In this paper are reported the local minimum problem by means of current greedy algorithm for training the empirical potential function of protein folding on 8623 non-native structures of 31 globular proteins and a so...In this paper are reported the local minimum problem by means of current greedy algorithm for training the empirical potential function of protein folding on 8623 non-native structures of 31 globular proteins and a solution of the problem based upon the simulated annealing algorithm. This simulated annealing algorithm is indispensable for developing and testing highly refined empirical potential functions.展开更多
In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(S...In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.展开更多
Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF（empirica...Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF（empirical orthogonal function） temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results. A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.展开更多
A new algorithm, named segmented second empirical mode decomposition （EMD） algorithm, is proposed in this paper in order to reduce the computing time of EMD and make EMD algorithm available to online time-frequency ...A new algorithm, named segmented second empirical mode decomposition （EMD） algorithm, is proposed in this paper in order to reduce the computing time of EMD and make EMD algorithm available to online time-frequency analysis. The original data is divided into some segments with the same length. Each segment data is processed based on the principle of the first-level EMD decomposition. The algorithm is compared with the traditional EMD and results show that it is more useful and effective for analyzing nonlinear and non-stationary signals.展开更多
In this paper, a novel empirical equation is proposed to calculate the relative permeability of low permeability reservoir. An improved item is introduced on the basis of Rose empirical formula and Al-Fattah empirical...In this paper, a novel empirical equation is proposed to calculate the relative permeability of low permeability reservoir. An improved item is introduced on the basis of Rose empirical formula and Al-Fattah empirical formula, with one simple model to describe oil/water relative permeability. The position displacement idea of bare bones particle swarm optimization is applied to change the mutation operator to improve the RNA genetic algorithm. The parameters of the new empirical equation are optimized with the hybrid RNA genetic algorithm(HRGA) based on the experimental data. The data is obtained from a typical low permeability reservoir well 54 core 27-1 in Gu Dong by unsteady method. We carry out matlab programming simulation with HRGA. The comparison and error analysis show that the empirical equation proposed is more accurate than the Rose empirical formula and the exponential model. The generalization of the empirical equation is also verified.展开更多
Accurate estimation of reference crop evapotranspiration(ET_(0))is essential for water resource management and irrigation scheduling.A multitude of empirical models have been employed to estimate ET_(0),yielding satis...Accurate estimation of reference crop evapotranspiration(ET_(0))is essential for water resource management and irrigation scheduling.A multitude of empirical models have been employed to estimate ET_(0),yielding satisfactory outcomes.However,the performance of each model is contingent upon the empirical parameters utilized.This study examines the applicability of four empirical ET_(0) models,namely the Makkink(Mak),Irmark-Allen(IA),improved Baier-Robertson(MBR),and Brutsaert-Stricker(BS)models.Meteorological data from 24 weather stations across various regions in China were procured and employed to assess the ET_(0) simulation results.The study employed the Differential Evolution(DE)optimization algorithm,Grey Wolf Optimizer(GWO)algorithm,and a hybrid algorithm that combines DE and GWO algorithms(DE-GWO algorithm)to optimize the parameters of the four empirical models.The findings revealed that the optimization algorithms significantly enhanced the regional adaptability of the four models,particularly the BS model.The DE-GWO algorithm demonstrated superior optimization performance(RMSE=0.055-0.372,R^(2)=0.912-0.998,MAE=0.037-0.311,and FS=0.864-0.982)compared to the DE(RMSE=0.101-2.015,R^(2)=0.529-0.997,MAE=0.075-1.695,and FS=0.383-0.967)and GWO(RMSE=0.158-0.915,R^(2)=0.694-0.987,MAE=0.111-0.701,and FS=0.688-0.947)algorithms.The DE-GWO-optimized BS model was the most accurate and improved,followed by the MBR model.The IA and Mak models also showed slightly better performance after optimization with the DE-GWO algorithm.The DE-GWO-optimized BS model performed better in the southern agricultural region than in other regions.It is recommended to utilize the DE-GWO to enhance the accurate prediction of empirical ET_(0) models across the nine agricultural regions of China.展开更多
Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach wa...Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.展开更多
To reconstruct the missing data of the total electron content （TEC） observations, a new method is proposed, which is based on the empirical orthogonal functions （EOF） decomposition and the value of eigenvalue itse...To reconstruct the missing data of the total electron content （TEC） observations, a new method is proposed, which is based on the empirical orthogonal functions （EOF） decomposition and the value of eigenvalue itself. It is a self-adaptive EOF decomposition without any prior information needed, and the error of reconstructed data can be estimated. The interval quartering algorithm and cross-validation algorithm are used to compute the optimal number of EOFs for reconstruction. The interval quartering algorithm can reduce the computation time. The application of the data interpolating empirical orthogonal functions （DINEOF） method to the real data have demonstrated that the method can reconstruct the TEC map with high accuracy, which can be employed on the real-time system in the future work.展开更多
