域外刑事评议程序之比较研究

刑事评议程序是介于庭审程序和宣告程序之间的一个重要程序,它既是庭审的自然延续,又是裁判结论形成的前提和基础.然而,欲保证刑事评议程序的正常运行,刑事评议主体之独立必须得到保证;刑事评议之客体还应被全面考虑,并对其进行细化,以确保刑事评议的针对性和有效性,防止出现"估堆"现象.本文以域外刑事评议程序为对象,从评议的主体、客体等角度进行梳理,得出规律性的认识,以期对我国关于刑事评议的立法有所裨益.     

单站发射多站接收的空间目标激光测距技术

激光测距作为空间目标测定轨精度最高的技术,对非合作目标的测量精度比微波雷达、光电探测等技术高1～2个数量级,非常有利于非合作目标的精密定位、轨道复核及精确编目,保障在轨空间飞行器的安全。激光在非合作目标表面会发生漫反射,返回光斑弥散、回波微弱,采用大口径望远镜接收系统是必要的。鉴于大口径望远镜研制难度大,提出基于单站发射多站接收的空间目标激光测距新方法,即采用多接收望远镜增加接收面积,实现目标测量能力提升。通过分析单站发射多站接收的激光测距技术特点,基于双望远镜系统开展空间合作目标测量实验,验证了多望远镜接收激光信号的可行性,为该测距技术发展奠定了实验基础。     

面向工程试验测试软件组件的设计与实现

基于COM设计的航空试验软件组件，不但具有更好的模块化，而且提高了软件的健壮性和可重用性，有利于测试软件开发效率的提高。在讨论COM技术的基础上，提出了面向航空工程试验ActiveX软件控件的开发。     

基于运动对象自动检测和查询的监控系统

针对现有的视频监控系统的缺陷,提出基于运动对象自动检测和查询的智能视频监控系统.利用帧间差的4次统计量的假设验证,自动检测运动对象.用形态学腐蚀和膨胀算子对检测的运动对象的二值图像进行处理,以提高检测运动对象的可靠性,降低误警率.为自动跟踪运动对象轨迹,提出检测块概念.依据检测的运动对象和事件,建立相应的监控信息标注数据库,进而由时间段、运动对象及场景中发生的事件来查询监控信息,实现"跳跃"式随机检索监控信息.实验结果证明提出的方法对实现智能的视频监控系统是有效且健壮的.      

类构件潜在可重用性预测技术

首先分析了Judith Barnard类构件潜在可重用性预测模型,针对其不足提出了改进意见.在此基础上,将"类构件"的概念进行扩展,提出了"可重用类群"的概念及相应的判定算法,该技术使得对类构件潜在可重用性的预测更加合理、准确.      

帧差法和Mean shift算法融合的高速无人机目标跟踪

为实时跟踪高速飞行无人机,图像跟踪算法必须满足快速性和准确性要求。文章给出一个融合算法,将帧差法和 Mean shift算法的优势结合起来。2个算法平行运行,差帧法实现快速跟踪,Mean shift算法则用于对帧差法结果进行准确度修正。还利用 Kalman滤波技术对计算周期内的无人机运动位移进行补偿,进一步提高实时跟踪的准确性,并给出 Matlab仿真例子验证本文方法的有效性。     

基于小样本数据增强的航天器表面损伤智能检测方法

在轨运行的航天器表面形成损伤有可能导致严重的后果,需要对航天器进行在轨实时损伤检测。针对航天器损伤检测图像样本难以获取的问题,本文采用智能化检测方法,提出了一种用于航天器表面损伤样本扩充的生成对抗网络,该网络能够学习单张输入图像的特征纹理表示,从而生成大量与输入图像特征相似的细粒度尺度样本,实现了少量图像数据样本的扩充。利用YOLO目标检测算法在扩充的图像样本中进行表面缺陷与损伤的检测识别,获取了较高的检测精度,为未来航天器健康状态监测与评估、通用化服务机器人应用及太空原位建设等提供了技术支撑。     

Reconciling space object observed and solar pressure albedo-areas via astrometric and photometric data fusion

There are many Resident Space Objects (RSOs) in the Geostationary Earth Orbit (GEO) regime, both operational and debris. The primary non-gravitational force acting on these RSOs is Solar Radiation Pressure (SRP), which is sensitive to the RSO’s area-to-mass ratio. Sparse observation data and mismodeling of non-gravitational forces has constrained the state of practice in tracking and characterizing RSOs. Accurate identification, characterization, tracking, and motion prediction of RSOs is a high priority research issue as it shall aid in assessing collision probabilities in the GEO regime, and orbital safety writ large. Previous work in characterizing RSOs has taken a preliminary step in exploiting fused astrometric and photometric data to estimate the RSO mass, shape, attitude, and size. This works, in theory, since angles data are sensitive to SRP albedo-area-to-mass ratio, and photometric data are sensitive to shape, attitude, and observed albedo-area. By fusing these two data types, mass and albedo-area both become observable parameters and can be estimated as independent quantities. However, previous work in mass and albedo-area estimation has not quantified and assessed the fundamental physical link between SRP albedo-area and observed albedo-area. The observed albedo-area is always a function of the SRP albedo-area along the line of sight of the observer. This is the physical relationship that this current research exploits. It is shown through simulation that due to this physical link, and through the fusion of astrometric and photometric data, it is possible to observe the mass of a space object when the area is not known. Results for data from 100 trajectories generated from randomly sampled initial conditions are shown. It is seen that even when the area of the object is not known, the uncertainty in mass can be lowered from an initial value of 800?kg to the range 500–700?kg for 72% of the samples, 200–500?kg for 13% of the samples, and 0–200?kg for 15% of the samples. It is further shown that although the uncertainties are large, the actual errors in mass are much lower, with the error RMS being less than 100?kg for 30% of the samples, between 100 and 200?kg for another 30%, and between 200 and 300?kg for 24% of the samples.     

MCMC-Particle-based group tracking of space objects within Bayesian framework

With the intense increase in space objects, especially space debris, it is necessary to efficiently track and catalog the extensive dense clusters of space objects. As the main instrument for low earth orbit (LEO) space surveillance, ground-based radar system is usually limited by its resolution while tracking small space debris with high density. Thus, the obtained measurement information could have been seriously missed, which makes the traditional tracking method inefficient. To address this issue, we conceived the concept of group tracking. For group tracking, the overall tendency of the group objects is expected to be revealed, and the trajectories of individual objects are simultaneously reconstructed explicitly. According to model the interaction between the group center and individual trajectories using the Markov random field (MRF) within Bayesian framework, the objects’ number and individual trajectory can be estimated more accurately in the condition of high miss alarm probability. The Markov chain Monte Carlo (MCMC)-Particle algorithm was utilized for solving the Bayesian integral problem. Furthermore, we introduced the mechanism for describing the behaviors of groups merging and splitting, which can expand the single group tracking algorithm to track variable multiple groups. Finally, simulation of the group tracking of space objects was carried out to validate the efficiency of the proposed method.     

电子出版物的数据递归定义设计

】介绍了电子出版物递归数据结构设计。在分析了电子出版物中数据安排的实际情况后，深入探讨了数据递归定义的内在机理，揭示了各种安排的实质。