干涉型光纤传感器随机相位漂移统计特性研究

提出描述干涉型光纤传感器随机相位漂移Δφ（ｔ）物理模型，给出了Δφ（ｔ）的自相关函数和功率谱密度函数，还给出了随机相位漂移周期的统计分布．     

光电稳定平台中微机械陀螺随机漂移实时滤波算法

微机械陀螺作为小型化光电稳定平台中的核心传感器,由于存在随机漂移,直接影响了其使用精度。小波算法以其多分辨特性,适合于非平稳信号的去噪,应用小波分析算法,实现了微机械陀螺的实时滤波。通过仿真及实验验证了算法的效果。结果表明,应用小波分析算法可以将漂移均方差降低为处理前的5%以内。     

Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption

Data from smart grids are challenging to analyze due to their very large size, high dimensionality, skewness, sparsity, and number of seasonal fluctuations, including daily and weekly effects. With the data arriving in a sequential form the underlying distribution is subject to changes over the time intervals. Time series data streams have their own specifics in terms of the data processing and data analysis because, usually, it is not possible to process the whole data in memory as the large data volumes are generated fast so the processing and the analysis should be done incrementally using sliding windows. Despite the proposal of many clustering techniques applicable for grouping the observations of a single data stream, only a few of them are focused on splitting the whole data streams into the clusters. In this article we aim to explore individual characteristics of electricity usage and recommend the most suitable tariff to the customer so they can benefit from lower prices. This work investigates various algorithms (and their improvements) what allows us to formulate the clusters, in real time, based on smart meter data.     

共光路/共模块自适应光学数据融合方法

研究了在CP／CM系统中,两个哈特曼—夏克波前传感器用于控制一套波前校正器件的几种数据融合方法,通过复原矩阵的条件数分析了斜率数据向量的扰动和复原矩阵的扰动对系统控制电压的影响,得到了相应的公式。实现了CP／CM自适应光学闭环实验,并且利用实际系统比较了几种数据融合方法,认为现有工程技术条件下,加修正因子斜率融合和电压融合是可行的办法,二者在控制电压的得到上是等价的,不同之处将表现在波前处理机。     

Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion

Dempster-Shafer (DS) evidence theory is widely used in various fields of uncertain information processing, but it may produce counterintuitive results when dealing with conflicting data. Therefore, this paper proposes a new data fusion method which combines the Deng entropy and the negation of basic probability assignment (BPA). In this method, the uncertain degree in the original BPA and the negation of BPA are considered simultaneously. The degree of uncertainty of BPA and negation of BPA is measured by the Deng entropy, and the two uncertain measurement results are integrated as the final uncertainty degree of the evidence. This new method can not only deal with the data fusion of conflicting evidence, but it can also obtain more uncertain information through the negation of BPA, which is of great help to improve the accuracy of information processing and to reduce the loss of information. We apply it to numerical examples and fault diagnosis experiments to verify the effectiveness and superiority of the method. In addition, some open issues existing in current work, such as the limitations of the Dempster-Shafer theory (DST) under the open world assumption and the necessary properties of uncertainty measurement methods, are also discussed in this paper.