Spontaneous combustion is one of the greatest disasters in coal mines. Early recognition is important because it may be a potential inducement for other coalmine accidents. However, early recognition is difficult beca...Spontaneous combustion is one of the greatest disasters in coal mines. Early recognition is important because it may be a potential inducement for other coalmine accidents. However, early recognition is difficult because of the complexity of different coal mines. Fuzzy clustering has been proposed to incorporate the uncertainty of spontaneous combustion in coal mines and it can give a clear degree of classification of combustion. Because FCM clustering tends to become trapped in local minima, a new approach of fuzzy c-means clustering based on a genetic algorithm is there- fore proposed. Genetic algorithm is capable of locating optimal or near optimal solutions to difficult problems. It can be applied in many fields without first obtaining detailed knowledge about correlation. It is helpful in improving the effec- tiveness of fuzzy clustering in detecting spontaneous combustion. The effectiveness of the method is demonstrated by means of an experiment.展开更多
A new error analysis method is presented via genetic algorithms for high precise heading determination model based on two total positioning stations （TPSs）. The method has the ability to search all possible solution...A new error analysis method is presented via genetic algorithms for high precise heading determination model based on two total positioning stations （TPSs）. The method has the ability to search all possible solution space by the genetic operators of elitist model and restriction. The result of analyzing the error of this model shows that the accuracy of this model is precise enough to meet the need of calibration for navigation systems on ship, and the search space is only 0. 03% of the total search space, and the precision of heading determination is 4＂ in a general dock.展开更多
Each physical process in a numerical weather prediction(NWP)system may have many different parameterization schemes.Early studies have shown that the performance of different physical parameterization schemes varies w...Each physical process in a numerical weather prediction(NWP)system may have many different parameterization schemes.Early studies have shown that the performance of different physical parameterization schemes varies with the weather situation to be simulated.Thus,it is necessary to select a suitable combination of physical parameterization schemes according to the variation of weather systems.However,it is rather difficult to identify an optimal combination among millions of possible parameterization scheme combinations.This study applied a simple genetic algorithm(SGA)to optimizing the combination of parameterization schemes in NWP models for typhoon forecasting.The feasibility of SGA was verified with the simulation of Typhoon Mujigae(2015)by using the Weather Research and Forecasting(WRF)model and Typhoon Higos(2020)by using the Coupled Ocean–Atmosphere–Wave–Sediment Transport(COAWST)modeling system.The results show that SGA can efficiently obtain the optimal combination of schemes.For Typhoon Mujigae(2015),the optimal combination can be found from the 1,304,576 possible combinations by running only 488 trials.Similar results can be obtained for Typhoon Higos(2020).Compared to the default combination proposed by the COAWST model system,the optimal combination scheme significantly improves the simulation of typhoon track and intensity.This study provides a feasible way to search for the optimal combinations of physical parameterization schemes in WRF and COAWST for more accurate typhoon simulation.This can help provide references for future development of NWP models,and for analyzing the coordination and adaptability of different physical process parameterization schemes under specific weather backgrounds.展开更多
文章给出了基于改进遗传算法的圆锥滚子轴承优化设计的方法。针对传统算法在圆锥滚子轴承优化设计中全局寻优能力较差问题,文章改进了标准遗传算法(Simple Genetic A lgorithm SGA)的算法参数,采用了最优解保存策略,并运用退火罚函数法...文章给出了基于改进遗传算法的圆锥滚子轴承优化设计的方法。针对传统算法在圆锥滚子轴承优化设计中全局寻优能力较差问题,文章改进了标准遗传算法(Simple Genetic A lgorithm SGA)的算法参数,采用了最优解保存策略,并运用退火罚函数法[1]处理约束。通过与标准遗传算法及综合约束双下降法比较,表明改进遗传算法具有良好全局寻优能力和稳定性。从而解决了开发圆锥滚子轴承CAD系统的关键问题。展开更多
基金Project 20050290010 supported by the Doctoral Foundation of Chinese Education Ministry
文摘Spontaneous combustion is one of the greatest disasters in coal mines. Early recognition is important because it may be a potential inducement for other coalmine accidents. However, early recognition is difficult because of the complexity of different coal mines. Fuzzy clustering has been proposed to incorporate the uncertainty of spontaneous combustion in coal mines and it can give a clear degree of classification of combustion. Because FCM clustering tends to become trapped in local minima, a new approach of fuzzy c-means clustering based on a genetic algorithm is there- fore proposed. Genetic algorithm is capable of locating optimal or near optimal solutions to difficult problems. It can be applied in many fields without first obtaining detailed knowledge about correlation. It is helpful in improving the effec- tiveness of fuzzy clustering in detecting spontaneous combustion. The effectiveness of the method is demonstrated by means of an experiment.
文摘A new error analysis method is presented via genetic algorithms for high precise heading determination model based on two total positioning stations （TPSs）. The method has the ability to search all possible solution space by the genetic operators of elitist model and restriction. The result of analyzing the error of this model shows that the accuracy of this model is precise enough to meet the need of calibration for navigation systems on ship, and the search space is only 0. 03% of the total search space, and the precision of heading determination is 4＂ in a general dock.
基金Supported by the National Natural Science Foundation of China(42130605)Shenzhen Science and Technology Program(JCYJ20210324131810029)Guangdong Province Introduction of Innovative Research and Development Team Project China(2019ZT08G669)。
文摘Each physical process in a numerical weather prediction(NWP)system may have many different parameterization schemes.Early studies have shown that the performance of different physical parameterization schemes varies with the weather situation to be simulated.Thus,it is necessary to select a suitable combination of physical parameterization schemes according to the variation of weather systems.However,it is rather difficult to identify an optimal combination among millions of possible parameterization scheme combinations.This study applied a simple genetic algorithm(SGA)to optimizing the combination of parameterization schemes in NWP models for typhoon forecasting.The feasibility of SGA was verified with the simulation of Typhoon Mujigae(2015)by using the Weather Research and Forecasting(WRF)model and Typhoon Higos(2020)by using the Coupled Ocean–Atmosphere–Wave–Sediment Transport(COAWST)modeling system.The results show that SGA can efficiently obtain the optimal combination of schemes.For Typhoon Mujigae(2015),the optimal combination can be found from the 1,304,576 possible combinations by running only 488 trials.Similar results can be obtained for Typhoon Higos(2020).Compared to the default combination proposed by the COAWST model system,the optimal combination scheme significantly improves the simulation of typhoon track and intensity.This study provides a feasible way to search for the optimal combinations of physical parameterization schemes in WRF and COAWST for more accurate typhoon simulation.This can help provide references for future development of NWP models,and for analyzing the coordination and adaptability of different physical process parameterization schemes under specific weather backgrounds.
文摘文章给出了基于改进遗传算法的圆锥滚子轴承优化设计的方法。针对传统算法在圆锥滚子轴承优化设计中全局寻优能力较差问题,文章改进了标准遗传算法(Simple Genetic A lgorithm SGA)的算法参数,采用了最优解保存策略,并运用退火罚函数法[1]处理约束。通过与标准遗传算法及综合约束双下降法比较,表明改进遗传算法具有良好全局寻优能力和稳定性。从而解决了开发圆锥滚子轴承CAD系统的关键问题。
