The present paper investigates the methods for estimating the maximum(positive)and the minimum(negative)peak wind force coefficients on domed free roofs based on the quasi-steady theory and the peak factor method,in w...The present paper investigates the methods for estimating the maximum(positive)and the minimum(negative)peak wind force coefficients on domed free roofs based on the quasi-steady theory and the peak factor method,in which the experimental results obtained from our previous studies(2019,2025)are used.Focus is on the distributions of the peak wind force coefficients along the centerline parallel to the wind direction considering that domed free roof is an axisymmetric body.Empirical formulas are provided to the distributions of mean wind force coefficient,RMS(root mean square)fluctuating wind force coefficient and peak factors as a function of the rise/span ratio of the roof and the turbulence intensity of the approach flow in the along-wind direction at the mean roof height.The proposed methods are validated by the experimental results for the peak wind force coefficients.The methods would provide useful information to structural engineers when estimating the design wind loads on cladding/components of domed free roofs.展开更多
To address the issues of unknown target size,blurred edges,background interference and low contrast in infrared small target detection,this paper proposes a method based on density peaks searching and weighted multi-f...To address the issues of unknown target size,blurred edges,background interference and low contrast in infrared small target detection,this paper proposes a method based on density peaks searching and weighted multi-feature local difference.Firstly,an improved high-boost filter is used for preprocessing to eliminate background clutter and high-brightness interference,thereby increasing the probability of capturing real targets in the density peak search.Secondly,a triple-layer window is used to extract features from the area surrounding candidate targets,addressing the uncertainty of small target sizes.By calculating multi-feature local differences between the triple-layer windows,the problems of blurred target edges and low contrast are resolved.To balance the contribution of different features,intra-class distance is used to calculate weights,achieving weighted fusion of multi-feature local differences to obtain the weighted multi-feature local differences of candidate targets.The real targets are then extracted using the interquartile range.Experiments on datasets such as SIRST and IRSTD-IK show that the proposed method is suitable for various complex types and demonstrates good robustness and detection performance.展开更多
The level of ground shaking,as determined by the peak ground acceleration(PGA),can be used to analyze seismic hazard at a certain location and is crucial for constructing earthquake-resistant structures.Predicting the...The level of ground shaking,as determined by the peak ground acceleration(PGA),can be used to analyze seismic hazard at a certain location and is crucial for constructing earthquake-resistant structures.Predicting the PGA immediately after an earthquake occurs allows for the issuing of a warning by an earthquake early warning system.In this study,we propose a deep learning model,ConvMixer,to predict the PGA recorded by weak-motion velocity seismometers in Japan.We use 5-s threecomponent seismograms,from 2 s before until 3 s after the P-wave arrival time of the earthquake.Our dataset comprised more than 50,000 single-station waveforms recorded by 10 seismic stations in the K-NET,Kiki-NET,and Hi-Net networks between 2004 and 2023.The proposed ConvMixer is a patch-based model that extracts global features from input seismic data and predicts the PGA of an earthquake by combining depth and pointwise convolutions.The proposed ConvMixer network had a mean absolute error of 2.143 when applied to the test set and outperformed benchmark deep learning models.In addition,the proposed ConvMixer demonstrated the ability to predict the PGA at the corresponding station site based on 1-second waveforms obtained immediately after the arrival time of the P-wave.展开更多
The primary objective of this work is to improve our understanding of the mechanical involvements of two-order roughness in shear.First,wavelet analysis is used to separate the waviness(first-order)and unevenness(seco...The primary objective of this work is to improve our understanding of the mechanical involvements of two-order roughness in shear.First,wavelet analysis is used to separate the waviness(first-order)and unevenness(second-order)from four granite joint surfaces,with roughness characterized using Grasselli’s 3D morphology parameters.The results reveal that first-order roughness is more pronounced than second-order roughness,highlighting the dominant role of waviness in joint surface roughness.Additionally,the variation in first-order roughness with strike direction corresponds to the total roughness,while second-order roughness remains largely constant,indicating that roughness anisotropy is primarily driven by waviness.Then,direct shear tests on joint replicas are performed to investigate the contributions of both roughness orders to peak shear strength.The results show that the peak dilation angle is closely related to first-order roughness,while the shear component angle is closely associated with second-order roughness,both exhibiting a linear correlation.Based on these findings,relationships are established between the angles and their respective roughness orders.Finally,a joint shear strength criterion based on two-order roughness is proposed.A comparative analysis of prediction accuracy reveals that the average relative error for the proposed criterion is 13.79%,while the errors for Xia's,Yang's,and Ban's criteria are 15.19%,16.29%,and 13.87%,respectively.It demonstrates the proposed criterion can predict the peak shear strength of rock joints.展开更多
Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead...Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead to changes in the network topology,thereby reducing cluster stability in urban scenarios.To address this issue,we propose a clustering model based on the density peak clustering(DPC)method and sparrow search algorithm(SSA),named SDPC.First,the model constructs a fitness function based on the parameters obtained from the DPC method and deploys the SSA for iterative optimization to select cluster heads(CHs).Then,the vehicles that have not been selected as CHs are assigned to appropriate clusters by comprehensively considering the distance parameter and link-reliability parameter.Finally,cluster maintenance strategies are considered to tackle the changes in the clusters’organizational structure.To verify the performance of the model,we conducted a simulation on a real-world scenario for multiple metrics related to clusters’stability.The results show that compared with the APROVE and the GAPC,SDPC showed clear performance advantages,indicating that SDPC can effectively ensure VANETs’cluster stability in urban scenarios.展开更多
