The Cloud Model for Climate Change

In 1995, the Intergovernmental Panel on Climate Change (IPCC) released a thermodynamic model based on the Greenhouse Effect, aiming to forecast global temperatures. This study delves into the intricacies of that model. Some interesting observations are revealed. The IPCC model equated average temperatures with average energy fluxes, which can cause significant errors. The model assumed that all energy fluxes remained constant, and the Earth emitted infrared radiation as if it were a blackbody. Neither of those conditions exists. The IPCC’s definition of Climate Change only includes events caused by human actions, excluding most causes. Satellite data aimed at the tops of clouds may have inferred a high Greenhouse Gas absorption flux. The model showed more energy coming from the atmosphere than absorbed from the sun, which may have caused a violation of the First and Second Laws of Thermodynamics. There were unexpectedly large gaps in the satellite data that aligned with various absorption bands of Greenhouse Gases, possibly caused by photon scattering associated with re-emissions. Based on science, we developed a cloud-based climate model that complied with the Radiation Laws and the First and Second Laws of Thermodynamics. The Cloud Model showed that 81.3% of the outgoing reflected and infrared radiation was applicable to the clouds and water vapor. In comparison, the involvement of CO2 was only 0.04%, making it too minuscule to measure reliably.

A Variable Separation Approach to Solve the Integrable and Nonintegrable Models:Coherent Structures of the (2 + 1)-Dimensional KdV Eqnation

We study the localized coherent structures ofa generally nonintegrable (2+ 1 )-dimensional KdV equation via a variable separation approach. In a special integrable case, the entrance of some arbitrary functions leads to abundant coherent structures. However, in the general nonintegrable case, an additional condition has to be introduced for these arbitrary functions. Although the additional condition has been introduced into the solutions of the nonintegrable KdV equation, there still exist many interesting solitary wave structures. Especially, the nonintegrable KdV equation possesses the breather-like localized excitations, and the similar static ring soliton solutions as in the integrable case. Furthermor,in the integrable case, the interaction between two travelling ring solitons is elastic, while in the nonintegrable case we cannot find even the single travelling ring soliton solution.

基于Sentinel-1/2改进极化指数和纹理特征的土壤含盐量反演模型

目前Sentinel-1/2协同反演植被土壤含盐量的研究大多是基于Sentinel-2光谱信息和Sentinel-1后向散射系数,没有考虑Sentinel-2光谱信息容易受土壤亮度等信息影响,Sentinel-1后向散射系数容易受土壤粗糙度和水分影响。为进一步提高Sentinel-1/2协同反演植被土壤含盐量的精度,用水云模型对雷达卫星后向散射系数进行校正,消除植被影响;然后协同Sentinel-2纹理特征,基于VIP、OOB、PCA 3种变量筛选和RF、ELM、Cubist 3种机器学习回归模型构建植被土壤含盐量反演模型。研究结果表明:经过水云模型去除植被影响后的雷达后向散射系数及其极化组合指数与土壤含盐量的相关性有一定程度的提高。不同变量选择方法与不同机器学习方法耦合模型在反演土壤含盐量中,OOB变量筛选方法与RF、ELM和Cubist 3种机器学习方法的耦合模型精度最佳,建模集和验证集的R2都在0.750以上,且验证集的RMSE和MAE均最小;其中OOB-Cubist耦合模型精度最高,且R_(v)^(2)/R_(c)^(2)为0.955,具有良好的鲁棒性。研究可为机器学习协同物理模型、光学卫星协同雷达卫星在土壤含盐量反演中的进一步应用提供思路。

Container Based Nomadic Vehicular Cloud Using Cell Transmission Model

Nomadic Vehicular Cloud(NVC)is envisaged in this work.The predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model(CTM).Containers are used in the place of Virtual Machines(VM),as containers’features are very apt to NVC’s dynamic environment.The specifications of 5G NR V2X PC5 interface are applied to NVC,for the feature of not relying on the network coverage.Nowa-days,the peak traffic on the road and the bottlenecks due to it are inevitable,which are seen here as the benefits for VC in terms of resource availability and residual in-network time.The speed range of high-end vehicles poses the issue of dis-connectivity among VC participants,that results the container migration failure.As the entire VC participants are on the move,to maintain proximity of the containers hosted by them,estimating their movements plays a vital role.To infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity,this paper proposes to apply the CTM to the container based and 5G NR V2X enabled NVC.The simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.