Water infiltration into soil is an important process in hydrologic cycle;however,its measurement is difficult,time-consuming and costly.Empirical and physical models have been developed to predict cumulative infiltrat...Water infiltration into soil is an important process in hydrologic cycle;however,its measurement is difficult,time-consuming and costly.Empirical and physical models have been developed to predict cumulative infiltration(CI),but are often inaccurate.In this study,several novel standalone machine learning algorithms(M5Prime(M5P),decision stump(DS),and sequential minimal optimization(SMO))and hybrid algorithms based on additive regression(AR)(i.e.,AR-M5P,AR-DS,and AR-SMO)and weighted instance handler wrapper(WIHW)(i.e.,WIHW-M5P,WIHW-DS,and WIHW-SMO)were developed for CI prediction.The Soil Conservation Service(SCS)model developed by the United States Department of Agriculture(USDA),one of the most popular empirical models to predict CI,was considered as a benchmark.Overall,154 measurements of CI(explanatory/input variables)were taken from 16 sites in a semi-arid region of Iran(Illam and Lorestan provinces).Six input variable combinations were considered based on Pearson correlations between candidate model inputs(time of measuring and soil bulk density,moisture content,and sand,clay,and silt percentages)and CI.The dataset was divided into two subgroups at random:70%of the data were used for model building(training dataset)and the remaining 30%were used for model validation(testing dataset).The various models were evaluated using different graphical approaches(bar charts,scatter plots,violin plots,and Taylor diagrams)and quantitative measures(root mean square error(RMSE),mean absolute error(MAE),Nash-Sutcliffe efficiency(NSE),and percent bias(PBIAS)).Time of measuring had the highest correlation with CI in the study area.The best input combinations were different for different algorithms.The results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone models.The AR-M5P model provided the most accurate CI predictions(RMSE=0.75 cm,MAE=0.59 cm,NSE=0.98),while the SCS model had the lowest performance(RMSE=4.77 cm,MAE=2.64 cm,NSE=0.23).The differences in RMSE between the best model(AR-M5P)and the second-best(WIHW-M5P)and worst(SCS)were 40%and 84%,respectively.展开更多
基金Supported by the National Nataral Science Foundation of China(No.39980 0 0 5 )
文摘In this paper are reported the local minimum problem by means of current greedy algorithm for training the empirical potential function of protein folding on 8623 non-native structures of 31 globular proteins and a solution of the problem based upon the simulated annealing algorithm. This simulated annealing algorithm is indispensable for developing and testing highly refined empirical potential functions.
基金Projects(61471370,61401479)supported by the National Natural Science Foundation of China
文摘In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.
基金Project supported by the National Natural Science Foundation of China (No.40375019) the Tropical Marine and Meteorology Science Foundation (No.200609) the Jiangsu Key Laboratory of Meteorological Disaster Foundation (No.KLME0507)
文摘Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF（empirical orthogonal function） temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results. A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.
文摘A new algorithm, named segmented second empirical mode decomposition （EMD） algorithm, is proposed in this paper in order to reduce the computing time of EMD and make EMD algorithm available to online time-frequency analysis. The original data is divided into some segments with the same length. Each segment data is processed based on the principle of the first-level EMD decomposition. The algorithm is compared with the traditional EMD and results show that it is more useful and effective for analyzing nonlinear and non-stationary signals.
基金Supported by the National Natural Science Foundation of China(60974039)the Natural Science Foundation of Shandong Province(ZR2011FM002)
文摘In this paper, a novel empirical equation is proposed to calculate the relative permeability of low permeability reservoir. An improved item is introduced on the basis of Rose empirical formula and Al-Fattah empirical formula, with one simple model to describe oil/water relative permeability. The position displacement idea of bare bones particle swarm optimization is applied to change the mutation operator to improve the RNA genetic algorithm. The parameters of the new empirical equation are optimized with the hybrid RNA genetic algorithm(HRGA) based on the experimental data. The data is obtained from a typical low permeability reservoir well 54 core 27-1 in Gu Dong by unsteady method. We carry out matlab programming simulation with HRGA. The comparison and error analysis show that the empirical equation proposed is more accurate than the Rose empirical formula and the exponential model. The generalization of the empirical equation is also verified.
基金supported by the National Natural Science Foundation of China(Grant No.52309050 and Grant No.32372680)the Key R&D and Promotion Projects in Henan Province(Science and Technology Development)(Grant No.232102110264)+2 种基金the Key Scientific Research Projects of Colleges and Universities in Henan Province(Grant No.24B416001)the Innovative Research Team(Science and Technology)in University of Henan Province(Grant No.23IRTSTHN024)the Youth Backbone Teacher Project of Henan University of Science and Technology(Grants No.13450013 and 13450010).
