基于因果推断的冷轧带钢厚度异常根因分析

厚度精度是衡量冷轧带钢质量的重要指标之一，快速诊断带钢厚度异常并定位异常发生的根本原因，对提升带钢质量及生产稳定性具有非常重要的意义。为此，通过轧制机理模型确定厚度影响因素，采用多元线性回归方法构建厚度增量残差模型，对残差进行核密度估计来检测厚度异常；针对厚度异常，基于因果推断计算影响因素的因果效应，辨识出厚度异常的根本原因。实际应用结果表明，该方法能准确地诊断厚度异常和辨识异常的根本原因，与常规方法相比，其对冷连轧过程变量之间具有高度相关性的工况更为有效。     

基于商空间的黄金价格SVM模型预测

结合商空间理论和支持向量机方法,根据黄金价格的价格因子对我国黄金价格进行预测。采用Person相关系数法,对现阶段黄金价格的9个价格因子与黄金价格的相关性进行比较,筛选出相关系数较大的5个价格因子,并通过Granger因果检验,得出可能导致黄金价格变化的2个价格因子;将Person相关系数法和Granger因果检验选出的7个因子作为黄金价格预测的主要价格因子,结合商空间理论,按照时间属性,将黄金价格论域划分为年、季、月3个粒度,建立3层商空间,并进行粒度的合成和计算。然后建立基于商空间理论的支持向量机预测模型,预测得出年、季、月粒度的黄金价格预测值分别为8122.4,7947.506和8089.5元/金衡盎司,合成结果为8053.1元/金衡盎司。将预测结果与GM(1,1)预测值和实际黄金价格进行对比,证明该模型的预测结果在误差允许范围内,优于传统的价格预测方法。     

基于广义可加模型的热轧变形抗力预报

基于热连轧生产过程实测数据,建立一种具有广义可加形式的热轧带钢变形抗力模型。首先,提出基于广义可加模型框架的热轧带钢变形抗力建模方法,包括变量预分析、模型设定、模型估计与结果分析等步骤;给出了估计模型各自变量平滑函数的back-fitting算法。接着,针对宝钢1880精轧机组进行建模实验,通过收集覆盖多钢种的带钢样本数据,建立了热轧带钢变形抗力的广义可加模型,采用三次光滑样条来估计各个模型自变量的单变量函数,获得了变形温度、变形程度、变形速率等因素对变形抗力的影响规律。实际建模实践表明,新模型的结构优于宝钢1880在线模型,具有计算精度高、适应钢种范围广等优点,可用于热连轧生产在线过程控制。     

小样本贫信息条件下高炉冶炼烧结终点组合预测法

由于烧结过程具有不确定性、多变量耦合、时变时滞的特点,并且烧结终点受多种因素的影响,采用传统控制方法难以将烧结终点控制在要求的范围内,提出应用支持向量机优良的时序预测性能,以及贝叶斯理论能够利用样本信息和先验知识来简化预测模型和优化参数的特性,建立了贝叶斯支持向量机烧结终点的预报模型.首先对烧结终点的机理分析,后分别叙述贝叶斯框架理论和LS-SVM算法,并将贝叶斯证据框架应用于最小二乘支持向量机模型参数的自动选择,建立起时间序列的烧结终点非线性预测模型.在贝叶斯推断的第一层,进行模型参数的选择;在贝叶斯推断的第二层,进行模型超参数的选择;在贝叶斯推断的第三层,估计模型核参数,然后利用贝叶斯最小二乘支持向量机算法(LS-SVM)对烧结终点进行预测,并在此基础上构造了烧结终点的贝叶斯最小二乘支持向量机模型.仿真结果和多种模型比较表明,本模型能在小样本贫信息条件下对烧结终点做出比较准确的预测,并具有预测精度高、所需样本少、计算简便等优点,取得了令人满意的结果.     

