Characteristics of Spatial and Temporal Variations of Monthly Mean Surface Air Temperature over Qinghai-Tibet Plateau

The recorded meteorological data of monthly mean surface air temperature from 72 meteorological stations over the Qinghal-Tibet Plateau in the period of 1960-2003 have been analyzed by using Empirical Orthogonal Function （EOF） method, to understand the detailed features of its temporal and spatial variations. The results show that there was a high consistency of the monthly mean surface air temperature, with a secondarily different variation between the north and the south of the plateau. Warming trend has existed at all stations since the 1960s, while the warming rates were different in various zones. The source regions of big rivers had intense warming tendency. June, November and December were the top three fast-warming months since the 1960s; while April, July and September presented dramatic warming tendency during the last decade.

Preliminary analysis of distribution and variation of perennialmonthly mean water masses in the Bohai Sea,the Huanghai Sea and the East China Sea

On the basis of perennial monthly mean temperature and salinity data, the classification of monthly water masses at the surface and the bottom in the Bohai Sea, the Huanghai Sea and the East China Sea, has been made by using the method of fuzzy cluster from the modified characteristic of water masses in the shallow water area. In this paper, the basic features, growth and decline patterns of water masses in relation to fishing grounds in the whole shelves of the Bohai Sea, the Huanghai Sea and the East China Sea are discussed with emphasis.

Some Annual Variation Characteristics for the Northern Hemispheric Monthly Mean Precipitation Fields

By the utilization of monthly precipitation data from all stations in the Northern Hemisphere annexed to the 'World Survey of climatology, Vol. 1-15', the distributions of the maximum precipitation months (MPM), the annual relative precipitation (ARP) and the monthly relative precipitation (percent of annual) in January and July are respectively mapped. Moreover the distributions of intermonthly relative precipitation variabilities from January to December are plotted as well. From these figures, the precipitation in the Northern Hemisphere may be classified into three types(continental, oceanic and transitional types) and 17 regions. The precipitation regime may also be divided into two patterns, the global and regional patterns. The global pattern consists of planetary front system and ITCZ and its inter-monthly variation shows the north-and-south shift of the rain belt; the regional pattern consists of the sea-land monsoon and plateau monsoon regime, in which the inter-monthly variation of rain belt shows a east-and-wcst shift.

一种改进的K-means蚁群聚类算法

现有的K-means蚁群聚类算法,首先进行K-means聚类算法操作,快速、粗略地确定初始聚类中心,接着根据上一步获得的聚类中心再进行蚁群算法聚类操作,有效地解决蚁群聚类算法收敛速度过慢的问题。研究发现,现有的Kmeans蚁群聚类算法并没有改善算法在迭代后期易出现收敛于非全局最优的缺陷。针对这一问题,提出一种改进的Kmeans蚁群聚类算法。每次迭代结束时,随机选择一个或多个簇,再从选中的簇里选择含有信息素最小的节点进行变异操作,把选中的节点变异到其他簇,计算评价值判断变异是否进行。仿真实验结果表明,用F值表示的平均值和最差结果都比原有的算法较好,有效解决了原有算法易收敛于非全局最优及早熟问题,但由于变异操作使算法运行时间相对较长。

基于均衡化函数的快速K-means算法

聚类分析的应用很广泛,传统的K-means算法要求事先给定k值,限制了很多实际的应用,由于聚类的质量主要考察类内的紧凑性和类间的距离,提出了均衡化的评价函数,使用最近邻搜索算法减少算法的计算量,不仅自动生成聚类的数目,同时均衡了类内差异和类间差异对于聚类结果的影响,实验结果证明改进的K-means算法的有效性。

基于角点特征融合的Mean-shift跟踪算法

传统Harris检测算法不能很好地适应跟踪环境。为此,提出一种基于角点特征融合的Mean-shift跟踪算法。考虑人体姿态变化或遮挡对多区域跟踪的影响,采用角点更新策略,将特征融合主色调模型的跟踪结果与多区域跟踪结果进行权衡。实验结果表明,该算法能克服人体姿态变化或遮挡对跟踪的影响,实时性满足一般跟踪系统的要求,且在非遮挡状况下,其跟踪准确率比传统算法高。

基于块的Mean-shift跟踪算法

针对传统Mean-shift跟踪算法在目标发生遮挡和形态变化时跟踪性能下降的缺点,提出了一种基于块的Mean-shift跟踪算法,该算法主要特点有:1)将跟踪目标平均分块,每小块独立进行传统Mean-shift跟踪,利用小块跟踪未被遮挡的目标部分;2)跟踪检测器检测目标小块跟踪的有效性,筛选出无效跟踪的目标小块,解决了目标分块造成跟踪性能下降的问题;3)归一化互相关检测器和邻域一致检测增加了对目标空间信息的检测,弥补了Mean-shift算法的局限性,增加了跟踪的鲁棒性。实验表明,该算法在目标发生遮挡和形态变化时仍然可以有效地实现跟踪。

