小波和稀疏分解在非连续性薄膜去噪中的应用

陈功; 朱锡芳; 许清泉; 徐安成; 杨辉

doi:10.7510/jgjs.issn.1001-3806.2014.04.022

小波和稀疏分解在非连续性薄膜去噪中的应用

常州工学院电子信息与电气工程学院, 常州 213022

基金项目:

江苏省自然科学基金资助项目（BK20130245）;江苏省常州市科技计划资助项目（CE20120071）;江苏省常州市高新区科技发展计划资助项目（XE120121408）;常州市光电子材料与器件重点实验室资助项目（20130694）

详细信息

中图分类号: TP274
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- 文章访问数: 11
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出版历程
- 收稿日期: 2013-08-19
- 修回日期: 2013-09-01
- 发布日期: 2014-07-24

Applications of wavelets and sparse decomposition in non-continuous film de-noising

School of Electronic Information and Electrical Engineering, Changzhou Institute of Technology, Changzhou 213022, China

摘要

摘要: 为了在传感器测量锂电池非连续性膜厚前不需测量C型机构的固有频率和扫描振动频率，采用3层小波-阈值判断-稀疏分解信号处理去噪方法，进行了理论分析和实验验证。该方法不需固有频率和扫描振动频率的先验知识，在不同C型机构扫描速率模式下，通过迭代选取最佳匹配的原子序列保留锂电池薄膜厚度分布，滤除局部噪声波动，实现稀疏迭代去噪。结果表明，相对于小波算法，在缺乏先验知识的条件下，稀疏分解算法具有较好的去噪性能，其均方差值达5μm~7μm，是一种操作简单、可行有效的方法。
- 信号处理 /
- 去噪 /
- 稀疏分解 /
- 锂电池薄膜
Abstract: In order to avoid measuring the inherent frequency and the scanning vibration frequency of C-dynamic scanning system before measuring discontinuous film thickness of lithium battery with laser sensors, the 3-layer wavelet-threshold judgment-sparse decomposition signal processing de-noising method was used. Theoretical analysis and experimental verification were made. Without prior knowledge of the inherent frequency and the scanning vibration frequency and under different C-dynamic scanning mode, the best-matching atomic sequence was selected by iteration and the film thickness distribution of lithium battery was reserved, fluctuations of the local noise were filtered and sparse iterative de-noising was realized. The results show that comparing with the wavelet algorithm and in the absence of the prior knowledge, sparse decomposition algorithm has better de-noising performance and is a simple, practical and effective method. Mean square error of sparse decomposition algorithm is 5μm~7μm.
- signal processing /
- de-noising /
- sparse decomposition /
- film of lithium battery

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参考文献(12)

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