As one of the carriers for human communication and interaction, images are prone to contamination by noise during transmission and reception, which is often uncontrollable and unknown. Therefore, how to denoise images...As one of the carriers for human communication and interaction, images are prone to contamination by noise during transmission and reception, which is often uncontrollable and unknown. Therefore, how to denoise images contaminated by unknown noise has gradually become one of the research focuses. In order to achieve blind denoising and separation to restore images, this paper proposes a method for image processing based on Root Mean Square Error (RMSE) by integrating multiple filtering methods for denoising. This method includes Wavelet Filtering, Gaussian Filtering, Median Filtering, Mean Filtering, Bilateral Filtering, Adaptive Bandpass Filtering, Non-local Means Filtering and Regularization Denoising suitable for different types of noise. We can apply this method to denoise images contaminated by blind noise sources and evaluate the denoising effects using RMSE. The smaller the RMSE, the better the denoising effect. The optimal denoising result is selected through comprehensively comparing the RMSE values of all methods. Experimental results demonstrate that the proposed method effectively denoises and restores images contaminated by blind noise sources.展开更多
目的/意义研究一种均方根误差最小准则的偏最小二乘筛选中药药效物质方法,以便全面地观察和分析中药的作用机理。方法/过程以均方根误差(root mean square error,RMSE)最小为主要准则,通过偏最小二乘法获得特征的变量投影重要性指标(var...目的/意义研究一种均方根误差最小准则的偏最小二乘筛选中药药效物质方法,以便全面地观察和分析中药的作用机理。方法/过程以均方根误差(root mean square error,RMSE)最小为主要准则,通过偏最小二乘法获得特征的变量投影重要性指标(variable importance in the projection,VIP)值,再以VIP值的大小对特征重要性排序,最后通过偏最小回归法与前向搜索法,以RMSE最小、交叉性验证结果最好为标准,确定特征子集。采用大承气汤配比治疗急性胰腺炎实验数据,以及麻杏石甘汤治咳、平喘、退热实验数据进行验证。结果/结论该方法能得到回归性能最好时的最小RMSE和药效物质子集。VIP值大于1的特征是相对重要的,VIP值小于1的特征也可能对模型性能有影响。展开更多
包络线跟踪电源相较于传统恒压供电大大提高了功放效率,但对硬件要求更高且会产生较高的额外时延。针对以上问题本文提出了一种基于BP神经网络的射频信号包络线的数据预测方案,用于提前生成控制开关变换器的基准信号。首先,基带数据经...包络线跟踪电源相较于传统恒压供电大大提高了功放效率,但对硬件要求更高且会产生较高的额外时延。针对以上问题本文提出了一种基于BP神经网络的射频信号包络线的数据预测方案,用于提前生成控制开关变换器的基准信号。首先,基带数据经正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术生成射频信号的包络线数据。其次,将处理好的数据用于训练包络线跟踪电源的预测模型。最后,改变OFDM调制的子载波数以及选取不同正交振幅调制方式(Quadrature Amplitude Modulation,QAM)分别训练网络。在12子载波下,分别选取16QAM~512QAM之间的6种映射方式进行仿真实验:512QAM在最佳隐含层节点16下训练得到的均方根误差值(Root Mean Squared Error,RMSE)为0.3143;在映射方式为16QAM,64QAM映射方式下,分别选取12、28、52子载波进行仿真,52子载波64QAM映射下的RMSE值最大为0.1752。预测结果中RMSE值均较小,满足预测误差的要求,且预测结果图中包络线与实际包络线拟合效果很好。通过对BP网络预测模型与传统调制模型浮点运算次数的计算,求取52子载波映射方式为64QAM的信号包络,计算次数节省率可以达到49.40%。展开更多
文摘As one of the carriers for human communication and interaction, images are prone to contamination by noise during transmission and reception, which is often uncontrollable and unknown. Therefore, how to denoise images contaminated by unknown noise has gradually become one of the research focuses. In order to achieve blind denoising and separation to restore images, this paper proposes a method for image processing based on Root Mean Square Error (RMSE) by integrating multiple filtering methods for denoising. This method includes Wavelet Filtering, Gaussian Filtering, Median Filtering, Mean Filtering, Bilateral Filtering, Adaptive Bandpass Filtering, Non-local Means Filtering and Regularization Denoising suitable for different types of noise. We can apply this method to denoise images contaminated by blind noise sources and evaluate the denoising effects using RMSE. The smaller the RMSE, the better the denoising effect. The optimal denoising result is selected through comprehensively comparing the RMSE values of all methods. Experimental results demonstrate that the proposed method effectively denoises and restores images contaminated by blind noise sources.
文摘目的/意义研究一种均方根误差最小准则的偏最小二乘筛选中药药效物质方法,以便全面地观察和分析中药的作用机理。方法/过程以均方根误差(root mean square error,RMSE)最小为主要准则,通过偏最小二乘法获得特征的变量投影重要性指标(variable importance in the projection,VIP)值,再以VIP值的大小对特征重要性排序,最后通过偏最小回归法与前向搜索法,以RMSE最小、交叉性验证结果最好为标准,确定特征子集。采用大承气汤配比治疗急性胰腺炎实验数据,以及麻杏石甘汤治咳、平喘、退热实验数据进行验证。结果/结论该方法能得到回归性能最好时的最小RMSE和药效物质子集。VIP值大于1的特征是相对重要的,VIP值小于1的特征也可能对模型性能有影响。
文摘包络线跟踪电源相较于传统恒压供电大大提高了功放效率,但对硬件要求更高且会产生较高的额外时延。针对以上问题本文提出了一种基于BP神经网络的射频信号包络线的数据预测方案,用于提前生成控制开关变换器的基准信号。首先,基带数据经正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术生成射频信号的包络线数据。其次,将处理好的数据用于训练包络线跟踪电源的预测模型。最后,改变OFDM调制的子载波数以及选取不同正交振幅调制方式(Quadrature Amplitude Modulation,QAM)分别训练网络。在12子载波下,分别选取16QAM~512QAM之间的6种映射方式进行仿真实验:512QAM在最佳隐含层节点16下训练得到的均方根误差值(Root Mean Squared Error,RMSE)为0.3143;在映射方式为16QAM,64QAM映射方式下,分别选取12、28、52子载波进行仿真,52子载波64QAM映射下的RMSE值最大为0.1752。预测结果中RMSE值均较小,满足预测误差的要求,且预测结果图中包络线与实际包络线拟合效果很好。通过对BP网络预测模型与传统调制模型浮点运算次数的计算,求取52子载波映射方式为64QAM的信号包络,计算次数节省率可以达到49.40%。