Wind tunnel experiment and CFD(computational fluid dynamics)simulation with LES(large eddy simulation)have been conducted to investigate the characteristics of peak wind force coefficients of porous panels mounted on ...Wind tunnel experiment and CFD(computational fluid dynamics)simulation with LES(large eddy simulation)have been conducted to investigate the characteristics of peak wind force coefficients of porous panels mounted on the roofs of high-rise buildings.First,aerodynamic modelling of porous panels was discussed.The relation between pressure loss coefficient and porosity was obtained.Then,a wind tunnel experiment was conducted to measure the wind forces(net wind pressures)acting on solid and porous panels mounted on the roof of a high-rise building.Because it was difficult to measure the pressures on both sides of thin,porous panel at the same location simultaneously,we proposed to use the roof edge pressures near the panel for the panel’s inside-surface pressures.This experimental method was validated by a CFD simulation reproducing the wind tunnel experiment.The characteristics of peak wind force coefficients of porous panels mounted on the roofs of high-rise buildings were made clear.Finally,positive and negative peak wind force coefficients for designing the rooftop porous panels were proposed.展开更多
To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control s...To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control strategy is proposed for peak shaving sce-narios.First,considering the difference between peak and valley loads and the operating costs of EVs,a peak shaving model for EVs is constructed.Second,the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer(GEO)algorithm.Subsequently,IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions.Next,using the k-means algorithm,EVs are dynamically divided into priority charging groups,backup groups,and priority discharging groups based on SOC differences.Finally,a dual layer power distribution scheme for EVs is designed.The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs,whereas the lower layer allocates the charging and discharging instructions for each group to each EV.The proposed strategy was simulated and verified,and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy.The pro-posed EV grouping control strategy effectively reduces the peak-valley difference in the power grid,reduces the operational life loss of EVs,and maintains a better SOC balance for EVs.展开更多
Climate changes in cold-temperate zones are increasingly altering the state of climatic constraints on photosynthesis and growth,leading to adaptive changes in plant phenology and subsequent seasonal carbon assimilati...Climate changes in cold-temperate zones are increasingly altering the state of climatic constraints on photosynthesis and growth,leading to adaptive changes in plant phenology and subsequent seasonal carbon assimilation.However,the spatio-temporal patterns of climatic constraints and seasonal carbon assimilation are poorly understood.In this study,the timing of peak photosynthetic activity(DOY_(pmax))was employed as a proxy for plant adap-tive state to climatic constraints on growth to examine the spatio-temporal dynamics of DOY_(pmax).By using multiple remote sensing metrics,DOY_(pmax)was characterized with changes in the solar-induced chlorophyll fluorescence(SIF)and leaf area index(LAI)from 2000 to 2018.Based on SIF,the DOY_(pmax)was generally around day 190,while based on LAI was about 10 d later.Peak photosynthetic activity of forests occurs earlier compared to other vegetation types.Overall,the advanced DOY_(pmax)were observed based on both SIF and LAI,with annual rates of 0.2(P=0.31)and 0.3(P<0.05)d,respectively.DOY_(pmax)dynamics were influ-enced by hot temperature extremes and vapor pressure defi-cits(VPD)during the early growing season,regardless of sub-zone and different vegetation type.The generalized lin-ear mixed model(GLMM)showed the largest contribution by hot extremes to DOY_(pmax)dynamics accounted for 55.5%(DOY_(pmax_SIF))and 49.1%(DOY_(pmax_LAI)),respectively,fol-lowed by VPD(DOY_(pmax_SIF):23.1%;DOY_(pmax_LAI):29.5%).These findings highlight the crucial role of climate extremes in shaping seasonal carbon dynamics and regional carbon balance.展开更多
Objective:To investigate the predictive value of diaphragm thickening fraction(DTF)combined with cough peak expiratory flow(CPEF)on the success rate of weaning from mechanical ventilation.Methods:The clinical data of ...Objective:To investigate the predictive value of diaphragm thickening fraction(DTF)combined with cough peak expiratory flow(CPEF)on the success rate of weaning from mechanical ventilation.Methods:The clinical data of patients undergoing invasive mechanical ventilation via oral endotracheal intubation in the ICU of our hospital from January 2022 to December 2023 were studied.All patients underwent a 30-minute spontaneous breathing trial(SBT)using low-level pressure support ventilation(PSV)after meeting the clinical weaning screening criteria.Among them,150 patients who met the clinical weaning criteria were weaned from the ventilator.They were divided into a successful weaning group(n=100)and a failed weaning group(n=50)based on the weaning outcome.Clinical data,including age,gender,APACHE II score,duration of mechanical ventilation,DTF,and CPEF,were collected from 150 patients.The differences in clinical data between the two groups were compared,and the correlation between DTF,CPEF,and the success rate of weaning was analyzed.Results:There were no significant differences between the two groups in gender ratio(χ^(2)=0.884,P=0.347>0.05),age(t=0.350,P=0.727>0.05),and APACHE II score(t=1.295,P=0.197>0.05),but there was a significant difference in the duration of mechanical ventilation(t=3.766,P<0.001).The DTF and CPEF values in the successful weaning group were significantly higher than those in the failed weaning group(P<0.05).ROC curves were drawn to predict the weaning results using DTF,CPEF,and the combination of DTF and CPEF.The results showed that the specificity of the combination of DTF and CPEF was comparable to that of either metric alone,but the sensitivity and AUC were significantly higher than those of either metric alone.Conclusion:The combination of DTF and CPEF can be used as an effective indicator to evaluate the weaning efficacy of mechanically ventilated patients,which has important clinical significance for guiding clinical weaning treatment,improving the success rate of weaning,reducing the incidence of ventilator-associated pneumonia,and shortening the length of hospital stay.展开更多
Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV pred...Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV prediction by combining conventional empirical equations with physics-informed neural networks(PINN)and optimizing the model parameters via the Particle Swarm Optimization(PSO)algorithm.The proposed PSO-PINN framework was rigorously benchmarked against seven established machine learning approaches:Multilayer Perceptron(MLP),Extreme Gradient Boosting(XGBoost),Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting Decision Tree(GBDT),Adaptive Boosting(Adaboost),and Gene Expression Programming(GEP).Comparative analysis showed that PSO-PINN outperformed these models,achieving RMSE reductions of 17.82-37.63%,MSE reductions of 32.47-61.10%,AR improvements of 2.97-21.19%,and R^(2)enhancements of 7.43-29.21%,demonstrating superior accuracy and generalization.Furthermore,the study determines the impact of incorporating empirical formulas as physical constraints in neural networks and examines the effects of different empirical equations,particle swarm size,iteration count in PSO,regularization coefficient,and learning rate in PINN on model performance.Lastly,a predictive system for blast vibration PPV is designed and implemented.The research outcomes offer theoretical references and practical recommendations for blast vibration forecasting in similar engineering applications.展开更多
Reducing the peak actuating force(PAF)and parasitic displacement is of high significance for improving the performance of compliant parallel mechanisms(CPMs).In this study,a 2-DOF 4-4R compliant parallel pointing mech...Reducing the peak actuating force(PAF)and parasitic displacement is of high significance for improving the performance of compliant parallel mechanisms(CPMs).In this study,a 2-DOF 4-4R compliant parallel pointing mechanism(4-4R CPPM)was used as the object,and the actuating force of the mechanism was optimized through redundant actuation.This was aimed at minimizing the PAF and parasitic displacement.First,a kinetostatic model of the redundantly actuated 4-4R CPPM was established to reveal the relationship between the input forces/displacements and the output displacements of the mobile platform.Subsequently,based on the established kinetostatic model,methods for optimizing the actuating force distribution with the aim of minimizing the PAF and parasitic displacement were introduced successively.Second,a simulated example of a mobile platform’s spatial pointing trajectory validated the accuracy of the kinetostatic model.The results show a less than 0.9%relative error between the analytical and finite element(FE)results,and the high consistency indicates the accuracy of the kinetostatic model.Then,the effectiveness of the method in minimizing the PAF and parasitic displacement was validated using two simulated examples.The results indicate that compared with the non-redundant actuation case,the PAF of the mechanism could be reduced by up to 50%,and the parasitic displacement was reduced by approximately three-four orders of magnitude by means of redundant actuation combined with the optimal distribution of the actuating force.As expected,with the reduction in parasitic displacement,the FE-results of the output angular displacements(θ_(x) andθ_(z))of the mobile platform were closer to the target oscillation trajectory.This further verified that the reduction in parasitic displacement is indeed effective in improving the motion accuracy of the mechanism.The advantage of this proposed method is that it reduces the PAF and parasitic displacement from the perspective of the actuating force control strategy,without the requirement of structural changes to the original mechanism.展开更多
In this paper,we present a high peak power passively Q-switched intracavity frequency-doubled green laser based on an efficient LED-pumped Nd:YAG dual-rod laser module.In quasi-continuous wave(QCW)running operation,th...In this paper,we present a high peak power passively Q-switched intracavity frequency-doubled green laser based on an efficient LED-pumped Nd:YAG dual-rod laser module.In quasi-continuous wave(QCW)running operation,the average output power of the fundamental laser at 1064 nm reaches as high as 20.98 W at a repetition rate of 50 Hz with a maximum single pulse energy of 419.6 mJ,corresponding to a maximum optical conversion efficiency of 38.8%and a slope efficiency of 41%.展开更多
Peak load and wind energy emission pressure rise more as wind energy penetration keeps growing,which affects the stabilization of the PS(power system).This paper suggests integrated optimal dispatching of thermal powe...Peak load and wind energy emission pressure rise more as wind energy penetration keeps growing,which affects the stabilization of the PS(power system).This paper suggests integrated optimal dispatching of thermal power generators and BESS(battery energy storage system)taking wind energy emission grading punishment and deep peak clipping into consideration.Firstly,in order to minimize wind abandonment,a hierarchical wind abandonment penalty strategy based on fuzzy control is designed and introduced,and the optimal grid-connected power of wind energy is determined as a result of minimizing the peak cutting cost of the system.Secondly,considering BESS and thermal power,the management approach of BESS-assisted virtual peak clipping of thermal power generators is aimed at reducing the degree of deep peak clipping of thermal power generators and optimizing the output of thermal power generators and the charging and discharging power of BESS.Finally,Give an example of how this strategy has been effective in reducing abandonment rates by 0.66% and 7.46% individually for different wind penetration programs,and the daily average can reduce the peak clipping power output of thermal power generators by 42.97 and 72.31 MWh and enhances the effect and economy of system peak clipping.展开更多
Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon...Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon emissions in the Yangtze River Delta(YRD)through path analysis,and to forecast carbon emissions in the YRD from the baseline scenario,factor regulation scenario and integrated scenario to reach the peak.The results show that:(1)Jiangsu's high carbon emission pattern is the main reason for the YRD hindering the synergistic regulation of carbon emissions.(2)Human factors,institutional factors and economic growth factors can all contribute to carbon emissions in the YRD region,while technological and industrial factors can generally suppress carbon emissions in the YRD region.(3)Under the capital regulation scenario,the YRD region has the highest level of carbon emission synergy,with Jiangsu reaching its peak five years earlier.Under the balanced regulation scenario,the YRD region as a whole,Jiangsu,Zhejiang and Anhui reach the peak as scheduled.展开更多
