基于模态分解和云粒子网络的大坝基岩地震动输入研究

在大坝抗震安全评价中基岩地震动输入多采用实测数据或人工生成等方式,而当坝址仪器损坏或历史震害资料不足时,确定基岩地震动就变得尤为困难。本文提出对大坝基岩地震动进行反演的研究思路,并开发了基于经验模态分解和云粒子网络的分解—训练—反演混合模型,在不依赖场地历史震害资料的情况下,仅用少量周边测站数据即可确定大坝的基岩地震动。首先,选取坝址周边地表及基岩的地震动实测记录,采用经验模态分解法将地震加速度序列分解;其次,通过粒子群算法建立与神经网络连接权值的映射,采用云理论优化粒子群算法的全局寻优能力,建立反演模型,将分解后的加速度序列作为训练集进行反演训练;然后,选取与大坝处于相似地质情况的地表实测地震动信息,结合反演模型对大坝基岩输入地震动进行反演;最后,以紫坪铺大坝为研究实例,通过对比传统输入方法,验证该模型的适用性。结果表明：本文所提的混合模型综合性能稳定,能较好地反演地震加速度序列,模型决定系数均大于0.9,平均绝对百分比误差均在11%左右;采用本文反演得到的基岩地震动进行计算,较已有研究成果计算误差降低0.79%~17.28%,与工程实际动力响应更为吻合。本文方法可为解决大坝基岩输入地震动的获取提供一条新途径。     

Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields

This paper presents a new approach to improve the performance of neural network method to PVT oil properties prediction. The true value of PVT properties which is determined based on the accurate data is a challenge of the petroleum industry. The main goal of the following investigation would be the performance comparison of various back-propagation learning algorithms in neural network that could be applied for PVT prediction. Up to now, no procedure has been presented to determine the network structure for some complicated cases, therefore; design and production of neural network would be almost dependent on the user's experience. To prevent this problem, neural network based recommended procedure in this study was applied to present the advantages. To show the performance of this procedure, several learning algorithms were investigated for comparison. One of the most common problems in neural network design is the topology and the parameter value accuracy that if those elements selection was correctly and optimally, the designer would achieve better results. Since, fluids of different regions have varying hydrocarbon properties, therefore, the empirical correlations in different hydrocarbon systems should be investigated to find their accuracies and limitations. In this study, an investigation of different empirical correlations along with the artificial neural networks in Iran oilfields has been presented. Then, the new model of artificial neural network for prediction of PVT oil properties in Iran crude oil presented. To test this new method, it was evaluated by collecting dataset from 23 different oilfields in Iran (south, central, western and continental shelf). In this study, two networks for prediction of bubble point pressure values (P_b) and the oil formation volume factor at bubble point (B_ob) were designed. The parameters and topology of the optimum neural networks were determined and in order to consider the effect of these networks designing on results, their performances were compared with various empirical correlations. According to comparison between the obtained results, it shows that the improved method presented has better performance rather than empirical and current methods in neural network designing in petroleum applications for these predictions.     

基于核主分量分析的经验模式分解及其工程应用

若信号的信噪比较小,经验模式分解不能正确分解出基本模式分量,分量中含有伪分量。根据此种情况,提出一种核主分量分析与经验模式分解相结合的方法。该方法首先建立信号相空间,利用核主分量分析方法提取相空间的核主分量,然后利用投影逆过程将得到的核主分量逆向投影回原相空间,从而重建信号相空间。最后对重建的相空间所对应的信号作经验模式分解。此方法可以有效消除噪声和冗余对经验模式分解的影响,提高经验模式分解的适应能力保证分解的有效性,确保其能够分解出正确的基本模式分量。通过工程实例进一步验证了该方法的可行性。     

