Application of ICA in on-line verification of the phase difference of the current sensor

The performance of the current sensor in power equipment may become worse affected by the environment. In this paper, based on ICA, we propose a method for on-line verification of the phase difference of the current sensor. However, not all source components are mutually independent in our application. In order to get an exact result, we have proposed a relative likelihood index to choose an optimal result from different runs. The index is based on the maximum likelihood evaluation theory and the independent subspace analysis. The feasibility of our method has been confirmed by experimental results.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Application of ICA in on-line verification of the phase difference of the current sensor

The performance of the current sensor in power equipment may become worse affected by the environment. In this paper, based on ICA, we propose a method for on-line verification of the phase difference of the current sensor. However, not all source components are mutually independent in our application. In order to get an exact result, we have proposed a relative likelihood index to choose an optimal result from different runs. The index is based on the maximum likelihood evaluation theory and the independent subspace analysis. The feasibility of our method has been confirmed by experimental results.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

核ICA在电流传感器相位差监测中的应用

介绍了核独立分量分析（ICA）的基本原理和算法，并将其用于对电流传感器输出的混合信号进行分离，通过比较分离出的单频测试信号输入前后的相位差，来标定传感器本身的相位差对其检测对象的影响。此外，还采用最大似然法对核ICA的分离效果进行评价。实验证明

基于ICA和BP网络的电流传感器相位差测量方法

提出了一种高精度在线测定电流传感器相位差的方法.在输入端加入测试信号,在输出端采用独立分量分析(ICA)将该测试信号与传感器正常工作的输出信号分离,通过比较该测试信号输出前后相位的变化确定传感器的相位差.但因ICA算法固有的局限性,同一混合信号每次分离结果误差不同,为了提高分离精度,利用BP网络学习分离结果中相位误差与最大似然指标和负熵的关系,提出了一个指示分离结果误差的评价量,以选取多次分离结果中的最优结果.实验结果证明该评价量的有效性.

基于仿生模式识别和PCA/ICA的DOA估计方法

本文提出了一种基于仿生模式识别和PCA/ICA的DOA估计方法.这种方法的建模过程是用在实际环境下采集的训练样本构造人工神经网络模型,对环境的适应能力较强;且这种方法采用PCA/ICA进行特征提取,使数据得到有效压缩,可以实现系统实时处理.实验结果表明:在信噪比为20dB和0dB时,该方法的正确估计率可达100%;在信噪比降为-20dB时,该方法仍有83%的可识别率.