一种无人飞行器监控数据预处理方法

阐述了对无人飞行器进行监控的必要性和重要意义,设计了一种无人飞行器监控数据预处理流程。首先采用空间对准算法将各传感器数据统一到空管系统坐标系下。然后根据无人飞行器的运动特点提出了基于改进“当前”统计模型的自适应卡尔曼目标滤波算法对航迹数据进行平滑滤波,该算法根据滤波新息的变化自适应调整机动频率,仿真表明改进后的算法能够实现对目标更为精确的跟踪。之后对航迹数据中的野值进行了判别和剔除。最后规范统一了各传感器的数据帧格式,实现了对监控数据进行预处理的目的。     

基于粗糙集理论的序列离群点检测

作为数据挖掘的一项重要任务,离群点检测已经引起人们的广泛关注.本文基于粗糙集理论来讨论离群点的定义与检测问题,提出了一种新的离群点定义--粗糙序列离群点以及相应的离群点检测算法RSOD.该算法利用粗糙集理论中的知识熵和属性重要性等概念来构建三种类型的序列,并通过分析序列中元素的变化情况来检测离群点.在UCI标准数据集上...     

SKLOF:一种新的超新星候选范围约减算法

超新星是宇宙学中的标准烛光,其在星系中爆发的概率很低,是一种特殊、稀少的天体,只有在大量观测的星系数据中才有机会遇到,而正处于爆发期的超新星会照亮其整个星系从而在观测获得的星系光谱中具有较明显的特征。但是,目前已发现的超新星数量相对于大量的天体而言又是非常稀少的,搜寻它们所用的计算时间成为能否进行后续观测的关键,因此需要寻找高效率的超新星搜寻方法。对超新星候选范围进行约减的LOF算法的时间复杂度较高,计算量大,不适用于大规模数据集。为此通过对LOF算法进行改进,提出了一种在海量星系光谱中快速约减超新星候范围的新方法(SKLOF)。首先对光谱数据集中离中心点近的数据点进行数据剪枝,剪掉那些肯定不是超新星候选体的光谱数据对象,然后利用改进的LOF算法计算剩余的光谱数据的孤立性因子并降序排列进行离群搜索,最后获得超新星候选体的较小的搜索范围以便进行后续的证认。实验结果表明,该算法十分有效,不仅在精确度上有所提高,而且相比于LOF算法还进一步缩短了算法的运行时间,提高了算法的执行效率。     

数据挖掘中统计方法的作用和问题点

本文讨论了数据挖掘与统计学间的关系,介绍了在数据挖掘中常用的统计方法和存在的问题。提出了统计怎样适应于数据挖掘的课题。     

机载设备测量数据中异常值判别与修正

研究机载设备异常数据的判别及修正问题, 提出了改进多项式外推拟合算法, 并在FPGA中对GPS和AHRS数据中的异常值进行判别和修正。根据机载数据的统计特性计算各物理量的标准差, 对多项式外推拟合算法进行改进, 简化了乘除法运算的硬件实现方式, 使之适合并行工作方式的FPGA进行异常值快速判断和处理。飞行试验数据处理结果表明, 该方法能够有效地判别、修正异常值, 满足飞行试验要求。     

基于线性ν-支持向量回归机的异常数据检测

讨论了线性v-支持向量回归机中参数v的意义,并给出了严格的理论证明。利用v-支持向量回归机中ε-不敏感损失函数及参数v的意义,提出一种回归数据中的异常值检测方法。采用线性模型使得该方法不仅速度快而且能处理大规模数据。数值实验证明其具有可行性和有效性。     

Methane hydrate: Modeling and assessing the experimental data of incipient stability conditions

Accurate knowledge of equilibrium conditions for methane hydrates dissociation is crucial to implement an appropriate hydrate-based technology/process. Hence, much importance is attached to utilize techniques for the modeling of hydrate stability zones and the existence of reliable data as well. This research employs the published experimental data, from 1940 to 2016, for modeling the incipient stability conditions of methane hydrate in pure water using Classification and Regression Tree (CART) as a novel methodology in gas hydrates research. Least squares support vector machine (LSSVM) and artificial neural network (ANN) methods were selected as a basis for comparison. Furthermore, the Leverage mathematical approach was used for evaluating the quality of the data as well as the correctness of the CART model. Results confirmed that the developed tree-based model provides excellent outcomes and no model can rival it for accuracy. In addition, applying the Leverage algorithm specified that: (a) the CART model is statistically valid and correct; (b) the published experimental data in the literature are different in quality; (c) there is no doubtful data in the studied database; and (d) the published data in the temperature range from 148.8 to 238.8?K are not sufficient compared with the whole of the collected database.     

电子产品寿命检测中异常数据的检验

讨论在电子产品的寿命数据中,对同时存在的异常大数据和异常小数据的检验方法,给出了一个明确的判别标准,并以一例说明其应用.     

A Recursive Algorithm to Restore Images Based on Robust Estimation of NSHP Autoregressive Models

The objective of this article is to present a new image restoration algorithm. First, each pixel in the image is classified into k categories. Then we assume that the gray levels in each category follow a nonsymmetric half-plane (NSHP) autoregressive model. Robust estimation of the parameters of the model is considered to attenuate the effect of the image contamination on the parameters. In each iteration we will construct a new image using a robustified version of the residuals. The introduction of the classification techniques as a first step of the algorithm reduces considerably the number of parameters to estimate. Hence, the computational time is also reduced because the robust estimations of the parameters are solutions of nonlinear system of equations. Some applications are presented to real synthetic aperture radar (SAR) images to illustrate how our algorithm restores an image in practice.