飞行学员英语自主学习情况调查及建议

通过定性和定量相结合的方法对飞行学员的英语自主学习情况进行了试验性研究。结果表明,飞行学员具有很强的自主学习意识,对英语学习态度积极,但学员自我管理能力较差,在学习中不善于制定计划,对学习环境和教师有很大的依赖,他们重视语言学习的过程而对语言学习的结果表示无所谓。此次调查为英语教师开展教学活动提供了建设性建议,教师应该督促学生制定计划,为学生创造英语学习环境,同时加大课堂语言输入量及输入的质量,对学生学习实施阶段性评价和终结性评价体系。     

试析马克思理论体系的两个思想资源

除了常说的三大理论来源以外,马克思的理论体系的形成还有很多丰富的思想资源,其中,基督教和<圣经>传统、古希腊神话和西方文学作品对马克思科学世界观、人生观、价值观的形成产生了深刻影响,马克思对他们采取了批判的借鉴的态度,从中汲取了丰富的营养,成为马克思理论体系重要的思想资源.     

将日本文化融入到二外日语教学的始终

每一种语言的形成和发展都与其社会环境密切相连，语言同时反映出一个国家和民族的历史和文化。从这个层面上说，教授语言同时也是在教授文化，学习语言的同时也是在学习文化。一个科学的语言教学体系，必须将文化教学紧密融入到语言的教学当中。鉴于当前的高校二外日语教学中鲜有独立开设日本文化课的情况，将日本文化融入到二外日语教学的始终，将会有效帮助学生在有限的学习时间里取得良好的学习效果。本文针对现阶段高校二外日语教学的特点，探讨了在日本文化背景下进行二外日语教学的重要性，并提出了一些文化导入的内容与方法。     

材料成型及控制专业“以赛促教，以赛促学”的教学改革探索

专业竞赛是培养学生创新能力和实践能力的一个重要手段。近几年来，我们组织材料成型及控制专业学生参加了各类竞赛，积累了经验，对材料成型及控制专业进行了以赛促教，以赛促学的教学改革探索。     

基于增量学习的高光谱图像目标检测

高光谱图像目标检测是高光谱图像分析中的重要研究内容之一。本文从经典有效的约束能量最小化算法出发,提出了一种基于增量学习的高光谱目标检测方法。当获得新的样本时,不需要重新计算所有样本的自相关矩阵即可对检测器模型进行更新,减轻了星上有限计算资源的负担。实验结果表明:本文提出的目标检测算法在压制背景光谱的同时可以更好地适应目标光谱,提高了算法的检测性能。     

Adaptive neural network control of nonlinear systems with unknown dynamics

In this study, an adaptive neural network control approach is proposed to achieve accurate and robust control of nonlinear systems with unknown dynamics, wherein the neural network is innovatively used to learn the inverse problem of system dynamics with guaranteed convergence. This study focuses on the following three contributions. First, the considered system is transformed into a multi-integrator system using an input–output linearization technique, and an extended state observation technique is used to identify the transformed states. Second, an iterative control learning algorithm is proposed to achieve the neural network training, and stability analysis is given to prove that the network’s predictions converge to ideal control inputs with guaranteed convergence. Third, an adaptive neural network controller is developed by combining the trained network and a proportional-integral controller, and the long-standing challenge of model-based methods for control determination of unknown dynamics is resolved. Simulation results of a virtual control mission and an aerospace altitude tracking mission are provided to substantiate the effectiveness of the proposed techniques and illustrate the adaptability and robustness of the proposed controller.     

Spatial modeling of flood probability using geo-environmental variables and machine learning models,case study: Tajan watershed,Iran

The main objective of this study was to produce flood susceptibility maps for Tajan watershed, Sari, Iran using three machine learning (ML) models including Self-Organization Map (SOM), Radial Basis Function Neural Network (RBFNN), and Multi-layers Perceptron (MLP). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. 212 flood inventory map was randomly divided into training and testing datasets, where 148 flood locations (70%) were used for training and the remaining 64 locations (30%) were employed for testing. Model validation was performed using several statistical indices and the area under the curve (AUC). The results of the correlation matrix showed, three factors slope (0.277), distance from river (0.263), and altitude (0.223) were the most important factors affecting flood. The accuracy evaluation of the flood susceptibility maps through the AUC method and K-index shows that in the validation phase RBFNN (AUC = 0.90) outperform the MLP (AUC = 0.839) and SOM (AUC = 0.882) models. The highest percentage flood susceptibility of the area in MLP, SOM and RBFNN models is related to moderate (28.7%), very low (40%) and low (37%), respectively. Also, the validation results of the models using the Relative Flood Density (RFD) approach showed that very high class had the highest RFD value.     

面向林火持续侦察的多无人机分布式控制方法

为解决目前面向林火持续侦察多无人机（UAV）协同控制实用性与自主性不足的问题，基于蔓延速度诱导元胞自动机（SVICA）林火蔓延算法、无人机与传感器建模，构建了较为真实的三维多无人机火场侦察仿真环境与侦察效能指标，提出了一种面向林火持续侦察的多无人机双层分布式控制架构，在行动层基于强化学习训练的人工神经网络（ANN），实现了有风条件下无人机自主火场环绕与地形跟随功能，在策略层设计通过时域均匀分布算法进行各无人机空速的离散自主调节，最终达到多无人机林火持续侦察时域分布的均匀性与即时性目的。通过一系列数值仿真实验，验证了所提出的无人机分布控制策略在无人机损失和补充突发情况下的自适应性。基于无人机数量与侦察效能指标关系的实验与研究，定义了无人机出动阈值并验证了无人机长时间出动回收策略。最终实验结果表明，针对林火持续侦察任务，所提出的多无人机分布式控制方法具备一定的有效性与实用性。     

Improving orbit prediction accuracy through supervised machine learning

Due to the lack of information such as the space environment condition and resident space objects’ (RSOs’) body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs’ trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO’s orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.     

我国高校无形资产经营管理分析

高等学校的无形资产经营管理是市场经济条件下各个高校需要解决的一个难题和重点。分析我国高校无形资产经营管理的现状及其问题,并给出相关政策建设。对加快相关的法规建设,规范高校无形资产经营管理具有现实意义。