基于特征组合分析的主泵异常检测方法

为了解决传统阈值法在核电站主泵状态数据异常检测中的误判、实时性差等问题,提出一种基于单维状态数据特征分析和多维状态数据特征分析相结合的方法。对于单维状态参量,使用AR(auto regressive)模型拟合获得模型参数,再结合SOM(self organizing maps)神经网络的量化结果得到单维状态参量随时间变化的过渡概率序列;对于多维状态参量,使用OPTICS(ordering points to identify the clustering structure)算法聚类生成不同的模式组;然后根据两类特征提取结果综合分析,得到异常检测模型;最后将检测模型应用于主泵状态数据异常检测,并与其他方法进行比较。实验结果表明此模型在准确性、实时性上更具优势。

Integrated Statistical and Engineering Process Control Based on Smooth Transition Autoregressive Model

Traditional studies on integrated statistical process control and engineering process control (SPC-EPC) are based on linear autoregressive integrated moving average (ARIMA) time series models to describe the dynamic noise of the system.However,linear models sometimes are unable to model complex nonlinear autocorrelation.To solve this problem,this paper presents an integrated SPC-EPC method based on smooth transition autoregressive (STAR) time series model,and builds a minimum mean squared error (MMSE) controller as well as an integrated SPC-EPC control system.The performance of this method for checking the trend and sustained shift is analyzed.The simulation results indicate that this integrated SPC-EPC control method based on STAR model is effective in controlling complex nonlinear systems.

混洗交换网络中解决路由冲突的一种新策略

路由分批是多级混洗交换网络中解决路由冲突的重要途径,但分批方法的复杂性和分批数量的不确定性影响了路由效率.本文在引入序列分割以及路由编码等概念的基础上,提出了一种新的冲突路由检测方法—分割检测法,该方法在时间效率上明显优于窗口检测法;另外,针对2n1级网络,提出一个与路由策略相关的新猜想,用构造性方法证明了n5时猜想的正确性,并基于猜想提出一种新的路由冲突解决方案,该方案实现了2n1(n5)级网络中所有入线信号不多于两批的路由,较好地解决了信号分批路由时的效率问题.