为了进一步提升红外与可见光图像融合方法的性能,本文提出了一种基于多尺度局部极值分解与深度学习网络ResNet152的红外与可见光图像融合方法。首先,利用多尺度局部极值分解(multiscale local extrema decomposition,MLED)方法将源图像...为了进一步提升红外与可见光图像融合方法的性能,本文提出了一种基于多尺度局部极值分解与深度学习网络ResNet152的红外与可见光图像融合方法。首先,利用多尺度局部极值分解(multiscale local extrema decomposition,MLED)方法将源图像分解为近似图像和细节图像,分离出源图像中重叠的重要特征信息。然后采用残差网络ResNet152深度提取源图像的多维显著特征,以l_(1)-范数作为活性测度生成显著特征图,对近似图像进行加权平均融合,以保持能量和残留细节信息不丢失。在细节图像中,利用“系数绝对值取大”规则获得初始决策图,源图像作为引导图像,初始决策图作为输入图像进行引导滤波处理,得到优化决策图,计算加权局部能量得到能量显著图,对细节图像进行加权平均融合,使融合图像具有丰富的纹理细节和良好的视觉边缘感知。最后,对近似融合图像和细节融合图像进行重构,得到融合图像。实验结果表明,与现有的典型融合方法相比,本文所提出的融合方法在客观评价和视觉感受方面都取得了最好的效果。展开更多
A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of ...A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.展开更多
齿轮振动信号中含有大量的噪声。通过用极值点分组模式分解(Extreme Point Packet Mode Decomposition,EPPMD)的方法提取出齿轮局部断齿故障特征,验证了EPPMD的有效性。该方法首先提取出齿轮振动信号极值点,并将极值点进行分组,再用三...齿轮振动信号中含有大量的噪声。通过用极值点分组模式分解(Extreme Point Packet Mode Decomposition,EPPMD)的方法提取出齿轮局部断齿故障特征,验证了EPPMD的有效性。该方法首先提取出齿轮振动信号极值点,并将极值点进行分组,再用三次样条函数拟合出所有分组极值点的曲线,求取其均值曲线,最后像经验模态分解(EMD)一样求取出各个IMF分量,进行IMF的频谱分析诊断故障。展开更多
For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the i...For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.展开更多
This paper investigates the form of complex a lgebraic differential equation with admissible meromorphic solutions and obtains two results which are more precise thatn that of the paper [2].
文摘为了进一步提升红外与可见光图像融合方法的性能,本文提出了一种基于多尺度局部极值分解与深度学习网络ResNet152的红外与可见光图像融合方法。首先,利用多尺度局部极值分解(multiscale local extrema decomposition,MLED)方法将源图像分解为近似图像和细节图像,分离出源图像中重叠的重要特征信息。然后采用残差网络ResNet152深度提取源图像的多维显著特征,以l_(1)-范数作为活性测度生成显著特征图,对近似图像进行加权平均融合,以保持能量和残留细节信息不丢失。在细节图像中,利用“系数绝对值取大”规则获得初始决策图,源图像作为引导图像,初始决策图作为输入图像进行引导滤波处理,得到优化决策图,计算加权局部能量得到能量显著图,对细节图像进行加权平均融合,使融合图像具有丰富的纹理细节和良好的视觉边缘感知。最后,对近似融合图像和细节融合图像进行重构,得到融合图像。实验结果表明,与现有的典型融合方法相比,本文所提出的融合方法在客观评价和视觉感受方面都取得了最好的效果。
基金Supported by the National Natural Science Foundation of China(62376214)the Natural Science Basic Research Program of Shaanxi(2023-JC-YB-533)Foundation of Ministry of Education Key Lab.of Cognitive Radio and Information Processing(Guilin University of Electronic Technology)(CRKL200203)。
文摘A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.
文摘齿轮振动信号中含有大量的噪声。通过用极值点分组模式分解(Extreme Point Packet Mode Decomposition,EPPMD)的方法提取出齿轮局部断齿故障特征,验证了EPPMD的有效性。该方法首先提取出齿轮振动信号极值点,并将极值点进行分组,再用三次样条函数拟合出所有分组极值点的曲线,求取其均值曲线,最后像经验模态分解(EMD)一样求取出各个IMF分量,进行IMF的频谱分析诊断故障。
基金National Natural Science Foundation of China(No.51467008)。
文摘For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.
文摘This paper investigates the form of complex a lgebraic differential equation with admissible meromorphic solutions and obtains two results which are more precise thatn that of the paper [2].
