2014 The Second International Conference on Advanced Cloud and Big Data(CBD 2014)

Organizing ＆ Program CommitteesGeneral Conference Co-Chairs - Yi Pan, Geot,gta State umversity, USA - You Chin Fuh, IBM

On the Influence Factors of Second-hand House Prices in First-tier Cities——Empirical Analysis Based on Panel Data

With the arrival of the ＂housing stock＂ in first - tier cities, the second - handhousing^market will become the dominant property market. This ardcle aim to the first - tiercities of second - hand housing prices and new home price index for the empirical analysis, thedata related to the cointegration analysis found that the result of the first -tier cities real estatemarket in China, the new home price index is the significant factors influencing the second -hand house price indexi For Beijing, Shanghai second - hand housing and new home price in-dex time series johans test, found that there exists cointegration relationship between two varia-bles,the new city real estate market prices out of a line on the secondary market have clearguide. Therefore, the real estate market regulation aiming at the first -tier cities and the＂housing stock＂ should take the second - hand housing market as the main direction, startingwith the sale price and influencing factors of new houses. At the same time, in different cities,we should adhere to the city＇ s policies, reflect the policy differentiation, promote the reformof the real estate supply side, and promote the return of housing properties.

Shallow Discussion about the Application of L-band Sounding Seconds Data in the Artificial Precipitation

[Objective] The research aimed to discuss shallowly the application of L-band sounding seconds data in the artificial precipitation. [Method] The characteristics, getting manner and displaying method of L-band sounding seconds data were introduced briefly. Moreover, its application prospect in the artificial precipitation operation was analyzed initially. We aimed to improve its application rate in the artificial precipitation operation. [Result] L-band sounding seconds data had the great improvement in the time-space resolution and the space positioning accuracy aspects when compared with the previous sounding data, and the precision reached the second level. It could provide the high-precision data basis for the assimilation of artificial precipitation numerical model initial field, and improve the numerical model. Moreover, the sounding product could provide the accurate scientific basis for the selection of artificial precipitation operation tool, the determination of operation height and range, and guide the artificial precipitation operation, and improve the operation efficiency. [Conclusion] The research provided the analysis and reference basis for the command of artificial precipitation operation.

Second-Order MaxEnt Predictive Modelling Methodology. II: Probabilistically Incorporated Computational Model (2nd-BERRU-PMP)

This work presents a comprehensive second-order predictive modeling (PM) methodology based on the maximum entropy (MaxEnt) principle for obtaining best-estimate mean values and correlations for model responses and parameters. This methodology is designated by the acronym 2nd-BERRU-PMP, where the attribute “2nd” indicates that this methodology incorporates second- order uncertainties (means and covariances) and second (and higher) order sensitivities of computed model responses to model parameters. The acronym BERRU stands for “Best-Estimate Results with Reduced Uncertainties” and the last letter (“P”) in the acronym indicates “probabilistic,” referring to the MaxEnt probabilistic inclusion of the computational model responses. This is in contradistinction to the 2nd-BERRU-PMD methodology, which deterministically combines the computed model responses with the experimental information, as presented in the accompanying work (Part I). Although both the 2nd-BERRU-PMP and the 2nd-BERRU-PMD methodologies yield expressions that include second (and higher) order sensitivities of responses to model parameters, the respective expressions for the predicted responses, for the calibrated predicted parameters and for their predicted uncertainties (covariances), are not identical to each other. Nevertheless, the results predicted by both the 2nd-BERRU-PMP and the 2nd-BERRU-PMD methodologies encompass, as particular cases, the results produced by the extant data assimilation and data adjustment procedures, which rely on the minimization, in a least-square sense, of a user-defined functional meant to represent the discrepancies between measured and computed model responses.