Finite-Size Scaling Analysis of a Three-Dimensional Blume-Capel Model in the Presence of External Magnetic Field

The Blume-Capel model in the presence of external magnetic field H has been simulated using a cellular automaton algorithm improved from the Creutz cellular automaton in three-dimension lattice. The field critical exponent 5 is estimated using the power law relations and the finite size scaling functions for the magnetization and the susceptibility in the range -0.1≤ h = H/J ≤0. The estimated value of the field critical exponent 5 is in good agreement with the universal value （δ = 5） in three dimensions. The simulations are carried out on a simple cubic lattice under periodic boundary conditions.

Study on the P2P Cloud Storage Architecture Based on Semantic Hypergraph

Cloud storage has the characteristics of distributed and virtual, and it makes the ownership rights and management rights of users data separated. The master-slave architecture of cloud storage has a problem of single point failure. In this paper, we provide a cloud storage architecture model based on Semantic equivalence. According to semantic matching degree, this architecture divides the nodes into node cluster by creating semantic tree and maintains system routing through semantic hypergraph. Through simulation experiments show that dividing network into semantic can enhance scalability and flexibility of the system, and it can improve the efficiency of network organization and the security of cloud storage system, at the same time, it can also reduce the cloud data storage and the delay of reading time.

基于Radarsat2与Landsat8协同反演植被覆盖地表土壤水分的一种新方法

主动微波遥感与被动光学遥感在反演地表土壤水分方面分别具有各自的优缺点,为了将这两者的优势结合弥补缺点,提出了一种基于Radarsat 2与Landsat 8数据协同反演植被覆盖地表土壤水分的半经验耦合模型.该模型基于水云模型,将光学遥感反演得到的植被冠层含水量作为水云模型的关键输入参数,并同时考虑植被冠层与土壤以及其之间的部分对雷达后向散射系数的影响,以此来去除雷达回波中的植被部分.最后选用内蒙古呼伦贝尔市额尔古纳市大兴安岭西侧研究区的Radarsat 2与Landsat 8遥感数据,利用新的耦合模型反演得到植被覆盖区土壤水分含量,并利用地面测量数据对模型进行验证.结果表明:利用Landsat 8数据反演植被含水量算法精度较高(R2=0.89),论文提出的耦合模型反演植被覆盖地表土壤水分精度比之前算法也有了较大的提高,其中HH极化效果最好,R2由0.27提高至0.65.这表明该耦合模型具有较好的反演精度,可以应用于植被覆盖区土壤水分含量的反演.

基于改进水云模型和Radarsat-2数据的农田土壤含水量估算

为了直接将雷达遥感中"水云模型"进行反演应用,该研究将"水云模型"中植被参数改为雷达植被指数,利用全极化数据直接支持遥感反演土壤含水量,无需遥感反演植被参数输入。改进模型为利用雷达遥感结合"水云模型"进行土壤含水量监测提供了一种高效便捷方法。基于Radarsat-2全极化数据对冬小麦覆盖的农田土壤含水量进行估算,利用2014年在陕西杨凌区获取的4个生育期内Radarsat-2卫星数据及同步田间测量108组冬小麦农田土壤含水量地面测量数据进行模型参数校正和精度验证。验证结果精度为:改进的雷达植被指数模型>原叶面积指数模型(实测叶面积指数验证)>原叶面积指数模型(光学遥感反演叶面积指数验证),且改进的雷达植被指数模型可以在多个生育期内对农田土壤含水量进行监测。

利用RADARSAT-2雷达数据与改进的水云模型反演冬小麦叶面积指数

利用微波遥感反演植被参数往往受到植被分布不均、稀疏植被覆盖、地表裸土等因素影响,导致微波遥感用于农业参数估计的效果不佳。为解决微波遥感反演地表植被参数的问题,本研究在原有的水云模型基础上引入植被覆盖度以及裸土对于雷达后向散射系数的直接作用信息,提出一种改进的水云模型,并充分考虑地表植被的覆盖分布情况,结合地面实测数据及RADARSAT-2雷达数据对改进模型进行验证,然后根据改进模型通过查找表法反演出植被含水量,最后利用叶面积指数与植被含水量的经验关系间接得到叶面积指数的估测值。结果表明,改进的水云模型对后向散射系数的模拟精度比原有的水云模型精度高,模拟的决定系数在HH和VV极化时分别为0.850和0.739,均方根误差分别为0.918dB和1.475dB。由此可见,改进的模型对研究区植被条件更为敏感,能够较好地分离出植被与土壤信息对雷达后向散射系数的影响,同时利用其反演得到的叶面积指数精度较高,决定系数达到0.841,均方根误差为0.233。

