谈英语词汇学习的语义因素

词汇学习和记忆是学习语言的最基本的手段,也是运用语言的先决条件。学习英语词汇除了从词汇的语音、拼写和一般定义入手外,还应特别注意词汇的语义。本文尝试从词汇的语义关系这个角度论述语义在英语词汇学习中的重要性。     

机载激光雷达数据的三维深度学习树种分类

针对传统基于激光雷达(Light Detection And Ranging,LiDAR)数据的树种分类方法难以直接且全面地利用点云的三维结构信息的问题,提出一种基于三维深度学习的机载LiDAR数据的树种分类方法.该方法直接从三维数据中抽象出高维特征,而无须将点云转化为体素或二维图像.以塞罕坝国家森林公园内白桦和落叶松...     

导弹突防后弹道机动调整策略强化学习

针对弹道导弹中段突防后飞行弹道与标准弹道产生较大偏离的弹道机动调整问题,建立了机动调整时机策略最优化模型。设计了机动调整逆序Q学习算法,采用Tile coding逼近器编码状态特征空间,并对其进行线性逼近。构建了Q学习算法与蒙特卡罗方法相结合的逆序更新策略机制,以对导弹机动调整最优时机进行训练。仿真测试分析结果表明,在给定场景参数下,通过10 000代强化学习算法训练得到的策略能够可靠地使用最少机动次数控制导弹突防后飞行弹道的调整决策,验证了方法的有效性。     

建筑消防设计中疏散时间的确定方法

本文针对建筑消防设计中疏散时间计算这一关键问题 ,利用疏散行为指数确定生活不能自理的人的疏散能力 ,进而确定其疏散时间 ,为建筑消防安全设计和防火管理工作提供理论依据。文中介绍的方法、计算公式及步骤简捷 ,具有一定的实用价值。     

党的执政经验和执政能力的辩证统一关系

中国共产党55年的执政经验与执政能力是一个不断总结和不断提高的过程,是一个既相互联系,又相互转化的辩证统一关系。党的四代领导集体在执政经验和执政能力上的继承与发展的辩证统一关系;党的执政经验与执政能力相互转化的辩证统一关系;党的执政经验与执政能力在自身理论认识上的辩证统一关系。     

谈军校图书馆在信息素质教育中的地位和作用

介绍了信息素质及其教育的相关概念和理论,提出信息素质是军校生最根本的素质,并对图书馆在军校生信息素质教育中的主体地位进行了分析和阐述,概述了对信息素质教育与图书馆未来建设的认识。     

对设计性实验的思考

设计性实验是巩固学生专业知识,培养学生动手能力、综合运用学科知识能力和自主创新能力,提高学生综合素质的重要方法。基于设计性实验的特点就如何有效开展设计性实验以及实验的组织实施、成果报告、效果评价的方法,提出了以教师为主导,以学生为主体的教学模式。     

背景音乐对人类学习效率影响的脑与行为研究进展

背景青乐是与主体的意识或行为无直接关系,通过非青乐鉴赏环境这一媒介间接作用于主体意识与行为的音乐。近些年来将背景音乐引入教育领域来探讨其对学习效率的影响是研究的新课题。从背景音乐对学习效率影响的相关理论、背景音乐对学习效率影响的实验研究以及背景音乐影响的研究趋势等方面进行了综述。     

背诵对提高口译能力的正迁移及在教学中的应用

背诵是成功的语言学习者常用的策略,也是不同条件下英语教学行之有效的教学方法。文章通过跟踪调查,试验对照的形式,结合SPSS对数据加以分析,在实证研究的基础上,揭示背诵对提高大学生口译能力的正迁移作用并提出如何将背诵科学、合理地应用于口译教学,以提高教学质量。     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.