In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken a...In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.展开更多
In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can eff...In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.展开更多
This paper proposes a novel method to predict the spur gear pair’s static transmission error based on the accuracy grade,in which manufacturing errors(MEs),assembly errors(AEs),tooth deflections(TDs)and profile modif...This paper proposes a novel method to predict the spur gear pair’s static transmission error based on the accuracy grade,in which manufacturing errors(MEs),assembly errors(AEs),tooth deflections(TDs)and profile modifications(PMs)are considered.For the prediction,a discrete gear model for generating the error tooth profile based on the ISO accuracy grade is presented.Then,the gear model and a tooth deflection model for calculating the tooth compliance on gear meshing are coupled with the transmission error model to make the prediction by checking the interference status between gear and pinion.The prediction method is validated by comparison with the experimental results from the literature,and a set of cases are simulated to study the effects of MEs,AEs,TDs and PMs on the static transmission error.In addition,the time-varying backlash caused by both MEs and AEs,and the contact ratio under load conditions are also investigated.The results show that the novel method can effectively predict the range of the static transmission error under different accuracy grades.The prediction results can provide references for the selection of gear design parameters and the optimization of transmission performance in the design stage of gear systems.展开更多
Persistent Heavy Rainfall(PHR)is the most influential extreme weather event in Asia in summer,and thus it has attracted intensive interests of many scientists.In this study,operational global ensemble forecasts from C...Persistent Heavy Rainfall(PHR)is the most influential extreme weather event in Asia in summer,and thus it has attracted intensive interests of many scientists.In this study,operational global ensemble forecasts from China Meteorological Administration(CMA)are used,and a new verification method applied to evaluate the predictability of PHR is investigated.A metrics called Index of Composite Predictability(ICP)established on basic verification indicators,i.e.,Equitable Threat Score(ETS)of 24 h accumulated precipitation and Root Mean Square Error(RMSE)of Height at 500 h Pa,are selected in this study to distinguish"good"and"poor"prediction from all ensemble members.With the use of the metrics of ICP,the predictability of two typical PHR events in June 2010 and June 2011 is estimated.The results show that the"good member"and"poor member"can be identified by ICP and there is an obvious discrepancy in their ability to predict the key weather system that affects PHR."Good member"shows a higher predictability both in synoptic scale and mesoscale weather system in their location,duration and the movement.The growth errors for"poor"members is mainly due to errors of initial conditions in northern polar region.The growth of perturbation errors and the reason for better or worse performance of ensemble member also have great value for future model improvement and further research.展开更多
The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on expe...The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.展开更多
Currently, simultaneously ensuring the machining accuracy and efficiency of thin-walled structures especially high performance parts still remains a challenge. Existing compensating methods are mainly focusing on 3-ai...Currently, simultaneously ensuring the machining accuracy and efficiency of thin-walled structures especially high performance parts still remains a challenge. Existing compensating methods are mainly focusing on 3-aixs machining, which sometimes only take one given point as the compensative point at each given cutter location. This paper presents a redesigned surface based machining strategy for peripheral milling of thin-walled parts. Based on an improved cutting force/heat model and finite element method（FEM） simulation environment, a deflection error prediction model, which takes sequence of cutter contact lines as compensation targets, is established. And an iterative algorithm is presented to determine feasible cutter axis positions. The final redesigned surface is subsequently generated by skinning all discrete cutter axis vectors after compensating by using the proposed algorithm. The proposed machining strategy incorporates the thermo-mechanical coupled effect in deflection prediction, and is also validated with flank milling experiment by using five-axis machine tool. At the same time, the deformation error is detected by using three-coordinate measuring machine. Error prediction values and experimental results indicate that they have a good consistency and the proposed approach is able to significantly reduce the dimension error under the same machining conditions compared with conventional methods. The proposed machining strategy has potential in high-efficiency precision machining of thin-walled parts.展开更多
The digital baseband predistorter is an effective technique to compensate for the nonlinearity of power amplifiers （PAs） with memory effects. However, most available adaptive predistorters based on direct learning a...The digital baseband predistorter is an effective technique to compensate for the nonlinearity of power amplifiers （PAs） with memory effects. However, most available adaptive predistorters based on direct learning architectures suffer from slow convergence speeds. In this paper, the recursive prediction error method is used to construct an adaptive Hammerstein predistorter based on the direct learning architecture, which is used to linearize the Wiener PA model. The effectiveness of the scheme is demonstrated on a digital video broadcasting-terrestrial system. Simulation results show that the predistorter outperforms previous predistorters based on direct learning architectures in terms of convergence speed and linearization. A similar algorithm can be applied to estimate the Wiener PA model, which will achieve high model accuracy.展开更多
基金This work is funded by the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the National Science Fund for Distinguished Young Scholars of China(Grant No.52222905).
