This paper provides a concise description of the philosophy, mathematics, and algorithms for estimating, detecting, and attributing climate changes. The estimation follows the spectral method by using empirical orthog...This paper provides a concise description of the philosophy, mathematics, and algorithms for estimating, detecting, and attributing climate changes. The estimation follows the spectral method by using empirical orthogonal functions, also called the method of reduced space optimal averaging. The detection follows the linear regression method, which can be found in most textbooks about multivariate statistical techniques. The detection algorithms are described by using the space-time approach to avoid the non-stationarity problem. The paper includes （1） the optimal averaging method for minimizing the uncertainties of the global change estimate, （2） the weighted least square detection of both single and multiple signals, （3） numerical examples, and （4） the limitations of the linear optimal averaging and detection methods.展开更多
This first nationwide survey was conducted to evaluate the overall performance of the circulating fluidized bed （CFB） incineration of municipal solid waste （MSW） in 2014-2015 in China. Total 23 CFB incineration po...This first nationwide survey was conducted to evaluate the overall performance of the circulating fluidized bed （CFB） incineration of municipal solid waste （MSW） in 2014-2015 in China. Total 23 CFB incineration power plants were evaluated. The data for monthly average flue gas emission of particles, CO, NOx, SO2 and HCl were collected over 12 consecutive months. The data were analyzed to assess the overall performance of CFB incineration by applying the Mahalanobis distance as a multivariate outlier detection method. Although the flue gas emission parameters had met the Chinese national emission standards, there were 11 total outliers （abnormal behavior） detected in 6 out of 23 CFB incineration oower olants from the oersoective of the MSW incineration performance. The results demonstrate that it is more important for a better perlbrmance of CFBs to reduce the lrequencles ot the MSW load changes, rather than the magnitudes of the MSW load changes, particularly reducing the frequencies in the range of 10% and more of the load changes, under the same and stable conditions. Furthermore, the overloading occurs more often than the underloading during the operation of the CFB incineration power plants in China. The frequent overloading is 0% to 30% ot the designed capacity. To achieve the stable performance of CFBs in practice, an appropriately designed MSW storage capacity is suggested to build in a plant to buffer and reduce the frequency of the load changes.展开更多
后验概率变化矢量分析(change vector analysis in posterior probability space,CVAPS)方法没有顾及到遥感影像波段之间和多时相之间的光谱相关性,可能会造成信息丢失而降低影像变化检测的精度。因此,结合多元变化检测(multivariate ch...后验概率变化矢量分析(change vector analysis in posterior probability space,CVAPS)方法没有顾及到遥感影像波段之间和多时相之间的光谱相关性,可能会造成信息丢失而降低影像变化检测的精度。因此,结合多元变化检测(multivariate change detection,MAD)技术与CVAPS方法,提出一种改进的土地利用/覆盖变化(land use/cover change,LUCC)分类自动更新方法。首先,引入MAD技术来降低多光谱影像波段间相关性的影响,从而改善对像元变化检测的精度,增强LUCC分类自动更新过程中训练样本的可靠性,提高LUCC分类自动更新的精度;然后,为减少分类图中"椒盐"噪声的影响,进一步利用迭代马尔科夫随机场(iterative Markov random field,IR-MRF)模型进行分类后空间邻域处理,以提高自动更新的精度。以福建省长汀县2013年获取的Landsat8影像数据以及相应的LUCC分类图为基准,利用2003年获取的Landsat5影像,对长汀县2003年的LUCC进行更新。实验结果表明,该方法的自动更新总体精度能够达到80%,比单独采用CVAPS方法的自动更新精度提高了约3%。展开更多
针对传统变化检测方法应用于高分辨率遥感影像变化检测时出现的变化信息分散、椒盐噪声影响严重等问题,将面向对象技术和迭代加权多变量变化检测方法结合起来,提出了一种面向对象的迭代加权多变量变化检测方法(iteratively reweighted m...针对传统变化检测方法应用于高分辨率遥感影像变化检测时出现的变化信息分散、椒盐噪声影响严重等问题,将面向对象技术和迭代加权多变量变化检测方法结合起来,提出了一种面向对象的迭代加权多变量变化检测方法(iteratively reweighted multivariate alternative detection,IR-MAD)。该方法主要通过结合卡方分布的概率密度函数和面向对象技术来对传统多变量变化识别方法(multivariate alternative detection,MAD)进行改进,卡方分布的概率密度函数对变化信息进行融合以获取信息集中的IR-MAD变量。此外,在对影像进行分割时结合叠置分割技术获取边界一致、同质性较好的影像对象。实验表明,面向对象IR-MAD方法能够有效集成变化信息,准确提取变化区域,同时较好地保持变化目标的结构与形状,减少椒盐噪声的影响,检测结果具有较高的可靠性。展开更多
文摘This paper provides a concise description of the philosophy, mathematics, and algorithms for estimating, detecting, and attributing climate changes. The estimation follows the spectral method by using empirical orthogonal functions, also called the method of reduced space optimal averaging. The detection follows the linear regression method, which can be found in most textbooks about multivariate statistical techniques. The detection algorithms are described by using the space-time approach to avoid the non-stationarity problem. The paper includes （1） the optimal averaging method for minimizing the uncertainties of the global change estimate, （2） the weighted least square detection of both single and multiple signals, （3） numerical examples, and （4） the limitations of the linear optimal averaging and detection methods.
