宽带有线综合信息网与多媒体计算机技术研究

本文简要介绍了宽带有线综合信息网与多媒体计算机技术，并对可以将宽带有线综合信息网和多媒体计算机终端实用化的一些关键技术进行了初步的探讨和研究。     

一种求最优风险证券组合的方法

本文给出了卖空限制下，包括无风险资产时求最优风险证券组合的方法。引入一种考虑风险补偿的效用分析法，对均值一方差分析中遗留下的有风险和无风险证券资产之间的分析问题进行了探讨。     

A comparative analysis of SLR,MLR, ANN,XGBoost and CNN for crop height estimation of sunflower using Sentinel-1 and Sentinel-2

Sustainable monitoring and determining the biophysical characteristics of crops is of global importance due to the increase in demand for food. In this context, remote sensing data provide valuable information on crops. This study investigates the relationship between the variables determined from both Synthetic Aperture Radar (SAR) and optical images and crop height. For this purpose, backscatter (σVH, σVV, σVH / σVV) and coherence (?VH, ?VV) of multi-temporal dual-polarized Sentinel-1 and vegetation indices of multi-temporal Sentinel-2 data are analyzed. Two indices, namely, Normalized Difference Vegetation Index (NDVI) and NDVI with the red-edge band (NDVIred), are interpreted to identify the contribution of the red-edge band over the near-infrared band. The Zile District of Tokat province in Turkey where dominantly sunflower cultivation is carried out, was selected as the study area. In the analysis of the data, Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Artificial Neural Network (ANN), EXtreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) were used. In the results of the study, ANN showed the lowest RMSE = 3.083 cm (RMSE%= 11.284) in the stem elongation period. The CNN followed the lowest RMSE for the Inflorescence development and flowering stages 19.223 cm (RMSE%=15.458) and 8.731 cm (RMSE%=5.821), respectively. In the ripening period, XGBoost achieved the lowest RMSE = 8.731 cm (RMSE%=6.091). All the best models in four methods were created using common variables of σVH, σVV, ?VH, ?VV and NDVIred, except ANN which exclude coherence variables. The results concluded that NDVIred contributed more than NDVI which is widely interpreted in previous studies.     

基于网络同步的航天器编队飞行姿态控制方法

航天器编队飞行需要协同控制系统进行统一的协调管理,以实现协同工作。文章将网络同步控制理论应用于航天器编队飞行姿态控制,以提高航天器编队飞行姿态控制的协同性。首先,采用修正的罗德里格斯参数描述航天器姿态动力学;然后,基于网络同步控制算法,设计航天器编队飞行的非线性姿态协同控制律;考虑成员航天器间姿态变化的差异,采用混合控制技术,设计航天器编队飞行混合控制策略。仿真结果验证了控制方法和控制律的有效性,并且相比单一反馈机制,混合控制具有更好的控制品质。     

基于小波变换的脉冲耦合神经网络预测模型及其应用

被称为第三代人工神经网络的脉冲耦合神经网络(PCNN)模型,以其耦合机制、脉冲输出两大基本特性广泛应用于图像处理等领域。文章在两大基本特性的基础上对PCNN进行了改进:增加了绝对误差控制公式;连接强度以相关系数来控制,调整阈值设置为最小误差;网络的运行机制与以往的PCNN原理有所不同;将ATrous小波变换与PCNN模型相结合,形成了组合预测模型。将改进后的组合模型用于年降雨量的预测中,以求检验模型的可行性。预测结果表明,基于小波变换的PCNN组合模型用在年降雨量预测中是可行的,并取得良好效果。为进一步深化PCNN的理论、拓宽PCNN的应用领域、解决水文水资源中的预测问题提供了新的思路和方法。     

测控信息传输可靠性保障方法研究

随着IP（互联网协议）在测控领域的广泛应用,丢包、时延等IP网络特性对测控信息传输的可靠性提出了挑战。针对遥/外测、引导及上行控制类数据在信息传输各个环节的技术特点,本文深入研究了各类测控数据的信息传输模型及工作流程,提出了适用于不同场景的可靠性保障方法,并通过数学仿真进行了计算分析。仿真结果表明,队列中断选切策略可有效保障遥/外测及引导类信息的可靠传输,N判M模式下的上行控制类信息传输可靠性优于N选1模式。     

柔性空间机械臂系统的模糊滑模神经网络控制及柔性振动主动抑制

研究了参数不确定漂浮基柔性空间机械臂关节空间的轨迹跟踪及柔性振动主动控制问题.运用虚拟力概念,生成能同时反映柔性振动和刚性运动的虚拟期望轨迹,设计了一种自适应非奇异Terminal滑模控制器来跟踪该虚拟期望轨迹,以实现载体姿态及关节稳定跟踪运动轨迹并对所产生的柔性振动进行主动抑制的控制目标.所设计的控制器结合了Terminal滑模控制快速收敛性,模糊小波神经网络优良的函数逼近特性及鲁棒技术处理逼近误差的优势,利用自适应算法在线自适应调节模糊小波神经网络的所有网络权值和参数,使控制器具有很强的鲁棒性.仿真实验证明了所提控制方案的有效性.      

Optical orbital debris spotter

The number of man-made debris objects orbiting the Earth, or orbital debris, is alarmingly increasing, resulting in the increased probability of degradation, damage, or destruction of operating spacecraft. In part, small objects (<10 cm) in Low Earth Orbit (LEO) are of concern because they are abundant and difficult to track or even to detect on a routine basis. Due to the increasing debris population it is reasonable to assume that improved capabilities for on-orbit damage attribution, in addition to increased capabilities to detect and track small objects are needed. Here we present a sensor concept to detect small debris with sizes between approximately 1.0 and 0.01 cm in the vicinity of a host spacecraft for near real time damage attribution and characterization of dense debris fields and potentially to provide additional data to existing debris models.     

指挥自动化网络信息安全的一种解决方案

针对基地指挥自动化网络信息安全要求,提出了基于USB身份认证卡技术,采用间接身份认证方式鉴别网络用户合法性,用双重加密算法对信息进行加密上传和解密下载的解决方案。此外,还设计了本地涉密文件加密隐藏方法。整个系统安全性高。独立性强,开发成本较低,具有较高的推广价值。     

基于深度学习的航天器组合体惯性参数在轨智能辨识

针对在轨服务过程形成新组合体的动力学参数未知的问题,借助深度学习在多参数寻优上的优势,提出了一种基于卷积神经网络的智能参数辨识算法,实现在外力作用下,线动量和角动量不守恒条件下的航天器组合体多参数辨识。利用卷积神经网络权值共享的特点,设计4层卷积神经网络,通过短时间内对大量特定存储形式的状态数据的训练,实现航天器组合体多参数快速高精度辨识。利用数学仿真试验对算法的可行性进行验证,结果表明：在24s内,质量与质心位置收敛;1190s内,惯量参数收敛,辨识精度在3%以内。说明所提方法在外界随机干扰力和力矩影响下能准确快速辨识出航天器组合体质量、质心位置和惯量矩阵。