The method of radiation energy （ER） of the earthquake wave measured by the peak velocity （r·v） of the ground motion is applied to a detailed study on the temporal and spatial distribution of the seismic appar...The method of radiation energy （ER） of the earthquake wave measured by the peak velocity （r·v） of the ground motion is applied to a detailed study on the temporal and spatial distribution of the seismic apparent stresses （σa） for the moderate and small earthquakes and two aftershock sequences in Yunnan area. The results show that there exists an obvious non-homogeneity for the seismic apparent stresses in the spatial distribution. The concentrated regions of the high apparent stresses are related to the active places of the moderate and small earthquakes. Before the Dayao M=6.2 earthquake, there was a period in which the apparent stresses were high and the value was 5 times of the average value, 0.25 MPa. The relatively high values of apparent stresses distribute around the epicentral area of the major shock and nearby. It indicates that the variation characteristics of the apparent stresses can be taken as a new kind of criterion for the earthquake-risk forecast. Usually the ratio of the apparent stresses of the aftershock sequence σaA to the ones σaM of main shock is less than 1.0.展开更多
针对视频情感识别中存在运算复杂度高的缺点,提出一种基于时空局部二值模式矩(Temporal-Spatial Local Binary Pattern Moment,TSLBPM)的双模态情感识别方法。首先对视频进行预处理获得表情和姿态序列;然后对表情和姿态序列分别提取TSL...针对视频情感识别中存在运算复杂度高的缺点,提出一种基于时空局部二值模式矩(Temporal-Spatial Local Binary Pattern Moment,TSLBPM)的双模态情感识别方法。首先对视频进行预处理获得表情和姿态序列;然后对表情和姿态序列分别提取TSLBPM特征,计算测试序列与已标记的情感训练集特征间的最小欧氏距离,并将其作为独立证据来构造基本概率分配(Basic Probability Assignment,BPA);最后使用Dempster-Shafer证据理论联合规则得到情感识别结果。在双模态表情和姿态情感数据库上的实验结果表明,本文提出的时空局部二值模式矩可以快速提取视频图像的时空特征,能有效识别情感状态。与其他方法的对比实验也验证了本文融合方法的优越性。展开更多
Existing learning-based super-resolution （SR） reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of ...Existing learning-based super-resolution （SR） reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of learning-based algorithms to video SR field, a novel video SR reconstruction algorithm based on deep convolutional neural network （CNN） and spatio-temporal similarity （STCNN-SR） was proposed in this paper. It is a deep learning method for video SR reconstruction, which considers not onlv the mapping relationship among associated low-resolution （LR） and high-resolution （HR） image blocks, but also the spatio-temporal non-local complementary and redundant information between adjacent low-resolution video frames. The reconstruction speed can be improved obviously with the pre-trained end-to-end reconstructed coefficients. Moreover, the performance of video SR will be further improved by the optimization process with spatio-temporal similarity. Experimental results demonstrated that the proposed algorithm achieves a competitive SR quality on both subjective and objective evaluations, when compared to other state-of-the-art algorithms.展开更多
基金Key project of Science and Technology from Yunnan Province (NG2001).
文摘The method of radiation energy （ER） of the earthquake wave measured by the peak velocity （r·v） of the ground motion is applied to a detailed study on the temporal and spatial distribution of the seismic apparent stresses （σa） for the moderate and small earthquakes and two aftershock sequences in Yunnan area. The results show that there exists an obvious non-homogeneity for the seismic apparent stresses in the spatial distribution. The concentrated regions of the high apparent stresses are related to the active places of the moderate and small earthquakes. Before the Dayao M=6.2 earthquake, there was a period in which the apparent stresses were high and the value was 5 times of the average value, 0.25 MPa. The relatively high values of apparent stresses distribute around the epicentral area of the major shock and nearby. It indicates that the variation characteristics of the apparent stresses can be taken as a new kind of criterion for the earthquake-risk forecast. Usually the ratio of the apparent stresses of the aftershock sequence σaA to the ones σaM of main shock is less than 1.0.
基金Projects(52022053, 52009073) supported by the National Natural Science Foundation of ChinaProject(BK20220987) supported by the Natural Science Foundation of Jiangsu Province,China+1 种基金Project(ZR201910270116) supported by the Natural Science Foundation of Shandong Province,ChinaProject(2022ZB189) supported by the Jiangsu Funding Program for Excellent Postdoctoral Talent,China。
文摘针对视频情感识别中存在运算复杂度高的缺点,提出一种基于时空局部二值模式矩(Temporal-Spatial Local Binary Pattern Moment,TSLBPM)的双模态情感识别方法。首先对视频进行预处理获得表情和姿态序列;然后对表情和姿态序列分别提取TSLBPM特征,计算测试序列与已标记的情感训练集特征间的最小欧氏距离,并将其作为独立证据来构造基本概率分配(Basic Probability Assignment,BPA);最后使用Dempster-Shafer证据理论联合规则得到情感识别结果。在双模态表情和姿态情感数据库上的实验结果表明,本文提出的时空局部二值模式矩可以快速提取视频图像的时空特征,能有效识别情感状态。与其他方法的对比实验也验证了本文融合方法的优越性。
基金supported by the National Natural Science Foundation of China (61320106006, 61532006, 61502042)
文摘Existing learning-based super-resolution （SR） reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of learning-based algorithms to video SR field, a novel video SR reconstruction algorithm based on deep convolutional neural network （CNN） and spatio-temporal similarity （STCNN-SR） was proposed in this paper. It is a deep learning method for video SR reconstruction, which considers not onlv the mapping relationship among associated low-resolution （LR） and high-resolution （HR） image blocks, but also the spatio-temporal non-local complementary and redundant information between adjacent low-resolution video frames. The reconstruction speed can be improved obviously with the pre-trained end-to-end reconstructed coefficients. Moreover, the performance of video SR will be further improved by the optimization process with spatio-temporal similarity. Experimental results demonstrated that the proposed algorithm achieves a competitive SR quality on both subjective and objective evaluations, when compared to other state-of-the-art algorithms.
