An ICA and AIS Based Method for Electromagnetic Compatibility Analysis

Zhao, Debin; Lu, Yu; Li, Hong Yi

doi:10.4028/www.scientific.net/amr.846-847.547

Cited by 6 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many works have been proposed to addressed the signal dimensionality reduction problem [1][2][3][4], such as principal component analysis (PCA), multidimensional scaling (MDS), isometric mapping (ISOMAP) and locally linear embedding (LLE). Besides, noisy signal components would deteriorate the encoding accuracy and classification result.…”

Section: Introductionmentioning

confidence: 99%

EMI Signal Encoding based on Deep Auto-encoder Combined with Wavelet Transformation

2021

Proceedings of 2021 the 11th International Workshop on Computer Science and Engineering

View full text Add to dashboard Cite

With the development of electronic technology, digital devices are becoming more sensitive to electromagnetic interference. As a result, the recognition methods are under scrutiny. To find the way to improve the accuracy of electromagnetic interference recognition, this paper presents a signal encoding method based on deep auto-encoder and wavelet transformation. We use wavelet de-noising as the first step on our method to reduce the noise interference. Then a deep auto-encoder is trained for extracting EMI signals' feature. Results on evaluation the reconstruction error and recognition accuracy demonstrate that our approach outperforms comparison methods, which indicates that the proposed method could better capture the data structure of the high dimensional EMI signals.

show abstract

Section: Introductionmentioning

confidence: 99%

EMI Signal Encoding based on Deep Auto-encoder Combined with Wavelet Transformation

2021

Proceedings of 2021 the 11th International Workshop on Computer Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Common methods for signal processing includes: independent component analysis [1], principle component analysis (PCA) [2], wavelet transform [3], Hilbert Huang transform [4], subspace learning [5][6][7], empirical mode decomposition [5,[7][8][9][10][11], and clustering [12][13][14][15]. To all of these methods, it is always crucial to determine how to select and extract features from a signal, especially from those high-dimension signals.…”

Section: Introductionmentioning

confidence: 99%

EMI signal feature enhancement based on extreme energy difference and deep auto‐encoder

Chen

et al. 2018

IET signal process.

Self Cite

View full text Add to dashboard Cite

To enhance features of different electromagnetic interference (EMI) signals, which are significant for further feature extraction and pattern recognition, the authors propose an EMI signal feature enhancement method based on extreme energy difference and a deep auto-encoder. Experimental results show that this method can effectively enhance features of EMI signals and improve recognition accuracy.

show abstract

“…Electromagnetic compatibility (EMC) [1][2][3][4] has been a popular research topic in recent years. As a main object of EMC study, the analysis of electromagnetic interference (EMI) is of significant importance.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction and basis function construction of electromagnetic interference source signals based on Toeplitz‐based singular value decomposition

Zhao

et al. 2017

IET signal process.

Self Cite

View full text Add to dashboard Cite

In this study, the authors propose a novel method, namely Toeplitz-based singular value decomposition (or TL-SVD for short) for the reconstruction and basis function construction of electromagnetic interference (EMI) source signals. Given a specific EMI source signal, they first construct a Toeplitz type data matrix. By applying singular value decomposition (SVD) to the constructed matrix, they obtain a set of singular values, which are further divided into two parts, corresponding to the clear and noisy components of input signals, respectively. The de-noised signal can then be reconstructed by reserving relatively larger singular values and abandoning smaller ones. Finally, by utilising the compositions of certain vectors resulting from the previous SVD step, the basis function can be constructed. To evaluate the performance of the proposed method, they conduct extensive experiments on both the synthetic data and real EMI signals, by comparing with several state-of-the-art signal reconstruction methods, such as discrete wavelet transform, EEMD, sparse representation based on K-SVD and OMP. Experimental results demonstrate that the proposed method can outperform comparison approaches.

show abstract

An ICA and AIS Based Method for Electromagnetic Compatibility Analysis

Cited by 6 publications

References 5 publications

EMI Signal Encoding based on Deep Auto-encoder Combined with Wavelet Transformation

EMI Signal Encoding based on Deep Auto-encoder Combined with Wavelet Transformation

EMI signal feature enhancement based on extreme energy difference and deep auto‐encoder

Reconstruction and basis function construction of electromagnetic interference source signals based on Toeplitz‐based singular value decomposition

Contact Info

Product

Resources

About