Second-Order MaxEnt Predictive Modelling Methodology. I: Deterministically Incorporated Computational Model (2nd-BERRU-PMD)

This work presents a comprehensive second-order predictive modeling (PM) methodology designated by the acronym 2nd-BERRU-PMD. The attribute “2nd” indicates that this methodology incorporates second-order uncertainties (means and covariances) and second-order sensitivities of computed model responses to model parameters. The acronym BERRU stands for “Best- Estimate Results with Reduced Uncertainties” and the last letter (“D”) in the acronym indicates “deterministic,” referring to the deterministic inclusion of the computational model responses. The 2nd-BERRU-PMD methodology is fundamentally based on the maximum entropy (MaxEnt) principle. This principle is in contradistinction to the fundamental principle that underlies the extant data assimilation and/or adjustment procedures which minimize in a least-square sense a subjective user-defined functional which is meant to represent the discrepancies between measured and computed model responses. It is shown that the 2nd-BERRU-PMD methodology generalizes and extends current data assimilation and/or data adjustment procedures while overcoming the fundamental limitations of these procedures. In the accompanying work (Part II), the alternative framework for developing the “second- order MaxEnt predictive modelling methodology” is presented by incorporating probabilistically (as opposed to “deterministically”) the computed model responses.

Second-Order MaxEnt Predictive Modelling Methodology. III: Illustrative Application to a Reactor Physics Benchmark

This work illustrates the innovative results obtained by applying the recently developed the 2nd-order predictive modeling methodology called “2nd- BERRU-PM”, where the acronym BERRU denotes “best-estimate results with reduced uncertainties” and “PM” denotes “predictive modeling.” The physical system selected for this illustrative application is a polyethylene-reflected plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation (involving 21,976 uncertain parameters), the solution of which is representative of “large-scale computations.” The results obtained in this work confirm the fact that the 2nd-BERRU-PM methodology predicts best-estimate results that fall in between the corresponding computed and measured values, while reducing the predicted standard deviations of the predicted results to values smaller than either the experimentally measured or the computed values of the respective standard deviations. The obtained results also indicate that 2nd-order response sensitivities must always be included to quantify the need for including (or not) the 3rd- and/or 4th-order sensitivities. When the parameters are known with high precision, the contributions of the higher-order sensitivities diminish with increasing order, so that the inclusion of the 1st- and 2nd-order sensitivities may suffice for obtaining accurate predicted best- estimate response values and best-estimate standard deviations. On the other hand, when the parameters’ standard deviations are sufficiently large to approach (or be outside of) the radius of convergence of the multivariate Taylor-series which represents the response in the phase-space of model parameters, the contributions stemming from the 3rd- and even 4th-order sensitivities are necessary to ensure consistency between the computed and measured response. In such cases, the use of only the 1st-order sensitivities erroneously indicates that the computed results are inconsistent with the respective measured response. Ongoing research aims at extending the 2nd-BERRU-PM methodology to fourth-order, thus enabling the computation of third-order response correlations (skewness) and fourth-order response correlations (kurtosis).

Distributed event-triggered consensus tracking of second-order multi-agent systems with a virtual leader

This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy.

“Second quantitative characterization”and its application in fractured-vuggy carbonate reservoirs

The Ordovician fracture-vug carbonate reservoirs of Tarim Basin,are featured by developed vugs,caves and fractures.The strong heterogeneity results in huge uncertainty when these reservoirs are quantitatively characterized using merely static seismic data.The effective quantitative characterization of the reservoirs has been an urgent problem to be solved.This study creatively proposes the"second quantitative characterization"technique with the combination of dynamic and static data based on the primary static quantitative characterization and fully considering lots of key influence factors when conducting characterization.In this technique,dynamic analysis methods such as well testing,production rate transient analysis,dynamic reserve evaluation and dynamic connectivity evaluation are used to get understandings on this kind of reservoir.These understandings are used as statistical parameters to constrain the inversion of seismic wave impedance to improve the relationship between wave impedance and porosity and determine the fracture-vug morphology,calculate dynamic reserves,and then a more accurate fracture-vugmodel can be selected and used to calculate the oil-water contact inversely based on the results of"second quantitative characterization".This method can lower the uncertainties in the primary quantitative characterization of fracture-vug reservoirs,enhance the accuracy of characterization results significantly,and has achieved good application results in the fracture-vug carbonate reservoirs of Tarim Basin.

