The CET 4 writing test is widely used in China,but whether it is a more reliable and valid test ofwriting for CET 4 than other methods of evaluation is still not clear.The purpose of this study was to examine the reli...The CET 4 writing test is widely used in China,but whether it is a more reliable and valid test ofwriting for CET 4 than other methods of evaluation is still not clear.The purpose of this study was to examine the reliability and the validity of a modification of this test.In carrying out the study,a total of 60 Band 4 students in Zhejiang University took the modified test,aCET 4 test and a First Certificate Practice Test.The results of this study demonstrate that themodified version is a significantly more reliable and valid writing test than the present CET 4 writingtest.These results suggest that the CET 4 writing test needs modification.展开更多
An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagno...An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagnosis. However, due to the performance differences caused by the tolerance of engine manufacturing and assembly, and performance degradation during continuously stringent environmental regulations, the model accuracy is severely reduced. In this paper, an adaptive modification method of turbofan engine nonlinear Component-Llevel Model(CLM) based on Long Short-Term Memory(LSTM) Neural Network(NN) and hybrid optimization algorithm is pro-posed. First, a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine under health condition. Then, a sensitivity analysis approach based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is developed to choose the unmeasurable health parameters to be adjusted. Finally, a parallel hybrid optimization algorithm is developed to complete the adaptive model modification when the performance degrades. The proposed method is verified on a military low-bypass twin-spool turbofan engine, and the experimental results show the effectiveness of the proposed method.展开更多
文摘The CET 4 writing test is widely used in China,but whether it is a more reliable and valid test ofwriting for CET 4 than other methods of evaluation is still not clear.The purpose of this study was to examine the reliability and the validity of a modification of this test.In carrying out the study,a total of 60 Band 4 students in Zhejiang University took the modified test,aCET 4 test and a First Certificate Practice Test.The results of this study demonstrate that themodified version is a significantly more reliable and valid writing test than the present CET 4 writingtest.These results suggest that the CET 4 writing test needs modification.
基金co-supported by the National Natural Science Foundation of China(Nos.61903061,61903059 and 61890925)Natural Science Foundation of Liaoning Province,China(No.2020-MS-098)+1 种基金Aeronautical Science Foundation of China(No.20200013063001)the Fundamental Research Funds for the Central Universities,China(No.DUT20JC22)。
文摘An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagnosis. However, due to the performance differences caused by the tolerance of engine manufacturing and assembly, and performance degradation during continuously stringent environmental regulations, the model accuracy is severely reduced. In this paper, an adaptive modification method of turbofan engine nonlinear Component-Llevel Model(CLM) based on Long Short-Term Memory(LSTM) Neural Network(NN) and hybrid optimization algorithm is pro-posed. First, a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine under health condition. Then, a sensitivity analysis approach based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is developed to choose the unmeasurable health parameters to be adjusted. Finally, a parallel hybrid optimization algorithm is developed to complete the adaptive model modification when the performance degrades. The proposed method is verified on a military low-bypass twin-spool turbofan engine, and the experimental results show the effectiveness of the proposed method.