基于变异系数法和K⁃means的电能表状态评价方法

智能电能表周期轮换存在着工作量大、计划性不强、重复检修等问题,如何合理地进行智能电能表状态评价是智能电能表轮换的关键环节。基于此,提出一种新的智能电能表状态评价方法。首先得到智能电能表的地区因素、可靠度、全事件、计量异常事件、电量过载和时钟电池欠压这6个指标数据;随后,一方面通过变异系数赋值法得到每个电能表的状态评分,另一方面将这6个指标数据作为输入数据,通过K⁃means聚类算法进行分类,得到相应类别。最后将两种算法结合得到新的智能电能表状态评价方法,并输出最后的评价结果。试验结果证明了该方法的科学性。

一种融合变异系数的k-mean聚类分析方法

K-means聚类算法的性能依赖于距离度量的选择,k-means算法将欧几里德距离作为最常用的距离度量方法。欧氏距离认为所有属性在聚类中作用是相同的,但是这种距离度量方法并不能准确反映样本间的相异性。针对这种不足,提出了融合变异系数的k-means聚类分析方法(CV-k-means),利用变异系数权重向量来减少不相关属性的影响。实验结果表明,该方法的聚类结果优于k-means算法。

一种改进的Mean Shift目标跟踪算法——针对视频对象部分遮挡和光照变化

针对经典的Mean Shift算法在目标部分遮挡或者场景光照变化时容易出现跟踪目标丢失的问题,文章运用一种基于分块权重的方法处理目标部分遮挡问题,每一部分的权重系数由来自不同的块和背景颜色信息共同决定.为了适应场景光照的变化,提出快速并且稳定的更新机制.实验分析,该方法可实现快速有效的跟踪.

Effect of Sea Level Variation on Tidal Characteristic Values for the East China Sea

Tidal waves in the East China Sea are simulated numerically with POM(Princeton Ocean Model) model for normal mean sea level, 30 cm higher, 60 cm higher, and 100 cm higher, respectively, and the simulated result is compared with the harmonic analysis result of hourly sea level data from 19 tide gauges for more than 19 years. It is indicated that the long-term mean sea level variation affects notably tidal waves in this region. Generally, the tidal amplitude increases when the mean sea level rises, but this relationship may be inverse for some sea areas. The maximal variation of tidal amplitude takes place in the zones near the Fujian coast and the Zhejiang coast, rather than the shallowest Bohai Sea. The maximum increase of M2 amplitude can exceed about 15 cm corresponding to the 60 cm rise of the mean sea level along the Fujian coast. The other regions with large variations of tidal amplitude are those along the Jiangsu coast, the south-east coast of Shandong, and the south-east coast of Dalian. The propagation of tidal waves is also related to mean sea level variation, and the tidal phase-lag decreases generally when the mean sea level rises. Almost all the regions where the tidal phase-lag increases with rising mean sea level are close to amphidromic points, meanwhile the spatial area of such regions is very small. Because the influence of mean sea level variation upon tidal waves is spatially marked, such spatial effect should be considered in calculation of the tidal characteristic value and engineering water level. In the region where the amplitudes of the major tidal constituents increase, the probable maximum high water level becomes higher, the probable maximum low water level becomes lower, and both design water level andcheck water level increase obviously. For example, the design water level at Xiamen increases by 13.5 cm due to the variation of tidal waves when the mean sea level rises 60 cm, the total increase of design water level being 73.5 cm.

The global mean sea surface model WHU2013

The mean sea surface （MSS） model is an important reference for the study of charting datum and sea level change. A global MSS model named WHU2013, with 2′ × 2′ spatial resolution between 80° S and 84°N, is established in this paper by combining nearly 20 years of multi-satellite altimetric data that include Topex/Poseidon （T/P）, Jason-1, Jason-2, ERS-2, ENVISAT and GFO Exact Repeat Mission （ERM） data, ERS-1/168, Jason-1/C geodetic mission data and Cryosat-2 low resolution mode （LRM） data. All the ERM data are adjusted by the collinear method to achieve the mean along-track sea surface height （SSH）, and the combined dataset of T/P, Jason-1 and Jason-2 from 1993 to 2012 after collinear adjustment is used as the reference data. The sea level variations in the non-ERM data （geodetic mission data and LRM data） are mainly investigated, and a combined method is proposed to correct the sea level variations between 66°S and 66°N by along-track sea level variation time series and beyond 66°S or 66°N by seasonal sea level variations. In the crossover adjustment between multi-altimetric data, a stepwise method is used to solve the problem of inconsistency in the reference data between the high and low latitude regions. The proposed model is compared with the CNES-CLS2011 and DTU13 MSS models, and the standard derivation （STD） of the differences between the models is about S cm between 80°S and 84°N, less than 3 cm between 66°S and 66°N, and less than 4 cm in the China Sea and its adjacent sea. Furthermore, the three models exhibit a good agreement in the SSH differences and the along-track gradient of SSH following comparisons with satellite altimetry data.

