针对“四强”特点 培育“四力”素质

网络信息时代，面对层出不穷的不良信息，如何提高官兵抵制负面影响的素质能力，概括起来就是：要针对不良信息“腐蚀性”强的特点，不断培育官兵“乐于追随”的不变定力；要针对不良信息“迷惑性”强的特点，不断培育官兵“善于思辨”的不凡智力；要针对不良信息“煽动性”强的特点，不断培育官兵“敢于斗争”的不惧魄力；要针对不良信息“持久性”强的特点，不断培育官兵“甘于奉献”的不竭动力。     

基于AdaBoost-SVM的P2P流量识别方法

针对传统的P2P流量识别技术存在识别率低和误判率高的缺点,将机器学习中Ada Boost算法的良好分类能力和SVM的泛化能力结合起来,提出一种基于Ada Boost-SVM组合算法的P2P网络流量识别模型,将SVM作为Ada Boost的基分类器,运用最小近邻法计算支持向量与训练集的样本间的距离实现分类进行P2P流量识别。最后,以4种P2P流量数据为研究对象在MATLAB上进行仿真,仿真结果表明,提出的Ada Boost-SVM的组合算法在P2P网络流量的分类性能和分类准确率上都优于单纯的Ada Boost和SVM,组合算法的P2P流量平均识别率高达98.7%,远高于Ada Boost和SVM的识别率。     

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。     

复杂海战场环境下AUV全局路径规划方法

针对无人自主水下航行器(Autonomous Underwater Vehicle, AUV)在复杂海战场环境中路径规划时环境模型复杂、约束条件多的情况,建立了包括战场地形、敌方威胁、障碍物和海流场等在内的比较完善的海战场环境模型。以AUV航行时间、威胁时间最短为优化目标,给出了一种基于振荡型入侵野草优化(Invasive Weeds Optimization, IWO)算法的AUV全局路径规划方法,并分别与标准IWO算法、全振荡型IWO算法以及粒子群算法等三种路径规划算法比较。仿真结果表明,所提方法具有较强的寻优能力和鲁棒性,可在复杂海战场环境下为AUV高效地规划出满足性能要求的航行路径。     

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.     

ARCS动机模型在初中英语语法教学中的应用

语法学习能在一定程度上保证语言的地道性、流畅性和实用性。但就目前来看,英语学习者学习语法的积极性普遍不高,不少英语学习者抱怨英语语法项目琐碎繁杂、语法学习枯燥无聊。针对此现状,本文尝试将ARCS模型的四类动机策略整合于初中英语语法教学,以期有效激发并维持学习者的语法学习热情,提高语法教学效果。