复杂系统因果图合成建模方法研究

因果图应用中,专家首先要建立复杂系统的因果图模型。但是复杂系统因果图模型非常复杂,专家直接构造非常困难。因此如何使构造的因果图能更真实地反映实际,减少偏差,是值得研究的问题。针对复杂系统建模困难,提出了复杂因果图的合成方法,可以对同一专家构造的因果图以及不同专家构造的因果图分别采用不同的方法进行合成。并通过实例进行了说明。研究表明：复杂系统因果图合成建模法可行有效,简化了因果图的建模方法,为因果图处理专家知识提供了一条有效的途径。     

Predicting software defects with causality tests

In this paper, we propose a defect prediction approach centered on more robust evidences towards causality between source code metrics (as predictors) and the occurrence of defects. More specifically, we rely on the Granger causality test to evaluate whether past variations in source code metrics values can be used to forecast changes in time series of defects. Our approach triggers alarms when changes made to the source code of a target system have a high chance of producing defects. We evaluated our approach in several life stages of four Java-based systems. We reached an average precision greater than 50% in three out of the four systems we evaluated. Moreover, by comparing our approach with baselines that are not based on causality tests, it achieved a better precision.     

Folded Hasse diagrams of combined traces

To represent concurrent behaviours one can use concepts originating from language theory, including traces and comtraces. Traces can express notions such as concurrency and causality, whereas comtraces can also capture weak causality and simultaneity. This paper is concerned with the development of efficient data structures and algorithms for manipulating comtraces. We introduce and investigate folded Hasse diagrams of comtraces which generalise Hasse diagrams defined for partial orders and traces. We also develop an efficient on-line algorithm for deriving Hasse diagrams from language theoretic representations of comtraces. Finally, we briefly discuss how folded Hasse diagrams could be used to implement efficiently some basic operations on comtraces.     

Mesoscopic effective material parameters for thin layers modeled as single and double grids of interacting loaded wires

As an example of thin composite layers we consider single and double grids of periodically arranged interacting wires loaded with a certain distributed reactive impedance. Currents induced to the wires by a normally incident plane wave are calculated analytically and the corresponding dipole moment densities are determined. Using this data and calculated averaged fields we assign mesoscopic material parameters for the proposed grid structures. These parameters depend on the number of grids, and measure the averaged induced polarizations. It is demonstrated that properly loaded double grids possess polarization response that over some frequency range can be described by assigning negative values for the mesoscopic parameters. Discussion is conducted on the physical meaningfulness to assign such material parameters for thin composite slabs. The results predicted by the proposed method for the double-grid structures are compared with the results obtained using the commonly adopted S-parameter retrieval procedure.     

基于因果图基本事件重要度的故障分析方法研究

因果图理论可以进行故障分析，但是缺乏对基本事件的重要性的分析。文章在引入基本事件重要度概念．并将这种新方法成功运用于煤矿瓦斯爆炸的故障分析中。通过基本事件的重要度，分析了各基本事件对顶事件的影响程度和重要程度，找出了故障原因和关键环节，并提出了切实可行的预防措施。研究结果表明，基于基本事件重要度的因果图．是有效地进行故障诊断和安全分析的定性和定量方法．能为事故预防提供可行的对策。     

High-speed train fault detection with unsupervised causality-based feature extraction methods

With the development of smart sensors, large amount of operating data collected from a complex system as a high-speed train providing opportunities in efficient and effective fault detection and diagnosis (FDD). The data brings also challenges in the FDD modelling process, since the various signals may be redundant, useless and noisy for the FDD modelling of a specific sub-system. The data-driven methods suffer also from the curse of dimensionality. Feature dimension reduction can reduce the dimension of the monitoring dataset and eliminate the useless information. Different from the classical methods based on the correlation among variables, recent studies have shown that causality-based methods can make the FDD model more explanatory and robust. From the adjacency matrix of the causal network diagram, three unsupervised causality-based feature extraction methods for FDD in the braking system of a high-speed train are proposed in this paper. By constructing the causal network diagram among the raw monitoring feature variables through the causal discovery algorithm, the proposed methods extract informative features based on the causal adjacency matrix or the full causal adjacency matrix proposed in this work. These methods are adopted for fault detection with real dataset collected from the braking system in a high-speed train to verify their effectiveness. The experimental results show that the proposed causality-based feature extraction methods are effective and have certain advantages in comparison with the classical correlation-based methods. Especially, the feature extraction method based on the correlation matrix constructed from full causal adjacency matrix achieves better and stable results than the benchmark methods in the experiment.     

Efficient distributed snapshots in an anonymous asynchronous message-passing system

We present a global snapshot algorithm with concurrent initiators, with termination detection in an anonymous asynchronous distributed message-passing system having FIFO channels. In anonymous systems, process identifiers are not available and an algorithm cannot use process identifiers in its operation. Such systems arise in several domains due to a variety of reasons. In the proposed snapshot algorithm for anonymous systems, each instance of algorithm initiation is identified by a random number (nonce); however, this is not used as an address in any form of communication. In the algorithm, each process can determine an instant when the local snapshot recordings at all the processes have terminated. This is a challenging problem when an algorithm cannot use process identifiers and a process does not know the number of processes in the system or the diameter of the network and cannot use a predefined topology overlay on the network, because there is no easy way to identify the global termination condition. The message complexity of our algorithm is (cn²)$\left(c{n}^2\right)$, where c$c$ is the number of concurrent initiators and n$n$ is the number of processes in the system, which is much better than that of the algorithm by Chalopin et al. (2012) [6]. Further, the algorithm by Chalopin et al. also requires knowledge of the network diameter.     

The relationship between energy and GNP: Further results

This paper reexamines the casuality between GNP and energy consumption by using updated US data for the period 1947–1979. As a secondary contribution, we investigate the causal relationship between energy consumption and employment. Applying Sims' technique, we find no causal relationship between GNP and energy consumption. We find further that there is a slight unidirectional flow running from employment to energy consumption. Economic interpretations of the empirical results are also presented.     

浙江省人口老龄化系数变动趋势预测

本文根据浙江省1992年至2006年户籍人口数据,利用格兰杰因果检验寻找影响浙江总人口和老年人口数变化的主要因素,从而建立了向量自回归模型。文中在出生率保持常数10.5‰和按年均增长0.1‰两种情况下,分别动态预测浙江省2007年至2050年总人口数和老年人口数、分析老龄化系数变动趋势,并提出了几点建议。     

‘Little wake turbulence,huh?’ Applying a contemporary model of learning and memory to the American Airlines Flight AA587 accident

Loss of control has become a leading cause of aviation accidents and human error is often recorded as the cause in favour of other factors. This has the effect of downgrading the significance of corrective actions to address deeper systemic issues, and serves the bad-apple theory of human error. This paper uses a model of learning and memory to expand on the analysis of negative training from the AA587 accident, which documented the actions of the First Officer as the probable cause and training as contributory. Evidence from the investigation and our contemporary understanding of learning and memory is used to explain how the experiences of the First Officer fit the scenario in which the accident occurred, such that it is plausible to extend probable cause beyond his actions. The paper develops a model of causal inference that enables analysts to continue probing causality as part of a systems approach.