详细介绍了一种新的机器学习的方法——流形学习。流形学习是一种新的非监督学习方法,可以有效地发现高维非线性数据集的内在维数并进行维数约简,近年来越来越受到机器学习和认知科学领域的研究者的重视。目前已经出现了很多有效的流形...详细介绍了一种新的机器学习的方法——流形学习。流形学习是一种新的非监督学习方法,可以有效地发现高维非线性数据集的内在维数并进行维数约简,近年来越来越受到机器学习和认知科学领域的研究者的重视。目前已经出现了很多有效的流形学习算法,如等度规映射(ISOMAP)、局部线性嵌套(Locally Linear Embedding,LLE)等。详细讲述了当前常用的几种流形学习算法以及在流形方面已经取得的研究成果,并对流形学习目前在各方面的应用作了较为细致的阐述。最后展望了流形学习的研究发展趋势,且提出了流形学习中仍需解决的关键问题。展开更多
The physical features exhibited by Hermite-Gaussian （HC） beams propagating in nonlocal nonlinear media with Gaussian-shaped response are discussed with an approximate variational method.Using direct numerical simula...The physical features exhibited by Hermite-Gaussian （HC） beams propagating in nonlocal nonlinear media with Gaussian-shaped response are discussed with an approximate variational method.Using direct numerical simulations,we find that the beam properties in the normalized system are different with the change of the degree of nonlocality.It is shown that initial HG profiles break up into several individual beams with propagation when the degree of nonlocality α is small.HG beams can propagate stably when a is large enough.展开更多
Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit tasks.Benefiting from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yie...Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit tasks.Benefiting from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yielded impressive achievements for spacecraft pose estimation.To improve the robustness and interpretability of CNNs,this paper proposes a Pose Estimation approach based on Variational Auto-Encoder structure(PE-VAE)and a Feature-Aided pose estimation approach based on Variational Auto-Encoder structure(FA-VAE),which aim to accurately estimate the 6 DoF pose of a target spacecraft.Both methods treat the pose vector as latent variables,employing an encoder-decoder network with a Variational Auto-Encoder(VAE)structure.To enhance the precision of pose estimation,PE-VAE uses the VAE structure to introduce reconstruction mechanism with the whole image.Furthermore,FA-VAE enforces feature shape constraints by exclusively reconstructing the segment of the target spacecraft with the desired shape.Comparative evaluation against leading methods on public datasets reveals similar accuracy with a threefold improvement in processing speed,showcasing the significant contribution of VAE structures to accuracy enhancement,and the additional benefit of incorporating global shape prior features.展开更多
文摘详细介绍了一种新的机器学习的方法——流形学习。流形学习是一种新的非监督学习方法,可以有效地发现高维非线性数据集的内在维数并进行维数约简,近年来越来越受到机器学习和认知科学领域的研究者的重视。目前已经出现了很多有效的流形学习算法,如等度规映射(ISOMAP)、局部线性嵌套(Locally Linear Embedding,LLE)等。详细讲述了当前常用的几种流形学习算法以及在流形方面已经取得的研究成果,并对流形学习目前在各方面的应用作了较为细致的阐述。最后展望了流形学习的研究发展趋势,且提出了流形学习中仍需解决的关键问题。
文摘The physical features exhibited by Hermite-Gaussian （HC） beams propagating in nonlocal nonlinear media with Gaussian-shaped response are discussed with an approximate variational method.Using direct numerical simulations,we find that the beam properties in the normalized system are different with the change of the degree of nonlocality.It is shown that initial HG profiles break up into several individual beams with propagation when the degree of nonlocality α is small.HG beams can propagate stably when a is large enough.
基金supported by the National Natural Science Foundation of China(No.52272390)the Natural Science Foundation of Heilongjiang Province of China(No.YQ2022A009)the Shanghai Sailing Program,China(No.20YF1417300).
文摘Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit tasks.Benefiting from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yielded impressive achievements for spacecraft pose estimation.To improve the robustness and interpretability of CNNs,this paper proposes a Pose Estimation approach based on Variational Auto-Encoder structure(PE-VAE)and a Feature-Aided pose estimation approach based on Variational Auto-Encoder structure(FA-VAE),which aim to accurately estimate the 6 DoF pose of a target spacecraft.Both methods treat the pose vector as latent variables,employing an encoder-decoder network with a Variational Auto-Encoder(VAE)structure.To enhance the precision of pose estimation,PE-VAE uses the VAE structure to introduce reconstruction mechanism with the whole image.Furthermore,FA-VAE enforces feature shape constraints by exclusively reconstructing the segment of the target spacecraft with the desired shape.Comparative evaluation against leading methods on public datasets reveals similar accuracy with a threefold improvement in processing speed,showcasing the significant contribution of VAE structures to accuracy enhancement,and the additional benefit of incorporating global shape prior features.
