Development of a Method of Selecting Important Alarms for Nuclear Power Plants

Presenting important alarms selected from a large number of activated alarms provides useful operational support under a transient status in a nuclear power plant. We have developed an alarm processing method which selects and presents important alarms depending on plant status.

In this method, important alarms are selected, first, based on physical relationships between alarms and component status including alarms themselves and second, even more important alarms are selected from the previously selected alarms according to the identified initial event causing the transient. Identification of the initial event is implemented by a neural network. The identified initial event and selected important alarms are presented to show the cause and influence of the transient.

A prototype based on the above alarm processing method was validated during the start-up test at Kashiwazaki Kariwa Nuclear Power Plant Unit Number 4 of Tokyo Electric Power Co. The initial events, which were load rejection, turbine trip and main steam isolation valve closure, were correctly identified and about 30% of all activated alarms were selected as important. It was verified by an operating expert that the presentation of the identified initial event and the selected important alarms were effective to understand rapidly and correctly the transient status of the plant.     

基于级联加性噪声模型的因果结构学习算法

现有级联非线性加性噪声模型可解决隐藏中间变量的因果方向推断问题,然而对于包含隐变量和级联传递因果关系的因果网络学习存在全局结构搜索、等价类无法识别等问题。设计一种面向非时序观测数据的两阶段因果结构学习算法,第一阶段根据观测数据变量间的条件独立性,构建基本的因果网络骨架,第二阶段基于级联非线性加性噪声模型,通过比较骨架中每个相邻因果对在不同因果方向假设下的边缘似然度进行因果方向推断。实验结果表明,该算法在虚拟因果结构数据集的不同隐变量数量、平均入度、结构维度、样本数量下均表现突出,且在真实因果结构数据集中的F1值相比主流因果结构学习算法平均提升了51%,具有更高的准确率和更强的鲁棒性。     

基于事理知识图谱的舆情推演方法

一直以来舆情态势发展的多元性、复杂性使其难以有效管控,一些负面舆情会激化矛盾,给社会安定带来不利影响.提出了一种基于事理知识图谱的舆情事件推演方法,通过神经网络挖掘事件因果逻辑,连接因果事件构成事理知识图谱.向量化事件节点以融合归并相似节点降低图谱冗余,增强图谱泛化性.根据事理知识图谱反映的发展逻辑对目标舆情事件的演化趋势进行预测.以自然灾害舆情事件为例,实验结果表明提出的方法能够有效预测舆情事件发展方向,可以为舆情监管提供一定支持.     

Does the alliance cause good outcome? Recommendations for future research on the alliance.

Research has consistently documented that the quality of the therapeutic alliance is related to the outcome of diverse psychotherapies. Insufficient attention, however, has been directed at identifying the nature and magnitude of the causal relationship between the alliance and outcome. In this commentary, we discuss the major threats to causal interpretation of alliance-outcome correlations and provide suggestions for future research that would help clarify the extent to which the alliance causes positive outcomes. Assuming the alliance is a causal factor in relation to outcome, we provide recommendations for research on the alliance that would attempt to improve patient care by enhancing the alliance. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Finances and Well-Being: A Dynamic Equilibrium Model of Resources.

This study of 513 Dutch farmers tested a dynamic equilibrium model of resources (an extension of the conservation of resources theory; S. E. Hobfoll, 1989, 1998, 2001). With structural equation modeling, the advantages of a 3-wave longitudinal design were comprehensively used, such as addressing bidirectional causal effects and within-individual vs. between-individual change. This allowed for a careful analysis of the management function of resources in the stress process. Results showed that well-being had stronger within-person stability than finances. Increased levels of financial problems temporarily increased psychological distress but not self-reported illness. Conversely, farmers with higher stable baselines of psychological distress also had higher baselines of self-reported illness and experienced more negative changes in their financial situation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

因果知识发现在关系数据集中的应用研究

因果发现是知识发现的一个新的研究方向,其目的是从观测数据中找出其隐含的因果关系。虽然应用前景十分美好,但结果评价困难等因素造成了其在应用方面研究的缺乏,并限制了它的进一步发展。文章对于一个人口统计方面的关系型数据集进行了因果发现,并对于结果进行了多方面的评估与分析。结果表明,因果发现不但切实可行,而且能在实际的知识发现应用中找出相对来说较高质量的知识。     

A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships

This paper presents a simple, efficient computer-based method for discovering causal relationships from databases that contain observational data. Observational data is passively observed, as contrasted with experimental data. Most of the databases available for data mining are observational. There is great potential for mining such databases to discover causal relationships. We illustrate how observational data can constrain the causal relationships among measured variables, sometimes to the point that we can conclude that one variable is causing another variable. The presentation here is based on a constraint-based approach to causal discovery. A primary purpose of this paper is to present the constraint-based causal discovery method in the simplest possible fashion in order to (1) readily convey the basic ideas that underlie more complex constraint-based causal discovery techniques, and (2) permit interested readers to rapidly program and apply the method to their own databases, as a start toward using more elaborate causal discovery algorithms.     

面向领域开源文本的因果知识提取

因果知识是一类十分常见的知识类型,也是领域知识库的重要组成部分。基于互联网信息资源自动提取因果相关知识,对社会计算系统的建模和智能系统的建造具有十分重要的意义。本文面向开源中文文本信息,研究建立并实现一种自动提取因果知识的方法,以有效支持网上知识工程和安全领域的因果情报自动获取与因果知识库的构建。     

Integrating integer programming and probabilistic deduction graphs for probabilistic reasoning

Optimal solutions of several variants of the probabilistic reasoning problem were found by a new technique that integrates integer programming and probabilistic deduction graphs (PDG). PDGs are extended from deduction graphs of the and-type via normal deduction graphs. The foregoing variants to be solved can involve multiple hypotheses and multiple evidences where the former is given and the latter is unknown and being found or vice versa. The relationship among these hypotheses and evidences with possible intermediaries is represented by a causal graph. The proposed method can handle a large causal graph of any type and find an optimal solution by invoking a linear integer programming package. In addition, formulating the reasoning problem to fit integer programming takes a polynomial time. H.-L. Li was visiting the Department of Computer Sciences, University of North Texas in 1988–1989. He is with the Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.     

Word, thought, and deed: The role of object categories in children's inductive inferences and exploratory play.

Previous research (e.g., S. A. Gelman & E. M. Markman, 1986; A. Gopnik & D. M. Sobel, 2000) suggests that children can use category labels to make inductive inferences about nonobvious causal properties of objects. However, such inductive generalizations can fail to predict objects' causal properties when (a) the property being projected varies within the category, (b) the category is arbitrary (e.g., things smaller than a bread box), or (c) the property being projected is due to an exogenous intervention rather than intrinsic to the object kind. In 4 studies, the authors showed that preschoolers (M = 48 months; range = 42-57 months) were sensitive to these constraints on induction and selectively engaged in exploration when evidence about objects' causal properties conflicted with inductive generalizations from the objects' kind to their causal powers. This suggests that the exploratory actions children generate in free play could support causal learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)