文摘In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.
基金Project supported by the National Natural Science Foundation of China （Grant Nos 40575036 and 40325015）.Acknowledgement The authors thank Drs Zhang Pei-Qun and Bao Ming very much for their valuable comments on the present paper.
文摘In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.
基金Project(51675061)supported by the National Natural Science Foundation of China。
文摘This paper proposes a novel method to predict the spur gear pair’s static transmission error based on the accuracy grade,in which manufacturing errors(MEs),assembly errors(AEs),tooth deflections(TDs)and profile modifications(PMs)are considered.For the prediction,a discrete gear model for generating the error tooth profile based on the ISO accuracy grade is presented.Then,the gear model and a tooth deflection model for calculating the tooth compliance on gear meshing are coupled with the transmission error model to make the prediction by checking the interference status between gear and pinion.The prediction method is validated by comparison with the experimental results from the literature,and a set of cases are simulated to study the effects of MEs,AEs,TDs and PMs on the static transmission error.In addition,the time-varying backlash caused by both MEs and AEs,and the contact ratio under load conditions are also investigated.The results show that the novel method can effectively predict the range of the static transmission error under different accuracy grades.The prediction results can provide references for the selection of gear design parameters and the optimization of transmission performance in the design stage of gear systems.
基金National 973 Program of China(2012CB417204)National Natural Science Foundation of China(41075035,41475044)Special Fund for Meteorological Scientific Research in the Public Interest(GYHY201006015)
文摘Persistent Heavy Rainfall(PHR)is the most influential extreme weather event in Asia in summer,and thus it has attracted intensive interests of many scientists.In this study,operational global ensemble forecasts from China Meteorological Administration(CMA)are used,and a new verification method applied to evaluate the predictability of PHR is investigated.A metrics called Index of Composite Predictability(ICP)established on basic verification indicators,i.e.,Equitable Threat Score(ETS)of 24 h accumulated precipitation and Root Mean Square Error(RMSE)of Height at 500 h Pa,are selected in this study to distinguish"good"and"poor"prediction from all ensemble members.With the use of the metrics of ICP,the predictability of two typical PHR events in June 2010 and June 2011 is estimated.The results show that the"good member"and"poor member"can be identified by ICP and there is an obvious discrepancy in their ability to predict the key weather system that affects PHR."Good member"shows a higher predictability both in synoptic scale and mesoscale weather system in their location,duration and the movement.The growth errors for"poor"members is mainly due to errors of initial conditions in northern polar region.The growth of perturbation errors and the reason for better or worse performance of ensemble member also have great value for future model improvement and further research.
基金This work was supported by the Scientific Research Projects of Tianjin Educational Committee(No.2020KJ029)。
文摘The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.
基金supported by Key Program of National Natural Science Foundation of China (Grant No. 50835001) General Program of National Natural Science Foundation of China (Grant No. 50775023)Program for New Century Excellent Talents of Ministry of Education of China (Grant No. NCET-08-081)
文摘Currently, simultaneously ensuring the machining accuracy and efficiency of thin-walled structures especially high performance parts still remains a challenge. Existing compensating methods are mainly focusing on 3-aixs machining, which sometimes only take one given point as the compensative point at each given cutter location. This paper presents a redesigned surface based machining strategy for peripheral milling of thin-walled parts. Based on an improved cutting force/heat model and finite element method（FEM） simulation environment, a deflection error prediction model, which takes sequence of cutter contact lines as compensation targets, is established. And an iterative algorithm is presented to determine feasible cutter axis positions. The final redesigned surface is subsequently generated by skinning all discrete cutter axis vectors after compensating by using the proposed algorithm. The proposed machining strategy incorporates the thermo-mechanical coupled effect in deflection prediction, and is also validated with flank milling experiment by using five-axis machine tool. At the same time, the deformation error is detected by using three-coordinate measuring machine. Error prediction values and experimental results indicate that they have a good consistency and the proposed approach is able to significantly reduce the dimension error under the same machining conditions compared with conventional methods. The proposed machining strategy has potential in high-efficiency precision machining of thin-walled parts.
文摘The digital baseband predistorter is an effective technique to compensate for the nonlinearity of power amplifiers （PAs） with memory effects. However, most available adaptive predistorters based on direct learning architectures suffer from slow convergence speeds. In this paper, the recursive prediction error method is used to construct an adaptive Hammerstein predistorter based on the direct learning architecture, which is used to linearize the Wiener PA model. The effectiveness of the scheme is demonstrated on a digital video broadcasting-terrestrial system. Simulation results show that the predistorter outperforms previous predistorters based on direct learning architectures in terms of convergence speed and linearization. A similar algorithm can be applied to estimate the Wiener PA model, which will achieve high model accuracy.
