基于摩擦效应的砂岩裂缝密度定量预测

以挤压型盆地内低渗砂岩储层为研究目标,研究应力场与裂缝参数的关系,通过岩石力学实验与理论推导相结合的方法,建立一套基于考虑裂缝面摩擦耗能的裂缝密度定量预测方法。研究表明:岩石变形准则与能量转换法相结合是建立应力场和裂缝参数力学模型的有效途径,当岩石内部应力状态超过破裂条件后,裂缝密度随应变能密度的增加而增大;裂缝的形成除了要克服岩石内在的黏聚力外,还要克服围压与摩擦效应形成的阻力,可使用库仑-莫尔破裂准则进行岩石破裂判别,进而明确应力-应变与岩石产生裂缝密度的数学关系。所建立的模型应用于准西北缘低渗砂岩裂缝密度预测取得了良好的效果。     

一种基于AC-RBF神经网络的网络安全态势预测方法

为了准确地把握网络安全发展态势,提出了一种基于自适应聚类径向基函数(adaptive clustering radical basis function,AC-RBF)神经网络的网络安全态势预测(network security situation prediction, NSSP)方法?该方法对网络安全态势样本自适应聚类,获得了神经网络隐层节点数,采用梯度下降法训练神经网络,寻找网络安全态势样本之间的非线性映射关系,利用该关系对未来时刻网络安全态势进行了预测? 仿真实验表明,相对于 K-均值 RBF 神经网络及支持向量机(support vector machine,SVM)预测模型,该方法在神经网络规模较小的情况下,不仅能够反映网络安全态势的总体趋势,而且还提高了预测精度,能够提供给网络安全管理员一个直观的网络安全态势图 ?     

基于连续隐Markov模型的理论线损率预测研究

提出一种基于连续隐马尔可夫模型的理论线损率预测方法,运用Baum-Welch参数重估修正公式实现CHMM参数优化,建立CHMM模型。依据CHMM模型概率输出判别各可选仿真模型相对于实际系统的有效性。结果表明,该模型运行速度快,在理论线损率预测中有更高的精度。     

基于改进RBF的空间插值算法及其在矿体三维可视化中的应用

为径向基神经网络确定更为优化的初始中心,增强径向基网络的性能。通过采用改进的模拟退火蚁群算法作为径向基神经网络径向基层的训练法,将改进的径向基神经网络模型应用于地层高程的面插值和矿体品位的空间体插值,并与普通克里金法进行交叉验证,优化效果明显,然后利用VC++与OpenGL开发环境开发出矿体可视化系统,结果在结合矿体实际数据进行实例应用的过程中,实效性明显。     

P油田H油组参数解释研究

P油田H油组属于河流——三角洲沉积体系的延伸,具有河流与湖泊双重作用的特点,地层非均质性明显,油水分布复杂,原参数解释标准与岩心分析及试油结论符合度偏低.针对这一问题,利用取心、录井、测井、试油、测试等资料,探究岩电对应规律,优化参数,建立适合研究区的孔隙度、渗透率测井解释模型,为下一步油藏开发调整提供可靠的地质依据.     

Settlement Prediction of Dredger Fill with the Optimal Combination Model

Post-construction settlement has gained increasing attention because it frequently causes engineering problems. A combined model is a commonly used prediction model that overcomes the difficulty of a single model（ i. e., cannot reflect various regulations of settlement at some stages or the entire process）. In this study,the correlation coefficient,maximum error values,and other values were obtained according to the fitting and predicted results of a single model. The coefficient of variation was then introduced to determine the weight of each model forming the combination. The proposed model was used to fit and predict for settlement and overcome the issue of utilizing a single model while determining the weight. The fitting predictive effect was also analyzed using the settlement fitting precision results. The fitting precision of optimizing the combination model is high. The predicted data of the post-construction settlement are closer to the calculated value of the settlement monitoring data. Moreover,the proposed model has good practicability,does not require the interval data of settlement,and restricts the model number. Thus,this model can be applied in the engineering field.     

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters（ i. e.,raw material index inputs） have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network（ KBANN） was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set（ RS）-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.     

上市公司财务困境教学案例分析——以民和牧业为例

本文主要以一家上市公司民和牧业(002234)为例,从盈利能力、营运能力、偿债能力、成长能力、现金流量、杠杆系数和非财务指标等方面分析该公司从2009年至2013年的公司发展营运状况,由此判断它在2014年被特别处理(ST)的原因。     

邢台矿建筑物下充填开采沉陷规律的研究

为了保护井巷、建筑物、水体以及铁路等,使它们免受或少受开采的有害影响,减少地下资源的浪费,邢台矿首次采用建筑物下综合机械化充填采煤技术进行工业广场下开采,本文介绍了建筑下综合机械化充填采煤技术的主要思路,针对7606充填工作面建立岩移观测站,总结了开采沉陷规律及预计参数,并通过7608充填工作面对预计参数进行验证。结果显示:预计移动参数选取合理,预计方法正确,预计结果能较好的反映矸石粉煤灰充填开采的地表移动变形规律。     

基于小波分解的统计模型对小时风速的预测

为提高小时风速的预测精度,提出了基于小波分解和AR模型的混合模型(WD-AR).模型应用小波分解技术将风速序列进行多层分解,再利用AR模型分别对各分解层的风速序列进行预测,最后将预测结果叠加得到预测值.采用河西地区风速观测数据对模型进行分析验证,结果表明:WD-AR模型预测精度指标R,RMSE和MAP E值分别是0.89,0.36和27%,与AR模型相比有了较大的改善,提高了小时风速的预测精度,说明WD-AR模型具有更好的预测能力.