In order to correct the test error caused by the dynamic characteristics of pressure sensor and avoid the influence of the error of sensor＇s dynamic model on compensation results,a dynamic compensation method of the ...In order to correct the test error caused by the dynamic characteristics of pressure sensor and avoid the influence of the error of sensor＇s dynamic model on compensation results,a dynamic compensation method of the pressure sensor is presented,which is based on quantum-behaved particle swarm optimization（QPSO）algorithm and the mean square error（MSE）.By using this method,the inverse model of the sensor is built and optimized and then the coefficients of the optimal compensator are got.This method is verified by the dynamic calibration with shock tube and the dynamic characteristics of the sensor before and after compensation are analyzed in time domain and frequency domain.The results show that the working bandwidth of the sensor is extended effectively.This method can reduce dynamic measuring error and improve test accuracy in actual measurement experiments.展开更多
We investigate the decay of a_1^+(1260) →π^+π^+π^-with the assumption that the a_1(1260) is dynamically generated from the coupled channel ρπ and KK~*interactions. In addition to the tree level diagrams that pro...We investigate the decay of a_1^+(1260) →π^+π^+π^-with the assumption that the a_1(1260) is dynamically generated from the coupled channel ρπ and KK~*interactions. In addition to the tree level diagrams that proceed via a_1^+(1260) →ρ~0π^+→π^+π^+π^-, we take into account also the final state interactions of ππ→ππ and KK →ππ. We calculate the invariant π^+π^-mass distribution and also the total decay width of a_1^+(1260) →π^+π^+π^-as a function of the mass of a_1(1260). The calculated total decay width of a_1(1260) is significantly different from other model calculations and tied to the dynamical nature of the a_1(1260) resonance. The future experimental observations could test of model calculations and would provide vary valuable information on the relevance of the ρπ component in the a_1(1260) wave function.展开更多
基金The 11th Postgraduate Technology Innovation Project of North University of China(No.20141147)
文摘In order to correct the test error caused by the dynamic characteristics of pressure sensor and avoid the influence of the error of sensor＇s dynamic model on compensation results,a dynamic compensation method of the pressure sensor is presented,which is based on quantum-behaved particle swarm optimization（QPSO）algorithm and the mean square error（MSE）.By using this method,the inverse model of the sensor is built and optimized and then the coefficients of the optimal compensator are got.This method is verified by the dynamic calibration with shock tube and the dynamic characteristics of the sensor before and after compensation are analyzed in time domain and frequency domain.The results show that the working bandwidth of the sensor is extended effectively.This method can reduce dynamic measuring error and improve test accuracy in actual measurement experiments.
基金Supported by the National Natural Science Foundation of China under Grant Nos.11475227 and 11735003supported by the Youth Innovation Promotion Association CAS(No.2016367)
文摘We investigate the decay of a_1^+(1260) →π^+π^+π^-with the assumption that the a_1(1260) is dynamically generated from the coupled channel ρπ and KK~*interactions. In addition to the tree level diagrams that proceed via a_1^+(1260) →ρ~0π^+→π^+π^+π^-, we take into account also the final state interactions of ππ→ππ and KK →ππ. We calculate the invariant π^+π^-mass distribution and also the total decay width of a_1^+(1260) →π^+π^+π^-as a function of the mass of a_1(1260). The calculated total decay width of a_1(1260) is significantly different from other model calculations and tied to the dynamical nature of the a_1(1260) resonance. The future experimental observations could test of model calculations and would provide vary valuable information on the relevance of the ρπ component in the a_1(1260) wave function.