应用双频合成孔径雷达(SAR)数据和干涉水云模型估算森林生物量

以河北省承德市塞罕坝机械林场为研究区域,应用哨兵1号(Sentinel-1A)C波段双极化数据和大地2号L波段全极化数据分别建立干涉水云模型(IWCM)估算森林地上生物量。结果表明:大地2号(ALOS-2)L波段全极化数据体散射比例为72%,二面角散射比例为4%;L波段HV极化和VH极化的估算结果较好,HV极化估算结果的决定系数(R^(2))为0.737,均方根误差(R_(MSE))为28.88 t/hm^(2);VH极化估算结果的决定系数(R^(2))为0.743,均方根误差(R_(MSE))为27.76 t/hm^(2);C+L波段的组合方式反演结果最优,决定系数(R^(2))为0.863,均方根误差(R_(MSE))为18.92 t/hm^(2)。采用干涉水云模型估算森林生物量,L波段合成孔径雷达(SAR)数据估算精度高于C波段估算精度,L波段SAR数据更适合进行森林生物量的估算;C波段和L波段SAR融合对森林生物量的估算精度显著提高。

基于Sentinel-1/2遥感数据的冬小麦覆盖地表土壤水分协同反演

冬小麦是我国重要粮食作物之一,对冬小麦覆盖地表土壤水分进行监测有助于解决因土壤供水导致的冬小麦歉收和农业用水浪费等问题。为了降低冬小麦覆盖地表土壤水分微波遥感反演过程中冬小麦对雷达后向散射系数的影响,该文基于Sentinel-1携带的合成孔径雷达(SAR)数据和Sentinel-2携带的多光谱成像仪(MSI)数据,结合水云模型,开展冬小麦覆盖地表土壤水分协同反演研究。首先,基于MSI数据,该文定义了一种新的植被指数,即融合植被指数(FVI),用于冬小麦含水量反演;然后,该文发展了一种基于主被动遥感数据的冬小麦覆盖地表土壤水分反演半经验模型,校正冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;最后,以河南省某地冬小麦农田为研究区域,开展归一化水体指数(NDWI)和FVI两种指数与VV,VH,VV/VH 3种极化组合而成的6种反演方式下的土壤水分反演对比实验。结果表明:以FVI为植被指数,能够更好地去除冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;6种反演方式中,FVI与VV/VH组合下的反演效果最优,其决定系数为0.7642,均方根误差为0.0209 cm^(3)/cm^(3),平均绝对误差为0.0174 cm^(3)/cm^(3),展示了该文所提土壤水分反演模型的研究价值和应用潜力。

Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm

A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise(DBSCAN)algorithm.To identify convective and stratiform clouds in different developmental phases,two-dimensional(2D)and three-dimensional(3D)models are proposed by applying reflectivity factors at 0.5°and at 0.5°,1.5°,and 2.4°elevation angles,respectively.According to the thresholds of the algorithm,which include echo intensity,the echo top height of 35 dBZ(ET),density threshold,andεneighborhood,cloud clusters can be marked into four types:deep-convective cloud(DCC),shallow-convective cloud(SCC),hybrid convective-stratiform cloud(HCS),and stratiform cloud(SFC)types.Each cloud cluster type is further identified as a core area and boundary area,which can provide more abundant cloud structure information.The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing,Xuzhou,and Qingdao.The results show that cloud clusters can be intuitively identified as core and boundary points,which change in area continuously during the process of convective evolution,by the improved DBSCAN algorithm.Therefore,the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification.Because density thresholds are different and multiple elevations are utilized in the 3D model,the identified echo types and areas are dissimilar between the 2D and 3D models.The 3D model identifies larger convective and stratiform clouds than the 2D model.However,the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds.In addition,the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.

Nonlinear dynamical wave structures of Zoomeron equation for population models

The nonlinear dynamical exact wave solutions to the non-fractional order and the time-fractional order of the biological population models are achieved for the first time in the framwork of the Paul-Painlevéapproach method(PPAM).When the variables appearing in the exact solutions take specific values,the solitary wave solutions will be easily obtained.The realized results prove the efficiency of this technique.

韧性理念下地铁火灾灾害风险二维云模型预警

在灾害韧性理念指导下,分析地铁消防系统韧性,进行地铁火灾灾害风险预警分析。首先分析孕灾环境、承灾体、致灾因子组成的地铁火灾灾害系统,考察地铁火灾灾害事故。结合韧性管理4M1E-4Rs构建了地铁火灾灾害风险预警分析框架。依据地铁防火相关标准,并参考借鉴地铁火灾灾害相关参考文献,建立地铁火灾灾害预警评价指标体系。针对地铁火灾灾害风险评价指标的模糊性和随机性,选用云模型进行预警分析。综合考虑风险矩阵中的风险可能性和风险后果,建立二维云模型进行分析。以风险概率和风险后果作为二维云模型的维度,采用熵权法和CRITIC法确定指标组合权重。最后利用云发生器matlab编程计算概率风险云和后果风险云的云数字特征,生成云滴及风险预警云图,并与标准云图进行对比。将可视化的云图配合接近度综合判定风险预警等级。运用该方法对某地铁车站进行火灾灾害风险预警分析,为地铁火灾灾害风险预警提供科学解决思路和方法。

