树形多体系统动力学约束力算法

多体系统高效动力学算法一直是多体系统动力学的重要研究方向. 近年来,众多高效算法虽然在提高解算效率方面取得了一定研究成果,但大多无法直接给出多体系统的显式动力学方程或解算系统约束力. 基于以上问题,研究了适用于任意树形多体系统动力学解算的约束力算法(constraint force algorithm, CFA) 及其串行化应用. 约束力算法可在解算多体系统动力学的过程中对系统约束力进行求解,该算法串行化后计算量仅与自由度成线性关系. 通过分析树形多体系统中任意节点处的动力学、运动学递推关系并讨论系统方程的组集方法,将仅适用于链状系统的算法推广至任意树形系统,并给出了其串行化应用方法以提高算法效率. 在数值仿真中,将所提算法与递推算法进行对比,验证了所提出的约束力算法的准确性;此外,通过对比4 种不同算法在相同工作环境下解算同一模型时的处理器运行时间,证实了串行化约束力算法的高效性.      

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.     

航天器地面综测系统软件间通信协议的现状与改进

传统的航天器地面综测系统软件间通信协议一般是根据不同航天器的测试任务和测试特点而制定的,没有形成统一规范,各航天器综测系统软件协议和接口的差别甚大,导致综测系统软件可移植性差;针对现有航天器地面综测系统软件间通信协议可扩展性能差的现状,以新型小卫星地面综测系统的设计研发为依托,探索并设计了一套基于XML描述与传输的通信协议,开发了地面综测系统软件,并对该通信协议进行了实现和试验;经试验证明,利用XML描述与传输的综测系统软件协议和XML串行化反串行化技术,该通信协议具备良好的操作性和可扩展性,较大的简化了航天器地面综测系统软件间的接口设计,增强了综测系统软件的可移植性。     

阶跃伏安法离散数据连续化及导数卷积的方法 Ⅰ.样条插值法

本文对采用样条函数插值法解决阶跃伏安法中离散数据连续化及进一步的导数卷积处理进行了详细讨论。用模拟数据和实验数据进行了该方法的效果测试。结果表明,样条函数插值法处理连续化问题,具有显著的优越性,能成功地解决阶跃伏安法在大阶梯增量(△E)快速扫描时存在的由于采样点不多而造成的波形显示、导数卷积后波形杂散等问题。     

红外光学系统系列化方案

分析了红外光学系统品种随意发展带来的问题,研究提出了专用组件系列化发展的技术途径。选择适合的系列化的数学方法,分析了红外光学系统的应用特性及系列化的条件,研究提出了红外光学系统系列化的方案和型谱架构。