学习迁移理论指导下的分层式课堂活动研究

在大学的英语课堂,学生在课堂活动中的有效参与直接影响到教学效果。这就要求教师以创新的教学方式来提高学生的参与度和主动性。以学习迁移理论为依据,提出分层次实施课堂教学活动,阐释了分层式课堂活动的设计与应用,对其优势进行了分析,以期对贯彻“因材施教”教学理念起到参考作用。     

新疆高校维吾尔族学生英语学习中母语负迁移现象的研究

本文从迁移理论出发,以调查维吾尔族母语负迁移为目的,结合访谈、调查问卷和写作,对分布在新疆不同高校里的维吾尔族学生进行了调查研究,最后得出结论：维吾尔族学生在英语学习中受母语负迁移的影响较大,其中与维吾尔语差异最大的语法方面所受母语影响最大,母语思维是产生负迁移的主要原因,其它方面受汉语影响也比较大。基于上述研究,为了避免负迁移的发生,本文提出了相应的建议。     

Visual-attention gabor filter based online multi-armored target tracking

The multi-armored target tracking (MATT) plays a crucial role in coordinated tracking and strike. The occlusion and insertion among targets and target scale variation is the key problems in MATT. Most state-of-the-art multi-object tracking (MOT) works adopt the tracking-by-detection strategy, which rely on compute-intensive sliding window or anchoring scheme in detection module and neglect the target scale variation in tracking module. In this work, we proposed a more efficient and effective spatial-temporal attention scheme to track multi-armored target in the ground battlefield. By simulating the structure of the retina, a novel visual-attention Gabor filter branch is proposed to enhance detection. By introducing temporal information, some online learned target-specific Convolutional Neural Networks (CNNs) are adopted to address occlusion. More importantly, we built a MOT dataset for armored targets, called Armored Target Tracking dataset (ATTD), based on which several comparable experiments with state-of-the-art methods are conducted. Experimental results show that the proposed method achieves outstanding tracking performance and meets the actual application requirements.     

提升大学生学习力之浅见

二十一世纪是终身学习、终身教育的社会。当代大学生要跟上社会发展的步伐,必须确立较强的学习力,储备良好的核心竞争力。本文在调查的基础上,经过分析总结,从学习力的内涵、现状分析、提升的途径和策略、采取相应措施等方面进行了初步的解析和阐述。     

试论英语学习中词典的使用

词典是汇集词语进行解释、为人们释疑解惑的工具书。初级英语学习者主要依靠教师授课进行学习,而中高级学习者有更多的时间利用英语词典进行自学。词典是英语学习者的必备工具书,但学习者未能充分利用。因此,有必要对以英语学习词典为主的工具书的使用进行探讨,从而得出结论:要使学习者从词典中充分获益,有必要对英语学习者进行词典使用方面的训练。     

探讨研究生学习动机与培养质量的相关性

以往研究研究生的学习动机侧重于量化研究,本文试图从质性研究的角度对当代研究生学习动机进行探讨,归纳出不同的学习类型,分析当代研究生学习动机与培养质量的关系,以及目前由于学习动机造成的培养质量问题,以提高研究生培养质量。     

深度置信网络在导弹攻击区分类中的应用

针对传统导弹攻击区解算方法忽略双方态势变化等问题,提出运用深度置信网络的导弹攻击区分类模型。根据导弹命中情况与目标机动间的关系,将导弹攻击区划分为五类。通过分析影响导弹攻击结果的态势参数,构建导弹攻击结果预测模型。在实验部分,结合重构误差和测试错误率确定深度置信网络的网络结构,通过逐层提取数据法分析模型参数特征并且讨论微调数据的采样方式。使用反向传播神经网络和支持向量机进行分类有效性对比实验。实验结果表明：深度置信网络运行速度和预测准确度明显优于其他两种方法,满足实时性和准确性要求,所提方法具有良好的应用价值。     

用户偏好提取MDP建模研究

将马尔可夫判决过程和智能强化学习算法相结合,给出了异构无线网络环境下用户业务偏好评估模型的技术框架。为动态环境下用户需求的感知、量化和适配特征的研究提供了基本的数学描述,对解决用户体验的评价问题和业务与业务环境的适配问题提供了新的研究思路。仿真结果表明所构建的MDP模型能够在多状态条件下学习用户偏好,根据用户需求智能选择业务。     

深度神经网络指导下的无线覆盖预测算法

为保证新一代移动无线网络能够根据实时覆盖情况动态地调节小区天线参数,需要实现高效且准确的无线覆盖预测。传统的求解方法通过精确的场强预测判断天线参数的优劣,虽然精度很高但需要大量的计算资源,无法满足5G和后5G移动网络通过实时覆盖预测进行射频参数动态调整的实际需求。现采用基于深度神经网络的算法对给定天线参数的覆盖效果进行预测,以取代对目标区域的精确场强预测。数值结果表明:该方法能够在保持计算准确性的同时显著减少计算量,为5G动态网络规划提供基础性参考数据。     

Gabor-CNN for object detection based on small samples

Object detection models based on convolutional neural networks (CNN) have achieved state-of-the-art performance by heavily rely on large-scale training samples. They are insufficient when used in specific applications, such as the detection of military objects, as in these instances, a large number of samples is hard to obtain. In order to solve this problem, this paper proposes the use of Gabor-CNN for object detection based on a small number of samples. First of all, a feature extraction convolution kernel library composed of multi-shape Gabor and color Gabor is constructed, and the optimal Gabor convolution kernel group is obtained by means of training and screening, which is convolved with the input image to obtain feature information of objects with strong auxiliary function. Then, the k-means clustering algorithm is adopted to construct several different sizes of anchor boxes, which improves the quality of the regional proposals. We call this regional proposal process the Gabor-assisted Region Proposal Network (Gabor-assisted RPN). Finally, the Deeply-Utilized Feature Pyramid Network (DU-FPN) method is proposed to strengthen the feature expression of objects in the image. A bottom-up and a top-down feature pyramid is constructed in ResNet-50 and feature information of objects is deeply utilized through the transverse connection and integration of features at various scales. Experimental results show that the method proposed in this paper achieves better results than the state-of-art contrast models on data sets with small samples in terms of accuracy and recall rate, and thus has a strong application prospect.