工作记忆及语言水平对英语学习者隐喻理解的影响

本研究旨在探讨工作记忆广度和语言水平差异在中国英语学习者对隐喻句这一特定句型理解的影响。通过英语工作记忆广 度、英语语言水平及英语隐喻理解的研究测试发现,中国英语学习者的英语工作记忆广度、英语语言水平与其隐喻理解能力高度 相关,工作记忆和语言水平高低两组t 检验结果均呈显著性差异。工作记忆广度及语言水平是影响中国英语学习者隐喻理解的两 大因素,故英语学习者可以通过知识面拓展及工作记忆训练来提高自己的隐喻理解能力。     

图标认知实验定量研究综述

图标作为人机界面的重要组成部分之一,长期以来受到用户界面研究人员和设计人员的关注,对图标视觉效果,生成机制,附加因素等的研究与探讨层出不穷。文章以一定程度的定量综述的方式,研究分析自2000年以来,在图标认知的实验方面的研究中,研究人员重点关注的实验变量和研究目的与方法,找出其中的相关性,为此领域的进一步研究提供参考。研究表明,研究涉及较多的变量包括图形因素,图标属性,图标隐喻和触觉听觉元素四类,主要研究目的包括视觉搜索,认知识别和用户偏好。且两者之间存在一定程度的相关性。     

基于概率评估差分进化的多峰值优化

多峰值优化问题要求算法同时找到一个问题的多个全局最优解。近年来,演化算法已被广泛用于求解多峰值优化问题。然而,如何在极其有限的适应值评估次数内找到问题的多个全局最优解依然为演化算法带来了巨大的挑战。通过分析个体的历史更新经验,为每个个体赋予双层适应值评估概率,对个体进行选择性评估,从而减少算法运行过程中无效或低效的适应值评估,提出了一种基于概率评估差分进化的多峰值优化算法。实验结果显示,概率评估机制可以为算法节省更多的适应值评估次数,增加迭代过程,效果远好于其他主流的多峰值优化算法。     

Circuit analog of a beam and its application to multimodal vibration damping,using piezoelectric transducers

In this paper we solve the synthesis problem of finding a completely passive electric circuit analog to a vibrating beam. The synthesis problem is of interest when one wants to suppress beam mechanical vibrations by using distributed piezoelectric transduction. Indeed, an effective electromechanical energy transduction is guaranteed when the electric circuit (interconnecting the transducers' terminals) is resonant at all mechanical resonance frequencies and is able to mimic all the mechanical modal shapes. The designed electric circuit behaves as an electric controller of mechanical vibrations (i.e. an electric vibration damper) once suitably endowed with a set of resistors. Because of its completely passive nature, it does not require external power units and stands as an economical means of controlling vibrations. Copyright © 2004 John Wiley & Sons, Ltd.     

AI虚拟数字人技术在融合媒体生产中的运用

重大活动无线电安全保障肩负着贯彻国家安全战略,维护社会稳定,维护国家形象的重要使命。通过对重大活动无线电安全保障工作内容的梳理,深化对无线电保障工作的认识,更好地服务于今后的重大活动无线电保障工作。     

数据受限条件下的多模态处理技术综述

随着多媒体技术的发展,可获取的媒体数据在种类和量级上大幅提升。受人类感知方式的启发,多种媒体数据互相融合处理,促进了人工智能在计算机视觉领域的研究发展,在遥感图像解译、生物医学和深度估计等方面有广泛的应用。尽管多模态数据在描述事物特征时具有明显优势,但仍面临着较大的挑战。1)受到不同成像设备和传感器的限制,难以收集到大规模、高质量的多模态数据集;2)多模态数据需要匹配成对用于研究,任一模态的缺失都会造成可用数据的减少;3)图像、视频数据在处理和标注上需要耗费较多的时间和人力成本,这些问题使得目前本领域的技术尚待攻关。本文立足于数据受限条件下的多模态学习方法,根据样本数量、标注信息和样本质量等不同的维度,将计算机视觉领域中的多模态数据受限方法分为小样本学习、缺乏强监督标注信息、主动学习、数据去噪和数据增强5个方向,详细阐述了各类方法的样本特点和模型方法的最新进展。并介绍了数据受限前提下的多模态学习方法使用的数据集及其应用方向(包括人体姿态估计、行人重识别等),对比分析了现有算法的优缺点以及未来的发展方向,对该领域的发展具有积极的意义。     

