate at higher temperatures intensifies the thermal cracking behavior in granite.This study effectively elucidates the thermomechanical coupling behavior of granite during heating and cooling processes,providing insights into the mechanisms of mechanical property changes with rising or decreasing temperatures.
基金Supported by Health Commission of Heilongjiang Province,No.20230404080031.
文摘Correction to“Liu QQ,Li YD,Chen JX,Zhang LL,Guan RC,Zhao W,Meng LY.Prognostic value of preoperative fibrinogen,neutrophil-to-lymphocyte ratio,serum alpha-fetoprotein,and prealbumin for patients with primary liver cancer undergoing transarterial chemoembolization.World J Gastrointest Oncol 2025;17(6):103198 PMID:40547171 DOI:10.4251/wjgo.v17.i6.103198”.The funding number listed in the"Supported by"section of this article needs to be corrected.
基金supported by the National Natural Science Foundation of China(No.41971339)the SDUST Research Fund(No.2019TDJH103)。
文摘The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.
基金supported by the National Science and Tech-nology Major Project of China(Nos.2017-II-0007-0021 and J2019-II-0017-0038)。
文摘Aerodynamic performances of axial compressors are significantly affected by variation of Reynolds number in aero-engines.In the design and analysis of compressors,previous correction methods for cascades and stages have difficulties in predicting comprehensively Reynolds number effects on airfoils,matching and characteristics curves.This study proposes Re-correction models for loss,deviation angle and endwall blockage based on classical theories and cascade tests,and loss and deviation models show good agreement in test data of NACA65 and C4 cascades.Throughflow method considering Reynolds number effects is developed by integrating the correction models into a verified Streamline Curvature(SLC)tool.A three-stage axial compressor is investigated through SLC and CFD methods from design Reynolds number(Red=2106)to low Re=4104,and the numerical methods are validated with test data of characteristic curves and spanwise distributions at Red.With Re reduction,SLC method with correction models well predicts variation in overall performances compared with CFD calculations and Wassell's model.Streamwise and spanwise matching such as total pressure and loss distributions in SLC predictions are basically consistent with those in CFD results at near-stall points under design and low Reynolds numbers.SLC and CFD methods share similar detections of stall risks in the third stage(Stg3),and their analyses of diffusion processes deviate to some extent due to different predictions in separated endwall flow.The correction models can be adopted to consider Reynolds number effects in through-flow design and analysis of axial compressors.
基金National Key Research and Development Program of China (2022YFC3004004)National Natural Science Foundation of China (42075155,12241104)National Natural Science Foundation of China Joint Fund (U2342213)。
文摘The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the initial field quality and the forecasting accuracy of the model. This study assimilated FY-4B AGRI data into the CMA-MESO model and analyzed the bias characteristics and correction methods. Analysis of the AGRI data revealed a clear diurnal variation in the bias, which was positively correlated with the solar elevation angle. However, the diurnal variation in the bias lagged behind the solar elevation angle, likely owing to temperature changes and delayed instrument responses resulting from solar radiation. To address this issue, we propose a correction method that utilizes the solar elevation angle after an optimal time shift. Using the time-shifted solar elevation angle as a predictor effectively reduces the diurnal variation in bias and significantly improves the correction effect. This approach provides theoretical support for the assimilation of FY-4B AGRI data into mesoscale numerical weather predictions, thereby enhancing the reliability of the assimilation results.
基金supported by the National Natural Science Foundation of China(Grant No.42077244)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0434).
文摘To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks.
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.
基金supported by the Meteorological Joint Funds of the National Natural Science Foundation of China(Grant No.U2142211)the National Natural Science Foundation of China(Grant Nos.42075141,42341202 and 62088101)+1 种基金the National Key Research and Development Program of China(Grant No.2020YFA0608000)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100).
文摘Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.
