在传统拓扑优化设计中,随着结构单元增加,迭代计算过程消耗了大量的时间。本文提出了一种基于深度学习的方法来加速拓扑优化设计过程,缩短了结构拓扑优化设计的迭代过程,并生成了高分辨率拓扑优化结构。利用深度学习方法,在低分辨率中...在传统拓扑优化设计中,随着结构单元增加,迭代计算过程消耗了大量的时间。本文提出了一种基于深度学习的方法来加速拓扑优化设计过程,缩短了结构拓扑优化设计的迭代过程,并生成了高分辨率拓扑优化结构。利用深度学习方法,在低分辨率中间构型与高分辨率拓扑构型之间创建高维映射关系,利用独立、连续和映射(ICM)方法建立深度学习网络所需要的数据集,训练神经网络以实现加速过程,将结构拓扑优化设计问题转化为图像处理中的风格迁移问题。通过引入条件生成对抗式神经网络CGAN(Conditional Generative and Adversarial Network)解决了跨分辨率拓扑优化问题,实验验证了优化过程效率的提高,该方法具有良好的泛化性能,研究模型在其他结构优化设计中具有可推广性。展开更多
High acceleration of radar targets is analyzed using Acceleration Ambiguity Function (AAF). The acceleration resolution based on AAF is defined. The AAF and acceleration resolution of rectangle pulse signal are deriva...High acceleration of radar targets is analyzed using Acceleration Ambiguity Function (AAF). The acceleration resolution based on AAF is defined. The AAF and acceleration resolution of rectangle pulse signal are derivated and the conclusion that its acceleration resolution is in inverse proportion with the square of its duration is drawn. In the end, these conclusions are applied to the parameter designing and performance evaluation for a certain type of pulse Doppler radar.展开更多
Seismic wave velocity is one of the most important processing parameters of seismic data,which also determines the accuracy of imaging.The conventional method of velocity analysis involves scanning through a series of...Seismic wave velocity is one of the most important processing parameters of seismic data,which also determines the accuracy of imaging.The conventional method of velocity analysis involves scanning through a series of equal intervals of velocity,producing the velocity spectrum by superposing energy or similarity coefficients.In this method,however,the sensitivity of the semblance spectrum to change of velocity is weak,so the resolution is poor.In this paper,to solve the above deficiencies of conventional velocity analysis,a method for obtaining a high-resolution velocity spectrum based on weighted similarity is proposed.By introducing two weighting functions,the resolution of the similarity spectrum in time and velocity is improved.Numerical examples and real seismic data indicate that the proposed method provides a velocity spectrum with higher resolution than conventional methods and can separate cross reflectors which are aliased in conventional semblance spectrums;at the same time,the method shows good noise-resistibility.展开更多
文摘在传统拓扑优化设计中,随着结构单元增加,迭代计算过程消耗了大量的时间。本文提出了一种基于深度学习的方法来加速拓扑优化设计过程,缩短了结构拓扑优化设计的迭代过程,并生成了高分辨率拓扑优化结构。利用深度学习方法,在低分辨率中间构型与高分辨率拓扑构型之间创建高维映射关系,利用独立、连续和映射(ICM)方法建立深度学习网络所需要的数据集,训练神经网络以实现加速过程,将结构拓扑优化设计问题转化为图像处理中的风格迁移问题。通过引入条件生成对抗式神经网络CGAN(Conditional Generative and Adversarial Network)解决了跨分辨率拓扑优化问题,实验验证了优化过程效率的提高,该方法具有良好的泛化性能,研究模型在其他结构优化设计中具有可推广性。
文摘High acceleration of radar targets is analyzed using Acceleration Ambiguity Function (AAF). The acceleration resolution based on AAF is defined. The AAF and acceleration resolution of rectangle pulse signal are derivated and the conclusion that its acceleration resolution is in inverse proportion with the square of its duration is drawn. In the end, these conclusions are applied to the parameter designing and performance evaluation for a certain type of pulse Doppler radar.
基金funded by the National Key Research and Development Plan (No. 2017YFB0202905)China Petroleum Corporation Technology Management Department “Deep-ultra-deep weak signal enhancement technology based on seismic physical simulation experiments”(No. 2017-5307073-000008-01)。
文摘Seismic wave velocity is one of the most important processing parameters of seismic data,which also determines the accuracy of imaging.The conventional method of velocity analysis involves scanning through a series of equal intervals of velocity,producing the velocity spectrum by superposing energy or similarity coefficients.In this method,however,the sensitivity of the semblance spectrum to change of velocity is weak,so the resolution is poor.In this paper,to solve the above deficiencies of conventional velocity analysis,a method for obtaining a high-resolution velocity spectrum based on weighted similarity is proposed.By introducing two weighting functions,the resolution of the similarity spectrum in time and velocity is improved.Numerical examples and real seismic data indicate that the proposed method provides a velocity spectrum with higher resolution than conventional methods and can separate cross reflectors which are aliased in conventional semblance spectrums;at the same time,the method shows good noise-resistibility.
