<div style="text-align:justify;"> We developed a Bessel-beam photoacoustic microscopical simulation platform by using the k-Wave: MATLAB toolbox. The simulation platform uses the ring slit method to ge...<div style="text-align:justify;"> We developed a Bessel-beam photoacoustic microscopical simulation platform by using the k-Wave: MATLAB toolbox. The simulation platform uses the ring slit method to generate Bessel beam. By controlling the inner and outer radius of the ring slit, the depth-of-field (DoF) of Bessel beam can be controlled. And the large volumetric image is obtained by point scanning. The simulation experiments on blood vessels were carried out to demonstrate the feasibility of the simulation platform. This simulation work can be used as an auxiliary tool for the research of Bessel-beam photoacoustic microscopy. </div>展开更多
为解决压电波动法在检测混凝土结构时面临的源信号信噪比低、背景噪声大与非平稳性问题,提出了一种K奇异值分解(K-means singular value decomposition,K-SVD)更新字典的压电信号滤波方法,并对混凝土结构损伤进行了识别。首先,对开裂状...为解决压电波动法在检测混凝土结构时面临的源信号信噪比低、背景噪声大与非平稳性问题,提出了一种K奇异值分解(K-means singular value decomposition,K-SVD)更新字典的压电信号滤波方法,并对混凝土结构损伤进行了识别。首先,对开裂状态与完整状态下混凝土结构的压电信号进行采集,并将采集的信号进行分类处理;其次,对上述采集的压电信号进行滤波处理,并对K-SVD字典学习滤波结果与未滤波结果进行对比分析,评价K-SVD字典学习滤波方法的适用性;最后,利用残差卷积神经网络(residual network,ResNet)对滤波后的压电信号进行分类识别。结果表明:利用基于K-SVD字典学习与ResNet模型,能够稳定地识别混凝土结构内部损伤的压电信号;训练集与测试集的损伤信号识别准确率分别为93.25%与92.38%,无损信号的识别准确率分别为95.41%与94.67%,相较于未滤波的采集信号,其准确率提升了10个百分点以上;利用K-SVD字典学习与ResNet对混凝土结构损伤进行有效识别,实现了对混凝土结构内部损伤区域的定位。研究结果可为混凝土结构健康监测的数据处理提供一种新的思路。展开更多
文摘<div style="text-align:justify;"> We developed a Bessel-beam photoacoustic microscopical simulation platform by using the k-Wave: MATLAB toolbox. The simulation platform uses the ring slit method to generate Bessel beam. By controlling the inner and outer radius of the ring slit, the depth-of-field (DoF) of Bessel beam can be controlled. And the large volumetric image is obtained by point scanning. The simulation experiments on blood vessels were carried out to demonstrate the feasibility of the simulation platform. This simulation work can be used as an auxiliary tool for the research of Bessel-beam photoacoustic microscopy. </div>
文摘为解决压电波动法在检测混凝土结构时面临的源信号信噪比低、背景噪声大与非平稳性问题,提出了一种K奇异值分解(K-means singular value decomposition,K-SVD)更新字典的压电信号滤波方法,并对混凝土结构损伤进行了识别。首先,对开裂状态与完整状态下混凝土结构的压电信号进行采集,并将采集的信号进行分类处理;其次,对上述采集的压电信号进行滤波处理,并对K-SVD字典学习滤波结果与未滤波结果进行对比分析,评价K-SVD字典学习滤波方法的适用性;最后,利用残差卷积神经网络(residual network,ResNet)对滤波后的压电信号进行分类识别。结果表明:利用基于K-SVD字典学习与ResNet模型,能够稳定地识别混凝土结构内部损伤的压电信号;训练集与测试集的损伤信号识别准确率分别为93.25%与92.38%,无损信号的识别准确率分别为95.41%与94.67%,相较于未滤波的采集信号,其准确率提升了10个百分点以上;利用K-SVD字典学习与ResNet对混凝土结构损伤进行有效识别,实现了对混凝土结构内部损伤区域的定位。研究结果可为混凝土结构健康监测的数据处理提供一种新的思路。
