Using the signals excited by the large-volume airgun source at the Binchuan transmitting seismic station from January to June,2016,arrival-time data was acquired at four stations near the epicenter of the Eryuan MS4.5...Using the signals excited by the large-volume airgun source at the Binchuan transmitting seismic station from January to June,2016,arrival-time data was acquired at four stations near the epicenter of the Eryuan MS4.5 and MS4.0 earthquakes on February 8,2016,as well as the epicenter of the Yunlong MS5.0 and Eryuan MS4.6 earthquakes on May 18,2016 through the waveform cross-correlation technique.The wave velocity ratio of the four stations was calculated using the single-station method.At the same time,the b-value and the focal mechanism consistency parameters of the study area were also calculated.The results show that:(1)the wave velocity ratio of each station experienced a process of decline-recovery-fast rise before the two strong earthquakes,and a significant quasi-synchronous rapid rise occurred within 3-12 days before the earthquake;(2)the timing of the rapid rise of the wave velocity ratio of the four stations before the Yunlong MS5.0 and Eryuan MS4.6 earthquakes were related to the epicentral distance.The station which observed the earliest increase in rapid rise is the farthest one from the epicenter,and the station where the rapid rise appeared in the latest is closest to the epicenter;(3)the form of change of the wave velocity ratio before the earthquake was different between stations located at different directions in the epicenter area;(4)the b-value and the focal mechanism consistency parameter which is commonly used to characterize the stress level both showed a downward trend before the two strong earthquakes,and were consistent with the change in the wave velocity ratio.According to the preliminary analysis,the wave velocity ratio obtained by using airgun source can better reflect the change in the stress state of the underground medium.展开更多
近年来,无人机因体积小、灵活性好等优势被广泛应用在车辆跟踪领域。当无人机在高空飞行时,其捕捉的图像中车辆目标存在像素点少、拥挤以及被遮挡的情况。现有的多目标跟踪研究方法在车辆被遮挡过程中发生非线性运动时,使用卡尔曼滤波预...近年来,无人机因体积小、灵活性好等优势被广泛应用在车辆跟踪领域。当无人机在高空飞行时,其捕捉的图像中车辆目标存在像素点少、拥挤以及被遮挡的情况。现有的多目标跟踪研究方法在车辆被遮挡过程中发生非线性运动时,使用卡尔曼滤波预测,会出现车辆位置预测不准确的问题。为了解决这些问题,采用先检测后跟踪(tracking by detection,TBD)范式,对YOLOv8检测算法进行改进,在网络结构中引入了BiFormer稀疏动态注意力模块,用于提取小目标特征信息。同时使用轻量级上采样算子CARAFE替换原最近邻插值上采样,减少上采样过程中小目标特征丢失的问题。提出一种轻量化跟踪模型FA-SORT,针对SORT算法提出三点改进:改进KF、添加速度方向一致性匹配和检测值匹配。在自制地组合了多个车辆数据集上验证改进的YOLOv8算法。实验结果表明,与YOLOv8相比,精确率(precision)提高了0.97%,召回率(recall)提高了0.898%。对所提出的FA-SORT算法使用UAVDT数据集进行验证,结果表明,与现有的多目标跟踪算法相比,HOTA指标首个达到70.05%,IDF1达到87.45%,跟踪速度达到29.93 FPS。验证了FA-SORT跟踪算法在多车辆跟踪任务中的优越性。展开更多
针对脉搏波波速法无创血压测量中血压计算模型建模困难和模型计算精度较低的问题,结合TPTT、ln(TPTT)及(1/TPTT)2等模量建立多模量血压计算模型。首先,利用99名随机测试者的实验数据确定多模量模型参数,并基于实验数据计算各模型性能评...针对脉搏波波速法无创血压测量中血压计算模型建模困难和模型计算精度较低的问题,结合TPTT、ln(TPTT)及(1/TPTT)2等模量建立多模量血压计算模型。首先,利用99名随机测试者的实验数据确定多模量模型参数,并基于实验数据计算各模型性能评价指标,其中多模量血压计算模型拟合相关系数最大,为0.891,误差方差最小,仅为6.1,实验表明,多模量血压计算模型具有更好的拟合效果和更低的计算误差。然后,利用医用水银血压计和自主设计的多模量血压测量系统两种方法采集另外36名随机测试者的收缩压和舒张压数据,并计算两种方法采集数据间的相关参数,其中收缩压差值的绝对值d<6 mm Hg,差值均值E_d=0.55 mm Hg,差值的标准差δ_d=2.98 mm Hg;舒张压差值的绝对值d<6 mm Hg,差值均值E_d=0.57 mm Hg,差值的标准差δ_d=3.42 mm Hg,完全符合美国医疗仪器促进协会SP10-199中对电子血压计测量差值<8 mm Hg的要求。最后,采用Bland-Altman差值法,对两种方法测量数据一致性进行检验,发现舒张压与收缩压的95%一致性界限分别为(-5.3,6.4)和(7.2,-6.2),完全在临床血压测量可接受范围之内,较好地证明多模量血压计算模型用于无创血压测量的有效性。研究结果表明,多模量血压计算模型可以应用于脉搏波波速法无创血压测量。展开更多