The accurate prediction of peak overpressure of explosion shockwaves is significant in fields such as explosion hazard assessment and structural protection, where explosion shockwaves serve as typical destructive elem...The accurate prediction of peak overpressure of explosion shockwaves is significant in fields such as explosion hazard assessment and structural protection, where explosion shockwaves serve as typical destructive elements. Aiming at the problem of insufficient accuracy of the existing physical models for predicting the peak overpressure of ground reflected waves, two physics-informed machine learning models are constructed. The results demonstrate that the machine learning models, which incorporate physical information by predicting the deviation between the physical model and actual values and adding a physical loss term in the loss function, can accurately predict both the training and out-oftraining dataset. Compared to existing physical models, the average relative error in the predicted training domain is reduced from 17.459%-48.588% to 2%, and the proportion of average relative error less than 20% increased from 0% to 59.4% to more than 99%. In addition, the relative average error outside the prediction training set range is reduced from 14.496%-29.389% to 5%, and the proportion of relative average error less than 20% increased from 0% to 71.39% to more than 99%. The inclusion of a physical loss term enforcing monotonicity in the loss function effectively improves the extrapolation performance of machine learning. The findings of this study provide valuable reference for explosion hazard assessment and anti-explosion structural design in various fields.展开更多
The navigation system plays a pivotal role in guiding aircraft along designated routes,ensuring precise and punctual arrival at destinations.The integration of scene matching with an inertial navigation system enhance...The navigation system plays a pivotal role in guiding aircraft along designated routes,ensuring precise and punctual arrival at destinations.The integration of scene matching with an inertial navigation system enhances the capability of providing a dependable guarantee for success-ful accomplishment of flight missions.Nonetheless,assuring reliability in scene matching encoun-ters significant challenges in areas characterized by repetitive or weak textures.To tackle these challenges,we propose a novel method to assess the reliability of scene matching based on the dis-tinctive characteristics of correlation peaks.The proposed method leverages the fact that the sim-ilarity of the optimal matching result is significantly higher than that of the surrounding area,and three novel indicators(e.g.,relative height,slope of a correlation peak,and ratio of a sub peak to the main peak)are determined to conjointly evaluate the reliability of scene matching.The pro-posed method entails matching a real-time image with a reference image to generate a correlation surface.A correlation peak is then obtained by extracting the portion of the correlation surface exhibiting a significant gradient.Additionally,the matching reliability is determined by considering the relative height,slope,and ratio of the peak collectively.Exhaustive experimental results with two sets of data demonstrate that the proposed method significantly outperforms traditional approaches in terms of precision,recall,and F1-score.These experiments also establish the efficacy of the proposed method in achieving reliable matching in challenging environments characterized by repetitive and weak textures.This enhancement holds the potential to significantly elevate scene-matching-based navigation.展开更多
Detrital geochronology fundamentally involves the quantification of major age ranges and their weights winthin an age distribution.This study presents a streamlined approach,modeling the age distribution of detrital z...Detrital geochronology fundamentally involves the quantification of major age ranges and their weights winthin an age distribution.This study presents a streamlined approach,modeling the age distribution of detrital zircons using a normal mixture model,and employs the Expectation-Maximization(EM)algorithm for precise estimations.A method is introduced to automatically select appropriate initial mean values for EM algorithm,enhancing its efficacy in detrital geochronology.This process entails multiple trials with varying numbers of age components leading to diverse k-component models.The model with the lowest Bayesian Information Criterion(BIC)is identified as the most suitable.For accurate component number and weight determination,a substantial sample size(n>200)is advisable. Our findings based on both synthetic and empirical datasets confirm that the normal mixture model,refined by the EM algorithm,reliably identifies key age parameters with minimal error.As a kind of probability density estimator,the normal mixture model offers a novel visualization tool for detrital data and an alternative foundation for KDE in calculating existing similarity metrics.Another focus of this study is the critical examination of quantitative metrics for comparing detrital zircon age patterns.Through a case study,this study demonstrates that metrics based on empirical cumulative probability distribution(such as K-S and Kuiper statistics)may lead to erroneous conclusions.The employment of the Kullback-Leibler(KL)divergence,a metric grounded in probability density estimation,is proposed.Reference critical values,simulated via the Monte Carlo method,provide more objective benchmarks for these quantitative metrics. All methodologies discussed are encapsulated in a series of MATLAB scripts,available as open-source code and a standalone application,facilitating wider adoption and application in the field.展开更多
文摘The present paper investigates the methods for estimating the maximum(positive)and the minimum(negative)peak wind force coefficients on domed free roofs based on the quasi-steady theory and the peak factor method,in which the experimental results obtained from our previous studies(2019,2025)are used.Focus is on the distributions of the peak wind force coefficients along the centerline parallel to the wind direction considering that domed free roof is an axisymmetric body.Empirical formulas are provided to the distributions of mean wind force coefficient,RMS(root mean square)fluctuating wind force coefficient and peak factors as a function of the rise/span ratio of the roof and the turbulence intensity of the approach flow in the along-wind direction at the mean roof height.The proposed methods are validated by the experimental results for the peak wind force coefficients.The methods would provide useful information to structural engineers when estimating the design wind loads on cladding/components of domed free roofs.
