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。     

我国税收收入与经济增长关系的协整性分析

利用协整理论,以中国1994年～2006年数据为基础,对中国税收收入总量与国民生产总值之间的协整性进行了检验,并建立了它们之间的误差修正模型.研究分析结果表明,中国税收收入与经济增长之间存在协整关系和短期调整的机制,即它们之间存在长期的均衡关系.同时通过对中国税收收入与经济增长两者之间的Granger因果关系检验,也表明了中国税收收入总量与国民生产总值两者之间存在相互促进的效应关系.     

Evidence of causality between the quantity and quality of energy consumption and economic growth

The aim of this paper is to re-examine the energy–GDP relationship for the US for the period 1946–2000 by redefining energy in terms of exergy (the amount of energy available for useful work) and the amount of useful work provided from energy inputs. This enables us to examine whether output growth depends on either the quantity of energy supplied and/or the efficiency of energy use. Two multivariate models were estimated involving GDP, capital, labour and the two measures of energy. We find that unidirectional causality runs from either energy measure to GDP. We attribute the causation to both short- and long-run effects in the case of exergy, but only long-run effects in the case of useful work. We find no evidence of causality running from GDP to either energy measure. We infer that output growth does not drive increased energy consumption and to sustain long-term growth it is necessary to either increase energy supplies or increase the efficiency of energy usage. Faced with energy security concerns and the negative externalities of fossil fuel use the latter option is preferred.     

Causal relationship between energy consumption (EC) and GDP: A Markov-switching (MS) causality

This paper re-examines the causal relationship between energy use and Gross Domestic Product (GDP) in the United States for the period 1960-2005. To that end, we use Markov-switching vector autoregressive (MS-VAR) models, rather than vector autoregressive (VAR) models, which allows for regime shifts. These models are capable of detecting changes in the relationship between variables; in addition, the coefficients of the model are time dependent and they depend on the states of the variables. Therefore, in contrast to VAR and vector error correction models (VECM), which assume a stable relationship, the relationship between the variables could be different in the separate regimes.Results from the estimation of MS models show changes in the pattern of causality relationship between GDP and energy use. That is, we found evidence of bidirectional Granger causality (GC) between the variables in the first regime, while there is no GC between the variables in the second regime. The first regime consists of 1971-1975, 1977-1982, 1989-1995, and from 2001 to the end of the sample. This regime includes the energy crises in 1970s, the recessions in the early 1980s, 1990s, and the recession in 2001.     

A causal approach to nonmonotonic reasoning

We introduce logical formalisms of production and causal inference relations based on input/output logics of Makinson and Van der Torre [J. Philos. Logic 29 (2000) 383–408]. These inference relations will be assigned, however, both standard semantics (giving interpretation to their rules), and natural nonmonotonic semantics based on the principle of explanation closure. The resulting nonmonotonic formalisms will be shown to provide a logical representation of abductive reasoning, and a complete characterization of causal nonmonotonic reasoning from McCain and Turner [Proc. AAAI-97, Providence, RI, 1997, pp. 460–465]. The results of the study suggest production and causal inference as general nonmonotonic formalisms providing an alternative representation for a significant part of nonmonotonic reasoning.     

Ethics,science, and the mechanisation of the world picture

A nascent science in the sixteenth century rejected explanations in terms of purpose in favour of causality, and this bias has persisted and grown stronger. It has unfortunate consequences in areas where social and ethical considerations should prevail, and the paper describes a search extending over 20 years for a way in which these consequences could be avoided.

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.     

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.     

Distributed algorithm to detect strong conjunctive predicates

This paper presents an on-line distributed algorithm for detection of Definitely(φ) for the class of conjunctive global predicates. The only known algorithm for detection of Definitely(φ) uses a centralized approach. A method for decentralizing the algorithm was also given, but the work load is not fairly distributed and the method uses a hierarchical structure. The centralized approach has a time, space, and total message complexity of O(n²m), where n is the number of processes and m is the maximum number of messages sent by any process. The proposed on-line distributed algorithm uses the concept of intervals rather than events, and assumes p is the maximum number of intervals at any process. The worst-case time complexity across all the processes is O(min(pn²,mn²)). The worst-case space overhead across all the processes is min(2mn²,2pn²).     

The future of research in understanding and controlling work-related low back disorders

Our knowledge of low back disorder (LBD) causation has progressed well over the years with in-depth understanding accelerating in the traditional disciplines of biomechanics, psychology, psychophysics, psychosocial, physiology, genetics, organizational psychology and rehabilitation. However, each of these disciplines has studied LBD causality in isolation of other disciplines. The underlying assumption is that each discipline can fully explain causality and each discipline is treated as if it were mutually exclusive and exhaustive of the other disciplines. Hence, the body of knowledge has progressed along research silos where we have in-depth knowledge along given research tracks that are defined by the boundaries of the discipline. Furthermore, a wealth of knowledge has been amassed within each of these research silos. How can they all be correct if they are indeed mutually exclusive and exhaustive? The answer is: they cannot be. This brief review of the state-of-the art in LBD research applied to ergonomics, suggests that instead of observing LBD through the myopic lens of each discipline, we need to begin to view LBD causality as a system. Recent work attempting to understand the interaction between these traditional disciplines has demonstrated that many of the findings along these silos are really interrelated and can be explained in terms of changes in the biomechanical loading at the tissue level. It is argued that further efforts to understand these interactions represent the next level of understanding causality of LBDs.