For random noise suppression of seismic data, we present a non-local Bayes （NL- Bayes） filtering algorithm. The NL-Bayes algorithm uses the Gaussian model instead of the weighted average of all similar patches in th...For random noise suppression of seismic data, we present a non-local Bayes （NL- Bayes） filtering algorithm. The NL-Bayes algorithm uses the Gaussian model instead of the weighted average of all similar patches in the NL-means algorithm to reduce the fuzzy of structural details, thereby improving the denoising performance. In the denoising process of seismic data, the size and the number of patches in the Gaussian model are adaptively calculated according to the standard deviation of noise. The NL-Bayes algorithm requires two iterations to complete seismic data denoising, but the second iteration makes use of denoised seismic data from the first iteration to calculate the better mean and covariance of the patch Gaussian model for improving the similarity of patches and achieving the purpose of denoising. Tests with synthetic and real data sets demonstrate that the NL-Bayes algorithm can effectively improve the SNR and preserve the fidelity of seismic data.展开更多
A homogeneous and lattice self-reconfigurable robot module is designed, and each module is composed of a center body and six connection planes which can independently rotate. A module can independently connect or disc...A homogeneous and lattice self-reconfigurable robot module is designed, and each module is composed of a center body and six connection planes which can independently rotate. A module can independently connect or disconnect with other modules, and then change its connection by collaborating with other modules. We discuss how to describe and discover configuration of robot. Furthermore, we describe its motion planning based on the appraisal function and the adjacency matrix which is effective to solve the computationally difficult problem and optimize the system motion path during the self-reconfiguration process. Finally, a simulation experiment is demonstrated, which verifies the correctness of locomotion method.展开更多
基金financially sponsored by Research Institute of Petroleum Exploration&Development(PETROCHINA)Scientific Research And Technology Development Projects(No.2016ycq02)China National Petroleum Corporation Science&Technology Development Projects(No.2015B-3712)Ministry of National Science&Technique(No.2016ZX05007-006)
文摘For random noise suppression of seismic data, we present a non-local Bayes （NL- Bayes） filtering algorithm. The NL-Bayes algorithm uses the Gaussian model instead of the weighted average of all similar patches in the NL-means algorithm to reduce the fuzzy of structural details, thereby improving the denoising performance. In the denoising process of seismic data, the size and the number of patches in the Gaussian model are adaptively calculated according to the standard deviation of noise. The NL-Bayes algorithm requires two iterations to complete seismic data denoising, but the second iteration makes use of denoised seismic data from the first iteration to calculate the better mean and covariance of the patch Gaussian model for improving the similarity of patches and achieving the purpose of denoising. Tests with synthetic and real data sets demonstrate that the NL-Bayes algorithm can effectively improve the SNR and preserve the fidelity of seismic data.
文摘A homogeneous and lattice self-reconfigurable robot module is designed, and each module is composed of a center body and six connection planes which can independently rotate. A module can independently connect or disconnect with other modules, and then change its connection by collaborating with other modules. We discuss how to describe and discover configuration of robot. Furthermore, we describe its motion planning based on the appraisal function and the adjacency matrix which is effective to solve the computationally difficult problem and optimize the system motion path during the self-reconfiguration process. Finally, a simulation experiment is demonstrated, which verifies the correctness of locomotion method.
