深空通信中的Turbo码技术

简介深空通信的发展现状和 Turbo码的基本原理。着重研究深空通信中 Turbo码技术的算法和应用 ;并通过仿真对 Turbo码和其它传统码的性能进行比较分析。针对深空通信的特点 ,就 Turbo码具体实现的技术作了探讨     

多级航天器在有限推力情况下的最优变轨问题探讨

与航天运载器类似,航天器也可以有单级与多级之分。一个多级航天器是由几个具有动力装置的航天器经串联而成的组合体。本文研究这类航天器的最优变轨问题。多级航天器在有限推力情况下的最优变轨问题,实质上属于多级最优控制问题。本文应用多级最优控制问题的求解原理,讨论有限推力情况下,两级航天器在两共面圆轨道之间的两类最优转移问题,给出了确定最优推力程序和最优转移轨迹的定解方程和定解条件,特别考察了级间分离时刻应满足的条件。本文给出的原理同样适用于非圆形、非共面轨道之间的最优转移问题。     

捆绑火箭模态的传递矩阵全耦合算法

本文发展了适用于捆绑火箭的各向耦合自由振动的传递矩阵法，并对捆绑结构的处理难点－捆绑边界条件的处理给出了理论的说明，因而本方法是适用于各种捆绑结构振动模态分析的通用方法，对捆绑火箭１／１０比例模型进行计。算比较表明，传递矩阵法是适合于捆绑结构模态计算的方法。     

Upregulation of erythropoietin receptor in UT-7/EPO cells inhibits simulated microgravity-induced cell apoptosis

Hematopoietic progenitor cell proliferation can be altered in either spaceflight or under simulated microgravity experiments on the ground, however, the underlying mechanism remains unknown. Our previous study showed that exposure of the human erythropoietin (EPO)-dependent leukemia cell line UT-7/EPO to conditions of simulated microgravity significantly inhibited the cellular proliferation rate and induced cell apoptosis. We postulated that the downregulation of the erythropoietin receptor (EPOR) expression in UT-7/EPO cells under simulated microgravity may be a possible reason for microgravity triggered apoptosis. In this paper, a human EPOR gene was transferred into UT-7/EPO cells and the resulting expression of EPOR on the surface of UT-7/EPO cells increased approximately 61% (p < 0.05) as selected by the antibiotic G418. It was also shown through cytometry assays and morphological observations that microgravity-induced apoptosis markedly decreased in these UT-7/EPO–EPOR cells. Thus, we concluded that upregulation of EPOR in UT-7/EPO cells could inhibit the simulated microgravity-induced cell apoptosis in this EPO dependent cell line.     

修正Rodrigues参数在飞行器定姿中的应用(英文)

There are two attitude estimation algorithms based on the different representations of attitude errors when modified Rodrigues parameters are applied to attitude estimation. The first is multiplicative error attitude estimator （MEAE）, whose attitude error is expressed by the modified Rodrigues parameters representing the rotation from the estimated to the true attitude. The second is subtractive error attitude estimator （SEAE）, whose attitude error is expressed by the arithmetic difference between the true and the estimated attitudes. It is proved that the two algorithms are equivalent in the case of small attitude errors. It is possible to describe rotation without encountering singularity by switching between the modified Rodrigues parameters and their shadow parameters. The attitude parameter switching does not bring disturbance to MEAE, but it does to SEAE. This article introduces a modification to eliminate the disturbance on SEAE, and simulation results demonstrate the efficacy of the presented algorithm.     

低频靶标法测量CCD相机调制传递函数的仿真

基于用高频靶标法测试调制传递函数（MTF）时不能准确地体现出CCD器件的平均采样的情况。该文探讨用低频靶标法测试MTF，介绍它的原理；通过数字仿真CCD的光学镜头的模糊和采样平均过程，分析不同频率靶标的MTF测试结果，并分析相位造成的影响。     

一种基于迁移学习的遥测数据异常检测方法

为解决卫星遥测数据异常检测面临的数据不平衡且缺乏有标签样本的问题，提出一种基于一维卷积神经网络(1dCNN)迁移学习的异常检测方法。首先利用源域卫星的遥测数据对1dCNN进行预训练，使得模型的卷积层具有卫星状态特征的提取能力；然后将训练好的模型迁移到缺乏标签数据的目标域卫星中；利用目标域有标签样本对预训练模型进行微调，从而实现了对目标域测试集样本的异常检测。为了使1dCNN能够适应遥测数据样本的不平衡性，引入了代价敏感训练策略，建立动态损失函数，从而提升代价敏感一维卷积神经网络(cs 1dCNN)对于异常样本的识别能力。以某两个卫星的电源分系统遥测数据进行了验证，实验结果表明该异常检测迁移方法具有较好的有效性和鲁棒性。     

移动代理(MA)综述

随着网络规模的日益扩大，如何保证网络管理系统的可扩展性和高效率性已成为网络管理人员迫切需要解决的问题。移动代理技术是新型的分布计算技术，具有很好的应用前景。移动代理系统是实现移动代理应用的基础框架。文章简述了移动代理(mobile agent)的定义和特点、代理的分类及移动代理的实现语言、以及移动的分类和移动机制；还提出了移动代理的特有技术优势，并对其发展前景进行了展望。文章在论述移动代理系统基本组成的同时，对IBM的移动代理平台aglet系统进行了介绍。     

广义Fuzzy数值测度的绝对连续性和奇异性探讨

给出了广义Fuzzy数值测度的绝对连续性和奇异性的定义,利用Hahn分解结论探讨了广义Fuzzy数值测度与其上变差、下变差和全变差的绝对连续性和奇异性的关系。     

Regression model for civil aero-engine gas path parameter deviation based on deep domain-adaptation with Res-BP neural network

The variations in gas path parameter deviations can fully reflect the healthy state of aero-engine gas path components and units; therefore, airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault diagnosis. In the past, the airlines could not obtain deviations autonomously. At present, a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the deviations. However, it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks. To obtain monitoring autonomy of each aero-engine model, it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine models. This paper adopts the Residual-Back Propagation Neural Network (Res-BPNN) to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy (MK-MMD) adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space (RKHS) for discrepancy measurement. To further reduce the distribution discrepancy of each aero-engine model, the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible, and then the learned features can be confused. Through the above methods, domain-invariant features can be extracted, and the optimal adaptation effect can be achieved. Finally, the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms.