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71.
72.
噪声是影响图像质量的重要因素,噪声的存在导致图像的某些特征细节不能被辨识以及图像的信噪比下降,所以图像去噪是图像处理中的一个重要问题.本文提出了基于小波框架的彩色图像的泊松去噪模型.在该模型中,赋权的l2项作为保真项,包含小波框架的l1项作为正则项.同时,又提出了解决该模型的重新赋权的split Bregman算法.最后,利用仿真实验以及PSNR(峰值信噪比)指标对该模型的去噪效果进行评估,评估结果表明该算法可行、有效. 相似文献
73.
为了了解区域云顶高度对过去气候变化的响应,基于卫星搭载的MODIS传感器提供的2000年3月至2018年2月MOD03_08_v6.0数据,分析了东亚地区云顶高度2000—2018年的时空变化特征,并探讨其长期变化的原因。研究发现,东亚地区云顶高度呈西南高东北低的特征。云顶高度在东亚地区以0.020 km/a的变率增长,其中大陆东部云顶高度的年际变率为0.035 km/a,东部海域年际变率为0.034 km/a。在东部海域地区云顶高度的变化同海表温度的变化相关性较高,相关系数为0.68,这表明云顶高度的变化受下垫面的影响。在东亚地区30°~40°N区域内,年平均云顶高度的增加较为明显。此外,夏季云顶高度在长江中下游盆地、塔里木盆地、吐鲁番盆地以及四川盆地东北部呈-0.03 km/a的减少趋势,这是由于更多低云的形成降低了云顶高度;冬季云顶高度在东亚地区40°N以北呈下降趋势,而在40°N以南呈增加趋势。 相似文献
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在大地电磁(MT)测深法中,大地电磁响应函数经常出现个别频点分散、误差棒较大、形态异常等现象,在作反演解释时,许多地质特征难以有效提取出来,这些问题严重阻碍了MT的实际应用效能和发展.本文利用高阶统计量方法检验了MT信号的高斯性. 结果表明,MT信号是非高斯的. 根据任何高斯过程,其高阶统计量(高阶累积量,高阶谱)均为零的性质,通过信号的高阶谱恢复功率谱,再由功率谱估算MT响应函数,能有效抑制高斯有色噪声的影响,提高MT资料的处理质量. 从应用效果看,这种方法在抑制高斯有色噪声、提取信号中有用信息方面优于传统功率谱方法. 相似文献
77.
??ITRF2008??????й???????5??IGS??????WUHN??BJFS??URUM??KUNM??SHAO??2012??????????????TriP?????????????????????????С??????????????????????????????????????????н?????????????????????????????????GAMIT/GLOBK????????????????????з?????????ж???????????????й??????IGS???????????д??????????????????????????????????????????????????URUM??KUNM??BJFS??SHAO?????????????????????BPPL+WN????WUHN??????N??E??U????????????????з?????????????BPPL+WN??FN+WN??PL+WN??????????BPPL+WN??????????а???????????????????????????2 mm??BPPL???????WN??????1/10???????0.28??0.89 mm????????????????С???????????????????????????????????С??????TriP?????????????????????????????????????????????????? 相似文献
78.
基于潜标测量的海洋环境噪声谱特性分析 总被引:3,自引:1,他引:2
利用海洋环境噪声测量潜标系统对南海典型海域开展了为期3个月的海洋环境噪声测量,16通道海洋环境噪声测量系统每小时测量两分钟噪声信号。数据处理结果表明,800~5 000Hz范围内,噪声谱与风速相关性最好,且风速越大相关性越好,噪声谱与风速的相关性好于与浪高的相关性。风关噪声谱级在海水中部基本不随接收深度发生变化,但由于测量水听器阵长度未能覆盖整个水深,因此未给出海面和海底处谱级变化规律。在400Hz以上的高频段整个风速范围内噪声谱级都随风速发生变化,且噪声谱级与对数风速具有很好的线性关系。 相似文献
79.
