图样的知识目标分类与例证性分析

根据教育目标分类学的观点分解课程教学目标，再分别对分解的目标进行例证性分析，能更客观地验证教育目标的实效性和教育过程的可行性。本文通过对图样的知识目标分类与例证性分析，为工程图学教育目标检测做一尝试。     

关联性与文化的解读和建构

根据Sperber和Wilson的关联理论及其认知语境观,说明外语读者如何在认知语境中寻找最佳关联,可以对作者所设置的文化缺省进行连贯解读,从而实现跨文化图式的建构。     

Modeling visuospatial reasoning

I apply my proposed modification of Soar/Spatial/Visual System and Kosslyn’s (1983) computational operations on images to problems within a 2 × 2 taxonomy that classifies research according to whether the coding involves static or dynamic relations within an object or between objects (Newcombe & Shipley, 2015). I then repeat this analysis for problems that are included in mathematics and science curricula. Because many of these problems involve reasoning from diagrams Hegarty’s (2011) framework for reasoning from visual-spatial displays provides additional support for organizing this topic. Two more relevant frameworks specify reasoning at different levels of abstraction (Reed, 2016) and with different combinations of actions and objects (Reed, 2018). The article concludes with suggestions for future directions.     

图形背景理论模式下英语强调结构的教学认知

在英语中,说话人要重点突出的部分叫做强调.强调结构一直是学生学习英语的重点和难点。传统的教学方法存在诸多弊端,学生不会综合运用,不懂融会贯通。从新的视角对英语强调结构的教学进行认知势在必行。认知语言学中图形-背景理论为重新感知和理解强调结构提供了理据,进行形象具体化教学,有效提高学习效果。     

一种步进频率信号认知雷达波形优化设计方法

认知雷达常用于完成探测、跟踪、成像及识别等多重任务,为提高其综合性能,需兼顾多方面因素研究其波形优化设计问题。基于此,提出一种基于压缩感知（CS）RIPless准则的步进频率信号认知雷达波形优化设计方法。首先,建立了目标回波信号稀疏模型,分析了其与发射信号模糊函数之间的关系。其次,根据模型中观测矩阵的构造,基于RIPless准则,将波形设计问题转化为概率分布的互相干参数及其协方差矩阵的条件数的优化问题,从而通过自适应寻优算法,获得优化的步进频率信号脉冲重复时间间隔序列和子脉冲频率序列。相较于传统方法,所提方法在信号发射与接收之间形成了信息实时反馈和信号优化重构的闭环,在高概率准确重构目标径向一维距离像的同时,也实现了发射信号模糊函数的优化。最后,仿真计算验证了所提方法的有效性。     

基于视觉注意机制的认知雷达数据关联算法

针对传统关联波门设计方法在应用于机动目标跟踪时容易引起失跟、以及概率数据关联算法不适于多交叉目标跟踪的问题,提出了一种基于人类视觉选择性注意机制和知觉客体的"特征整合"理论的认知雷达数据关联算法。算法以综合交互式多模型概率数据关联算法为基础,采取假设目标最大机动水平已知的"当前"统计模型和匀速运动模型作为模型集,通过实时交互使关联波门能够随目标机动动态调整,较好地兼顾了雷达计算耗时和跟踪成功率。在利用目标位置特征的基础上,进一步提取、整合目标运动特征,对关联波门交叉区域公共量测进行分类,使多交叉目标跟踪问题转化为多个单目标跟踪问题,优化了传统概率数据关联算法。仿真结果表明：与传统关联波门设计方法相比,算法跟踪失败率和计算耗时明显降低;而且在计算资源增加不大的情况下,杂波环境适应性也得到了显著增强。     

基于人的认知失误分析模型的跑道侵入风险评估研究

本文主要对导致跑道侵入的人为因素进行研究分析,运用认知可靠性与失误分析法(CREAM)的认知模型建立跑道侵入风险评估模型,并以南苑机场为实例验证该模型的可靠性,计算出来的南苑机场发生跑道侵入事件的风险值较为合理,结果表明运用人的可靠性分析模型对跑道侵入的风险进行评估具有实用价值。     

基于多目标优化的星空认知网络鲁棒波束成形算法

卫星通信(SC)网和无人机(UAV)通信网之间频谱共享、相互融合,能较大提升频谱效率,有望成为第6代移动通信的关键技术之一。与现有文献不同,本文在系统非完美信道状态信息(CSI)的条件下,考虑了次级用户可达速率最大化和发射功率最小化2种准则,利用加权切比雪夫方法构建满足概率约束的多目标优化问题(MOO);由于该问题较为非凸,利用概率公式、半正定松弛等方法将其转化为凸问题,并进一步通过半正定规划(SDP)求解,得到鲁棒波束成形权矢量,获得2种性能指标间的帕累托最优权衡。计算机仿真验证了所提鲁棒波束成形算法的有效性和优越性。     

基于EEG的驾驶舱告警信息设计研究

针对直升机驾驶舱人机交互中认知负荷问题,设计飞行模拟试验,探究视觉和听觉告警对飞行员认知负荷的影响。基于多元线性回归方法,通过主观评价结果、反应时间、准确率和脑电信号等指标,构建了直升机驾驶舱人机交互认知负荷评估模型。结果表明文字颜色、语音提示两种告警特征对飞行员信息加工均会产生影响,使用多元线形回归模型可对不同告警方式下受试对象的认知负荷水平进行评估,构建的评估模型能够较准确地评估直升机驾驶舱中飞行员的认知负荷,可为优化驾驶舱交互设计、降低飞行员认知负荷、减少人为差错提供依据。     

Mining Spatial Associations with Limited Sensory Information

Abstract

Human navigation in an unknown environment requires an understanding of the spatial relationships of the terrain. For example, a soldier who is on a reconnaissance mission in a new city needs to “know” the spatial layout of the surroundings with high confidence. Oftentimes, this understanding must be acquired within a very short amount of time and with limited sensory inputs. The soldier would benefit from a digital avatar that draws inferences about the spatial layout of the city based on an initial set of observations and guides the soldier either in further exploring the environment or in making decisions based on these inferences. In this paper, we present and evaluate an inductive approach to learning spatial associations using sensory data that is available from the simulation environment of a computer game, Unreal Tournament. We study two kinds of spatial relationships between nodes on a level of a game map: nodes that are placed near each other to satisfy some spatial requirement and nodes that are placed near each other to satisfy the design preferences of a level architect. We show that we can infer both kinds of relationships using an association rule mining algorithm. Furthermore, we show how to use an ontology to distinguish between these relationships in order to discover different types of spatial arrangements on a specific map. We discuss how the inferred associations can be used to control an avatar that makes recommendations for navigating unexplored areas on a map. We conclude with some thoughts on the applicability of our methods to scenarios in the real world, beyond the simulation environment of a game, and on how the learned associations can be represented and queried by a simple question-answer type system.