To reduce the cost, size and complexity, a consumer digital camera usually uses a single sensor overlaid with a color filter array(CFA) to sample one of the red-green-blue primary color values, and uses demosaicking a...To reduce the cost, size and complexity, a consumer digital camera usually uses a single sensor overlaid with a color filter array(CFA) to sample one of the red-green-blue primary color values, and uses demosaicking algorithm to estimate the missing color values at each pixel. A novel image correlation and support vector machine(SVM) based edge-adaptive algorithm was proposed, which can reduce edge artifacts and false color artifacts, effectively. Firstly, image pixels were separated into edge region and smooth region with an edge detection algorithm. Then, a hybrid approach switching between a simple demosaicking algorithm on the smooth region and SVM based demosaicking algorithm on the edge region was performed. Image spatial and spectral correlations were employed to create middle planes for the interpolation. Experimental result shows that the proposed approach produced visually pleasing full-color result images and obtained higher CPSNR and smaller S-CIELAB*ab?E than other conventional demosaicking algorithms.展开更多
电力系统作为实时信息与能源高度融合的电力信息物理融合系统(cyber-physical power system,CPPS),虚假数据注入攻击(false data injection attacks,FDIAs)的准确辨识将有效保证CPPS安全稳定运行。为准确、高效地完成日前负荷预测,首先...电力系统作为实时信息与能源高度融合的电力信息物理融合系统(cyber-physical power system,CPPS),虚假数据注入攻击(false data injection attacks,FDIAs)的准确辨识将有效保证CPPS安全稳定运行。为准确、高效地完成日前负荷预测,首先使用肯德尔相关系数(Kendall's tau-b)量化日期类型的取值,引入加权灰色关联分析选取相似日,再建立基于最小二乘支持向量机(least squares support vector machine,LSSVM)的日前负荷预测模型。将预测负荷通过潮流计算求解的系统节点状态量与无迹卡尔曼滤波(unscented Kalman filter,UKF)动态状态估计得到的状态量进行自适应加权混合,最后基于混合预测值和静态估计值间的偏差变量提出了攻击检测指数(attack detection index,ADI),根据ADI的分布检测FDIAs。若检测到FDIAs,使用混合预测状态量对该时刻的量测量进行修正。使用IEEE-14和IEEE-39节点系统进行仿真,结果验证了所提方法的有效性与可行性。展开更多
针对在刀具磨损实时监测过程中受外界噪声影响而导致预测准确度较低问题,提出一种基于皮尔逊相关系数(Pearson Correlation Coefficient,PCC)和灰狼优化支持向量机(Grey Wolf Optimization Support Vector Machine,GWO-SVM)的刀具磨损...针对在刀具磨损实时监测过程中受外界噪声影响而导致预测准确度较低问题,提出一种基于皮尔逊相关系数(Pearson Correlation Coefficient,PCC)和灰狼优化支持向量机(Grey Wolf Optimization Support Vector Machine,GWO-SVM)的刀具磨损量预测模型。该模型采用时域、频域和时频联合域上的特征提取方法,能有效捕捉刀具磨损过程中不同方面的信息;通过PCC优化方法筛选与刀具磨损高度相关的特征数据,提高模型的特征提取能力;利用灰狼算法获取搜索狼群中具有最佳适应度值的位置,即对应的SVM惩罚因子C和核函数参数σ作为SVM的最优参数进行构建和训练,提高预测精度。实验结果表明,PCC-GWO-SVM模型在球头铣刀磨损预测任务中的均方误差MSE为0.0181mm^(2),平均相对误差MAPE为0.187%,决定系数R^(2)为0.9827,均优于预测模型GA-SVM和BES-LSSVM,验证了该模型的有效性和可行性。展开更多
基金Projects(51174258,11105002)supported by the National Natural Science Foundation of ChinaProject(KJ2013B087)supported by Anhui Provincial Natural Science Research Projects in Central Universities,China+1 种基金Projects(2011B31,2013A4017)support by the Guidance Science and Technology Plan Projects of Huainan,ChinaProject(2012QNZ06)supported by the Youth Foundation of Anhui University of Science&technology of China
文摘To reduce the cost, size and complexity, a consumer digital camera usually uses a single sensor overlaid with a color filter array(CFA) to sample one of the red-green-blue primary color values, and uses demosaicking algorithm to estimate the missing color values at each pixel. A novel image correlation and support vector machine(SVM) based edge-adaptive algorithm was proposed, which can reduce edge artifacts and false color artifacts, effectively. Firstly, image pixels were separated into edge region and smooth region with an edge detection algorithm. Then, a hybrid approach switching between a simple demosaicking algorithm on the smooth region and SVM based demosaicking algorithm on the edge region was performed. Image spatial and spectral correlations were employed to create middle planes for the interpolation. Experimental result shows that the proposed approach produced visually pleasing full-color result images and obtained higher CPSNR and smaller S-CIELAB*ab?E than other conventional demosaicking algorithms.
文摘电力系统作为实时信息与能源高度融合的电力信息物理融合系统(cyber-physical power system,CPPS),虚假数据注入攻击(false data injection attacks,FDIAs)的准确辨识将有效保证CPPS安全稳定运行。为准确、高效地完成日前负荷预测,首先使用肯德尔相关系数(Kendall's tau-b)量化日期类型的取值,引入加权灰色关联分析选取相似日,再建立基于最小二乘支持向量机(least squares support vector machine,LSSVM)的日前负荷预测模型。将预测负荷通过潮流计算求解的系统节点状态量与无迹卡尔曼滤波(unscented Kalman filter,UKF)动态状态估计得到的状态量进行自适应加权混合,最后基于混合预测值和静态估计值间的偏差变量提出了攻击检测指数(attack detection index,ADI),根据ADI的分布检测FDIAs。若检测到FDIAs,使用混合预测状态量对该时刻的量测量进行修正。使用IEEE-14和IEEE-39节点系统进行仿真,结果验证了所提方法的有效性与可行性。
文摘针对在刀具磨损实时监测过程中受外界噪声影响而导致预测准确度较低问题,提出一种基于皮尔逊相关系数(Pearson Correlation Coefficient,PCC)和灰狼优化支持向量机(Grey Wolf Optimization Support Vector Machine,GWO-SVM)的刀具磨损量预测模型。该模型采用时域、频域和时频联合域上的特征提取方法,能有效捕捉刀具磨损过程中不同方面的信息;通过PCC优化方法筛选与刀具磨损高度相关的特征数据,提高模型的特征提取能力;利用灰狼算法获取搜索狼群中具有最佳适应度值的位置,即对应的SVM惩罚因子C和核函数参数σ作为SVM的最优参数进行构建和训练,提高预测精度。实验结果表明,PCC-GWO-SVM模型在球头铣刀磨损预测任务中的均方误差MSE为0.0181mm^(2),平均相对误差MAPE为0.187%,决定系数R^(2)为0.9827,均优于预测模型GA-SVM和BES-LSSVM,验证了该模型的有效性和可行性。
