iGMAS分析中心产品一致性分析及其应用研究

通过比较不同分析中心提供的精密轨道与钟差产品发现，不同分析中心的轨道和钟差表现出明显差异，并且轨道径向和钟差的相对偏差存在很强的相关性和周期特性。经过数据分析，BDS、GPS、GLONASS的轨道径向和钟差相对偏差具有12h和24h周期项，而Galileo具有12h周期项。因此，提出了一种新的钟差拟合及加权综合方法，通过建立多项式+不同周期项模型对钟差进行拟合求得残差序列，利用残差序列对不同分析中心产品的钟差值进行定权，并将加权均值作为钟差综合值。通过与ISC钟差对比发现，提出的钟差综合模型可以明显提高部分分析中心产品的钟差精度，并优化iGMAS分析中心钟差产品的一致性。     

间接言语行为的话语含义

本文从关联理论、认知语境两个方面对间接言语行为的话语含义进行了阐释。只有当话语含义得以成功表达时,间接言语行为才能够得以成功实施。     

Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life (RUL) estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine (RVM) is a data-driven algorithm used to estimate a battery’s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery’s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.