针对传统最小二乘配置(traditional least squares collocation,TLSC)算法在大尺度区域地壳运动速度场高精度拟合中,在监测站点稀疏区域与块体边缘处速度场拟合结果会出现异常与不平滑的问题,结合K-means聚类算法与TLSC算法发展了一种基...针对传统最小二乘配置(traditional least squares collocation,TLSC)算法在大尺度区域地壳运动速度场高精度拟合中,在监测站点稀疏区域与块体边缘处速度场拟合结果会出现异常与不平滑的问题,结合K-means聚类算法与TLSC算法发展了一种基于K-means聚类的最小二乘配置法(KLSC),并在青藏高原GNSS地壳运动实测速度场中验证该方法的有效性。结果表明:1)相较于TLSC算法,KLSC算法利用K-means算法在无监督分类中的优势,基于GNSS速度场本身特征先将研究区域划分为多个速度相似的子区域,然后在每个子区域内分别利用TLSC进行速度场拟合,避免了局部复杂地质环境对区域速度场拟合精度的影响;2)KLSC算法以各网格点到各聚类中心的距离最近为依据选取拟合参数,解决了数据稀疏区域速度场拟合结果较差的问题;3)KLSC算法利用次近距离拟合并结合卷积滤波,有效解决了块体边缘处速度场拟合结果不平滑的问题;4)KLSC算法拟合的速度场的RMSE精度和相关性均优于TLSC算法,东、北向拟合速度场RMSE精度分别提高37%~48.2%和52.1%~67.2%,相关性分别提高24.1%~24.7%和4.7%~5.2%。展开更多
This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial esti...This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x , at pixel location i , in an image X , depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image.展开更多
在对LMS算法进行MATLAB仿真的基础上,采用硬件描述语言VHDL和FPGA完成LMS自适应算法的硬件实现。自适应均衡器的设计采用自上向下的设计思想、串并行相结合的流水线操作方法、定点运算方法,在Quartus II 4.1平台和Stratix II系列芯片上...在对LMS算法进行MATLAB仿真的基础上,采用硬件描述语言VHDL和FPGA完成LMS自适应算法的硬件实现。自适应均衡器的设计采用自上向下的设计思想、串并行相结合的流水线操作方法、定点运算方法,在Quartus II 4.1平台和Stratix II系列芯片上进行了综合和仿真。结果表明,该设计结果符合要求,能实现自适应过程。展开更多
文摘针对传统最小二乘配置(traditional least squares collocation,TLSC)算法在大尺度区域地壳运动速度场高精度拟合中,在监测站点稀疏区域与块体边缘处速度场拟合结果会出现异常与不平滑的问题,结合K-means聚类算法与TLSC算法发展了一种基于K-means聚类的最小二乘配置法(KLSC),并在青藏高原GNSS地壳运动实测速度场中验证该方法的有效性。结果表明:1)相较于TLSC算法,KLSC算法利用K-means算法在无监督分类中的优势,基于GNSS速度场本身特征先将研究区域划分为多个速度相似的子区域,然后在每个子区域内分别利用TLSC进行速度场拟合,避免了局部复杂地质环境对区域速度场拟合精度的影响;2)KLSC算法以各网格点到各聚类中心的距离最近为依据选取拟合参数,解决了数据稀疏区域速度场拟合结果较差的问题;3)KLSC算法利用次近距离拟合并结合卷积滤波,有效解决了块体边缘处速度场拟合结果不平滑的问题;4)KLSC算法拟合的速度场的RMSE精度和相关性均优于TLSC算法,东、北向拟合速度场RMSE精度分别提高37%~48.2%和52.1%~67.2%,相关性分别提高24.1%~24.7%和4.7%~5.2%。
文摘This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x , at pixel location i , in an image X , depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image.
文摘在对LMS算法进行MATLAB仿真的基础上,采用硬件描述语言VHDL和FPGA完成LMS自适应算法的硬件实现。自适应均衡器的设计采用自上向下的设计思想、串并行相结合的流水线操作方法、定点运算方法,在Quartus II 4.1平台和Stratix II系列芯片上进行了综合和仿真。结果表明,该设计结果符合要求,能实现自适应过程。