基于VAR模型的氧化钕价格分析及预测

从氧化钕供求关系、宏观经济指标、联产品间的价格联动性三个维度对影响氧化钕价格的因素进行分析,并应用Pearson相关系数、共线性诊断、逐步回归、Johansen检验、Granger因果检验等方法筛选出对预测氧化钕价格有意义的解释变量,构建了VAR(1)模型,得到了2019年1月至2020年12月的氧化钕价格预测值。结果表明,未来两年氧化钕月价格呈缓慢上升的趋势,以每月0.18%的增长率增长。     

框架填充墙结构基于性能的抗震评估

在现有研究基础上,经统计分析提出了框架与填充墙各自的性能水平划分及其层间位移角限值.建立了框架填充墙非线性有限元模型.对6、9和12层三个典型框架填充墙结构进行小震、中震和大震下的静力弹塑性分析,得出框架填充墙结构在不同水平地震作用下层间位移角分布.依据抗震性能指标,分析和评估了填充墙框架结构的抗震性能,指出目前规范的不足.根据计算结果,给出了用于评估结构性能的图表.      

大型高炉炉体框架的结构形式及内力分析方法

简略介绍了我国现有高炉炉体框架的结构形式和荷载工况，重点阐述了炉体框架的内力分析方法。对炉体框架采用《钢结构设计规范》GB50017提出的近似分析方法进行计算分析对比，验证该近似方法在大型复杂空间结构中的适用性和可行性。通过两个大型有限元程序对不同结构形式的炉体框架进行一阶弹性和二阶弹性分析，其结果表明，二阶效应不明显：在工程设计中，对炉体框架可只进行空间结构的一阶弹性分析，能满足设计精度要求。     

基于层次分析法的钻井井塌卡钻事故分析研究

研究分析了钻井井塌卡钻事故的成因机理及其危害性,运用因果图分析法从影响井塌卡钻的地质因素、工艺因素、人为因素及管理因素等方面进行了定性系统分析.在此基础上结合层次分析法的数学理论,建立井塌卡钻事故模型,对井塌卡钻事故的致因因素权重进行计算确定,并进行一致性检验,判断计算结果的科学性,对所有致因因素权重进行排序比较,从而最终确定井塌卡钻的关键因素,分梯次进行预防,为现场施工和管理提供科学的决策依据.     

构建第三方支付的技术采纳模型

为研究消费者对第三方支付的认知过程和接受过程,明晰关键影响构念,借鉴技术结构采纳模型的分析方法和相关理论,构建了基于第三方支付的技术采纳模型（TAM）,并对模型展开了阐述。同时提出了消费者对第三方支付的实际使用是受其使用意愿的影响,而使用意愿则是由消费者对第三方支付的有用认知、易用认知、第三方支付的经济性等构念以及消费者的使用经验共同影响的,并对“有用认知”的影响构念、变量进行了逐一分析。     

理想化认知模型理论对小品幽默话语的认知分析——以赵本山小品为例

Lakoff认为,理想化认知模型理论作为认知语言学中一个十分重要的概念,可以被用来解释语义范畴和概念结构.这对语义研究来说是一种创新.尝试在认知语言学的框架内,以理想化认知模型理论为基础,着重从三个方面对赵本山小品中幽默言语的生成进行认知解读,即:基于或突显某一模型、某一模型的隐喻或转喻说法以及修改某一模型,旨在为幽默话语研究提供一个新的视角.     

Dissociating Processes Supporting Causal Perception and Causal Inference in the Brain.

An understanding of relations between causes and effects is essential for making sense of the dynamic physical world. It has been argued that this understanding of causality depends on both perceptual and inferential components. To investigate whether causal perception and causal inference rely on common or on distinct processes, the authors tested 2 callosotomy (split-brain) patients and a group of neurologically intact participants. The authors show that the direct perception of causality and the ability to infer causality depend on different hemispheres of the divided brain. This finding implies that understanding causality is not a unitary process and that causal perception and causal inference can proceed independently. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Judging probable cause.