Based on dynamic response signals a damage detection algorithm is developed for marine risers. Damage detection methods based on numerous modal properties have encountered issues in the researches in offshore oil community. For example, significant increase in structure mass due to marine plant/animal growth and changes in modal properties by equipment noise are not the result of damage for riser structures. In an attempt to eliminate the need to determine modal parameters, a data-based method is developed. The implementation of the method requires that vibration data are first standardized to remove the influence of different loading conditions and the autoregressive moving average (ARMA) model is used to fit vibration response signals. In addition, a damage feature factor is introduced based on the autoregressive (AR) parameters. After that, the Euclidean distance between ARMA models is subtracted as a damage indicator for damage detection and localization and a top tensioned riser simulation model with different damage scenarios is analyzed using the proposed method with dynamic acceleration responses of a marine riser as sensor data. Finally, the influence of measured noise is analyzed. According to the damage localization results, the proposed method provides accurate damage locations of risers and is robust to overcome noise effect. 相似文献
80.
【Title】
There are knowledge gaps in our understanding of vegetation responses to multi-scale climate-related variables in tropical/subtropical mountainous islands in the Asia-Pacific region. Therefore, this study investigated inter-annual vegetation dynamics and regular/irregular climate patterns in Taiwan. We applied principal component analysis (PCA) on 11 years (2001~2011) of high-dimensional monthly photosynthetically active vegetation cover (PV) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and investigated the relationships between spatiotemporal patterns of the eigenvectors and loadings of each component through time and multi-scale climate-related variations. Results showed that the first five components contributed to 96.4% of the total variance. The first component (PC1, explaining 94.5% of variance) loadings, as expected, were significantly correlated with the temporal dynamics of the PV (r = 0.94), which was mainly governed by regional climate. The temporal loadings of PC2 and PC3 (0.8% and 0.6% of variance, respectively) were significantly correlated with the temporal dynamics of the PV of forests (r = 0.72) and the farmlands (r = 0.80), respectively. The low-order components (PC4 and PC5, 0.3% and 0.2% of variance, respectively) were closely related to the occurrence of drought (r = 0.49) and to irregular ENSO associated climate anomalies (r = -0.54), respectively. Pronounced correlations were also observed between PC5 and the Southern Oscillation Index (SOI) with one to three months of time lags (r = -0.35 ~ -0.43, respectively), revealing biophysical memory effects on the time-series pattern of the vegetation through ENSO-related rainfall patterns. Our findings reveal that the sensitivity of the ecosystems in this tropical/subtropical mountainous island may not only be regulated by regional climate and human activities but also be susceptible to large-scale climate anomalies which are crucial and comparable to previous large scale analyses. This study demonstrates that PCA can be an effective tool for analyzing seasonal and inter-annual variability of vegetation dynamics across this tropical/subtropical mountainous islandin the Pacific Ocean, which provides an opportunity to forecast the responses and feedbacks of terrestrial environments to future climate scenarios. 相似文献
There are knowledge gaps in our understanding of vegetation responses to multi-scale climate-related variables in tropical/subtropical mountainous islands in the Asia-Pacific region. Therefore, this study investigated inter-annual vegetation dynamics and regular/irregular climate patterns in Taiwan. We applied principal component analysis (PCA) on 11 years (2001~2011) of high-dimensional monthly photosynthetically active vegetation cover (PV) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and investigated the relationships between spatiotemporal patterns of the eigenvectors and loadings of each component through time and multi-scale climate-related variations. Results showed that the first five components contributed to 96.4% of the total variance. The first component (PC1, explaining 94.5% of variance) loadings, as expected, were significantly correlated with the temporal dynamics of the PV (r = 0.94), which was mainly governed by regional climate. The temporal loadings of PC2 and PC3 (0.8% and 0.6% of variance, respectively) were significantly correlated with the temporal dynamics of the PV of forests (r = 0.72) and the farmlands (r = 0.80), respectively. The low-order components (PC4 and PC5, 0.3% and 0.2% of variance, respectively) were closely related to the occurrence of drought (r = 0.49) and to irregular ENSO associated climate anomalies (r = -0.54), respectively. Pronounced correlations were also observed between PC5 and the Southern Oscillation Index (SOI) with one to three months of time lags (r = -0.35 ~ -0.43, respectively), revealing biophysical memory effects on the time-series pattern of the vegetation through ENSO-related rainfall patterns. Our findings reveal that the sensitivity of the ecosystems in this tropical/subtropical mountainous island may not only be regulated by regional climate and human activities but also be susceptible to large-scale climate anomalies which are crucial and comparable to previous large scale analyses. This study demonstrates that PCA can be an effective tool for analyzing seasonal and inter-annual variability of vegetation dynamics across this tropical/subtropical mountainous islandin the Pacific Ocean, which provides an opportunity to forecast the responses and feedbacks of terrestrial environments to future climate scenarios. 相似文献