大规模定性仿真模型的建立

针对当前已有的定性理论难以分析磊规模系统的缺陷，本文提出一种模型建立理论，对人们常识中的因果关系作了正式的定义，并以之为基础，以明确的仿真目标为指导，给出了系统概念实体识别和模型结构建立方法。     

基于AHP算法的地铁AFC系统运营风险分析

针对地铁AFC(Automatic Fare Collection System )系统运营存在风险繁多的特点,运用层次分析法AHP(Analytical Hierar-chy Process)对地铁AFC系统运营风险进行了定量分析,为系统管理者作出正确的决策提供了相对可靠、科学的依据.分析显示:应该重点注意车站终端设备、票卡、车站计算机系统这三个主要因素,采取相应的措施尽量让每个子因素达到要求标准.同时,中央计算机系统、清分系统风险等几个因素对项目实施的影响也不容忽视.     

An end-to-end tabular information-oriented causality event evolutionary knowledge graph for manufacturing documents

Industrial tabular information extraction and its semantic fusion with text (ITIESF) is of great significance in converting and fusing industrial unstructured data into structured knowledge to guide cognitive intelligence analysis in the manufacturing industry. A novel end-to-end ITIESF approach is proposed to integrate tabular information and construct a tabular information-oriented causality event evolutionary knowledge graph (TCEEKG). Specifically, an end-to-end joint learning strategy is presented to mine the semantic information in tables. The definition and modeling method of the intrinsic relationships between tables with their rows and columns in engineering documents are provided to model the tabular information. Due to this, an end-to-end joint entity relationship extraction method for textual and tabular information from engineering documents is proposed to construct text-based knowledge graphs (KG) and tabular information-based causality event evolutionary graphs (CEEG). Then, a novel NSGCN (neighborhoods sample graph convolution network)-based entity alignment is proposed to fuse the cross-knowledge graphs into a unified knowledge base. Furthermore, a translation-based graph structure-driven Q&A (question and answer) approach is designed to respond to cause analysis and problem tracing. Our models can be easily integrated into a prototype system to provide a joint information processing and cognitive analysis. Finally, the approach is evaluated by employing the aerospace machining documents to illustrate that the TCEEKG can considerably help workers strengthen their skills in the cause-and-effect analysis of machining quality issues from a global perspective.     

满足因果约束的多速率单值广义预测控制

针对输入更新频率和输出采样频率均不相同的多速率采样系统,提出一种多速率广义预测控制算法．通过采用在系统周期内根据不同的采样时刻构造扩展模型序列的方法,有效地解决了由于多速率采样而引起的因果约束问题．在此基础上,对算法的闭环稳定性进行了分析,仿真结果证明了算法的有效性．     

Multivariate granger causality between electricity consumption,exports and GDP: Evidence from a panel of Middle Eastern countries

This paper examines the causal relationship between electricity consumption, exports and gross domestic product (GDP) for a panel of Middle Eastern countries. We find that for the panel as a whole there are statistically significant feedback effects between these variables. A 1 per cent increase in electricity consumption increases GDP by 0.04 per cent, a 1 per cent increase in exports increases GDP by 0.17 per cent and a 1 per cent increase in GDP generates a 0.95 per cent increase in electricity consumption. The policy implications are that for the panel as a whole these countries should invest in electricity infrastructure and step up electricity conservation policies to avoid a reduction in electricity consumption adversely affecting economic growth. Further policy implications are that for the panel as a whole promoting exports, particularly non-oil exports, is a means to promote economic growth and that expansion of exports can be realized without having adverse effects on energy conservation policies.     

Optimal multilinear estimation of a random vector under constraints of causality and limited memory

A new technique is provided for random vector estimation from noisy data under the constraints that the estimator is causal and dependent on at most a finite number p of observations. Nonlinear estimators defined by multilinear operators of degree r are employed, the choice of r allowing a trade-off between the accuracy of the optimal filter and the complexity of the calculations. The techniques utilise an exact correspondence of the nonlinear problem to a corresponding linear one. This is then solved by a new procedure, the least squares singular pivot algorithm, whereby the linear problem can be repeated reduced to smaller structurally similar problems. Invertibility of the relevant covariance matrices is not assumed. Numerical experiments with real data are used to illustrate the efficacy of the new algorithm.     

Run-time label propagation for forensic audit data

It is desirable to be able to gather more forensically valuable audit data from computing systems than is currently done or possible. This is useful for the analysis of events that took place on the system for the purpose of digital forensic investigations. In this paper we propose a mechanism that allows arbitrary meta-information bound to principals on a system to be propagated based on causality and influenced by information flow. We further discuss how to implement such a mechanism for the FreeBSD operating system and present a proof-of-concept implementation that has little overhead compared to the system without label propagation.     

梯形模糊因果图及归一化研究

因果图理论是一种基于概率论的推理方法。文章在分析因果图理论发展和存在的问题基础上，将模糊数学引入因果图理论，即模糊因果图，从而可以克服因果图分析中概率难以精确赋值的缺点，将因果图理论应用扩大到了模糊领域。文章主要对事件概率为梯形模糊教进行讨论，提出了模糊因果图的算子，得到了模糊条件概率的计算公式，讨论了模糊概率的归一化方法。最后，以核电站的一个子系统为例进行仿真实验，实验结果与实际一致，归一化方法可行。研究表明：模糊因果图能有效地用于故障分析，比原来的因果图方法具有更大的灵活性和适应性.     

Understanding motivation in internet gaming among Singaporean youth: The role of passion

This study examined the motivation of young people in internet gaming using the dualistic model of passion. Path analysis was used to examine the relationships between the two types of passion: obsessive and harmonious passion, behavioral regulations, and flow. A total of 1074 male secondary school students from Singapore took part in the study. The results of the path analysis showed that external, introjected, and identified regulations positively predicted obsessive passion, while harmonious passion was predicted by identified and intrinsic regulations. Flow in digital gaming was predicted directly by harmonious passion, as well as indirectly through intrinsic regulation. This study supports the proposed dualistic model of passion in explaining young people’s motivation in internet gaming.     

The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo

Vertigo is a common complaint with many potential causes involving otology, neurology and general medicine, and it is fairly difficult to distinguish the vertiginous disorders from each other accurately even for experienced physicians. Based on comprehensive investigations to relevant characteristics of vertigo, we propose a diagnostic modeling and reasoning methodology using Dynamic Uncertain Causality Graph. The symptoms, signs, findings of examinations, medical histories, etiology and pathogenesis, and so on, are incorporated in the diagnostic model. A modularized modeling scheme is presented to reduce the difficulty in model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. We resort to the “chaining” inference algorithm and weighted logic operation mechanism, which guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal insights into underlying interactions among diseases and symptoms intuitively demonstrate the reasoning process in a graphical manner. These solutions make the conclusions and advices more explicable and convincing, further increasing the objectivity of clinical decision-making. Verification experiments and empirical evaluations are performed with clinical vertigo cases. The results reveal that, even with incomplete observations, this methodology achieves encouraging diagnostic accuracy and effectiveness. This study provides a promising assistance tool for physicians in diagnosis of vertigo.