数据驱动的异常检测技术被广泛应用于复杂机械设备状态监测中,工况(operating conditions,简称OCs)变化会导致监测数据的分布漂移,使传统数据驱动的异常检测方法的准确性受到极大干扰。为了解决时变工况下工况和健康状态之间的耦合问题...数据驱动的异常检测技术被广泛应用于复杂机械设备状态监测中,工况(operating conditions,简称OCs)变化会导致监测数据的分布漂移,使传统数据驱动的异常检测方法的准确性受到极大干扰。为了解决时变工况下工况和健康状态之间的耦合问题,提出了一个新的特征解耦学习框架。首先,基于变分自动编码器(variation auto encoder,简称VAE)构建一个特征解耦条件变分自动编码器(conditional variation auto encoder,简称CVAE)网络,实现工况和健康状态的解耦;其次,对解耦后的健康状态相关特征进行降维处理,构建异常指标(anomaly indicator,简称ANI);然后,将ANI与统计异常阈值相结合,实现时变工况下轴承的异常检测;最后,通过基于时变转速退化的轴承加速疲劳退化实验,验证了该方法的有效性以及所构建的健康指标在消除时变工况干扰方面的优越性。展开更多
Data-driven garment animation is a current topic of interest in the computer graphics industry.Existing approaches generally establish the mapping between a single human pose or a temporal pose sequence,and garment de...Data-driven garment animation is a current topic of interest in the computer graphics industry.Existing approaches generally establish the mapping between a single human pose or a temporal pose sequence,and garment deformation,but it is difficult to quickly generate diverse clothed human animations.We address this problem with a method to automatically synthesize dressed human animations with temporal consistency from a specified human motion label.At the heart of our method is a twostage strategy.Specifically,we first learn a latent space encoding the sequence-level distribution of human motions utilizing a transformer-based conditional variational autoencoder(Transformer-CVAE).Then a garment simulator synthesizes dynamic garment shapes using a transformer encoder-decoder architecture.Since the learned latent space comes from varied human motions,our method can generate a variety of styles of motions given a specific motion label.By means of a novel beginning of sequence(BOS)learning strategy and a self-supervised refinement procedure,our garment simulator is capable of efficiently synthesizing garment deformation sequences corresponding to the generated human motions while maintaining temporal and spatial consistency.We verify our ideas experimentally.This is the first generative model that directly dresses human animation.展开更多
Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper,a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learnin...Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper,a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learning.A Conditional Variational Auto Encoder(CVAE)and an integrated generative network CVAE-GAN that combines the CVAE with the Wasserstein Generative Adversarial Networks(WGAN),are conducted as generative models.They are used to generate target wall Mach distributions for the inverse design that matches specified features,such as locations of suction peak,shock and aft loading.Qualitative and quantitative results show that both adopted generative models can generate diverse and realistic wall Mach number distributions satisfying the given features.The CVAE-GAN model outperforms the CVAE model and achieves better reconstruction accuracies for all the samples in the dataset.Furthermore,a deep neural network for nonlinear mapping is adopted to obtain the airfoil shape corresponding to the target wall Mach number distribution.The performances of the designed deep neural network are fully demonstrated and a smoothness measurement is proposed to quantify small oscillations in the airfoil surface,proving the authenticity and accuracy of the generated airfoil shapes.展开更多
文摘数据驱动的异常检测技术被广泛应用于复杂机械设备状态监测中,工况(operating conditions,简称OCs)变化会导致监测数据的分布漂移,使传统数据驱动的异常检测方法的准确性受到极大干扰。为了解决时变工况下工况和健康状态之间的耦合问题,提出了一个新的特征解耦学习框架。首先,基于变分自动编码器(variation auto encoder,简称VAE)构建一个特征解耦条件变分自动编码器(conditional variation auto encoder,简称CVAE)网络,实现工况和健康状态的解耦;其次,对解耦后的健康状态相关特征进行降维处理,构建异常指标(anomaly indicator,简称ANI);然后,将ANI与统计异常阈值相结合,实现时变工况下轴承的异常检测;最后,通过基于时变转速退化的轴承加速疲劳退化实验,验证了该方法的有效性以及所构建的健康指标在消除时变工况干扰方面的优越性。
基金supported by the National Natural Science Foundation of China(Grant No.61972379).
文摘Data-driven garment animation is a current topic of interest in the computer graphics industry.Existing approaches generally establish the mapping between a single human pose or a temporal pose sequence,and garment deformation,but it is difficult to quickly generate diverse clothed human animations.We address this problem with a method to automatically synthesize dressed human animations with temporal consistency from a specified human motion label.At the heart of our method is a twostage strategy.Specifically,we first learn a latent space encoding the sequence-level distribution of human motions utilizing a transformer-based conditional variational autoencoder(Transformer-CVAE).Then a garment simulator synthesizes dynamic garment shapes using a transformer encoder-decoder architecture.Since the learned latent space comes from varied human motions,our method can generate a variety of styles of motions given a specific motion label.By means of a novel beginning of sequence(BOS)learning strategy and a self-supervised refinement procedure,our garment simulator is capable of efficiently synthesizing garment deformation sequences corresponding to the generated human motions while maintaining temporal and spatial consistency.We verify our ideas experimentally.This is the first generative model that directly dresses human animation.
基金co-supported by the National Key Project of China(No.GJXM92579)the National Natural Science Foundation of China(Nos.92052203,61903178 and61906081)。
文摘Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper,a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learning.A Conditional Variational Auto Encoder(CVAE)and an integrated generative network CVAE-GAN that combines the CVAE with the Wasserstein Generative Adversarial Networks(WGAN),are conducted as generative models.They are used to generate target wall Mach distributions for the inverse design that matches specified features,such as locations of suction peak,shock and aft loading.Qualitative and quantitative results show that both adopted generative models can generate diverse and realistic wall Mach number distributions satisfying the given features.The CVAE-GAN model outperforms the CVAE model and achieves better reconstruction accuracies for all the samples in the dataset.Furthermore,a deep neural network for nonlinear mapping is adopted to obtain the airfoil shape corresponding to the target wall Mach number distribution.The performances of the designed deep neural network are fully demonstrated and a smoothness measurement is proposed to quantify small oscillations in the airfoil surface,proving the authenticity and accuracy of the generated airfoil shapes.
