基于深度学习的单排孔气膜冷却性能预测

气膜冷却是增强涡轮叶片的高温耐受力,间接提高涡轮进口温度的有效手段之一。目前气膜冷却孔布局的主流设计方法是先通过计算流体力学（CFD）筛选和优化初始方案,再进行模型实验。这种方法设计周期长,时间成本高。传统上用于快速评估冷却效率的经验公式法存在函数形式复杂,拟合精度有限,参数适用范围较窄等问题。因此基于深度学习原理,设计了一种基于多层感知器模型（MLP）的深度神经网络,建立了绝热气膜冷却效率的预测模型。使用CFD数据训练网络,结果表明：深度学习模型在训练集和验证集上具有大于0.95的拟合度,在测试集上具有大于0.99的拟合度,可以较好地识别数据集中的抽象特征,具有较高的精度和较好的泛化能力。此外,在满足精度要求的前提下,一个完成训练的深度学习模型能够有效减少预测耗时,提高预测效率,在快速评估冷却布局性能方面具有较好的应用前景。     

Actual TDoA-based augmentation system for enhancing cybersecurity in ADS-B

Currently, cybersecurity and cyber resilience are emerging and urgent issues in next-generation air traffic surveillance systems, which depend primarily on Automatic Dependent Surveillance-Broadcast (ADS-B) owing to its low cost and high accuracy. Unfortunately, ADS-B is prone to cyber-attacks. To verify the ADS-B positioning data of aircraft, multilateration (MLAT) techniques that use Time Differences of Arrivals (TDoAs) have been proposed. MLAT exhibits low accuracy in determining aircraft positions. Recently, a novel technique using a theoretically calculated TDoA fingerprint map has been proposed. This technique is less dependent on the geometry of sensor deployment and achieves better accuracy than MLAT. However, the accuracy of the existing technique is not sufficiently precise for determining aircraft positions and requires a long computation time. In contrast, this paper presents a reliable surveillance framework using an Actual TDoA-Based Augmentation System (ATBAS). It uses historically recorded real-data from the OpenSky network to train our TDoA fingerprint grid network. Our results show that the accuracy of the proposed ATBAS framework in determining the aircraft positions is significantly better than those of the MLAT and expected TDoA techniques by 56.93% and 48.86%, respectively. Additionally, the proposed framework reduced the computation time by 77% compared with the expected TDoA technique.     

未来智能空战发展综述

随着装备战斗力生成模式逐渐向机械化、信息化、智能化"三化融合"发展演变,未来航空主战装备的定位、形态及运用将可能发生根本性变革。为应对新时期空战任务所面临的环境高复杂性、博弈强对抗性、响应高实时性、信息不完整性、边界不确定性等一系列挑战,交叉融合人工智能理论与空战对抗技术,研发智能空战系统,将有望在下一代无人制空装备谱系中构建不对称"智能代差",成为制胜未来空天战场的核心关键。本文完整梳理了智能空战研究的发展脉络,总结了以专家机动逻辑、自动规则生成、规则演进、机器学习等方法为代表的智能空战基础理论。从体系、应用及技术视角全面剖析了智能空战的发展趋势,以智能空战的不确定性、安全性、解释性、迁移性、协同性为切入点阐述了智能空战应用落地的若干问题,以期为未来智能空战技术研究勾勒出一条新的探索路径,为人工智能理论与航空科学技术的跨领域交叉融合提供新的发展思路。     

高职院校学习型辅导员队伍构建策略探析

在高职院校,普遍存在着辅导员学习研究意识淡漠,缺乏学科背景支持、学习氛围不浓等问题,针对这些问题,高职院校须构建一支学习型辅导员队伍,可采取的具体策略包括：建设学习型学生工作领导队伍、培养问题研究意识、加强团队学习和培训指导等策略。     

A machine learning approach to crater detection from topographic data

Craters are distinctive features on the surfaces of most terrestrial planets. Craters reveal the relative ages of surface units and provide information on surface geology. Extracting craters is one of the fundamental tasks in planetary research. Although many automated crater detection algorithms have been developed to exact craters from image or topographic data, most of them are applicable only in particular regions, and only a few can be widely used, especially in complex surface settings. In this study, we present a machine learning approach to crater detection from topographic data. This approach includes two steps: detecting square regions which contain one crater with the use of a boosting algorithm and delineating the rims of the crater in each square region by local terrain analysis and circular Hough transform. A new variant of Haar-like features (scaled Haar-like features) is proposed and combined with traditional Haar-like features and local binary pattern features to enhance the performance of the classifier. Experimental results with the use of Mars topographic data demonstrate that the developed approach can significantly decrease the false positive detection rate while maintaining a relatively high true positive detection rate even in challenging sites.     

基于网络语言学习中心的公共英语口语辅助教学设计

为了推进高校公共英语教学,以国家示范性院校建设为契机,学院建立了英语语言学习中心,开始了结合网络自主学习概念的公共英语教学改革的尝试。英语口语教学作为公共英语教学的主要组成部分,主要是以基于网络语言自主学习的辅助教学展开设计方案。     

基于过程神经网络的液体火箭发动机状态预测

提出一种基于极限学习算法的离散过程神经网络模型,用于解决液体火箭发动机状态预测这一难题。首先,在历史数据的基础上建立离散过程神经网络（DPNN）预测模型;然后,根据在线更新的数据样本,采用递推极限学习（EL）算法对双并联前馈离散过程神经网络（DPFDPNN）隐层到输出层的权值进行更新,并应用权值更新后的过程神经网络对发动机状态进行预测;最后,以液体火箭发动机状态预测中氢涡轮泵扬程预测为例,分别采用有权值更新和无权值更新两种预测模型进行了试验。结果表明,通过更新过程神经网络权值可以使模型具有更高的预测精度和更好的适应能力,该方法能够为液体火箭发动机状态预测提供一种有效的解决途径。      

航空网络关键节点辨识的核极限学习机算法研究

通过技术手段准确识别航空网络的关键节点,对航空网络平时的正常运行以及战时的防御和修复,具有重要的理论意义和参考价值。提出一种基于核极限学习机的航空网络关键节点识别方法,首先,采用层次分析法对节点综合重要度进行评估;然后,选取三个简单指标,基于核极限学习机学习简单指标与综合重要度之间的映射关系,建立重要度评估模型;最后,以中美两国航空网络为例进行仿真。结果表明:仅需计算 40 个节点的复杂指标值,就可对关键节点取得较满意的辨识效果,降低了计算复杂度,提高了辨识效率,即采用本文方法辨识航空网络的关键节点是有效、可行的。     

基于网络平台的大学英语自主学习现状与对策研究

基于网络平台的大学英语自主学习对大学英语教学产生了积极的影响。通过问卷调查发现,存在着学习目标不明确、学习内容不系统、自我监控能力较差等问题。应通过加强对学生自主学习策略的培养,落实学生的主体地位,加强网络监控来提高自主学习的效率。     

纵列式双旋翼重型直升机操纵系统及飞控系统方案研究

通过对纵列式双旋翼重型直升机的静稳定性和动稳定性进行理论分析,找出了影响纵列式双旋翼重型直升机最关键的迎角静不稳定和速度静不稳定问题,针对上述问题提出了相应的操纵模式,并制定了操纵系统及飞控系统的初步技术方案。