原型理论对中国大学生英语词汇学习的启示

词汇学习是我国大学生英语学习的重点以及难点,传统的机械性背记方法耗时多,收效少。就提高我国大学生英语词汇学习效率这一问题,从认知语言学的原型理论出发,为我国大学生学习词汇提供了更科学,更高效的学习方法。     

Interface Familiarity Restores Active Advantage in a Virtual Exploration and Reconstruction Task in Children

Abstract

Active exploration is reportedly better than passive observation of spatial displacements in real environments, for the acquisition of relational spatial information, especially by children. However, a previous study using a virtual environment (VE) showed that children in a passive observation condition performed better than actives when asked to reconstruct in reality the environment explored virtually. Active children were unpractised in using the input device, which may have detracted from any active advantage, since input device operation may be regarded as a concurrent task, increasing cognitive load and spatial working memory demands. To examine this possibility, 7–8-year-old children in the present study were given 5 minutes of training with the joystick input device. When compared with passive participants for spatial learning, active participants gave a better performance than passives, placing objects significantly more accurately. The importance of interface training when using VEs for assessment and training was discussed.     

FAST FEATURE RANKING AND ITS APPLICATION TO FACE RECOGNITION

A fast feature ranking algorithm for classification in the presence of high dimensionahty and small sample size is proposed. The basic idea is that the important features force the data points of the same class to maintain their intrinsic neighbor relations, whereas neighboring points of different classes are no longer to stick to one an- other. Applying this assumption, an optimization problem weighting each feature is derived. The algorithm does not involve the dense matrix eigen-decomposition which can be computationally expensive in time. Extensive exper- iments are conducted to validate the significance of selected features using the Yale, Extended YaleB and PIE data- sets. The thorough evaluation shows that, using one-nearest neighbor classifier, the recognition rates using 100-- 500 leading features selected by the algorithm distinctively outperform those with features selected by the baseline feature selection algorithms, while using support vector machine features selected by the algorithm show less prominent improvement. Moreover, the experiments demonstrate that the proposed algorithm is particularly effi- cient for multi-class face recognition problem.     

W波段FMCW体制ISAR系统成像及试验验证

毫米波雷达具有高分辨率、小型化、轻型化等特点,是现代雷达应用的一个重要发展方向。随着W波段元器件的突破,W波段逆合成孔径雷达(ISAR)系统的研究引起了世界发达国家的重视。W波段ISAR图像分辨率高,目标散射细节更丰富,可提高目标分类、识别精度,在军民领域均有很大的应用价值。介绍了一种W波段调频连续波(FMCW)体制ISAR系统,探讨了该体制ISAR系统性能并介绍了W波段FMCW ISAR成像处理算法。该系统发射信号中心频率为94 GHz,带宽为5 GHz。利用该系统开展了ISAR转台试验,并利用RD算法得到了ISAR系统初步成像结果。     

基于神经网络的机器人迭代学习控制

针对机器人动力学模型的不确定性和负载扰动，提出了一种采用神经网络的机器人迭代学习控制方法。该方法将反馈控制和神经网络学习控制相结合，反馈控制沿时间轴方向使关节运动跟踪期望轨迹，神经网络学习控制沿迭代轴方向使关节运动逼近期望轨迹。文中还给出了基于ＢＰ神经网络的学习控制算法。仿真结果表明，该方法能克服机器人动力学模型的不确定性和负载扰动，具有良好的鲁棒性和控制性能。     

旋翼桨叶动力学设计技术

桨叶动力学设计,其实质是求解非线性高维动力学逆问题的具体实施。本文较全面地阐述了这方面的进展情况,提出了切实可行、有效实用的数值方法。     

基于深度学习的混合翼型前缘压力分布预测

提出了一种基于深度学习的混合翼型前缘压力分布预测方法，通过对翼型几何特征提取、压力分布曲线的参数化，建立了卷积神经网络模型（CNN），并利用计算流体力学（CFD）的计算结果作为其训练样本，实现对混合翼型前缘压力分布的预测。结果表明:两种方法计算结果的拟合优度大于0.98，基于深度学习的计算方法耗时1.7 s，CFD方法耗时大于50 s，计算时间大大缩短。该方法能够在满足计算精度的条件下提高计算效率并可应用于其他的翼型设计过程。      

英语习得与人为目的语环境的创造

"语言习得"与"语言学习"是外语教学理论研究中的两个重要概念,克拉申认为只有"习得"来的语言才能成为流利的口头表达,而"学习"只能起"监查"作用.在"学习"的条件下,通过营造人为目的语习得环境,以用为本,立足实践,学习者同样可以达到习得外语的目标.     

基于自适应深度网络的无人机目标跟踪算法

针对无人机（UAV）视频中目标易受到遮挡、形变、复杂背景干扰等问题，提出一种基于自适应深度网络的无人机目标跟踪算法。首先，基于主成分分析（PCA）和卷积神经网络（CNN）算法，设计3阶的自适应深度网络进行目标特征提取，该网络对图像的H、S、I通道分别进行主成分分析学习，将得到的特征向量输入网络进行分层卷积，优化了网络结构，提高了网络的收敛速度和精度。其次，将目标深度特征输入核相关滤波算法进行目标跟踪，通过分析相邻2帧图像的变化率，采用分段自适应调整学习率的算法进行目标模板更新，有效地改善目标遮挡问题。仿真实验结果表明，该算法有效地避免了复杂因素干扰导致的跟踪精度下降，具有较好的鲁棒性，相较于全卷积跟踪（FCNT）算法平均跟踪精度提高了9.62%，平均跟踪成功率提高了11.9%。     

Practical compensation for nonlinear dynamic thrust measurement system

The real dynamic thrust measurement system usually tends to be nonlinear due to the complex characteristics of the rig, pipes connection, etc. For a real dynamic measuring system,the nonlinearity must be eliminated by some adequate methods. In this paper, a nonlinear model of dynamic thrust measurement system is established by using radial basis function neural network(RBF-NN), where a novel multi-step force generator is designed to stimulate the nonlinearity of the system, and a practical compensation method for the measurement system using left inverse model is proposed. Left inverse model can be considered as a perfect dynamic compensation of the dynamic thrust measurement system, and in practice, it can be approximated by RBF-NN based on least mean square(LMS) algorithms. Different weights are set for producing the multi-step force, which is the ideal input signal of the nonlinear dynamic thrust measurement system. The validity of the compensation method depends on the engine’s performance and the tolerance error0.5%, which is commonly demanded in engineering. Results from simulations and experiments show that the practical compensation using left inverse model based on RBF-NN in dynamic thrust measuring system can yield high tracking accuracy than the conventional methods.