Estimating Functional Brain Network with Low-Rank Structure via Matrix Factorization for MCI/ASD Identification

Functional brain networks (FBNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Due to its importance, many FBN construction methods have been proposed currently, including the low-order Pearson’s correlation (PC) and sparse representation (SR), as well as the high-order functional connection (HoFC). However, most existing methods usually ignore the information of topological structures of FBN, such as low-rank structure which can reduce the noise and improve modularity to enhance the stability of networks. In this paper, we propose a novel method for improving the estimated FBNs utilizing matrix factorization (MF). More specifically, we firstly construct FBNs based on three traditional methods, including PC, SR, and HoFC. Then, we reduce the rank of these FBNs via MF model for estimating FBN with low-rank structure. Finally, to evaluate the effectiveness of the proposed method, experiments have been conducted to identify the subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) from norm controls (NCs) using the estimated FBNs. The results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrate that the classification performances achieved by our proposed method are better than the selected baseline methods.

高次旋转多项式的夹爪驱动盘设计分析

针对机械夹爪驱动盘运动轨迹平滑性设计问题,提出转盘类时间指数的高次多项式与一次旋转方程相融合的方法。在平面坐标系中对时间类指数高次多项式函数进行参数矩阵(基圆半径、联动圆盘类时间指数、运动行程角、运动行程)设定。将平面坐标系中相关高次多项式离散点带入旋转坐标系中实现偏转,得到高次旋转多项式轨迹方程。采用一次到八次多项式分别对机械夹爪驱动盘运动轨迹曲线的位移、类速度、类加速度、类超加速度进行分析,得知二次以上旋转多项式的机械夹爪驱动盘运动轨迹基本处于平滑状态。实验证明:机械夹爪驱动盘运动的平稳性与次数有关。

基于高阶矩阵函数的广义逆波束形成改进算法

广义逆波束形成是一种高效的声源识别定位方法,然而其计算稳健性易受随机噪声影响,阻碍了其声源识别动力学水平进一步提高。为改善广义逆波束形成声源识别方法的稳健性,基于高阶矩阵函数提出一种广义逆波束形成改进算法:定义了基于广义逆波束形成的正则化矩阵;对正则化矩阵与波束形成输出进行迭代运算;利用高阶矩阵函数对迭代求解所得广义逆波束形成输出的互谱进行优化。通过数值仿真详细分析了声源频率对波束形成矩阵函数阶次取值的影响,得到阶次的最优取值区间。最后通过数值模型和实验算例对单极子与相干声源进行定位识别,结果表明:改进算法在准确识别声源基础上能有效抑制旁瓣干扰,且具有更高的声源识别精度。

基于矩阵特殊运算的高阶流形单元分析

在数值流形法的单元分析中广泛采用单纯形积分进行精确积分,然而在高阶数值流形法中采用单纯形积分并不容易,因为要求被积函数为显式多项式函数。采用Kronecker积、Hadamard积和拉直等矩阵特殊运算进行高阶流形单元分析,使得单元矩阵的推导过程简单且被积函数易表示为多项式形式。在此基础上,开发了三维弹性连续体静力分析的高阶数值流形法程序。通过实例验证了公式和程序的正确性。

带有变时滞的高阶模糊细胞神经网络的指数稳定性(英文)

研究了带有变时滞的高阶模糊细胞神经网络(HFCNNs)的全局指数稳定性.通过引入非奇异M-矩阵和使用Lyapunov泛函方法,得到了带有常时滞和变时滞的高阶模糊细胞神经网络全局指数稳定性的充分条件.

分析纤维增强层合板固有特性的动态有限元

本文提出了一种用于分析正交各向异性的纤维增强层合板之固有特性的动态有限元。考虑层板的特点,采用八节点四十个自由度的矩形单元.构造了满足单元边界条件的动态位移函数,导出了动态元意义下的层板单元特性。据此编制了层板固有特性的动态元分析程序。将实例分析结果与常规元分析结果、实验结果进行了比较,表明在层板固有持性分析中动态有限元法较之于常规有限正法有一定的优越性。