独立元子空间算法及其在故障检测上的应用

针对高维数据建模问题,提出一种独立元子空间算法(ICSM),作为一种新的集成学习算法,ICSM利用独立元在不同变量上的贡献度来选取子空间,符合了集成学习的要求,具备了明确的物理意义,有效地克服了随机子空间算法(RSM)的主要缺点。在此基础上,进一步将ICSM应用于工业过程监控,提出了一种新的ICSM-PCA故障检测算法。首先在各个子空间内分别建立相应的PCA监测模型,然后根据T~2和SPE统计量的值计算出集成时各自的权重,最后构造两个集成统计量对工业过程进行监测。通过在Tennessee Eastman(TE)模型上的仿真研究,说明提出的算法具有较好的建模效果和故障检测能力。     

一种基于聚类融合和最小生成树的重叠社交网络划分方法

为解决重叠社交网络划分过程中涉及群体内容私密性的问题,提出一种基于聚类和最小生成树的社交网络划分方法。该方法首先以社交网络固有特征——成员之间联系的紧密度等属性来衡量节点之间的距离,然后使用K means算法产生初始聚类,并将聚类结果表述为矩阵和不带环无向图的形式,并对无向图采用Prim算法得出最小生成树,删减最小生成树中距离较远的K条边得到K个最小生成树,最后利用相似度解决离群点以及重叠点的社交网络成员的归属。实验表明：该算法并未涉及社交网络成员的隐私,得到的社区结构质量较高,并有效地考虑了重叠社区成员以及孤立成员的划分。     

Partial oxidation of methane to synthesis gas over α-Al2O3-supported bimetallic Pt–Co catalysts

The partial oxidation of methane to synthesis gas was studied at atmospheric pressure and in the temperature range of 550–800°C over -Al₂O₃-supported bimetallic Pt–Co, and monometallic Pt and Co catalysts, respectively. Both methane conversion and CO selectivity over a bimetallic Pt_0.5Co₁ catalyst were higher than those over monometallic Pt_0.5 and Co₁ catalysts. Furthermore, the addition of platinum in Pt–Co bimetallic catalysts effectively improved their resistance to carbon deposition with no coking occurring on Pt_0.5Co₁ during 80 h reaction. The FTIR study of CO adsorption observed only linearly bonded CO on bimetallic Pt–Co catalysts. TPR and XPS showed enhanced formation of a cobalt surface phase (CSP) in bimetallic Pt–Co catalysts. The origins of the good coking resistivity of bimetallic Pt–Co catalysts were discussed.     

聚合经验模态分解和小波变换相结合的地震信号衰减分析

常规经验模态分解（EMD）方法存在模态混叠等问题会造成多解性,从而影响含气层弱信号的识别。为此,引入可改进模态混叠的聚合EMD（EEMD）方法,结合小波变换从地震数据中可提取具有明确物理和地质意义的新地震属性进行含气性检测。具体步骤为：1对地震道进行EEMD。选取具有强相关性的IMF进行重构生成地震道的特征高频子信号;2衰减梯度计算。针对EEMD处理后的IMF高频信号特征,采用改进的动态频率域窗函数,逐点计算地震道特征子信号小波时频图中各点处的对数能量,并逐道、逐点计算地震数据体的衰减梯度;3利用最小二乘法和选择频率域动态窗函数提高衰减梯度的预测精度。对川西海相碳酸盐岩的储层预测结果表明,利用文中方法可以刻画各相带储层的不同强振幅异常特征,尤其对台内滩相储层的强振幅异常更灵敏,可以有效地识别宽带地震响应中特定频率的强振幅异常,同时能较好地抑制地层等影响因素,给出的含烃类统计性解释结果与已知井的含气测试结果相吻合。     

基于多次数据吸收集合平滑算法的自动油藏历史拟合研究

针对常见历史拟合方法存在计算量大、油藏参数更新异常、油藏模型修正失真等问题.采用集合平滑算法,通过引入集合卡尔曼滤波算法(EnKF)中多次迭代思路,对相同数据重复吸收,推导出多次数据吸收集合平滑算法(ES-MDA)的核心公式,并编写了自动油藏历史拟合软件.以北海布伦特油田海相砂岩油藏为例,将基于ES-MDA算法的油藏自...     