Decreasing Birth Rate Determining Worldwide Incidence and Regional Variation of Female Breast Cancer

Purpose: Urbanization, obesity and ageing associated with lifestyle changes (Westernized diet patterns, pollution, physical inactivity) have been proposed as the major contributing factors for the global rise in breast cancer (BCa) and have been the variables used to predict the future breast cancer rate. At the same time, socio-economic level, instead of birth rate, has been proposed for explanation of dramatic regional variations of breast cancer incidence. We sought to determine which factor plays the determining role in predicting worldwide breast cancer incidence rates and regional variations. Methods: Bivariate correlation was conducted to examine the relationships between country-specific estimates of birth rate, BCa incidence, urbanization, overweight, ageing and GDP. Partial correlation was performed to identify the correlation between BCa incidence with each independent variable while we controlled the other four variables. Multiple linear regression was used to identify the most significant predictors of BCa incidence. Post hoc Scheff and independent T-Test analysis were performed to compare mean differences in BCa incidence rates and residuals of BCa standardised on birth rate in the WHO regions, and UN developed and developing regions respectively. Results: Worldwide, BCa incidence rate tends to increase while birth rate decreases and urbanization, overweight, ageing and GDP increase. However, birth rate was the only variable that had a significant correlation with BCa incidence when controlled for the other four variables. Birth rate was the only significant predictor of BCa incidence in regression analysis. Multiple mean differences of BCa incidence between regions were significant, but all disappeared when the contributing effect of birth rate on BCa incidence rate was removed. Conclusions: Birth rate plays a determining role in worldwide BCa incidence rate and regional variations. Current BCa projection methods may estimate future rates of BCa poorly if they fail to incorporate the impact of birth rate.

Comparison of two approximal proximal point algorithms for monotone variational inequalities

Proximal point algorithms （PPA） are attractive methods for solving monotone variational inequalities （MVI）. Since solving the sub-problem exactly in each iteration is costly or sometimes impossible, various approximate versions ofPPA （APPA） are developed for practical applications. In this paper, we compare two APPA methods, both of which can be viewed as prediction-correction methods. The only difference is that they use different search directions in the correction-step. By extending the general forward-backward splitting methods, we obtain Algorithm Ⅰ; in the same way, Algorithm Ⅱ is proposed by spreading the general extra-gradient methods. Our analysis explains theoretically why Algorithm Ⅱ usually outperforms Algorithm Ⅰ. For computation practice, we consider a class of MVI with a special structure, and choose the extending Algorithm Ⅱ to implement, which is inspired by the idea of Gauss-Seidel iteration method making full use of information about the latest iteration. And in particular, self-adaptive techniques are adopted to adjust relevant parameters for faster convergence. Finally, some numerical experiments are reported on the separated MVI. Numerical results showed that the extending Algorithm II is feasible and easy to implement with relatively low computation load.

基于改进k-means聚类算法的入侵检测研究

提出了一种k-means改进算法,通过考虑样本密度、距离因素选择初始聚类中心,有效克服了经典k-means算法初始值敏感、收敛结果容易陷入局部最优解的缺点。同时引入变异系数法对样本的不同属性在聚类过程中所起的作用不同进行加权处理,全面反映了各个属性对聚类结果的影响程度。最后利用KDD Cup 1999数据集进行仿真实验,结果表明,改进算法有效地提高了入侵检测质量。

UNIFORM CONVERGENCE OF CES■RO MEANS OF NEGATIVE ORDER OF DOUBLE TRIGONOMETRIC FOURIER SERIES

In this paper we prove that if f ∈ C （[-π, π]^2） and the function f is bounded partial p-variation for some p ∈[1, ＋∞）, then the double trigonometric Fourier series of a function f is uniformly （C;-α,-β） summable （α＋β 〈 1/p,α,β 〉 0） in the sense of Pringsheim. If α ＋ β ≥ 1/p, then there exists a continuous function f0 of bounded partial p-variation on [-π,π]^2 such that the Cesàro （C;-α,-β） means σn,m^-α,-β（f0;0,0） of the double trigonometric Fourier series of f0 diverge over cubes.