Evaluation and Improvement of a SVD-Based Empirical Atmospheric Model

T An empirical atmospheric model (EAM) based on the singular value decomposition (SVD) method is evaluated using the composite El Nino/Southern Oscillation (ENSO) patterns of sea surface temperature (SST) and wind anomalies as the target scenario. Two versions of the SVD-based EAM were presented for comparisons. The first version estimates the wind anomalies in response to SST variations based on modes that were calculated from a pair of global wind and SST fields (i.e., conventional EAM or CEAM). The second version utilizes the same model design but is based on modes that were calculated in a region-wise manner by separating the tropical domain from the remaining extratropical regions (i.e., region-wise EAM or REAM). Our study shows that, while CEAM has shown successful model performance over some tropical areas, such as the equatorial eastern Pacific (EEP), the western North Pacific (WNP), and the tropical Indian Ocean (TIO), its performance over the North Pacific (NP) seems poor. When REAM is used to estimate the wind anomalies instead of CEAM, a marked improvement over NP readily emerges. Analyses of coupled modes indicate that such an improvement can be attributed to a much stronger coupled variability captured by the first region-wise SVD mode at higher latitudes compared with that captured by the conventional one. The newly proposed way of constructing the EAM (i.e., REAM) can be very useful in the coupled studies because it gives the model a wider application beyond the commonly accepted tropical domain.     

基于经验模式分解三相流型信号去噪方法研究

经验模式分解(EMD)作为一种信号处理技术,它是基于数据本身的,且能在空间域中将信号进行分解,从而可以区分噪声和有用信号。应用经验模式分解(EMD)对模拟信号及实测油气水三相流压差波动信号进行滤波去噪处理,分别与db8小波及Haar小波的滤波去噪效果进行定量比较。结果表明:EMD方法与小波方法一样能有效地处理短时瞬态及含宽带噪声的信号,但EMD方法更直接,更方便,且不受小波基函数选择的影响,因此EMD方法更具有通用性和稳定性,从而为三相流流型信号的预处理提供了又一种有效的方法。     

SAR测雨反演算法MREA的研究

介绍了利用X-SAR的归一化雷达散射截面(NRCS)来反演地表降雨量的算法:改进型回归经验算法(MREA),依据测雨反演算法的基本原理,重点分析了MREA算法的理论依据,对于矩形降雨分布在地表降雨量R=50 mm/h和R=100 mm/h的条件下用MREA算法进行反演,指出了NRCS随x轴坐标不断变化这一导致MREA算...     

基于周期波动及其影响分析的电力市场中长期电价预测

电力市场中的中长期电价受众多不确定因素影响,很难用传统的经验统计方法建模预测。现有的预测方法一般从系统模拟角度出发进行预测,需要较多的系统信息资料,难度很大。本文采用经验模式分解将电价分解成多个相对独立的分量,详细分析不同的因素对于这些分量的影响,从不同的时间尺度分析电价的影响因素。根据分量的特点有针对性地选择时间序列和支持向量机建模预测。实例研究表明该方法适合于分析和预测复杂的中长期电价。     

基于数据降噪的冷轧板形调控功效系数获取

 针对轧制过程实际数据噪声大、难以获取准确板形调控功效系数的问题,提出了一种融合集成经验模态分解(EEMD)和小波变换(WT)的数据降噪方法。将含有噪声的实际生产数据经过EEMD分解后,利用小波变换方法对噪声主导的本征模态分量(IMF)进行降噪处理,处理后的噪声分量与其余分量重构得到降噪后数据,并结合结构方程模型(SEM)计算得到板形功效系数。利用1 450 mm五机架冷连轧生产线实际数据进行试验,结果表明,EEMD-WT-SEM方法可以有效降低数据噪声,有效提升板形调控功效系数的准确性。     

Recent Developments in Predicting Impact and Shock Sensitivities of Energetic Materials （英）

Empirical, quantum mechanical and artificial neural network methods are three usual methods in recent years that were used to predict sensitivity of different classes of high explosives. Some recent developments in predicting sensitivity by various methods are reviewed and discussed for various classes of energetic materials.     

相依样本下带有讨厌参数的经验似然比置信区域

本文将条件θ_0=EH(X,μ)推广到更一般的估计方程EH(X,θ_0,μ)=0,并且在样本为强平稳φ混合序列情形下讨论带有讨厌参数的经验似然比置信区域。