Security anomaly detection in software‐defined networking based on a prediction technique

Nowadays, software‐defined networking (SDN) is regarded as the best solution for the centralized handling and monitoring of large networks. However, it should be noted that SDN architecture suffers from the same security issues, which are the case with common networks. As a case in point, one of the shortcomings of SDNs is related to its high vulnerability to distributed denial of service (DDoS) attacks and other similar ones. Indeed, anomaly detection systems have been considered to deal with these attacks. The challenges are related to designing these systems including gathering data, extracting effective features, and selecting the best model for anomaly detection. In this paper, a novel combined approach is proposed; this method uses NetFlow protocol for gathering information and generating dataset, information gain ratio (IGR), in order to select the effective and relevant features and ensemble learning scheme (Stacking) for developing a structure with desirable performance and efficiency for detecting anomaly in SDN environment. The results obtained from the experiments revealed that the proposed method performs better than other methods in terms of enhancing accuracy (AC) and detection rate (DR) and reducing classification error (CE) and false alarm rate (FAR). The AC, DR, CE, and FAR of the proposed model were measured as 99.92%, 99.83%, 0.08%, and 0.03%, respectively. Furthermore, the proposed method prevents the occurrence of excessive overload on the controller and OpenFlow.     

An Improved Particle Swarm Optimization Algorithm Based on Ensemble Technique

An improved particle swarm optimization （PSO） algorithm based on ensemble technique is presented. The algorithm combines some previous best positions （pbest） of the particles to get an ensemble position （Epbest）, which is used to replace the global best position （gbest）. It is compared with the standard PSO algorithm invented by Kennedy and Eberhart and some improved＇PSO algorithms based on three different benchmark functions. The simulation results show that the improved PSO based on ensemble technique can get better solutions than the standard PSO and some other improved algorithms under all test cases.     

一种新的外测数据随机误差分离方法

主要研究了外测数据随机误差分离问题。利用模型函数逼近趋势项的方法建模较为复杂,小波变换的方法需预先确定小波基和分解层数。基于经验模式分解(EMD)的趋势项消除方法由于EMD分解可能会有模态混叠效应和分解的本征模态函数层数冗余的特性,导致该方法分解的余项不一定能准确逼近趋势项。在此方法基础上提出了基于总体经验模式分解(EEMD)的随机误差分离方法,利用EMD分解的频率特性和能量特性,提出了利用能量比例法来确定用来逼近趋势项的本征模态的层数,进而分离随机误差的方法。理论仿真和实测数据证明了该算法的有效性和实用性。     

基于量子云模型反馈的协同精英属性均衡优势集成约简

为进一步增强进化算法在最小属性约简过程中全局求解性能,提出了一种基于量子云模型反馈的协同精英属性均衡优势集成约简算法,该算法首先设计一种基于云模型反馈的量子自适应旋转角调整策略,使量子蛙群精英在云模型定性知识和罚因子反馈指导下自适应控制属性搜索空间范围;然后构建一种有限理性区域下协同精英均衡优势属性分解框架,在动态精英演化区域内使参与属性约简的量子蛙群精英在平均权重裕度下协同化达到Nash均衡优势区域;最后量子蛙群精英采用集成化操作机制在各自均衡优势区域内协同提取属性约简子集,从而稳定取得全局最优约简特征集。实验结果表明本文算法求解全局最优属性约简集效率、精度和稳定性等具有明显优势,应用到孕龄新生儿脑MRIs电子病历分割进一步表明该算法具有较强的应用性能。