Argues that people use systematic rules for assessing cause, both in science and everyday inference. By explicating the processes that underlie the judgment of causation, the authors review and integrate various theories of causality proposed by psychologists, philosophers, statisticians, and others. Because causal judgment involves inference and uncertainty, the literature on judgment under uncertainty is also considered. It is suggested that the idea of a "causal field" is central for determining causal relevance, differentiating causes from conditions, determining the salience of alternative explanations, and affecting molar versus molecular explanations. Various "cues-to-causality" such as covariation, temporal order, contiguity in time and space, and similarity of cause and effect are discussed, and it is shown how these cues can conflict with probabilistic ideas. A model for combining the cues and the causal field is outlined that explicates methodological issues such as spurious correlation, "causalation," and causal inference in case studies. The discounting of an explanation by specific alternatives is discussed as a special case of the sequential updating of beliefs. Conjunctive explanations in multiple causation are also considered. (120 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal judgments about relations between multilevel variables.

Three experiments presented stimulus information about cause and effect variables taking 3 quantitative values. Judgments tended to vary in accordance with considerations of conditions affecting the validity of causal inference from correlational data: whether causal candidates were presented simultaneously or in a temporal order such that one could affect the other and whether candidates were confounded with each other. The results supported a general hypothesis that causal judgments are moderated in accordance with acquired methodological intuitions. The 4th experiment showed that tendencies in correlation judgment were different from those in causal judgment, further supporting the hypothesis that causal judgment from multilevel variable information is, to some extent, determined by processes or conceptual frameworks specific to the domain of causal cognition. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Ideas about causation in philosophy and psychology.

Philosophical theories summarized here include regularity and necessity theories from D. Hume (1739 [1978], 1740 [1978]) to the present; manipulability theory; the theory of powerful particulars; causation as connected changes within a defined state of affairs; departures from "normal" events or from some standard for comparison; causation as a transfer of something between objects; and causal propagation and production. Issues found in this literature and of relevance for psychology include whether actual causal relations can be perceived or known; what sorts of things people believe can be causes; different levels of causal analysis; the distinction between the causal relation itself and cues to causal relations; causal frames or fields; internal and external causes; and understanding of causation in different realms of the world, such as the natural and artificial realms. A full theory of causal inference by laypeople should address all of these issues. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Collider Principle in Causal Reasoning: Why the Monty Hall Dilemma Is So Hard.

The authors tested the thesis that people find the Monty Hall dilemma (MHD) hard because they fail to understand the implications of its causal structure, a collider structure in which 2 independent causal factors influence a single outcome. In 4 experiments, participants performed better in versions of the MHD involving competition, which emphasizes causality. This manipulation resulted in more correct responses to questions about the process in the MHD and a counterfactual that changed its causal structure. Correct responses to these questions were associated with solving the MHD regardless of condition. In addition, training on the collider principle transferred to a standard version of the MHD. The MHD taps a deeper question: When is knowing about one thing informative about another? (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Driven by power? Probe question and presentation format effects on causal judgment.

It has been proposed that causal power (defined as the probability with which a candidate cause would produce an effect in the absence of any other background causes) can be intuitively computed from cause-effect covariation information. Estimation of power is assumed to require a special type of counterfactual probe question, worded to remove potential sources of ambiguity. The present study analyzes the adequacy of such questions to evoke normative causal power estimation. The authors report that judgments to counterfactual probes do not conform to causal power and that they strongly depend on both the probe question wording and the way that covariation information is presented. The data are parsimoniously accounted for by an alternative model of causal judgment, the Evidence Integration rule. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Learning a theory of causality.

The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned—an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Seeing it happen and knowing how it works: how children understand the relation between perceptual causality and underlying mechanism

Two experiments studied how 5- to 10-year-olds integrate perceptual causality with their knowledge of the underlying causal mechanism. Children learned about 2 devices by which a ball dropped into one end of a box made a bell ring at the other end, either immediately (contiguous mechanism) or after a delay (noncontiguous mechanism). When 1 ball was dropped first and a 2nd ball was dropped after a delay, and then the bell rang immediately, 5- and 7-year-olds chose the contiguous cause regardless of the mechanism inside. This was not due to lack of specific knowledge or problems with salient distractors. The results suggest a link between temporal contiguity and causality in children's understanding. Children also considered causal mechanism, in agreement with previous research, but they may not understand that mechanism is superordinate to perceptual cues for causality.