We report two models of the lateral displacement of acoustic-wave scattering on a fluid-solid interface that reveal an acoustic analog of the Goos-Hainchen effect in optics. This acoustic analog is called the acoustic...We report two models of the lateral displacement of acoustic-wave scattering on a fluid-solid interface that reveal an acoustic analog of the Goos-Hainchen effect in optics. This acoustic analog is called the acoustic Goos-Hainchen effect. Using newly proposed models, we made numerical calculations for the system ofa water-Perspex interface. Specifically, in the post-critical-angle region, we observed a lateral displacement （and transition time） of the reflected P-wave with respect to the incident P-wave. The first arrival of the acoustic signal from the interface is found to be a reflected P-wave rather than the sliding-refraction P-wave usually described in traditional acoustic-logging sliding P-wave theory. For both proposed models, the effective propagation speed of the reflected P-wave along the interface depends on not only the physical properties of the interracial media but also the incident angle. These observations are intriguing and warrant further investigation.展开更多
Two-stage hybrid flow shop scheduling has been extensively considered in single-factory settings.However,the distributed two-stage hybrid flow shop scheduling problem(DTHFSP)with fuzzy processing time is seldom invest...Two-stage hybrid flow shop scheduling has been extensively considered in single-factory settings.However,the distributed two-stage hybrid flow shop scheduling problem(DTHFSP)with fuzzy processing time is seldom investigated in multiple factories.Furthermore,the integration of reinforcement learning and metaheuristic is seldom applied to solve DTHFSP.In the current study,DTHFSP with fuzzy processing time was investigated,and a novel Q-learning-based teaching-learning based optimization(QTLBO)was constructed to minimize makespan.Several teachers were recruited for this study.The teacher phase,learner phase,teacher’s self-learning phase,and learner’s self-learning phase were designed.The Q-learning algorithm was implemented by 9 states,4 actions defined as combinations of the above phases,a reward,and an adaptive action selection,which were applied to dynamically adjust the algorithm structure.A number of experiments were conducted.The computational results demonstrate that the new strategies of QTLBO are effective;furthermore,it presents promising results on the considered DTHFSP.展开更多
基金the Xi’an University of Posts and Telecommunicationsthe Physical Sciences Division at the University of Chicagothe Scientific Research Program(Grant No.15JK1685)of the Shaanxi Provincial Education Department
文摘We report two models of the lateral displacement of acoustic-wave scattering on a fluid-solid interface that reveal an acoustic analog of the Goos-Hainchen effect in optics. This acoustic analog is called the acoustic Goos-Hainchen effect. Using newly proposed models, we made numerical calculations for the system ofa water-Perspex interface. Specifically, in the post-critical-angle region, we observed a lateral displacement （and transition time） of the reflected P-wave with respect to the incident P-wave. The first arrival of the acoustic signal from the interface is found to be a reflected P-wave rather than the sliding-refraction P-wave usually described in traditional acoustic-logging sliding P-wave theory. For both proposed models, the effective propagation speed of the reflected P-wave along the interface depends on not only the physical properties of the interracial media but also the incident angle. These observations are intriguing and warrant further investigation.
文摘Two-stage hybrid flow shop scheduling has been extensively considered in single-factory settings.However,the distributed two-stage hybrid flow shop scheduling problem(DTHFSP)with fuzzy processing time is seldom investigated in multiple factories.Furthermore,the integration of reinforcement learning and metaheuristic is seldom applied to solve DTHFSP.In the current study,DTHFSP with fuzzy processing time was investigated,and a novel Q-learning-based teaching-learning based optimization(QTLBO)was constructed to minimize makespan.Several teachers were recruited for this study.The teacher phase,learner phase,teacher’s self-learning phase,and learner’s self-learning phase were designed.The Q-learning algorithm was implemented by 9 states,4 actions defined as combinations of the above phases,a reward,and an adaptive action selection,which were applied to dynamically adjust the algorithm structure.A number of experiments were conducted.The computational results demonstrate that the new strategies of QTLBO are effective;furthermore,it presents promising results on the considered DTHFSP.