文摘This first nationwide survey was conducted to evaluate the overall performance of the circulating fluidized bed （CFB） incineration of municipal solid waste （MSW） in 2014-2015 in China. Total 23 CFB incineration power plants were evaluated. The data for monthly average flue gas emission of particles, CO, NOx, SO2 and HCl were collected over 12 consecutive months. The data were analyzed to assess the overall performance of CFB incineration by applying the Mahalanobis distance as a multivariate outlier detection method. Although the flue gas emission parameters had met the Chinese national emission standards, there were 11 total outliers （abnormal behavior） detected in 6 out of 23 CFB incineration oower olants from the oersoective of the MSW incineration performance. The results demonstrate that it is more important for a better perlbrmance of CFBs to reduce the lrequencles ot the MSW load changes, rather than the magnitudes of the MSW load changes, particularly reducing the frequencies in the range of 10% and more of the load changes, under the same and stable conditions. Furthermore, the overloading occurs more often than the underloading during the operation of the CFB incineration power plants in China. The frequent overloading is 0% to 30% ot the designed capacity. To achieve the stable performance of CFBs in practice, an appropriately designed MSW storage capacity is suggested to build in a plant to buffer and reduce the frequency of the load changes.
文摘后验概率变化矢量分析(change vector analysis in posterior probability space,CVAPS)方法没有顾及到遥感影像波段之间和多时相之间的光谱相关性,可能会造成信息丢失而降低影像变化检测的精度。因此,结合多元变化检测(multivariate change detection,MAD)技术与CVAPS方法,提出一种改进的土地利用/覆盖变化(land use/cover change,LUCC)分类自动更新方法。首先,引入MAD技术来降低多光谱影像波段间相关性的影响,从而改善对像元变化检测的精度,增强LUCC分类自动更新过程中训练样本的可靠性,提高LUCC分类自动更新的精度;然后,为减少分类图中"椒盐"噪声的影响,进一步利用迭代马尔科夫随机场(iterative Markov random field,IR-MRF)模型进行分类后空间邻域处理,以提高自动更新的精度。以福建省长汀县2013年获取的Landsat8影像数据以及相应的LUCC分类图为基准,利用2003年获取的Landsat5影像,对长汀县2003年的LUCC进行更新。实验结果表明,该方法的自动更新总体精度能够达到80%,比单独采用CVAPS方法的自动更新精度提高了约3%。
文摘针对传统变化检测方法应用于高分辨率遥感影像变化检测时出现的变化信息分散、椒盐噪声影响严重等问题,将面向对象技术和迭代加权多变量变化检测方法结合起来,提出了一种面向对象的迭代加权多变量变化检测方法(iteratively reweighted multivariate alternative detection,IR-MAD)。该方法主要通过结合卡方分布的概率密度函数和面向对象技术来对传统多变量变化识别方法(multivariate alternative detection,MAD)进行改进,卡方分布的概率密度函数对变化信息进行融合以获取信息集中的IR-MAD变量。此外,在对影像进行分割时结合叠置分割技术获取边界一致、同质性较好的影像对象。实验表明,面向对象IR-MAD方法能够有效集成变化信息,准确提取变化区域,同时较好地保持变化目标的结构与形状,减少椒盐噪声的影响,检测结果具有较高的可靠性。
