矿井电动机事故原因分析和预防办法

电动机事故在煤矿生产中居高不下 ,严重影响煤矿安全生产。针对电动机事故的原因进行了分析 ,采取积极有效的措施 ,最大限度地减少电动机事故的发生 ,提高煤矿的经济效益和安全效益     

基于解析冗余式的风机传动系统故障诊断

为了以系统有序的方法对风机的具体元件参数进行故障诊断,给出一种基于键合图模型的故障诊断方法。键合图模型是一种跨能域的元件级模型,因此能够定位到具体的故障元件。同时键合图模型能够清晰地表明各元件之间的关系,因此适用于推导解析冗余式。通过解析冗余式可以对系统进行故障检测和隔离。为了能够系统地得到尽量多的解析冗余式,提高故障的可隔离性,该方法首先建立风机的键合图模型,然后由键合图模型导出时间因果图,由时间因果图导出变量关系图,最后由变量关系图消除键合图中结点方程的未知变量得到解析冗余式。实验结果验证了采用该方法推导的解析冗余式能够用于风机参数故障的诊断。     

Developing efficient management strategies for a water supply system using system dynamics modelling

In this paper, the causal feedback relationships among the components that make up the working mechanism of water supply systems management, including key factors and their relationship to the management of water pipes, were identified based on the conceptual framework established for water supply systems management. Subsequently, a system dynamics computer simulation model, which can be used to aid efficient management of water supply systems, was developed. The computer model consisted of water supply, pipe maintenance, and water supply business finance sub-models. The model was verified using historical data from a water supply service case study. Using the verified model, long-term managerial and operating conditions of the case study system were predicted under optimistic, basic, and pessimistic management condition scenarios. In addition, sensitivity analyses on major indicators of the case study system have been presented to show that the developed model can facilitate identification of the best policy for achieving a specific management objective of a water supply system.     

混合局部因果结构学习

局部因果结构学习是发现和学习给定一个目标变量的直接原因和直接结果而无需学习一个完整因果网络的过程。目前已有算法通常由两个步骤完成:步骤1使用约束类算法利用独立性测试学习目标变量的马尔科夫毯(MB)或父子节点集(PC),但是该步骤由于受到有限的数据样本量等因素影响使得独立性测试存在一定的错误性,而导致该步骤精度通常不是很高;步骤2利用V结构及Meek规则来进行边的定向,但是该步骤由于极其依赖于V结构的发现且同样受到有限样本的影响,使得算法精度相对不是很高。基于上述问题,提出利用打分和限制相结合的混合方式来缓减有限样本问题且提高算法精度。步骤1通过在基于限制的算法中加入打分思想来提高数据有效性,进而提出SIAPC算法;步骤2通过利用PC算法得到的定向结果和对部分数据集打分得到的定向结果的交集来确定边的方向,以此来降低对V结构的依赖性且缓减有限样本问题,之后使用独立性测试修正边的定向结果来进一步提高算法精度,进而提出HLCS算法。在标准贝叶斯网络上,实验验证了该算法相对于已有算法在精度方面具有更好的性能且能够有效缓减数据效率问题。     

Depressed people are not less motivated by personal goals but are more pessimistic about attaining them.

Despite its theoretical importance, personal goal motivation has rarely been examined in clinical depression. Here we investigate whether clinically depressed persons (n = 23) differ from never-depressed persons (n = 26) on number of freely generated approach and avoidance goals, appraisals of these goals, and reasons why these goals would and would not be achieved. Participants listed approach and avoidance goals separately and generated explanations for why they would (pro) and would not (con) achieve their most important approach and avoidance goals, before rating the importance, likelihood, and perceived control of goal outcomes. Counter to hypothesis, depressed persons did not differ from never-depressed controls on number of approach or avoidance goals, or on the perceived importance of these goals. However, compared to never-depressed controls, depressed individuals gave lower likelihood judgments for desirable approach goal outcomes, tended to give higher likelihood judgments for undesirable to-be-avoided goal outcomes, and gave lower ratings of their control over goal outcomes. Furthermore, although controls generated significantly more pro than con reasons for goal achievement, depressed participants did not. These results suggest that depressed persons do not lack valued goals but are more pessimistic about their likelihood, controllability, and reasons for successful goal attainment. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Expectations and interpretations during causal learning.

In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A–1C, participants' interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Imitation in young children: When who gets copied is more important than what gets copied.

Unlike other animals, human children will copy all of an adult's goal-directed actions, including ones that are clearly unnecessary for achieving the demonstrated goal. Here we highlight how social affiliation is key to this species-specific behavior. Preschoolers watched 2 adults retrieve a toy from a novel apparatus. One adult included irrelevant actions in her demonstration; the other only used actions causally related to opening the apparatus. After both adults took turns demonstrating, 1 left the test room, and the remaining adult gave the apparatus to the child. Children reproduced the irrelevant actions only when given the apparatus by the adult who had demonstrated them, even though the departed adult's actions emphasized how unnecessary these redundant actions were. Our results highlight the specialized skills for participating in cultural groups that have evolved in humans and provide insight into why finding such high fidelity copying in other animals has proven elusive. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Judgments about forces in described interactions between objects.

In 4 experiments, participants made judgments about forces exerted and resistances put up by objects involved in described interactions. Two competing hypotheses were tested: (1) that judgments are derived from the same knowledge base that is thought to be the source of perceptual impressions of forces that occur with visual stimuli, and (2) that judgments are generated using simple models or heuristics for deriving force judgments from kinematic information. The results show some similarities with perceptual impression research, particularly that an active object is judged to exert more force on an inactive object than the latter exerts on the former. However, most of the available kinematic information had little or no effect on judgment, supporting the hypothesis that force judgments are generated according to simple rules. Presentation of information about damage to the objects resulted in use of all available kinds of information; this could reflect greater imageability of damage information than kinematic information. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

For want of a nail: How absences cause events.

Causation by omission is instantiated when an effect occurs from an absence, as in The absence of nicotine causes withdrawal or Not watering the plant caused it to wilt. The phenomenon has been viewed as an insurmountable problem for process theories of causation, which specify causation in terms of conserved quantities, like force, but not for theories that specify causation in terms of statistical or counterfactual dependencies. A new account of causation challenges these assumptions. According to the force theory, absences are causal when the removal of a force leads to an effect. Evidence in support of this account was found in 3 experiments in which people classified animations of complex causal chains involving force removal, as well as chains involving virtual forces, that is, forces that were anticipated but never realized. In a 4th experiment, the force theory's ability to predict synonymy relationships between different types of causal expressions provided further evidence for this theory over dependency theories. The findings show not only how causation by omission can be grounded in the physical world but also why only certain absences, among the potentially infinite number of absences, are causal. (PsycINFO Database Record (c) 2010 APA, all rights reserved)