3D Shape Recognition Based on Multimodal \newline Relation Modeling

To make full use of the local spatial relation between point cloud and multi-view data to further improve the accuracy of three-dimensional (3D) shape recognition, a 3D shape recognition network based on multimodal relation is proposed. Firstly, a Multimodal Relation Module (MRM) is designed, which can extract the relation information between the local features of any point cloud and that of any multi-view to obtain the corresponding relation features. Then, a cascade pooling consisting of maximum pooling and generalized mean pooling is applied to process the relation feature tensor and obtain the global relation feature. There are two types of multimodal relation modules, which output the point-view relation feature and the view-point relation feature, respectively. The proposed gating module adopts a self-attentive mechanism to find the relation information within the features so that the aggregated global features can be weighted to suppress redundant information. Extensive experiments show that the multimodal relation module can make the network obtain stronger representational ability; the gating module can make the final global feature more discriminative and boost the performance of the retrieval task. The proposed network achieves classification accuracy of 93.8% and 95.0%, as well as average retrieval precision of 90.5% and 93.4% on two standard 3D shape recognition datasets (ModelNet40 and ModelNet10), respectively, which outperforms the existing works.     

多模态融合的特征提取方法在SA检测中的应用

为解决睡眠呼吸暂停(Sleep Apnea,SA)检测中使用传统的机器学习方法需花大量工作在特征工程上导致效率低下,以及模型多以单通道信号进行特征提取存在识别效果不佳的问题,提出一种基于时序卷积网络(Temporal Convolutional Network,TCN)和堆叠稀疏降噪自编码器(Stacked Sparse Denoismg Auto-Encoder,SSDAEs)的多模态特征融合模型来实现特征自动提取。该模型以心电和呼吸2种信号作为输入,首先利用TCN网络提取输入信号的时序特征,然后通过SSDAEs提取信号的浅层与深层的高维特征,对于不同特征空间的心电信号特征和呼吸信号特征采用一个小型神经网络进行特征融合,将该模型与随机森林算法结合,用于解决SA片段检测问题。实验结果表明,该方法在SA片段检测的准确率、灵敏度、特异性分别是91.5%、88.9%、90.8%。通过与以往相关研究对比,验证了该模型的SA检测性能更好,效率更高。     

多峰函数优化的黄金分割斐波那契树优化算法

针对多峰函数优化问题，基于斐波那契树优化算法，结合黄金分割思想，提出一种黄金分割斐波那契树优化算法.该算法利用斐波那契树优化算法全局局部交替寻优特性，通过在寻优过程中对优化问题解空间进行黄金分割比例压缩，从而提高算法局部搜索能力与小峰值搜索能力.多峰函数优化的仿真结果表明，该算法多峰优化能力强、速度快、精度高.     

Effects of different metaphor usage on hypertext learning

There are many studies that offer different opinions on the effects of hypertext usage as an educational tool. Given the differences of opinion, it is useful to research the effects of metaphor usage in hypertext education and the use of hypertext as an educational tool. In this study, the effects of metaphors' uses in constructing the characteristics of the hypertext teaching have been approved. A between-group design experiment was conducted to study the effects of four metaphors on four different groups. Survey results emphasise the importance of metaphor usage and the manner in which metaphor is used. It was also discovered that metaphor usage in the hypertext teaching affects the construction duration of the hypertext but does not have any effects on the acquisition of knowledge.