多策略ATLAS数据优选与影像匹配相结合的SAR高程控制点提取

为了精化星载SAR影像几何参数并提高立体定位精度,借鉴星载激光测高数据光学遥感影像高程控制点提取思路,设计了一种多策略高级地形激光测高系统(ATLAS)数据优选与影像匹配相结合的SAR高程控制点提取方法。该方法采用非夜间观测光子滤除、高置信度光子选取、SRTM DEM辅助的粗差剔除、大偏心率椭圆滤波核平坦区域光子筛选等多种策略,从ATLAS数据ATL03级产品中提取高质量、平坦区域的激光高程点,再依据SRTM DEM对斜距SAR影像进行地理编码,按激光高程点的平面坐标选取局部谷歌地球影像作为足印影像,采用秩自相似描述子进行足印影像与SAR地理编码影像的匹配,得到与激光高程点对应的SAR影像像点坐标,从而提取SAR高程控制点。采用中国登封市、日本横须贺市两个区域的ATLAS数据进行了高分三号SAR高程控制点提取实验,利用提取的高程控制点进行SAR影像几何参数精化,大幅提升了立体定位精度,验证了该文高程控制点提取方法的可行性和有效性。

基于二维多规则云模型定性推理的场地分类方法

针对场地土及其分类的模糊性和随机性特征,建立了基于二维多规则云模型定性推理的场地分类方法,实现了场地分类边界不确定性划分.设计了场地分类的二维多规则定性推理生成器,将其应用于场地类别和特征周期的识别过程中,实现了场地分类和特征周期的连续化,克服了在分类边界处易出现类别跳跃等不合理现象,基本实现了现行建(构)筑物抗震规范在场地分类方面的统一.最后利用该方法对74个实例进行分析,场地分类的正确率达到97.3%,表明了该方法是可行的、有效的.

基于云模型的用户群负荷调控算法

为平衡风电功率输出波动,优化调控用户群负荷资源,提出一种基于云模型的用户群负荷调控算法。用户群依托以负荷聚合商为中间商的运营模式实现用户群负荷的调控,以风电信息和用户负荷状态信息的实时交互为手段,动态跟踪风电功率波动。设计二维云任务发生器内部映射规则矩阵,以超短期风电功率波动为二维云任务发生器输入,以负荷聚合商管辖的用户群负荷可调余量为约束条件,得出负荷聚合商实时调控任务。通过对其管辖范围内的用户群负荷的优化配置,有效平衡风电功率输出波动。最后,通过仿真分析验证了该算法的有效性。

定性推理生成器在驾驶疲劳检测中的应用

疲劳驾驶是导致交通死亡事故的原因之一。为检测识别驾驶疲劳状态,该文鉴于人的眼动行为存在随机性及模糊性的特点,建立了基于"眼睛闭合时间比"(Per-clos)和眨眼时间均值的二维云模型,根据这2个眼动参数的云模型特征,构建了14条定性规则。依据二维单规则生成器,构造了二维多规则定性推理生成器。对60例数据,进行识别判断。结果表明:平均识别率达到73.98%。同组实验数据下,使用该方法比采用K最近邻(KNN)及支持向量机(SVM)分类算法的检测率要高。随着训练样本人数增多,生成器的识别率预计可进一步提高。

基于二维云模型的数据库安全等级评估研究

针对信息安全等级保护测评中数据库安全等级判定存在模糊性和不完整性,提出了关于二维云模型的数据库安全等级评估方法。通过对数据库和数据自身的测评结果等级进行划分,构造数据库安全等级评估规则;通过云发生器依据每条规则的两组数字特征构建二维云模型的数据库安全等级评估云图,再根据评估云图投影出等级评估的投影面,实现数据库安全等级评估的定量划分。通过实例验证,上述方法简单有效可行,具有一定的应用价值。

基于拟合的二维正态云模型

分析了现有的正向/逆向云生成器实现算法的不足之处,并在此基础上提出了基于拟合思想的正向/逆向云生成算法。通过将概念空间划分为几个子空间,然后再在各子空间上使用正向/逆向正态隶属云生成算法来拟合整个概念空间上问题的描述。在拟合过程中,子空间的划分数目是影响拟合效果的主要因素。通过一系列实验分析就子空间的划分数目给出了一个建议数目。最后,实例分析和比较结果表明了该算法的可行性和效率。