随机误差传递的高空气象秒级数据快速校验方法

【目的】为保证气象预报结果的准确性。【方法】提出了考虑随机误差传递的高空气象秒级数据快速校验方法。对高空气象秒级数据进行预处理,包括数据中心化处理、特征点提取。数据中心化的处理方式为实施各组数据的加权平均处理,获得新的数据组作为基准数据;特征点提取使用的处理方法为遍历法,需要遍历全部数据点。考虑数据获取过程中的随机误差传递情况,对预处理后的高空气象秒级数据实施空间一致性检查、内部一致性检查。应用集合经验模态分解EEMD(Ensemble Empirical Mode Decomposition)算法与对比源反演CSI(Contrast Source Inversion)算法构建基于CSI-EEMD的高空气象秒级数据快速校验模型,实施数据得到快速校验。【结果】测试结果表明,该方法能够实现4个气象站数据的快速校验,校验结果的均方根误差与平均绝对误差均低于0.1,实现了数据的质量控制。【结论】将该方法应用于气象站数据校验中,可以实现精准度的数据校验,具有一定的实用价值。

基于L波段探空观测的FY-4A云顶温度产品评估及其在冬季降水相态判识中的应用

利用2019—2021年冬季上海宝山站L波段探空资料对FY-4A云顶温度(CTT)产品进行评估,分析发现,FY-4A的云顶温度产品能够较好地反映单层云的云顶温度,对双层云或多层云的云顶温度则普遍高估(平均高估幅度超过14℃);当探空观测到的云顶高度不超过6 km或云顶温度不低于-20℃时,FY-4A云顶温度产品误差较小,平均偏差约为3℃。根据2021—2022年冬季长三角地区地面站观测的降水现象统计云顶温度与降水相态的关系,并对典型个例进行分析,结果表明,云顶温度低是出现降雪的必要条件之一,绝大部分降雪出现在云顶温度低于-12℃的情况下。FY-4A的云顶温度产品时空分辨率高、精度尚可,可以辅助预报员判识雨雪落区,在雨雪转换过程的预报服务中有较大的应用潜力。

基于自编码器-受限时序卷积网络的数据驱动配电网无功优化策略

配电网中可再生能源渗透率的提高带来了频繁的电压越限问题。作为一种被广泛研究的方法,无功优化方法已经成功应用到配电网中以降低网损、优化电压质量。该文提出一种基于自编码器-受限时序卷积网络的新型数据驱动配电网无功优化策略,该策略通过3个阶段来协调光伏逆变器、电容器组等多种多时间尺度的无功调节设备。首先,将无功优化问题建模为混合整数二阶锥规划问题,求解出历史最优无功调度策略;然后,使用历史运行数据和最优策略训练所提网络模型,并通过矫正层规避不合理结果;在实际运行中,训练好的模型依据系统测量值给出无功优化策略以应对配电网的波动。最后,通过改进IEEE 33节点算例仿真实验验证,所提方法能够达到混合整数二阶锥模型98.80%的准确度而仅消耗其7.14%的时间;与其他流行的深度学习方法相比,具有更佳的性能和更好的实用性。

非线性模型在储渗体预测中的应用

储渗体为地层中具有一定渗透能力的孔隙与裂缝相互叠加而成的独立储集体,储渗体的分布大多与孔、缝、洞的发育存在直接关系。储渗体预测与常规储层预测最大的不同之处在于由于储渗体常分布在裂缝发育地区,所以需要对裂缝进行预测。蓬莱地区须二段致密砂岩孔隙结构复杂,孔渗相关性较差,常规测井资料和方法基本无法识别裂缝,影响了储渗体分类预测。本文选取具有参数少,建模快并适用于研究区测井资料多变量参数特征的随机森林分类算法建立分类模型,可自主评估变量重要性且能平衡误差的随机森林分类算法实现了单井储渗体纵向分布预测。通过对典型井的分析,结合已有钻井的含气性、测试、试采等与孔渗分布区间的相关性总结了储层分类评价标准。运用部分典型井进行建模,达到了96%的储层预测正确率。由此模型作出的储层类型预测解释图,可直观的看出不同类型的储渗体所在井段的位置及有效储层厚度,解决了蓬莱地区须二段致密砂岩储渗体分类预测的难题,为非均质性极强的致密砂岩储渗体分类预测工作提供了一个新的解决办法,具有一定的借鉴性。

