To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, ...To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, a two-dimensional stochastic airfoil optimization design method based on neural networks is presented. To provide highly efficient and credible analysis, four BP neural networks are built as surrogate models to predict the airfoil aerodynamic coefficients and geometry parameter. These networks are combined with the probability density function obeying normal distribution and the genetic algorithm, thus forming an optimization design method. Using the method, for GA（W）-2 airfoil, a stochastic optimization is implemented in a two-dimensional flight area about Mach number and angle of attack. Compared with original airfoil and single point optimization design airfoil, results show that the two-dimensional stochastic method can improve the performance in a specific flight area, and increase the airfoil adaptability to the stochastic changes of multiple flight parameters.展开更多
Objective: To explore the affecting factors of liver cancer recurrence after hepatectomy. Methods:The BP artificial neural network - Cox regression was introduced to analyze the factors of recurrence in1 457 patients....Objective: To explore the affecting factors of liver cancer recurrence after hepatectomy. Methods:The BP artificial neural network - Cox regression was introduced to analyze the factors of recurrence in1 457 patients. Results: The affecting factors statistically significant to liver cancer prognosis was selected.There were 18 factors to be selected by uni-factor analysis, and 9 factors to be selected by multi-factor analysis. Conclusion: The 9 factors selected can be used as important indexes to evaluate the recurrence of liver cancer after hepatectomy. The artificial neural network is a better method to analyze the clinical data, which provides scientific and objective data for evaluating prognosis of liver cancer.展开更多
The goal of this study was to apply artificial neural networks to predict rain-fed wheat yield using meteorological data a few days to few months before harvesting. The climatic observation data used; were mean of dai...The goal of this study was to apply artificial neural networks to predict rain-fed wheat yield using meteorological data a few days to few months before harvesting. The climatic observation data used; were mean of daily minimum and maximum temperature, extreme of daily minimum and maximum temperature, sum of daily rainfall, number of rainy days, sum of daily sun hours, mean of daily wind speed, extreme of daily wind speed, mean of daily relative humidity, and sum of daily water requirements that were collected during 1990-1999 in Sararood Station for wheat phenological stages consisting; sowing, germination, emergence, 3rd leaves, tillering, stem formation, heading, flowering, milk maturity, wax maturity, full maturity, separately for each growing season. Then, they arranged in a matrix whose rows form each of the statistical years and the columns are meteorological factors at each phenological stage. Finally, the obtained model had the following capabilities： Prediction of wheat yield with maximum errors of 45-60 kg/ha at least two months before full maturity stage, determination of the sensitivity of each phenological stage with respect to meteorological factors, and determination of the priority order and importance of each meteorological factor effective in plant growth and crop yield.展开更多
The distribution characteristics of rare earth elements （REE） in bottomsediments are influenced by many factors. Hence, conducting a quantitative analysis isdifficult. A qualitative analysis of the relationships bet...The distribution characteristics of rare earth elements （REE） in bottomsediments are influenced by many factors. Hence, conducting a quantitative analysis isdifficult. A qualitative analysis of the relationships between ΣREE content andprovenance, hydrodynamics, grain size and mineral distribution in the Beibu Gulf showsthat terrestrial rocks control the ΣREE composition. Both weaker hydrodynamics andfiner grain size lead to a higher ΣREE content. Relative curves revealing therelationships between individual impact factors and ΣREE content were obtained fromthe combination of qualitative and quantitative analyses of the BP neural network,which trained the position of samples, gravel content, sand content, silt content, claycontent and clay mineral content. The results are consistent with those of thequantitative analysis. The self-learning algorithm is automatically determined andcalculated quantitatively. The impact of each factor on REEs and how each factorcontrols the ΣREE distribution is identified. Thus, environmental changes and thegeological evolution of the region can be inferred based on curve variation and the geological evolution of the region can be inferred based on curve variation and theactual situation. This method also provides useful theoretical guidance for the analysisof REE enrichment and dispersion.展开更多
文摘To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, a two-dimensional stochastic airfoil optimization design method based on neural networks is presented. To provide highly efficient and credible analysis, four BP neural networks are built as surrogate models to predict the airfoil aerodynamic coefficients and geometry parameter. These networks are combined with the probability density function obeying normal distribution and the genetic algorithm, thus forming an optimization design method. Using the method, for GA（W）-2 airfoil, a stochastic optimization is implemented in a two-dimensional flight area about Mach number and angle of attack. Compared with original airfoil and single point optimization design airfoil, results show that the two-dimensional stochastic method can improve the performance in a specific flight area, and increase the airfoil adaptability to the stochastic changes of multiple flight parameters.
基金Supported by the National Natural Science Foundation of China (No. 39770835)
文摘Objective: To explore the affecting factors of liver cancer recurrence after hepatectomy. Methods:The BP artificial neural network - Cox regression was introduced to analyze the factors of recurrence in1 457 patients. Results: The affecting factors statistically significant to liver cancer prognosis was selected.There were 18 factors to be selected by uni-factor analysis, and 9 factors to be selected by multi-factor analysis. Conclusion: The 9 factors selected can be used as important indexes to evaluate the recurrence of liver cancer after hepatectomy. The artificial neural network is a better method to analyze the clinical data, which provides scientific and objective data for evaluating prognosis of liver cancer.
文摘The goal of this study was to apply artificial neural networks to predict rain-fed wheat yield using meteorological data a few days to few months before harvesting. The climatic observation data used; were mean of daily minimum and maximum temperature, extreme of daily minimum and maximum temperature, sum of daily rainfall, number of rainy days, sum of daily sun hours, mean of daily wind speed, extreme of daily wind speed, mean of daily relative humidity, and sum of daily water requirements that were collected during 1990-1999 in Sararood Station for wheat phenological stages consisting; sowing, germination, emergence, 3rd leaves, tillering, stem formation, heading, flowering, milk maturity, wax maturity, full maturity, separately for each growing season. Then, they arranged in a matrix whose rows form each of the statistical years and the columns are meteorological factors at each phenological stage. Finally, the obtained model had the following capabilities： Prediction of wheat yield with maximum errors of 45-60 kg/ha at least two months before full maturity stage, determination of the sensitivity of each phenological stage with respect to meteorological factors, and determination of the priority order and importance of each meteorological factor effective in plant growth and crop yield.
文摘The distribution characteristics of rare earth elements （REE） in bottomsediments are influenced by many factors. Hence, conducting a quantitative analysis isdifficult. A qualitative analysis of the relationships between ΣREE content andprovenance, hydrodynamics, grain size and mineral distribution in the Beibu Gulf showsthat terrestrial rocks control the ΣREE composition. Both weaker hydrodynamics andfiner grain size lead to a higher ΣREE content. Relative curves revealing therelationships between individual impact factors and ΣREE content were obtained fromthe combination of qualitative and quantitative analyses of the BP neural network,which trained the position of samples, gravel content, sand content, silt content, claycontent and clay mineral content. The results are consistent with those of thequantitative analysis. The self-learning algorithm is automatically determined andcalculated quantitatively. The impact of each factor on REEs and how each factorcontrols the ΣREE distribution is identified. Thus, environmental changes and thegeological evolution of the region can be inferred based on curve variation and the geological evolution of the region can be inferred based on curve variation and theactual situation. This method also provides useful theoretical guidance for the analysisof REE enrichment and dispersion.