基金supported by the National Natural Science Foundation of China (No.52205548)。
文摘To address the issues of unknown target size,blurred edges,background interference and low contrast in infrared small target detection,this paper proposes a method based on density peaks searching and weighted multi-feature local difference.Firstly,an improved high-boost filter is used for preprocessing to eliminate background clutter and high-brightness interference,thereby increasing the probability of capturing real targets in the density peak search.Secondly,a triple-layer window is used to extract features from the area surrounding candidate targets,addressing the uncertainty of small target sizes.By calculating multi-feature local differences between the triple-layer windows,the problems of blurred target edges and low contrast are resolved.To balance the contribution of different features,intra-class distance is used to calculate weights,achieving weighted fusion of multi-feature local differences to obtain the weighted multi-feature local differences of candidate targets.The real targets are then extracted using the interquartile range.Experiments on datasets such as SIRST and IRSTD-IK show that the proposed method is suitable for various complex types and demonstrates good robustness and detection performance.
基金the National Research Institute of Astronomy and Geophysics (NRIAG) for supporting this work
文摘The level of ground shaking,as determined by the peak ground acceleration(PGA),can be used to analyze seismic hazard at a certain location and is crucial for constructing earthquake-resistant structures.Predicting the PGA immediately after an earthquake occurs allows for the issuing of a warning by an earthquake early warning system.In this study,we propose a deep learning model,ConvMixer,to predict the PGA recorded by weak-motion velocity seismometers in Japan.We use 5-s threecomponent seismograms,from 2 s before until 3 s after the P-wave arrival time of the earthquake.Our dataset comprised more than 50,000 single-station waveforms recorded by 10 seismic stations in the K-NET,Kiki-NET,and Hi-Net networks between 2004 and 2023.The proposed ConvMixer is a patch-based model that extracts global features from input seismic data and predicts the PGA of an earthquake by combining depth and pointwise convolutions.The proposed ConvMixer network had a mean absolute error of 2.143 when applied to the test set and outperformed benchmark deep learning models.In addition,the proposed ConvMixer demonstrated the ability to predict the PGA at the corresponding station site based on 1-second waveforms obtained immediately after the arrival time of the P-wave.
基金funded by the National Natural Science Foundation of China (Grant nos. 42272333 and 42377154)the China Association for Science and Technology Youth Talent Support Program for PhD Students.
文摘The primary objective of this work is to improve our understanding of the mechanical involvements of two-order roughness in shear.First,wavelet analysis is used to separate the waviness(first-order)and unevenness(second-order)from four granite joint surfaces,with roughness characterized using Grasselli’s 3D morphology parameters.The results reveal that first-order roughness is more pronounced than second-order roughness,highlighting the dominant role of waviness in joint surface roughness.Additionally,the variation in first-order roughness with strike direction corresponds to the total roughness,while second-order roughness remains largely constant,indicating that roughness anisotropy is primarily driven by waviness.Then,direct shear tests on joint replicas are performed to investigate the contributions of both roughness orders to peak shear strength.The results show that the peak dilation angle is closely related to first-order roughness,while the shear component angle is closely associated with second-order roughness,both exhibiting a linear correlation.Based on these findings,relationships are established between the angles and their respective roughness orders.Finally,a joint shear strength criterion based on two-order roughness is proposed.A comparative analysis of prediction accuracy reveals that the average relative error for the proposed criterion is 13.79%,while the errors for Xia's,Yang's,and Ban's criteria are 15.19%,16.29%,and 13.87%,respectively.It demonstrates the proposed criterion can predict the peak shear strength of rock joints.
文摘Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead to changes in the network topology,thereby reducing cluster stability in urban scenarios.To address this issue,we propose a clustering model based on the density peak clustering(DPC)method and sparrow search algorithm(SSA),named SDPC.First,the model constructs a fitness function based on the parameters obtained from the DPC method and deploys the SSA for iterative optimization to select cluster heads(CHs).Then,the vehicles that have not been selected as CHs are assigned to appropriate clusters by comprehensively considering the distance parameter and link-reliability parameter.Finally,cluster maintenance strategies are considered to tackle the changes in the clusters’organizational structure.To verify the performance of the model,we conducted a simulation on a real-world scenario for multiple metrics related to clusters’stability.The results show that compared with the APROVE and the GAPC,SDPC showed clear performance advantages,indicating that SDPC can effectively ensure VANETs’cluster stability in urban scenarios.
文摘Wind tunnel experiment and CFD(computational fluid dynamics)simulation with LES(large eddy simulation)have been conducted to investigate the characteristics of peak wind force coefficients of porous panels mounted on the roofs of high-rise buildings.First,aerodynamic modelling of porous panels was discussed.The relation between pressure loss coefficient and porosity was obtained.Then,a wind tunnel experiment was conducted to measure the wind forces(net wind pressures)acting on solid and porous panels mounted on the roof of a high-rise building.Because it was difficult to measure the pressures on both sides of thin,porous panel at the same location simultaneously,we proposed to use the roof edge pressures near the panel for the panel’s inside-surface pressures.This experimental method was validated by a CFD simulation reproducing the wind tunnel experiment.The characteristics of peak wind force coefficients of porous panels mounted on the roofs of high-rise buildings were made clear.Finally,positive and negative peak wind force coefficients for designing the rooftop porous panels were proposed.