基于孕中期临床资料构建子痫前期疾病预测模型的研究

目的探讨孕中期临床资料对构建子痫前期疾病的预测价值。方法选取2019年1月—2022年12月济宁市第三人民医院275例妊娠期孕妇为研究对象,105例子痫前期患者作为子痫前期组,170例妊娠妇女为对照组。通过logistic回归分析、nomogram图、受试者操作特征(receiver operating characteristics,ROC)曲线等方法构建预测模型。结果2组年龄、孕次、产次、流产、存活子女、经产妇、不良孕产史、剖宫产史、身高等差异无统计学意义(P>0.05);经logistic回归分析,身体质量指数(body mass index,BMI)、国际标准化比值(International standard ratio,INR)、纤维蛋白原(fibrinogen,FIB)、舒张压是子痫前期高危风险因素,差异有统计学意义(P<0.05);经ROC曲线、nomogram图分析,BMI、FIB、白蛋白、舒张压能用于诊断子痫前期,差异有统计学意义(P<0.05)。结论孕中期BMI、FIB、白蛋白、舒张压对子痫前期疾病的诊断具有一定预测作用。

基于集合经验模态分解的河北唐山井同震响应特征

集合经验模态分解(EEMD)能够客观真实地从非线性、非平稳信号中提取有用信息,地震观测井的井水位表征的波形信号也是典型的非线性、非平稳信号,因此,集合经验模态分解在获取井水位同震响应信息方面具有重要的应用潜力。通过观测河北唐山井2016~2023年多次井水位同震响应,研究集合经验模态分解对井水位分析处理的优缺点,识别唐山井对远震、近震的井水位同震响应特征,应用地震能量密度经验公式推测唐山井记震能力。结果表明:唐山井水位观测数据秒值在经过集合经验模态分解后,对合适的高频分量进行重构可以压制噪声干扰,有利于观察井水位同震响应特征;对于远场大震引起的振荡型同震响应可以客观真实地进行识别和提取;对于近场地震引起的脉冲型和阶变型同震响应,需结合原始数据进行研究;井水位观测数据秒值有利于揭示区域应力场的变化,因观测数据秒值记震精度提高,唐山井能够记录到地震能量密度为1.77×10^(-7) J·m^(-3)的地震,观测井对不同方位地震的敏感度可用于研究其所在断裂带的裂隙走向。对于超过一定距离的远场地震,井-含水层系统能够记录到的井水位同震响应频率可在一定的范围内估算观测井的固有振动频率,唐山井固有振动频率和地震瑞丽面波频率接近。

高龄二胎产妇剖宫产后子宫瘢痕愈合不良的Nomogram预测模型的构建及其应用价值

目的 构建高龄二胎产妇剖宫产后子宫瘢痕愈合不良(PHUI)的列线图(Nomogram)预测模型,并探讨其临床应用价值。方法 回顾性分析2019年1月至2021年12月新乡医学院第一附属医院收治的277例高龄二胎剖宫产产妇的临床资料,依据所有产妇的子宫瘢痕愈合情况分为子宫瘢痕愈合良好(GHUI)组204例和PHUI组73例,经单因素分析后采用Logistic回归分析高龄二胎产妇剖宫产后PHUI的影响因素,构建Nomogram预测模型,并采用受试者工作特征曲线(ROC)及曲线下面积(AUC)进行临床应用性验证。结果 经单因素分析结果显示,年龄、孕期增重、贫血、既往剖宫产史、子宫位置、手术时机、术者年资、切口与宫颈内口距离、胎膜早破、羊水污染、术后感染是高龄二胎产妇剖宫产后PHUI的影响因素(P<0.05);经Logistic回归分析结果显示,年龄、贫血、既往剖宫产史、子宫位置、手术时机、切口与宫颈内口距离、胎膜早破、羊水污染、术后感染是高龄二胎产妇剖宫产后PHUI的独立影响因素(P<0.05);根据Logistic回归方程构建高龄二胎产妇剖宫产后PHUI的Nomograms预测模型,经检验,该模型C-index为0.921,校准度为0.873,预测高龄二胎产妇剖宫产后PHUI的AUC为0.868 (95%CI:0.822~0.916),敏感度为84.93%,特异度为70.10%;且经临床验证,该模型预测高龄二胎产妇剖宫产后PHUI的AUC为0.885 (95%CI:0.825~0.949),敏感度为85.29%,特异度为77.66%。结论 基于年龄、贫血、既往剖宫产史、子宫位置、手术时机、切口与宫颈内口距离、胎膜早破、羊水污染、术后感染等临床资料构建高龄二胎产妇剖宫产后PHUI的预测模型具有可行性,且其预测价值高,临床应用性可靠。