文摘Accurate estimation of reference crop evapotranspiration(ET_(0))is essential for water resource management and irrigation scheduling.A multitude of empirical models have been employed to estimate ET_(0),yielding satisfactory outcomes.However,the performance of each model is contingent upon the empirical parameters utilized.This study examines the applicability of four empirical ET_(0) models,namely the Makkink(Mak),Irmark-Allen(IA),improved Baier-Robertson(MBR),and Brutsaert-Stricker(BS)models.Meteorological data from 24 weather stations across various regions in China were procured and employed to assess the ET_(0) simulation results.The study employed the Differential Evolution(DE)optimization algorithm,Grey Wolf Optimizer(GWO)algorithm,and a hybrid algorithm that combines DE and GWO algorithms(DE-GWO algorithm)to optimize the parameters of the four empirical models.The findings revealed that the optimization algorithms significantly enhanced the regional adaptability of the four models,particularly the BS model.The DE-GWO algorithm demonstrated superior optimization performance(RMSE=0.055-0.372,R^(2)=0.912-0.998,MAE=0.037-0.311,and FS=0.864-0.982)compared to the DE(RMSE=0.101-2.015,R^(2)=0.529-0.997,MAE=0.075-1.695,and FS=0.383-0.967)and GWO(RMSE=0.158-0.915,R^(2)=0.694-0.987,MAE=0.111-0.701,and FS=0.688-0.947)algorithms.The DE-GWO-optimized BS model was the most accurate and improved,followed by the MBR model.The IA and Mak models also showed slightly better performance after optimization with the DE-GWO algorithm.The DE-GWO-optimized BS model performed better in the southern agricultural region than in other regions.It is recommended to utilize the DE-GWO to enhance the accurate prediction of empirical ET_(0) models across the nine agricultural regions of China.
文摘Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.
基金supported by the National Natural Science Foundation of China(Grant No.41105013,41375028,and 61271106)the National Natural Science Foundation of Jiangsu Province,China(Grant No.BK2011122)the Key Laboratory of Meteorological Observation and Information Processing Scientific Research Fund of Jiangsu Province,China(Grant No.KDXS1205)
文摘To reconstruct the missing data of the total electron content （TEC） observations, a new method is proposed, which is based on the empirical orthogonal functions （EOF） decomposition and the value of eigenvalue itself. It is a self-adaptive EOF decomposition without any prior information needed, and the error of reconstructed data can be estimated. The interval quartering algorithm and cross-validation algorithm are used to compute the optimal number of EOFs for reconstruction. The interval quartering algorithm can reduce the computation time. The application of the data interpolating empirical orthogonal functions （DINEOF） method to the real data have demonstrated that the method can reconstruct the TEC map with high accuracy, which can be employed on the real-time system in the future work.
文摘Water infiltration into soil is an important process in hydrologic cycle;however,its measurement is difficult,time-consuming and costly.Empirical and physical models have been developed to predict cumulative infiltration(CI),but are often inaccurate.In this study,several novel standalone machine learning algorithms(M5Prime(M5P),decision stump(DS),and sequential minimal optimization(SMO))and hybrid algorithms based on additive regression(AR)(i.e.,AR-M5P,AR-DS,and AR-SMO)and weighted instance handler wrapper(WIHW)(i.e.,WIHW-M5P,WIHW-DS,and WIHW-SMO)were developed for CI prediction.The Soil Conservation Service(SCS)model developed by the United States Department of Agriculture(USDA),one of the most popular empirical models to predict CI,was considered as a benchmark.Overall,154 measurements of CI(explanatory/input variables)were taken from 16 sites in a semi-arid region of Iran(Illam and Lorestan provinces).Six input variable combinations were considered based on Pearson correlations between candidate model inputs(time of measuring and soil bulk density,moisture content,and sand,clay,and silt percentages)and CI.The dataset was divided into two subgroups at random:70%of the data were used for model building(training dataset)and the remaining 30%were used for model validation(testing dataset).The various models were evaluated using different graphical approaches(bar charts,scatter plots,violin plots,and Taylor diagrams)and quantitative measures(root mean square error(RMSE),mean absolute error(MAE),Nash-Sutcliffe efficiency(NSE),and percent bias(PBIAS)).Time of measuring had the highest correlation with CI in the study area.The best input combinations were different for different algorithms.The results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone models.The AR-M5P model provided the most accurate CI predictions(RMSE=0.75 cm,MAE=0.59 cm,NSE=0.98),while the SCS model had the lowest performance(RMSE=4.77 cm,MAE=2.64 cm,NSE=0.23).The differences in RMSE between the best model(AR-M5P)and the second-best(WIHW-M5P)and worst(SCS)were 40%and 84%,respectively.