基金supported by the National Natural Science Foundation of China(52077078)China Southern Power Grid Company Limited 036000KK52220004(GDKJXM20220147).
文摘To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control strategy is proposed for peak shaving sce-narios.First,considering the difference between peak and valley loads and the operating costs of EVs,a peak shaving model for EVs is constructed.Second,the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer(GEO)algorithm.Subsequently,IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions.Next,using the k-means algorithm,EVs are dynamically divided into priority charging groups,backup groups,and priority discharging groups based on SOC differences.Finally,a dual layer power distribution scheme for EVs is designed.The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs,whereas the lower layer allocates the charging and discharging instructions for each group to each EV.The proposed strategy was simulated and verified,and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy.The pro-posed EV grouping control strategy effectively reduces the peak-valley difference in the power grid,reduces the operational life loss of EVs,and maintains a better SOC balance for EVs.
基金supported by the National Key R&D Program of China(2021YFD2200405)the National Natural Science Foundationof China(Nos.U23A2002,31930078,and 31670450)the Fundamental Research Funds for ICBR(1632021023,1632019006,and1630032023002).
文摘Climate changes in cold-temperate zones are increasingly altering the state of climatic constraints on photosynthesis and growth,leading to adaptive changes in plant phenology and subsequent seasonal carbon assimilation.However,the spatio-temporal patterns of climatic constraints and seasonal carbon assimilation are poorly understood.In this study,the timing of peak photosynthetic activity(DOY_(pmax))was employed as a proxy for plant adap-tive state to climatic constraints on growth to examine the spatio-temporal dynamics of DOY_(pmax).By using multiple remote sensing metrics,DOY_(pmax)was characterized with changes in the solar-induced chlorophyll fluorescence(SIF)and leaf area index(LAI)from 2000 to 2018.Based on SIF,the DOY_(pmax)was generally around day 190,while based on LAI was about 10 d later.Peak photosynthetic activity of forests occurs earlier compared to other vegetation types.Overall,the advanced DOY_(pmax)were observed based on both SIF and LAI,with annual rates of 0.2(P=0.31)and 0.3(P<0.05)d,respectively.DOY_(pmax)dynamics were influ-enced by hot temperature extremes and vapor pressure defi-cits(VPD)during the early growing season,regardless of sub-zone and different vegetation type.The generalized lin-ear mixed model(GLMM)showed the largest contribution by hot extremes to DOY_(pmax)dynamics accounted for 55.5%(DOY_(pmax_SIF))and 49.1%(DOY_(pmax_LAI)),respectively,fol-lowed by VPD(DOY_(pmax_SIF):23.1%;DOY_(pmax_LAI):29.5%).These findings highlight the crucial role of climate extremes in shaping seasonal carbon dynamics and regional carbon balance.
文摘Objective:To investigate the predictive value of diaphragm thickening fraction(DTF)combined with cough peak expiratory flow(CPEF)on the success rate of weaning from mechanical ventilation.Methods:The clinical data of patients undergoing invasive mechanical ventilation via oral endotracheal intubation in the ICU of our hospital from January 2022 to December 2023 were studied.All patients underwent a 30-minute spontaneous breathing trial(SBT)using low-level pressure support ventilation(PSV)after meeting the clinical weaning screening criteria.Among them,150 patients who met the clinical weaning criteria were weaned from the ventilator.They were divided into a successful weaning group(n=100)and a failed weaning group(n=50)based on the weaning outcome.Clinical data,including age,gender,APACHE II score,duration of mechanical ventilation,DTF,and CPEF,were collected from 150 patients.The differences in clinical data between the two groups were compared,and the correlation between DTF,CPEF,and the success rate of weaning was analyzed.Results:There were no significant differences between the two groups in gender ratio(χ^(2)=0.884,P=0.347>0.05),age(t=0.350,P=0.727>0.05),and APACHE II score(t=1.295,P=0.197>0.05),but there was a significant difference in the duration of mechanical ventilation(t=3.766,P<0.001).The DTF and CPEF values in the successful weaning group were significantly higher than those in the failed weaning group(P<0.05).ROC curves were drawn to predict the weaning results using DTF,CPEF,and the combination of DTF and CPEF.The results showed that the specificity of the combination of DTF and CPEF was comparable to that of either metric alone,but the sensitivity and AUC were significantly higher than those of either metric alone.Conclusion:The combination of DTF and CPEF can be used as an effective indicator to evaluate the weaning efficacy of mechanically ventilated patients,which has important clinical significance for guiding clinical weaning treatment,improving the success rate of weaning,reducing the incidence of ventilator-associated pneumonia,and shortening the length of hospital stay.
基金supported by the National Natural Science Foundation of China(Grant No.52409143)the Basic Scientific Research Fund of Changjiang River Scientific Research Institute for Central-level Public Welfare Research Institutes(Grant No.CKSF2025184/YT)the Hubei Provincial Natural Science Foundation of China(Grant No.2022CFB673).