LOW FREQUENCY VARIABILITY OF INTERANNUAL CHANGE PATTERNS FOR GLOBAL MEAN TEMPERATURE DURING THE RECENT 100 YEARS

The TEEOF method that expands temporally is used to conduct a diagnostic study of the variation patterns of 1, 3, 6 and 10 years with regard to mean air temperature over the globe and Southern and Northern Hemispheres over the course of 100 years. The results show that the first mode of TEEOF takes up more than 50% in the total variance, with each of the first mode in the interannual oscillations generally standing for annually varying patterns which are related with climate and reflecting long-term tendency of change in air temperature. It is particularly true for the first mode on the 10-year scale, which shows an obvious ascending trend concerning the temperature in winter and consistently the primary component of time goes in a way that is very close to the sequence of actual temperature. Apart from the first mode of all time sections of TEEOF for the globe and the two hemispheres and the second mode of the 1-year TEEOF, interannual variation described by other characteristic vectors are showing various patterns, with corresponding primary components having relation with long-term variability of specific interannual quasi-periodic oscillation structures. A 2T test applied to the annual variation pattern shows that the abrupt changes for the Southern Hemisphere and the globe come closer to the result of a uni-element t test for mean temperature than those for the Northern Hemisphere do. It indicates that the 2Ttest, when carried out with patterns of multiple variables, seems more reasonable than the t test with single elements.

A Support Construction for CT Image Based on K-Means Clustering

Computer Tomography in medical imaging provides human internal body pictures in the digital form. The more quality images it provides, the better information we get. Normally, medical imaging can be constructed by projection data from several perspectives. In this paper, our research challenges and describes a numerical method for refining the image of a Region of Interest (ROI) by constructing support within a standard CT image. It is obvious that the quality of tomographic slice is affected by artifacts. CT using filter and K-means clustering provides a way to reconstruct an ROI with minimal artifacts and improve the degree of the spatial resolution. Experimental results are presented for improving the reconstructed images, showing that the approach enhances the overall resolution and contrast of ROI images. Our method provides a number of advantages: robustness with noise in projection data and support construction without the need to acquire any additional setup.

Observed Solar Cycle Variation of the Stratospheric QBO Generated in the Mesosphere and Amplified by Upward Propagating Waves

With an analysis of zonal wind observations over 40 years, Salby and Callaghan [1] showed that the Quasi-biennial Oscillation (QBO) at 20 km is modulated by 11-year solar cycle (SC) variations from about 12 to 20 m/s (Figure 2). The observations are reproduced qualitatively in a study with the 3D Numerical Spectral Model, which shows that the SC effect of the stratospheric QBO is produced by dynamical downward coupling originating in the mesosphere. In this modeling study, the SC period is taken to be 10 years, and a realistic heat source is applied varying exponentially with altitude: 0.2%, surface;2%, 50 km;20%, 100 km and above. The numerical results show that the variable solar radiation in the mesosphere around 65 km generates a hemispheric symmetric Equatorial Annual Oscillation (EAO), which is modulated by relatively large SC variations. Under the influence of wave mean flow interactions, the EAO propagates into the lower atmosphere and is the dynamical source or pacemaker for the large SC modulation of the QBO. The numerical results show that the upward propagating small-scale gravity waves from the troposphere amplify the SC modulations of the QBO and EAO in the stratosphere, part of the SC mechanism. The zonal winds of the equatorial QBO and EAO produce through the meridional circulation measurable SC variations in the temperature of the stratosphere and troposphere at high latitudes. Analysis of NCEP temperature and zonal wind data (1958 to 2006) provides observational evidence of the EAO with SC variations around 11 years.

基于变分图自动编码器与K均值聚类的虚拟网络嵌入算法应用

将虚拟网络映射到物理网络是网络功能虚拟化中一项重要的任务.为了有效地分配虚拟网络请求,需要将虚拟网络嵌入到物理网络拓扑中.然而,由于虚拟网络的复杂性和物理网络的限制,这一任务变得非常具有挑战性.鉴于此,研究在现有虚拟网络嵌入算法(Virtual Network Embedding, VNE)模型基础上进行改进,融入了变分图自动编码器(Variational Graph Auto-Encoders, VGAE),提出了一种新型虚拟网络嵌入算法模型.通过编码器对虚拟网络的嵌入特征进行提取,随后利用K-means聚类算法对所得到的嵌入特征进行分类,最终得到合适的嵌入分配方法.实验结果表明,该新模型相较于其他同类型的嵌入算法性能表现最佳,稳定性最好,其平均嵌入请求接受率为60%,长期平均CPU资源利用率最高达97%.综上所述,研究提出的新型虚拟网络嵌入算法在资源利用率和嵌入质量方面表现出色,能够有效应对复杂的网络环境和大规模的虚拟网络请求.