从美的流变浅析美的本质

自从十八世纪中页鲍姆嘉通为美学奠定基础以来,众多哲学家、美学家写了无数论著来探讨美的含义.那么,美的本质究竟是什么呢?为什么美的问题会如此难以明晰?直到马克思主义美学产生后,这一美学命题才得到突破性地进展.马克思主义美学以实践为基础,认为实践是人类存在和发展的本质特征,从而提出"美是人的本质力量的对象化."将审美主体和审美客体有机地统一起来.     

Sailing at the brink – The no-limits of near-/now-term-technology solar sails and SEP spacecraft in (multiple) NEO rendezvous

Near-Earth object (NEO) in-situ exploration can provide invaluable information for science, possible future deflection actions and resource utilisation. This is only possible with space missions which approach the asteroid from its vicinity, i.e. rendezvous. This paper explores the use of solar sailing as means of propulsion for NEO rendezvous missions. Given the current state of sail technology, we search for multiple rendezvous missions of up to ten years and characteristic acceleration of up to 0.10 mm/s². Using a tree-search technique and subsequent trajectory optimisation, we find numerous options of up to three NEO encounters in the launch window 2019–2027. In addition, we explore steerable and throttleable low-thrust (e.g. solar-electric) rendezvous to a particular group of NEOs, the Taurid swarm. We show that an acceleration of 0.23 mm/s² would suffice for a rendezvous in approximately 2000 days, while shorter transfers are available as the acceleration increases. Finally, we show low-thrust options (0.3 mm/s²) to the fictitious asteroid 2019 PDC, as part of an asteroid deflection exercise.     

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.     

多媒体智能数据库系统MIDS／BUAA

ＩＤＢ（智能数据库）思想的提出，预示着人类的信息处理即将步入一个崭新的时代．ＩＤＢ将计算机科学中近年来日趋发展成熟的五大主要技术，ΟΟ（面向对象）技术、ＤＢ（数据库）技术、ＡＩ（人工智能）、Ｈｙｐｅｒｔｅｘｔ／Ｈｙｐｅｒｍｅｄｉａ（超文本／超媒体）和正文数据库与联机信息检索技术，集成为一体．其中ΟΟ、ＡＩ和ＤＢ是ＩＤＢ中的三大支柱技术．本文论述ＭＩＤＳ／ＢＵＡＡ数据模型（Ｄａｔａｍｏｄｅｌｉｎｇ）的哲学，并介绍ＭＩＤＳ／ＢＵＡＡ系统的设计思想、动机、核心数据模型，系统的功能构成和它的Ｃｌｉｅｎｔ／Ｓｅｒｖｅｒ体系结构．     

异质网上互操作—从数据交换到程序交换

对分布式计算和软件集成中的互操作性问题进行了讨论，叙述了遵循特定的标准解决互操作性的办法，比较几种常见的互操作标准，包括ＳＴＥＰ／ＳＤＡＩ，ＣＯＲＢＡ和Ｊａｖａ，描述了Ｊａｖａ语言出现后程序交换的新特点，并对互操作标准的现状作了评述。     

无人机地面控制站系统的应用研究

研究了一套功能完善的无人机地面控制站系统,由地面飞控站和地面导航站两部分组成。地面飞控站通过无线电链路直接与无人机自动驾驶仪进行通信;地面导航站是地面控制站的一个航迹系统,运用以太网实现它与地面飞控站之间的通信。     

高级体系结构仿真系统建模研究

高级体系结构(HLA)是一种支持互操作和可重用的分布交互仿真(DIS)标准。本文分析了HLA中的模型,然后提出了仿真系统构建中的虚拟功能模块,并给出了可行的实现方法,以期达到仿真与建模的高效性。     

基于深度卷积神经网络的遥感影像目标检测

随着遥感影像数据规模的快速扩张,如何高效准确地识别遥感影像中的典型目标成为当前的研究热点。为解决传统遥感影像目标检测方法准确率低的问题,用基于深度卷积神经网络进行遥感影像目标检测,在遥感影像数据集上用基于Faster-RCNN的神经网络模型对VGG16卷积网络进行训练,对输入的遥感影像通过区域推荐网络标注出待检目标的包围框和置信度,实现对遥感影像的目标检测。以飞机和油罐为例,在TensorFlow深度学习框架下实现了数据预处理、网络训练、目标检测等功能,并在当前测试数据集上取得了较高的检测准确率和置信度。该研究成果可应用于遥感影像解译和处理等相关领域。     

融合背景图像信息和多特征压缩的相关滤波跟踪算法

针对相关滤波类跟踪算法目标背景图像信息利用率较低、目标特征表达能力较弱的问题,提出了一种融合背景图像信息的多特征压缩跟踪算法。首先,在上下文感知滤波器的基础上,将背景图像信息加入位置滤波器。其次,提取颜色名(Color Name, CN)特征与梯度直方图(Histogram of Oriented Gradient, HOG)特征,使用最大响应因子及平均峰相关能量(Average Peak-to-Correlation Energy, APCE)评估跟踪结果的可信度,实现两种特征的自适应融合。最后,利用特征降维简化模型的复杂度,实现算法运行速度的提升。实验结果表明,改进后的算法在遮挡、形变、尺度变化等复杂环境下均具有较高的鲁棒性,其跟踪精度和成功率指标均优于DSST及其他主流的跟踪算法,并且仍保持了实时性。     

基于深度前馈网络的自适应相对导航滤波

针对空间非合作翻滚目标近距离相对导航中出现测量异常偏差导致滤波精度下降甚至发散的问题,研究了具有抗差能力的自适应滤波估计方法。在设计了相对导航滤波模型的基础上,提出了基于深度前馈网络的自适应扩展卡尔曼滤波算法,并且详细设计了智能故障检测与估计的深度前馈网络模型与训练方法。数学仿真结果表明:深度前馈网络能够有效估计测量异常偏差且估计误差小于异常偏差值的15%,基于深度前馈网络的自适应扩展卡尔曼滤波结果显著优于常规扩展卡尔曼滤波。