基金supported by the National Key Research and Development Program Project(2023YFC3107804)Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education(24YJA880097)the Graduate Education Reform Project in North China University of Technology(217051360025XN095-17)。
文摘Marine forecasting is critical for navigation safety and disaster prevention.However,traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features.To address the limitations,the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism.The model treats target forecast values as internal variables,and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer.Using a self-attention structure,the model captures correlations between exogenous variables and target sequences,explores intrinsic multi-dimensional relationships,and subsequently corrects endogenous variables with the mined exogenous features.The model’s performance is evaluated using metrics including MSE(Mean Squared Error),MAE(Mean Absolute Error),RMSE(Root Mean Square Error),MAPE(Mean Absolute Percentage Error),MSPE(Mean Square Percentage Error),and computational time,with TimeXer and PatchTST models serving as benchmarks.Experiment results show that the proposed model achieves lower errors and higher correction accuracy for both one-day and seven-day forecasts.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金Project supported by the National Key Research and Development Program of China(Grant No.2024YFA1208800)the National Natural Science Foundation of China(Grant Nos.62404253,62304254,U23A20322)。
文摘Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and power consumption overhead in the analog-to-digital conversion.In this work,we propose an analog-domain image correction architecture based on a proposed small-scale UNet,which implements a compact noise correction network within a one-transistor-one-memristor(1T1R)array.The statistical non-idealities of the fabricated 1T1R array(e.g.,device variability)are rigorously incorporated into the network's training and inference simulations.This correction network architecture leverages memristors for conducting multiply-accumulate operations aimed at rectifying non-uniform noise,defective pixels(stuck-at-bright/dark),and exposure mismatch.Compared to systems without correction,the proposed architecture achieves up to 50.13%improvement in recognition accuracy while demonstrating robust tolerance to memristor device-level errors.The proposed system achieves a 2.13-fold latency reduction and three orders of magnitude higher energy efficiency compared to conventional architecture.This work establishes a new paradigm for advancing the development of low-power,low-latency,and high-precision image processing systems.
基金supported bythe Special Fund for Basic Research and Operation of the Chinese Academy of Meteorological Sciences (GrantNo. 2011Y004)the Research and Development Special Fund for Public Welfare Industry (Meteorology+2 种基金Grant No.GYHY201006042)the National Natural Science Foundation of China (Grant No. 40975014)the Open Research Fund for State Key Laboratory of Hydroscience and Engineering of Tsinghua University (the search of basin QPE and QPF based on new generation of weather and numerical models)
文摘The errors in radar quantitative precipitation estimations consist not only of systematic biases caused by random noises but also spatially nonuniform biases in radar rainfall at individual rain-gauge stations. In this study, a real-time adjustment to the radar reflectivity rainfall rates （Z R） relationship scheme and the gauge-corrected, radar-based, estimation scheme with inverse distance weighting interpolation was devel- oped. Based on the characteristics of the two schemes, the two-step correction technique of radar quantitative precipitation estimation is proposed. To minimize the errors between radar quantitative precipitation es- timations and rain gauge observations, a real-time adjustment to the Z R relationship scheme is used to remove systematic bias on the time-domain. The gauge-corrected, radar-based, estimation scheme is then used to eliminate non-uniform errors in space. Based on radar data and rain gauge observations near the Huaihe River, the two-step correction technique was evaluated using two heavy-precipitation events. The results show that the proposed scheme improved not only in the underestimation of rainfall but also reduced the root-mean-square error and the mean relative error of radar-rain gauge pairs.
基金Priority Academic Program Development of Jiangsu Higher Education Institutions under Grant No.1105007002National Natural Science Foundation of China under Grant No.51378107 and No.51678147
文摘In real-time hybrid simulation（RTHS）, it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This paper proposes a new force correction method based on online discrete tangent stiffness estimation（online DTSE） to provide accurate online estimation of the instantaneous stiffness of the physical substructure. Following the discrete curve parameter recognition theory, the online DTSE method estimates the instantaneous stiffness mainly through adaptively building a fuzzy segment with the latest measurements, constructing several strict bounding lines of the segment and calculating the slope of the strict bounding lines, which significantly improves the calculation efficiency and accuracy for the instantaneous stiffness estimation. The results of both computational simulation and real-time hybrid simulation show that：（1） the online DTSE method has high calculation efficiency, of which the relatively short computation time will not interrupt RTHS; and（2） the online DTSE method provides better estimation for the instantaneous stiffness, compared with other existing estimation methods. Due to the quick and accurate estimation of instantaneous stiffness, the online DTSE method therefore provides a promising technique to correct restoring forces in RTHS.