基金sponsored by the subproject of Relocation of Earthquakes in Yunnan Area under the project of the Major Seismicity Trend in 2019 of Department of Monitoring and Prediction of CEA,the National Natural Science Foundation of China(41474048,41574059)the Science for Earthquake Resilience of China Earthquake Administration(XH18042Y)
文摘Using the signals excited by the large-volume airgun source at the Binchuan transmitting seismic station from January to June,2016,arrival-time data was acquired at four stations near the epicenter of the Eryuan MS4.5 and MS4.0 earthquakes on February 8,2016,as well as the epicenter of the Yunlong MS5.0 and Eryuan MS4.6 earthquakes on May 18,2016 through the waveform cross-correlation technique.The wave velocity ratio of the four stations was calculated using the single-station method.At the same time,the b-value and the focal mechanism consistency parameters of the study area were also calculated.The results show that:(1)the wave velocity ratio of each station experienced a process of decline-recovery-fast rise before the two strong earthquakes,and a significant quasi-synchronous rapid rise occurred within 3-12 days before the earthquake;(2)the timing of the rapid rise of the wave velocity ratio of the four stations before the Yunlong MS5.0 and Eryuan MS4.6 earthquakes were related to the epicentral distance.The station which observed the earliest increase in rapid rise is the farthest one from the epicenter,and the station where the rapid rise appeared in the latest is closest to the epicenter;(3)the form of change of the wave velocity ratio before the earthquake was different between stations located at different directions in the epicenter area;(4)the b-value and the focal mechanism consistency parameter which is commonly used to characterize the stress level both showed a downward trend before the two strong earthquakes,and were consistent with the change in the wave velocity ratio.According to the preliminary analysis,the wave velocity ratio obtained by using airgun source can better reflect the change in the stress state of the underground medium.
文摘近年来,无人机因体积小、灵活性好等优势被广泛应用在车辆跟踪领域。当无人机在高空飞行时,其捕捉的图像中车辆目标存在像素点少、拥挤以及被遮挡的情况。现有的多目标跟踪研究方法在车辆被遮挡过程中发生非线性运动时,使用卡尔曼滤波预测,会出现车辆位置预测不准确的问题。为了解决这些问题,采用先检测后跟踪(tracking by detection,TBD)范式,对YOLOv8检测算法进行改进,在网络结构中引入了BiFormer稀疏动态注意力模块,用于提取小目标特征信息。同时使用轻量级上采样算子CARAFE替换原最近邻插值上采样,减少上采样过程中小目标特征丢失的问题。提出一种轻量化跟踪模型FA-SORT,针对SORT算法提出三点改进:改进KF、添加速度方向一致性匹配和检测值匹配。在自制地组合了多个车辆数据集上验证改进的YOLOv8算法。实验结果表明,与YOLOv8相比,精确率(precision)提高了0.97%,召回率(recall)提高了0.898%。对所提出的FA-SORT算法使用UAVDT数据集进行验证,结果表明,与现有的多目标跟踪算法相比,HOTA指标首个达到70.05%,IDF1达到87.45%,跟踪速度达到29.93 FPS。验证了FA-SORT跟踪算法在多车辆跟踪任务中的优越性。
文摘针对脉搏波波速法无创血压测量中血压计算模型建模困难和模型计算精度较低的问题,结合TPTT、ln(TPTT)及(1/TPTT)2等模量建立多模量血压计算模型。首先,利用99名随机测试者的实验数据确定多模量模型参数,并基于实验数据计算各模型性能评价指标,其中多模量血压计算模型拟合相关系数最大,为0.891,误差方差最小,仅为6.1,实验表明,多模量血压计算模型具有更好的拟合效果和更低的计算误差。然后,利用医用水银血压计和自主设计的多模量血压测量系统两种方法采集另外36名随机测试者的收缩压和舒张压数据,并计算两种方法采集数据间的相关参数,其中收缩压差值的绝对值d<6 mm Hg,差值均值E_d=0.55 mm Hg,差值的标准差δ_d=2.98 mm Hg;舒张压差值的绝对值d<6 mm Hg,差值均值E_d=0.57 mm Hg,差值的标准差δ_d=3.42 mm Hg,完全符合美国医疗仪器促进协会SP10-199中对电子血压计测量差值<8 mm Hg的要求。最后,采用Bland-Altman差值法,对两种方法测量数据一致性进行检验,发现舒张压与收缩压的95%一致性界限分别为(-5.3,6.4)和(7.2,-6.2),完全在临床血压测量可接受范围之内,较好地证明多模量血压计算模型用于无创血压测量的有效性。研究结果表明,多模量血压计算模型可以应用于脉搏波波速法无创血压测量。