基于杂波和噪声联合稀疏特性的直接数据域STAP方法

针对机载预警雷达空时自适应处理(space-time adaptive processing,STAP)所面临的异构杂波环境,基于杂波和噪声的联合稀疏特性提出了一种直接数据域(direct data domain,D3)STAP方法。首先通过子孔径平滑技术扩充训练样本集合;然后基于杂波谱二阶表征理论构造STAP功率字典矩阵、导出目标函数,并解得待检测单元信号的空时功率谱;最后根据杂波先验信息重构无孔径损失的杂波加噪声协方差矩阵。数值实验验证了所提方法的协方差矩阵估计精度高于传统的稀疏恢复D3-STAP算法,且在理想情况和存在阵列误差的情况下,所提方法皆具备更好的低速目标检测性能。

基于改进CFSFDP算法的聚类联邦学习

联邦学习是一种基于多个用户协作训练全局模型的分布式训练范式,旨在解决由数据孤岛造成的数据碎片化问题。然而,在真实环境中,参与联邦学习的各个节点的数据分布通常是不平衡的,这会导致联邦学习模型的精确率下降。针对上述问题,提出了基于改进CFSFDP算法的聚类联邦学习方法——FL_CDP。此方法对CFSFDP算法中的局部密度定义进行了优化,并通过二阶差分实现自动选择聚类中心。通过评估客户端模型的推理相似度,对客户端节点进行了聚类,进而缓解了联邦学习中因节点数据分布不均所带来的模型准确度下降问题。在MNIST和FashionMNIST数据集上的实验结果证明,相较于传统的联邦学习算法,基于改进CFSFDP算法的聚类联邦学习算法在模型准确率方面有着显著提升。

基于Python爬虫的阜阳二手房数据可视化分析

以阜阳市二手房市场为例,文章首先利用Python爬虫技术从“58同城”二手房交易网站爬取了阜阳三区五县的在售二手房数据,随后对爬取的数据进行了清洗及预处理,旨在确保数据的准确性和可用性,接着应用Python语言及多种可视化工具对清洗后的数据进行了多维度分析,以揭示阜阳市二手房市场的区域差异、价格分析及热门户型等关键信息,为潜在二手房购买者与房地产交易从业者提供了决策支持。

2021年玛多M_(S)7.4和2022年门源M_(S)6.9地震引起的山东井水位同震响应特征分析

2021年5月22日青海玛多M S7.4地震和2022年1月8日青海门源M_(S)6.9地震引起栖霞鲁07井、枣庄鲁15井和菏泽鲁27井水位不同程度的同震响应。基于秒数据,对比分析3口井水位同震变化形态、幅度与分钟值记录的差异,并从含水层渗透性变化、地震能量密度等方面进行同震响应机理探讨。结果表明,井水位秒数据能够更加完整地记录水震波信息,更加精确展现水位同震变化形态、幅度;井水位对于远场大震的震荡形态主要受含水层水文地质条件的影响。在正常应力背景下,远场大震引起枣庄鲁15井水位同震响应的地震能量密度阈值大约是1.54×10^(-4)J·m^(-3)。

基于高垂直分辨率探空数据对五种典型下垫面湍流分布特征分析

由于湍流的时空尺度小、变化率大,缺乏大规模的现场观测,导致对全国范围内的大气湍流特征研究甚少。自2011年起,我国开始存储秒级高垂直分辨率探空数据,为深入研究中国各地的湍流特征提供了可能。在用探空资料诊断分析湍流特征时,首先分析了中国地区仪器噪音对分析结果的影响,证实噪音会使对流层平均的湍流耗散率偏小,证明了去除噪音的必要性。在此基础上,采用Thorpe分析法详细分析了湍流的强度(湍流耗散率)与下垫面类型的关系。结果显示,在对流层,全年平均草地的湍流最强,然后依次是旱地作物、水田作物、灌木、荒地。在平流层下层依然受到下垫面的影响,全年平均草地的湍流依旧最强,旱地作物次之。湍流在不同下垫面均表现出明显的季节性,且夏季对流层上层的强度和频率最高。研究结果将有助于理解不同下垫面对流层的湍流特征,为航空器飞行安全保障、大气污染扩散模式、天气预报模式的湍流参数化方案改进提供参考。