Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated fro...Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.展开更多
为了提高噪声估计的准确性,改进语音增强方法性能,在改进的最小控制递归平均算法(Improved Minima Controlled Recursive Averaging,IMCRA)的基础上提出了一种基于噪声分类的语音增强方法。该方法首先对含噪语音进行噪声类型的判断,然...为了提高噪声估计的准确性,改进语音增强方法性能,在改进的最小控制递归平均算法(Improved Minima Controlled Recursive Averaging,IMCRA)的基础上提出了一种基于噪声分类的语音增强方法。该方法首先对含噪语音进行噪声类型的判断,然后根据判定的噪声类型选取相应的最优参数进行噪声估计,最后采用最优修正的对数谱幅度语音估计计算增强后的语音。该方法相对于传统IMCRA算法,在语音信号的还原和背景噪声的抑制两方面都有较好的性能。展开更多
目的调查评价西宁市某特钢厂噪声作业危害程度,为企业噪声危害防治提供依据。方法采用现场职业卫生学调查与检测方法,对该厂各岗位人员接触噪声强度进行测量并做噪声作业危害程度分级。结果原料上料系统各工种接触噪声LEX,8h低于限值,...目的调查评价西宁市某特钢厂噪声作业危害程度,为企业噪声危害防治提供依据。方法采用现场职业卫生学调查与检测方法,对该厂各岗位人员接触噪声强度进行测量并做噪声作业危害程度分级。结果原料上料系统各工种接触噪声LEX,8h低于限值,但送料工已接近85d B（A）;炼钢系统冶炼工除操枪手外,接触噪声LEX,8h均超标,达94.4-98.8 d B（A）,危害程度等级为II至III级;精炼工接触噪声LEX,8h均超标,为85.0-87.3 d B（A）,危害程度等级均为I级;连铸系统连铸工5个岗位中,换包和一次切割岗位接触噪声LEX,8h超标,分别为92.6和98.6 d B（A）,危害程度等级对应为II至III级;辅助生产系统10个岗位中,维修工段电焊工、除尘系统除尘工（放灰、巡检）、水泵站水泵工接触噪声LEX,8h超标,为85.7-89.9 d B（A）,危害程度等级均为I级。结论该厂噪声危害较为严重,III级危害岗位5个,II级危害岗位3个,I级危害岗位7个。需采取综合防噪措施以降低劳动者实际接触水平。展开更多
基金Under the auspices of Knowledge Innovation Programs of Chinese Academy of Sciences (No.KZCX2-YW-449,KSCX-YW-09)National Natural Science Foundation of China (No.40971025,40901030,50969003)
文摘Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.
文摘现有的面向大规模数据分类的支持向量机(support vector machine,SVM)对噪声样本敏感,针对这一问题,通过定义软性核凸包和引入pinball损失函数,提出了一种新的软性核凸包支持向量机(soft kernel convex hull support vector machine for large scale noisy datasets,SCH-SVM).SCH-SVM首先定义了软性核凸包的概念,然后选择出能代表样本在核空间几何轮廓的软性核凸包向量,再将其对应的原始空间样本作为训练样本并基于pinball损失函数来寻找两类软性核凸包之间的最大分位数距离.相关理论和实验结果亦证明了所提分类器在训练时间,抗噪能力和支持向量数上的有效性.
文摘为了提高噪声估计的准确性,改进语音增强方法性能,在改进的最小控制递归平均算法(Improved Minima Controlled Recursive Averaging,IMCRA)的基础上提出了一种基于噪声分类的语音增强方法。该方法首先对含噪语音进行噪声类型的判断,然后根据判定的噪声类型选取相应的最优参数进行噪声估计,最后采用最优修正的对数谱幅度语音估计计算增强后的语音。该方法相对于传统IMCRA算法,在语音信号的还原和背景噪声的抑制两方面都有较好的性能。
文摘目的调查评价西宁市某特钢厂噪声作业危害程度,为企业噪声危害防治提供依据。方法采用现场职业卫生学调查与检测方法,对该厂各岗位人员接触噪声强度进行测量并做噪声作业危害程度分级。结果原料上料系统各工种接触噪声LEX,8h低于限值,但送料工已接近85d B（A）;炼钢系统冶炼工除操枪手外,接触噪声LEX,8h均超标,达94.4-98.8 d B（A）,危害程度等级为II至III级;精炼工接触噪声LEX,8h均超标,为85.0-87.3 d B（A）,危害程度等级均为I级;连铸系统连铸工5个岗位中,换包和一次切割岗位接触噪声LEX,8h超标,分别为92.6和98.6 d B（A）,危害程度等级对应为II至III级;辅助生产系统10个岗位中,维修工段电焊工、除尘系统除尘工（放灰、巡检）、水泵站水泵工接触噪声LEX,8h超标,为85.7-89.9 d B（A）,危害程度等级均为I级。结论该厂噪声危害较为严重,III级危害岗位5个,II级危害岗位3个,I级危害岗位7个。需采取综合防噪措施以降低劳动者实际接触水平。