文摘Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV prediction by combining conventional empirical equations with physics-informed neural networks(PINN)and optimizing the model parameters via the Particle Swarm Optimization(PSO)algorithm.The proposed PSO-PINN framework was rigorously benchmarked against seven established machine learning approaches:Multilayer Perceptron(MLP),Extreme Gradient Boosting(XGBoost),Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting Decision Tree(GBDT),Adaptive Boosting(Adaboost),and Gene Expression Programming(GEP).Comparative analysis showed that PSO-PINN outperformed these models,achieving RMSE reductions of 17.82-37.63%,MSE reductions of 32.47-61.10%,AR improvements of 2.97-21.19%,and R^(2)enhancements of 7.43-29.21%,demonstrating superior accuracy and generalization.Furthermore,the study determines the impact of incorporating empirical formulas as physical constraints in neural networks and examines the effects of different empirical equations,particle swarm size,iteration count in PSO,regularization coefficient,and learning rate in PINN on model performance.Lastly,a predictive system for blast vibration PPV is designed and implemented.The research outcomes offer theoretical references and practical recommendations for blast vibration forecasting in similar engineering applications.
基金Supported by Key Project of Hubei Provincial Department of Education Research Program(Grant No.D20211401).
文摘Reducing the peak actuating force(PAF)and parasitic displacement is of high significance for improving the performance of compliant parallel mechanisms(CPMs).In this study,a 2-DOF 4-4R compliant parallel pointing mechanism(4-4R CPPM)was used as the object,and the actuating force of the mechanism was optimized through redundant actuation.This was aimed at minimizing the PAF and parasitic displacement.First,a kinetostatic model of the redundantly actuated 4-4R CPPM was established to reveal the relationship between the input forces/displacements and the output displacements of the mobile platform.Subsequently,based on the established kinetostatic model,methods for optimizing the actuating force distribution with the aim of minimizing the PAF and parasitic displacement were introduced successively.Second,a simulated example of a mobile platform’s spatial pointing trajectory validated the accuracy of the kinetostatic model.The results show a less than 0.9%relative error between the analytical and finite element(FE)results,and the high consistency indicates the accuracy of the kinetostatic model.Then,the effectiveness of the method in minimizing the PAF and parasitic displacement was validated using two simulated examples.The results indicate that compared with the non-redundant actuation case,the PAF of the mechanism could be reduced by up to 50%,and the parasitic displacement was reduced by approximately three-four orders of magnitude by means of redundant actuation combined with the optimal distribution of the actuating force.As expected,with the reduction in parasitic displacement,the FE-results of the output angular displacements(θ_(x) andθ_(z))of the mobile platform were closer to the target oscillation trajectory.This further verified that the reduction in parasitic displacement is indeed effective in improving the motion accuracy of the mechanism.The advantage of this proposed method is that it reduces the PAF and parasitic displacement from the perspective of the actuating force control strategy,without the requirement of structural changes to the original mechanism.
基金supported by the Nanjing University of Posts and Telecommunications Foundation,China(Grant Nos.JUH219002 and JUH219007)the Key R&D Program of Shandong Province,China(Grant No.2021CXGC010202)。
文摘In this paper,we present a high peak power passively Q-switched intracavity frequency-doubled green laser based on an efficient LED-pumped Nd:YAG dual-rod laser module.In quasi-continuous wave(QCW)running operation,the average output power of the fundamental laser at 1064 nm reaches as high as 20.98 W at a repetition rate of 50 Hz with a maximum single pulse energy of 419.6 mJ,corresponding to a maximum optical conversion efficiency of 38.8%and a slope efficiency of 41%.
基金supported by Jilin Province Higher Education Teaching Reform Research Project in 2021(JLJY202186163419).
文摘Peak load and wind energy emission pressure rise more as wind energy penetration keeps growing,which affects the stabilization of the PS(power system).This paper suggests integrated optimal dispatching of thermal power generators and BESS(battery energy storage system)taking wind energy emission grading punishment and deep peak clipping into consideration.Firstly,in order to minimize wind abandonment,a hierarchical wind abandonment penalty strategy based on fuzzy control is designed and introduced,and the optimal grid-connected power of wind energy is determined as a result of minimizing the peak cutting cost of the system.Secondly,considering BESS and thermal power,the management approach of BESS-assisted virtual peak clipping of thermal power generators is aimed at reducing the degree of deep peak clipping of thermal power generators and optimizing the output of thermal power generators and the charging and discharging power of BESS.Finally,Give an example of how this strategy has been effective in reducing abandonment rates by 0.66% and 7.46% individually for different wind penetration programs,and the daily average can reduce the peak clipping power output of thermal power generators by 42.97 and 72.31 MWh and enhances the effect and economy of system peak clipping.
