CBERS-1卫星CCD图像高效去噪方法

文章介绍了CBERS - 1卫星 0 1/ 0 2星 (CBERS - 0 1/ 0 2 )CCD相机图像的高效去噪方法。首先介绍CCD相机图像存在的噪声种类 ,然后重点讨论了 3类噪声的快速定位算法 ,并对不同的噪声施加不同的方式滤波处理 ,最后给出图像的去噪效果     

基于深度森林的卫星ACS执行机构与传感器故障识别

针对卫星姿态控制系统（ACS）闭环回路的故障难以辨识的问题,引入深度森林算法,实现执行机构与传感器故障识别。首先针对可获取的少量卫星姿态控制系统遥测数据,结合系统动力学特性,研究合适的特征选择和特征提取方法,再结合深度森林算法进行故障信息学习与辨识,建立故障预测模型,实现执行机构故障与传感器故障的识别。半物理仿真结果表明：在存在气浮台干扰力矩、卫星转动惯量未知、飞轮非线性特性、闭环故障传播等多种不利因素情况下,深度森林算法对于执行机构和传感器故障具有高效的识别能力。     

冰灾对车八岭山地常绿阔叶林区系和群落的影响

研究了2008年年初南方冰雪冻雨灾害对车八岭常绿阔叶林不同区系的冠层植被所造成的影响.结果显示,研究样地内温带分布的乔木受损比例高于热带分布的乔木受损比例.X~2检验说明不同区系间的冠层乔木个体受损比例差异显著(P<0.01);东亚分布及其变型受损个体比例最小,热带亚洲至热带非洲分布及变型、东亚和北美间断分布及其变型、热带亚洲分布及其变型受损个体比例较大.冠层乔木都以重度受损为主,中度受损和轻度受损个体数都远小于前者.CCA多元分析显示,株数密度、平均胸高断面积、坡度、海拔各自对不同物种的受损乔木的分布有显著影响(Monte Carlo test,P=0.010 0).优势种米槠Castanopsis carlesii受损植株的分布与群落的平均胸高断面积显著正相关;亚优势种栲Castanopsis fargesii、鸭公青Neolitsea chuii、尾尖叶柃Eurya acuminata受损植株的分布与坡度显著负相关;亚优势种荷木Schima superb与海拔显著正相关.总之,在相同的生境中能够占据林冠优势层的区系类型往往受灾更严重.     

隔振支承系统动力学试验与辨识

通过对聚氨酯泡沫塑料的动力学特性进行试验和理论研究，发现聚氨酯泡沫塑料在不同频率与不同振幅作用下其隔振力与位移呈现出典型的迟滞非线性，而且系统的动刚度是频率的非线性函数，与振幅基本无关；阻尼是振幅，速度，频率以及位移的非线性函数，在此基础上本文提出了描述隔振支承系统的动力学方程，并进行了参数辩识。计算与试验比较，吻合较好。     

A comparative study of decision tree,random forest,and convolutional neural network for spread-F identification

Ionospheric spread-F (SF) is a commonly observed phenomenon of electron density perturbation in the F-layer. The ionospheric irregularities structure has an adverse effect on the propagation of electromagnetic waves in the ionosphere. The automatic identification of ionospheric spread-F and statistical study of the formation of spread-F are of great significance to the study of the physical mechanism of ionospheric inhomogeneity and for prediction of ionospheric irregularities. In this paper, we describe and implement three automatic identification methods of spread-F based on machine learning: decision tree, random forest, and convolutional neural network (CNN). The performance of these automatic identification methods was verified using a large set of test data. Results show that the accuracy of all three methods on identifying ionograms with spread-F exceeded 90%. After comparing the results of the three methods, we found that the decision tree method was the simplest and with the structure easiest to be understood, and it required the shortest interpretation time. In terms of the identification results, the random forest method provided better results than the decision tree method, and the CNN method was the best at accurately identifying ionograms with spread-F.     

Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in the environmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolution Worldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART, Stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods: random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniques was used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data. Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed. In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boosting model is the best performing method whilst based on independent validation there was no significant difference between the performances of classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine had better processing speed than other.     

最优特征选择下多层次分割的城市道路提取

针对道路提取过程中特征维数过高的问题,提出了一种基于ReliefF过滤式和Wrapper封装式的特征选择方法.将粒子群优化算法(PSO)作为Wrapper的搜索算法,优化过的随机森林算法(OPRF)作为Wrapper的分类器构成PSO_OPRF封装式子集评估器,对ReliefF预选后的特征子集进行评估,降低特征维度,选...     

密林环境空地协同GNSS/UWB快速高精度定位技术

针对密林中卫星信号遮挡难以实现快速高精度定位的问题,提出了一种密林环境空地协同全球卫星导航系统/超宽带(GNSS/UWB)高精度定位方法。该方法综合考虑无人机平台在空旷环境快速运动与超宽带的强穿透性测量等特征,通过无人机携带GNSS/UWB集成化载荷,以移动单基站模拟多基站,配合密林中UWB标签组网测距,完成密林中UWB标签定位。对基站布设方案展开研究,为密林环境下空地协同GNSS/UWB快速高精度定位技术应用提供基站布设指导和依据,并且利用密林环境下实测测距实验+仿真定位实验对提出的方法进行验证。结果表明,所述方法可有效实现密林环境下的快速高精度定位,精度达到分米级,并且通过优选基站布设网型、范围及高度可有效提高定位精度,为密林环境下快速高精度定位方法提供了理论支撑。     

基于随机森林的月球表面软着陆实时最优控制

针对传统月球表面软着陆在处理入轨偏差或降落过程轨迹偏离实时性差,文章提出一种通过监督学习离线训练随机森林结构,使得着陆器在降落过程中根据其状态,通过训练的随机森林结构在线计算其控制量,从而达到实时控制的目的。文章还提出一种基于随机森林的模型对月球着陆过程轨迹重规划技术,通过动力学建模将月球着陆过程分成制动段、接近段和着陆段共3个阶段,利用离线训练好的模型根据航天器状态在线计算其控制量,并通过三段下降过程逐渐降低航天器位置速度误差。仿真结果表明,针对入轨偏离500m的情况,通过第一阶段将位置误差缩短至50m,保证了航天器状态位于第二阶段训练集内,经过接近段后再将位置误差缩小至10m范围内,速度误差降至0.01m/s量级,满足着陆误差要求,且控制量计算时间短,达到了轨迹实时重规划的目的。     

一种航空发动机运行可靠性评估方法

航空发动机运行过程中,可靠性评估是航空发动机可靠性评估领域的关键问题之一,而合理的评估方法能够提高可靠性分析的效率和精度,因此本文提出一种支持评估飞机任务过程中航空发动机运行可靠性的方法。结合飞行任务特点和航空发动机工作特性,以快速存取记录器信息为分析数据,考虑当前运行环境、飞机瞬时状态、发动机当前工作状态 3 类因素对运行可靠性进行分析;将随机森林算法与分层抽样法结合对数据进行拟合、预测并计算特征重要度;以 B737-800 机型一次北京—乌鲁木齐的飞行任务为例,对方法的有效性和可行性进行验证。结果表明：本文提出的可靠性评估方法解决了航空发动机运行过程中数据量大、维度高导致的数据处理困难问题。