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 ?     

浅析尾矿库勘察中的钻探工艺

尾矿库勘察中钻探工艺的选取在一定程度上决定了勘察精度,也直接影响着施工成本,笔者根据多年来的现场钻探施工管理,剖析了在钻探施工过程中应着重注意的问题,总结出一套较为行之有效的施工工艺,以飨读者,供同类工程参考。     

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。     

鲁西隆起区奥陶系碳酸盐岩成岩环境演化

根据野外剖面观察和室内薄片鉴定,鲁西隆起区岩石类型主要为碳酸盐岩,包括白云岩、石灰岩及其过渡类型,其次为蒸发岩和膏溶角砾岩。研究区经历了多期次、多类型的成岩作用。储层储集空间主要包括晶间孔、粒间和粒内溶孔、残余粒内孔和粒间孔、铸模孔、溶洞和裂缝。结合研究区地质背景、岩石学特征和成岩作用分析,将该区成岩环境划分为海水成岩环境、表生成岩环境和埋藏成岩环境。其中:表生成岩环境中发育的溶蚀作用是优质储层形成的关键;海水成岩环境和中埋藏成岩环境中发育的白云石化作用对储层也起到了积极作用;而埋藏成岩环境中发育的胶结作用是造成储层物性变差的主要原因。     

裂缝性低渗透油藏储层物性的主控因素及其对产能的影响——以大情字井油田黑43区块为例

裂缝性低渗透岩性油藏物性复杂,控制因素多样,开采难度大,寻找储层物性的主控因素是该类油藏有效开发的基础。以大情字井油田黑43块为例,在对青山口组青一段储层沉积相、岩性特征、裂缝方向、低幅度构造综合评价基础上,分析了低渗透裂缝性油藏储集特征,探讨了储层性质对单井产能的影响。分析认为,黑43区块低渗透储层物性主要受沉积相和裂缝控制,剩余储量分布与构造幅度、油藏内的油水分异差别、储集层岩性变化密切相关,为裂缝性低渗透油藏有效开发提供参考。     

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。