文摘目的:筛选预测初发高危急性早幼粒细胞白血病(acute promyelocytic leukemia,APL)发生分化综合征的因素,比较翻倍白细胞(WBC_(Double))与峰值白细胞(WBC_(Peak))的预测价值。方法:回顾性分析我院血液科收治的78例经亚砷酸诱导治疗的初诊高危APL患者的临床特征,比较WBC_(Double)和WBC_(Peak)分化综合征的分化程度。结果:分化综合征的发生率为57.69%(45/78),分化综合征最常见的临床症状为发热,重度分化综合征组发热、肺部浸润、心包积液发生率明显高于轻度分化综合征组,差异有统计学意义(P<0.05)。高危APL患者分化综合征的发生与化疗后WBC_(Peak)(45.21×10^(9)/L vs 25.24×10^(9)/L,P<0.001)和化疗后WBC_(Double)(37.59×10^(9)/L vs 17.46×10^(9)/L,P=0.007)有关。与WBC_(Peak)组比较,WBC_(Double)组早于分化事件的发生率高(68.57%vs 40.00%,P=0.016),2组间重度分化、轻度分化和未分化差异均有统计学意义(P=0.002);WBC_(Peak)组出现4个以上分化综合征症状的患者比例更多(28.57%vs 5.71%,P=0.012)。与WBCNO-_(Double)组比较,WBC_(Double)组患者诊断时白细胞计数(P=0.003)、谷丙转氨酶(P=0.040)、外周血早幼粒细胞数(P=0.047)差异有统计学意义。结论:化疗后WBC_(Double)和化疗后WBC_(Peak)是分化综合征的危险因素,化疗后WBC_(Double)较WBC_(Peak)提示分化综合征的发生更有优势。
文摘Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon emissions in the Yangtze River Delta(YRD)through path analysis,and to forecast carbon emissions in the YRD from the baseline scenario,factor regulation scenario and integrated scenario to reach the peak.The results show that:(1)Jiangsu's high carbon emission pattern is the main reason for the YRD hindering the synergistic regulation of carbon emissions.(2)Human factors,institutional factors and economic growth factors can all contribute to carbon emissions in the YRD region,while technological and industrial factors can generally suppress carbon emissions in the YRD region.(3)Under the capital regulation scenario,the YRD region has the highest level of carbon emission synergy,with Jiangsu reaching its peak five years earlier.Under the balanced regulation scenario,the YRD region as a whole,Jiangsu,Zhejiang and Anhui reach the peak as scheduled.
文摘The accurate prediction of peak overpressure of explosion shockwaves is significant in fields such as explosion hazard assessment and structural protection, where explosion shockwaves serve as typical destructive elements. Aiming at the problem of insufficient accuracy of the existing physical models for predicting the peak overpressure of ground reflected waves, two physics-informed machine learning models are constructed. The results demonstrate that the machine learning models, which incorporate physical information by predicting the deviation between the physical model and actual values and adding a physical loss term in the loss function, can accurately predict both the training and out-oftraining dataset. Compared to existing physical models, the average relative error in the predicted training domain is reduced from 17.459%-48.588% to 2%, and the proportion of average relative error less than 20% increased from 0% to 59.4% to more than 99%. In addition, the relative average error outside the prediction training set range is reduced from 14.496%-29.389% to 5%, and the proportion of relative average error less than 20% increased from 0% to 71.39% to more than 99%. The inclusion of a physical loss term enforcing monotonicity in the loss function effectively improves the extrapolation performance of machine learning. The findings of this study provide valuable reference for explosion hazard assessment and anti-explosion structural design in various fields.
基金supported by the National Natural Science Foundation of China(No.42271446).
文摘The navigation system plays a pivotal role in guiding aircraft along designated routes,ensuring precise and punctual arrival at destinations.The integration of scene matching with an inertial navigation system enhances the capability of providing a dependable guarantee for success-ful accomplishment of flight missions.Nonetheless,assuring reliability in scene matching encoun-ters significant challenges in areas characterized by repetitive or weak textures.To tackle these challenges,we propose a novel method to assess the reliability of scene matching based on the dis-tinctive characteristics of correlation peaks.The proposed method leverages the fact that the sim-ilarity of the optimal matching result is significantly higher than that of the surrounding area,and three novel indicators(e.g.,relative height,slope of a correlation peak,and ratio of a sub peak to the main peak)are determined to conjointly evaluate the reliability of scene matching.The pro-posed method entails matching a real-time image with a reference image to generate a correlation surface.A correlation peak is then obtained by extracting the portion of the correlation surface exhibiting a significant gradient.Additionally,the matching reliability is determined by considering the relative height,slope,and ratio of the peak collectively.Exhaustive experimental results with two sets of data demonstrate that the proposed method significantly outperforms traditional approaches in terms of precision,recall,and F1-score.These experiments also establish the efficacy of the proposed method in achieving reliable matching in challenging environments characterized by repetitive and weak textures.This enhancement holds the potential to significantly elevate scene-matching-based navigation.
文摘Detrital geochronology fundamentally involves the quantification of major age ranges and their weights winthin an age distribution.This study presents a streamlined approach,modeling the age distribution of detrital zircons using a normal mixture model,and employs the Expectation-Maximization(EM)algorithm for precise estimations.A method is introduced to automatically select appropriate initial mean values for EM algorithm,enhancing its efficacy in detrital geochronology.This process entails multiple trials with varying numbers of age components leading to diverse k-component models.The model with the lowest Bayesian Information Criterion(BIC)is identified as the most suitable.For accurate component number and weight determination,a substantial sample size(n>200)is advisable. Our findings based on both synthetic and empirical datasets confirm that the normal mixture model,refined by the EM algorithm,reliably identifies key age parameters with minimal error.As a kind of probability density estimator,the normal mixture model offers a novel visualization tool for detrital data and an alternative foundation for KDE in calculating existing similarity metrics.Another focus of this study is the critical examination of quantitative metrics for comparing detrital zircon age patterns.Through a case study,this study demonstrates that metrics based on empirical cumulative probability distribution(such as K-S and Kuiper statistics)may lead to erroneous conclusions.The employment of the Kullback-Leibler(KL)divergence,a metric grounded in probability density estimation,is proposed.Reference critical values,simulated via the Monte Carlo method,provide more objective benchmarks for these quantitative metrics. All methodologies discussed are encapsulated in a series of MATLAB scripts,available as open-source code and a standalone application,facilitating wider adoption and application in the field.
