Practical implementation of Hilbert-Huang Transform algorithm

Hilbert-Huang Transform (HHT) is a newly developed powerful method for nonlinear and non-stationary time series analysis. The empirical mode decomposition is the key part of HHT, while its algorithm was protected by NASA as a US patent, which limits the wide application among the scientific community. Two approaches, mirror periodic and extrema extending methods, have been developed for handling the end effects of empirical mode decomposition. The implementation of the HHT is realized in detail to widen the application. The detailed comparison of the results from two methods with that from Huang et al. (1998, 1999), and the comparison between two methods are presented. Generally, both methods reproduce faithful results as those of Huang et al. For mirror periodic method (MPM), the data are extended once forever. Ideally, it is a way for handling the end effects of the HHT, especially for the signal that has symmetric waveform. The extrema extending method (EEM) behaves as good as MPM, and it is better t     

Singular points of the polarization tensor

The problem to compute the magnetic field above the chromosphere using data of the vector = B _t/Bt that gives the projected field direction can be solved with different approximations. The field of direction vectors is, however, not the only field accessible to observations. The Stokes parameters, which are components of the radiation tensor, can be measured at each point of the image plane. The directions of the eigenvectors of the radiation tensor define two mutually orthogonal systems of integral curves in the image plane. These families of curves have singular points, which are generally of different type than those of the vector field. When the morphology of H chromospheric fibrils are used to infer the topology of the magnetic field, a similar problem is met, suggesting that singular points should also be present there.     

Patterns in the Turkish reinsurance claim data, 1998–2002: An application of the extreme value theory

The devastating 1999 Marmara and Düzce earthquakes led to a significant increase in the earthquake studies in Turkey in geological, engineering and financial aspects. Extreme Value Theory (EVT) has a range of applications from stock market changes to natural disasters like floods and hurricanes. Here EVT is fitted to the ordinary and earthquake reinsurance claims of Turkey.     

中国重力异常的小波变换与多尺度分析

阐述了小波变换及多尺度分析方法的原理，利用二维小波多尺度分析方法对中国布格重力异常进行了分解，并从其中划分出有意义的剩余异常．模型结果证明方法有效．由于本文方法可以将重力异常分解成各种几何尺度意义下的不同成分，这为重力解释和研究地壳的结构提供更多更新的依据．     

1990—2018年长沙市土地利用变化对生态系统服务价值的影响

评估生态系统服务价值(Ecosystem Service Values,ESV)有利于衡量生态系统服务供给潜力和生态效益的大小,对城市生态规划与土地利用管控起到重要的支撑作用。选择长沙市为研究区域,基于1990、2000、2010和2018年4期土地利用现状遥感监测数据集,利用ArcGIS软件和经本地化校正的当量因子法,分析1990—2018年间的土地利用变化特征,并探讨土地利用变化对ESV的影响。结果表明: ①1990—2018年长沙市土地利用变化轨迹以耕地→林地互相转化以及建设用地对耕地、林地的侵占为主; ②1990—2018年长沙市的ESV共减少53.17×10⁸ 元,降幅3.59%,除水域外各类ESV均呈下降趋势,林地和耕地服务价值减少最明显; ③在各单项服务功能中,水文调节的价值最大,其次是气候调节,两者之和占总价值的54%; ④“退耕还林”“退田还湖”“兴工强市”等政策引起的土地利用变化势必对ESV造成影响,城市快速扩张、土地开发会导致ESV不断下降。综合分析认为,在调整研究区土地利用结构、优化土地配置时应充分重视水域、林地等生态系统服务价值较高的土地利用类型,减缓ESV的下降趋势。     

GRAPES全球奇异向量方法改进及试验分析

基于总能量模的奇异向量扰动常用于构造集合预报的初始条件。以建立GRAPES（Global and Regional Assimilation PrEdiction System）全球集合预报系统为目的,基于前期研发的GRAPES全球模式奇异向量方法,在GRAPES全球切线性模式和伴随模式2.0版的框架下,开展了引入线性化边界层方案来改善奇异向量结构,并提高奇异向量计算效率的研究。通过连续试验,从奇异向量的扰动能量结构、扰动能量谱及扰动空间分布等方面,综合分析改进GRAPES全球奇异向量的结构及演变特征。试验结果表明,改进后的GRAPES奇异向量方法有效抑制了之前扰动能量在近地面层不合理的快速增长,同时,奇异向量最优扰动的结构更客观地体现了中高纬度区域大气初始条件中的斜压不稳定扰动及其演变,如在初始时刻奇异向量扰动能量主要位于对流层中层,并呈现出随高度向西倾斜的大气斜压特征;经过线性化演变,扰动能量向较大水平尺度转移,并在垂直结构上表现出向对流层高层上传及向对流层低层下传的特征等。针对GRAPES奇异向量迭代求解中伴随模式计算耗时为主的情况,改进伴随模式中广义共轭余差方案的调用方式,并采用大内存存储法来提高其计算效率,进而将奇异向量总计算时间缩短了25%。总之,改进后的GRAPES奇异向量方法,可应用于构建面向业务应用的GRAPES全球集合预报系统。      

腐解9个月后杉木枯枝落叶化感物质对杉木的化感作用

采用有机溶剂与蒸馏水混和浸提杉木枯枝落叶腐解9个月后化感物质,应用杉木种子发芽试验进行生物检测.结果表明:杉木枯枝落叶腐解9个月后,杉木枯枝落叶中的弱极性化感物质对绝对发芽率、绝对发芽势、胚根长、胚轴长及干物质量等指标表现为抑制作用,对鲜物质量则表现为轻微的促进作用;极性化感物质对杉木种子发芽各指标均等表现为抑制作用.腐解土中的弱极性化感物质对杉木种子发芽各指标均表现为抑制作用,极性化感物质对绝对发芽率、绝对发芽势、胚轴长及干物质量等表现为抑制作用,但对胚根长、鲜物质量表现为促进作用.     

基于EEMD和ARIMA的海温预测模型研究

类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。     

Research on the Method of De-noising the Short-wave Time Signal at Low SNR

A voice enhancement algorithm based on the Empirical Mode Decomposition (EMD) and the improved spectral subtraction is proposed for the low-SNR (Signal Noise Ratio) shortwave time signal. This method is proposed to solve the problem that the shortwave time signal cannot be used for timing in complex noisy environments. The core idea of this method is to use the Hilbert-Huang Transform (HHT) algorithm to make the empirical mode decomposition on the noisy shortwave signal, and to select the intrinsic mode functions containing the shortwave signal information for the signal reconstruction by through the maximum correlation. Then, to make the spectral subtraction on the reconstructed signal to achieve the purpose of noise reduction. The experimental result shows that this method has a better noise reduction than the traditional methods.     

希尔伯特-黄变换的端点延拓

希尔伯特-黄变换(HHT)是近年来发展起来的一种新的时间序列信号分析方法.该文在对HHT深入研究与充分肯定的基础上,发展了信号的镜像闭合延拓和包络的极值延拓两种方法.通过几个典型的例子检验了两种方法,并与Huang等(1998,1999)进行了比较,得到了令人满意的结果.镜像闭合延拓法根据信号端点的分布特性,把镜子放在具有对称性的极值位置,通过镜像法把镜内信号映射成一个周期性的信号,不存在端点,从根本上避免了经验模态分解和希尔伯特变换的端点问题.极值延拓法简单易行,具有与镜像闭合法相当的效果,在处理非对称波形信号时更显其优越性.