宽带相控阵雷达信号的二分推理提取算法

宽带相控阵雷达是电子对抗侦察的重点关注对象,在接收机截获的交错脉冲数据中将这一类雷达信号优先提取出来具有重要军事价值。针对接收机截获的全脉冲数据,文中提出了一种宽带相控阵雷达信号优先提取方法。该方法利用宽带相控阵雷达采用电子扫描方式的特点,分析其在同一工作模式下脉冲幅度的不变特性,引入一种生物信息学中的二分推理算法,并结合宽带信号的脉宽特性。首先,对经过脉宽阈值过滤的全脉冲数据按照脉幅大小重新排序;然后,利用二分推理算法进行脉幅突变点检测,将脉冲序列分为若干个脉幅相同的片段;最后,对每一片段进行脉冲个数阈值过滤,实现对宽带相控阵雷达脉冲的提取。仿真实验验证了方法的有效性。     

Two analogy strategies: the cases of mind metaphors and introspection

Analogical reasoning is often employed in problem-solving and metaphor interpretation. This paper submits that, as a default, analogical reasoning addressing these different tasks employs different mapping strategies. In problem-solving, it employs analogy-maximising strategies (like structure mapping, Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52, 45–56); in metaphor interpretation, analogy-minimising strategies (like ATT-Meta, Barnden, J. A. (2015). Open-ended elaborations in creative metaphor. In T. R. Besold, M. Schorlemmer, & A. Smaill (Eds.), Computational creativity research: Towards creative machines (pp. 217–242). Berlin: Springer). The two strategies interact in analogical reasoning with conceptual metaphors. This interaction leads to predictable fallacies. The paper supports these hypotheses through case-studies on “mind” metaphors from ordinary discourse, and abstract problem-solving in the philosophy of mind, respectively. It shows that (1) default metaphorical interpretations for vision- and space-cognition metaphors can be derived with a variant of the analogy-minimising ATT-Meta approach, (2) philosophically influential introspective conceptions of the mind can be derived with conceptual metaphors only through an analogy-maximising strategy, and (3) the interaction of these strategies leads to hitherto unrecognised fallacies in analogical reasoning with metaphors. This yields a debunking explanation of introspective conceptions.     

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)     

船舶避碰智能决策支持系统研究

建立了一个基于RBR(Case-Based Reasoning,基于案例推理)和CBR(Rule-Based Reasoning,基于规则推理)结合的船舶避碰决策支持模型,将这个模型引入到船舶避碰智能决策支持系统IDSSVCA(Intelligent Decision Support System for Vessel...     

Specificity of meta-emotion effects on moral decision-making.

A recently proposed dual process theory of moral decision-making posits that utilitarian reasoning (approving of harmful actions that maximize good consequences) is the result of cognitive control of emotion. This suggests that deficits in emotional awareness will contribute to increased utilitarianism. The present study explored the relative contributions of the different facets of alexithymia and the closely related constructs of emotional intelligence and mood awareness to utilitarian decision making. Participants (N = 86) completed the Toronto Alexithymia Scale, Trait Meta Mood Scale, the Mood Awareness Scale, and a series of high-conflict, personal moral dilemmas validated by Greene et al. (2008). A brief neuropsychological battery was also administered to assess the possible confounds of verbal reasoning and abstract thinking ability. Principal components analysis revealed two latent factors—clarity of emotion and attention to emotion—which cut across all three meta-emotion instruments. Of these, low clarity of emotion—reflecting difficulty in reasoning thoughtfully about one's emotions—predicted utilitarian outcomes and provided unique variance beyond that of verbal and abstract reasoning abilities. Results are discussed in the context of individual differences in emotion regulation. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

A novel task-oriented framework for dual-arm robotic assembly task

Frontiers of Mechanical Engineering - In industrial manufacturing, the deployment of dual-arm robots in assembly tasks has become a trend. However, making the dual-arm robots more intelligent in...     

一种基于云预测模型的电网综合风险评估方法

电网综合风险评估能有效提高电网安全运行水平,为电力系统可靠运行提供安全保障。提出了一种基于云预测模型的电网综合风险评估方法。将云理论应用于输变电设备故障不确定推理预测,构建基于条件云发生器的云语言预测规则,在此基础上提出了基于云推理的输变电设备故障率预测模型。同时,基于效用理论构建了一套包括电压越限、极限输送功率、潜在连锁故障和系统稳定指数的电网综合风险评估指标体系。该方法能够科学合理地对电网进行综合风险评估,扫描电网运行薄弱环节。结合某地区电网运行实际数据进行了计算分析,计算结果表明该方法有效且实用。     

基于模糊证据推理的铁路隧道施工风险评估模型建构

研究基于模糊证据推理的铁路隧道施工风险评估模型,是一个模糊神经网络配合加权指标模型。其模糊神经网络的数据模糊使用数据投影法、归一化法、去量纲法等前置模糊算法,形成14个Double格式数据并输入到多列神经网络后,使用4个相对独立的解模糊模块对数据进行解析,形成针对4个特定事故风险的评价结果。使用加权指标法对4个指标进行整合,形成总体评价指标。通过真实数据的仿真分析,对比技术领域常用的2套成型软件,在评价敏感性不变的前提下,其评价特异性从10%提升至95%以上。     

基于诱导有序加权证据推理方法的装备使用质量综合评价

针对装备质量评价工作涉及的因子赋权以及通过调查问卷进行评价时存在合理的“信息缺失”等问题,采用诱导有序加权证据推理(induced ordered weighted evidential reasoning,IOWER)方法进行装备使用质量评价。分析多策略的合成赋权方法,介绍混合赋权策略,利用Matlab实现求解;分析不完全信息下基于证据推理的装备使用质量综合评价,并对某炮兵营装备的使用结果进行分析。分析结果表明,运用IOWER方法有利于得出更准确的评价结论。     

Na?ve theories of causal force and compression of elapsed time judgments.

Recent research has shown that when people perceive a causal relation between 2 events, they “compress” the intervening elapsed time. The present work shows that a na?ve mechanical–physical conception of causality, in which causal forces are believed to dissipate over time, underlies the estimates of shorter elapsed time. Being primed with alternative, nondissipative causal mechanisms and having the cognitive capacity to consider such mechanisms moderates the compression effect. The studies rule out similarity, mnemonic association, and anchoring as alternative accounts for the effect. Taken together, the findings support the hypothesis that causal cognition plays a major role in judgments of elapsed time. The implications of the compression effect on the timing of future actions, persistence, and causal learning are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)