基于BDPCA聚类算法的航空发动机故障数据标记

航空发动机作为飞行器的动力核心对飞行器的安全飞行有着举足轻重的作用,保证航空发动机的平稳运行对飞行安全有着重大意义。在基于有监督学习的航空发动机故障诊断技术不断取得进展的同时,如何将平时获取的大量未标记数据转换为能够用来训练故障诊断模型的带标记数据,成为了制约行业发展的一大瓶颈。针对这一问题引入了基于无监督学习的DPCA算法,用以实现对未标记数据集的准确分类与标记,并针对DPCA算法在局部密度计算与簇类别数选择方面的缺陷进行了优化:针对原始DPCA算法应用标准高斯核计算局部密度易造成误识别的状况,引入共享邻域算法对局部密度的计算方法进行优化;针对原始DPCA算法需要人工研判确定簇类别数易造成的误识别状况,引入BIC选择准则对簇类别数的选择方法进行优化;提出了原始DPCA算法与共享邻域算法以及BIC选择准则相结合的BDPCA算法。最后应用航空发动机转子故障数据对BDPCA算法进行了性能验证并取得了良好的结果,证实了BDPCA算法在航空发动机气路故障诊断领域有较高的实用价值。     

Mixture model selection via hierarchical BIC

The Bayesian information criterion (BIC) is one of the most popular criteria for model selection in finite mixture models. However, it implausibly penalizes the complexity of each component using the whole sample size and completely ignores the clustered structure inherent in the data, resulting in over-penalization. To overcome this problem, a novel criterion called hierarchical BIC (HBIC) is proposed which penalizes the component complexity only using its local sample size and matches the clustered data structure well. Theoretically, HBIC is an approximation of the variational Bayesian (VB) lower bound when sample size is large and the widely used BIC is a less accurate approximation. An empirical study is conducted to verify this theoretical result and a series of experiments is performed on simulated and real data sets to compare HBIC and BIC. The results show that HBIC outperforms BIC substantially and BIC suffers from underestimation.     

Estimating the number of components in a mixture of multilayer perceptrons

Bayesian information criterion (BIC) criterion is widely used by the neural-network community for model selection tasks, although its convergence properties are not always theoretically established. In this paper we will focus on estimating the number of components in a mixture of multilayer perceptrons and proving the convergence of the BIC criterion in this frame. The penalized marginal-likelihood for mixture models and hidden Markov models introduced by Keribin [Consistent estimation of the order of mixture models, Sankhya Indian J. Stat. 62 (2000) 49–66] and, respectively, Gassiat [Likelihood ratio inequalities with applications to various mixtures, Ann. Inst. Henri Poincare 38 (2002) 897–906] is extended to mixtures of multilayer perceptrons for which a penalized-likelihood criterion is proposed. We prove its convergence under some hypothesis which involve essentially the bracketing entropy of the generalized score-function class and illustrate it by some numerical examples.     

Book Review

Book reviewed: B. W. Silverman and J. C. Vassillious Wavelets, (eds.) The key to intermittent information?     

Data-guided model combination by decomposition and aggregation

Model selection and model combination is a general problem in many areas. Especially, when we have several different candidate models and also have gathered a new data set, we want to construct a more accurate and precise model in order to help predict future events. In this paper, we propose a new data-guided model combination method by decomposition and aggregation. With the aid of influence diagrams, we analyze the dependence among candidate models and apply latent factors to characterize such dependence. After analyzing model structures in this framework, we derive an optimal composite model. Two widely used data analysis tools, namely, Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are applied for the purpose of factor extraction from the class of candidate models. Once factors are ready, they are sorted and aggregated in order to produce composite models. During the course of factor aggregation, another important issue, namely factor selection, is also touched on. Finally, a numerical study shows how this method works and an application using physical data is also presented. Editor: Dan Roth     

计及模型定阶的低频振荡模式类噪声信号辨识

弱阻尼低频振荡是影响互联电网安全稳定运行的主要因素,但目前基于测量信息只能在振荡发生后进行告警,而不能预警.大量广域实测数据表明,因负荷的随机变化,电网内持续存在类似噪声信号的小幅波动.文中基于这种类噪声信号,采用自回归滑动平均(ARMA)法进行低频振荡模式辨识,从而实现电网正常运行状态下的动态稳定性预警.模型定阶是利用ARMA法进行振荡模式辨识的关键步骤,直接关系到结果的准确性.在分析比较各种定阶准则优缺点的基础上,选用贝叶斯准则(BIC)确定ARMA模型阶数,进一步面向在线实际应用,采用ARMA(2n,2n-1)建模方案提高辨识速度.最后,将该方法用于对36节点系统仿真数据和南方电网实测类噪声信号进行处理,辨识结果说明了该方法的有效性.     

高速拥塞控制端算法的仿真与分析

传统的TCP拥塞控制算法是一种可在网络速度、负载增加的情况下都能正常工作的稳健算法。然而随着诸如网格等新应用的出现和网络带宽的提高,现有的TCP协议不能在高带宽延迟乘积网络中高效工作,已成为网络性能瓶颈。针对这一不足,研究了最近提出的高速拥塞控制协议,利用NS2工具仿真了这些协议,从瓶颈带宽、公平性、TCP-Friendly等指标上进行了对比和分析。结果表明,它们均存在着一定的缺陷,最后讨论了下一步的研究方向。     

A Simple Method for Generating Additive Clustering Models with Limited Complexity

Additive clustering was originally developed within cognitive psychology to enable the development of featural models of human mental representation. The representational flexibility of additive clustering, however, suggests its more general application to modeling complicated relationships between objects in non-psychological domains of interest. This paper describes, demonstrates, and evaluates a simple method for learning additive clustering models, based on the combinatorial optimization approach known as Population-Based Incremental Learning. The performance of this new method is shown to be comparable with previously developed methods over a set of benchmark data sets. In addition, the method developed here has the potential, by using a Bayesian analysis of model complexity that relies on an estimate of data precision, to determine the appropriate number of clusters to include in a model.     

Phébus-FP: Results and significance for plant safety in Switzerland

Phébus-FP is a multi-national collaborative programme comprising four integral and one debris bed melting and fission product release experiments. The integral experiments simulate the heat-up, degradation and fission product release and transport, thermal hydraulic response, aerosol behaviour and iodine chemistry in the containment. The total programme, including interpretation effort, runs from 1993 to ca. 2007. The experiments demonstrated new characteristics of core degradation, fission product and iodine chemistry and accident integral behaviour. PSI has participated extensively through planning support, interpretation of data from the experiments and analyses of the results to establish confidence in the models being used for plant application. A significant part of the PSI effort was performed to interpret the significance of silver iodide as an effective retention agent under typical reactor conditions. The conclusions are of an interim nature, since the data reduction and interpretation are not yet complete. The significance of the Phébus results for plant safety will be fully realised only after successful benchmarking of the computer codes against Phébus, and application to plant sequences. Such work remains in progress within the Phébus project and in various national programmes.     

一种有效的农业气象灾害的定量性定义方法

至今为止,关于农业气象灾害只有定性的定义而没有统一的定量定义,针对这种情况,该文给出了统一的定量性定义方法。其中关于确定趋势产量的研究,提出了用AIC、BIC来给出统一的标准。在气象产量表示方法的选取方面,提出应对绝对气象产量序列做正态性检验的准则。并以吉林省的30多个市、县的三种主要农作物的数据为例,阐述了如何应用上述方法对农业气象灾害进行定量定义及预测,计算结果表明此方法给出了统一标准,不仅理论上合理,而且实际效果也很好。