针对传统现场接触式测量获取岩体结构面参数效率低、工作量大、结果精确性受人为因素影响等问题,本文结合数字摄影测量技术与运动法(structure from motion,SFM)进行岩体三维数字表面模型重建,并在此基础上建立了岩体结构面自动识别方...针对传统现场接触式测量获取岩体结构面参数效率低、工作量大、结果精确性受人为因素影响等问题,本文结合数字摄影测量技术与运动法(structure from motion,SFM)进行岩体三维数字表面模型重建,并在此基础上建立了岩体结构面自动识别方法。岩体数字表面模型重建步骤主要为岩体影像资料采集,基于尺度不变特征变换(Scale-Invariant Feature Transform,SIFT)算法进行图像特征匹配、稀疏点云构建、点云稠密化以及岩体曲面模型重构。结构面识别方法流程主要为:首先平滑岩体数字表面模型;通过改变搜索半径和角度阈值实现模型平面分割;基于区域生长原理进行结构面搜索;最后基于随机采样一致性拟合结构面得到结构面产状。将该方法应用于甘肃北山地下实验巷道,实现了巷道三维数字表面模型的重建与结构面产状数据获取,最后将识别到的结构面分组表征在模型表面。与人工实地测量方法以及现有的结构面识别软件相比,本文提出的方法具有良好的准确性,可为工程应用提供一定的参考。展开更多
Partial epilepsy is characterized by recurrent seizures that arise from a localized pathological brain region. During the onset of partial epilepsy, the seizure evolution commonly exhibits typical timescale separation...Partial epilepsy is characterized by recurrent seizures that arise from a localized pathological brain region. During the onset of partial epilepsy, the seizure evolution commonly exhibits typical timescale separation phenomenon. This timescale separation behavior can be mimicked by a paradigmatic model termed as Epileptor, which consists of coupled fast-slow neural populations via a permittivity variable. By incorporating permittivity noise into the Epileptor model, we show here that stochastic fluctuations of permittivity coupling participate in the modulation of seizure dynamics in partial epilepsy. In particular, introducing a certain level of permittivity noise can make the model produce more comparable seizure-like events that capture the temporal variability in realistic partial seizures. Furthermore, we observe that with the help of permittivity noise our stochastic Epileptor model can trigger the seizure dynamics even when it operates in the theoretical nonepileptogenic regime. These findings establish a deep mechanistic understanding on how stochastic fluctuations of permittivity coupling shape the seizure dynamics in partial epilepsy,and provide insightful biological implications.展开更多
文摘针对传统现场接触式测量获取岩体结构面参数效率低、工作量大、结果精确性受人为因素影响等问题,本文结合数字摄影测量技术与运动法(structure from motion,SFM)进行岩体三维数字表面模型重建,并在此基础上建立了岩体结构面自动识别方法。岩体数字表面模型重建步骤主要为岩体影像资料采集,基于尺度不变特征变换(Scale-Invariant Feature Transform,SIFT)算法进行图像特征匹配、稀疏点云构建、点云稠密化以及岩体曲面模型重构。结构面识别方法流程主要为:首先平滑岩体数字表面模型;通过改变搜索半径和角度阈值实现模型平面分割;基于区域生长原理进行结构面搜索;最后基于随机采样一致性拟合结构面得到结构面产状。将该方法应用于甘肃北山地下实验巷道,实现了巷道三维数字表面模型的重建与结构面产状数据获取,最后将识别到的结构面分组表征在模型表面。与人工实地测量方法以及现有的结构面识别软件相比,本文提出的方法具有良好的准确性,可为工程应用提供一定的参考。
基金supported by the National Natural Science Foundation of China(Grant Nos.81571770,61527815,81371636 and 81330032)
文摘Partial epilepsy is characterized by recurrent seizures that arise from a localized pathological brain region. During the onset of partial epilepsy, the seizure evolution commonly exhibits typical timescale separation phenomenon. This timescale separation behavior can be mimicked by a paradigmatic model termed as Epileptor, which consists of coupled fast-slow neural populations via a permittivity variable. By incorporating permittivity noise into the Epileptor model, we show here that stochastic fluctuations of permittivity coupling participate in the modulation of seizure dynamics in partial epilepsy. In particular, introducing a certain level of permittivity noise can make the model produce more comparable seizure-like events that capture the temporal variability in realistic partial seizures. Furthermore, we observe that with the help of permittivity noise our stochastic Epileptor model can trigger the seizure dynamics even when it operates in the theoretical nonepileptogenic regime. These findings establish a deep mechanistic understanding on how stochastic fluctuations of permittivity coupling shape the seizure dynamics in partial epilepsy,and provide insightful biological implications.
