Fault Diagnosis for Power Cables Based on Convolutional Neural Network With Chaotic System and Discrete Wavelet Transform

Wang, Menghui; Lu, Shiue‐Der; Liao, Rui-Min

doi:10.1109/tpwrd.2021.3065342

Cited by 33 publications

(15 citation statements)

References 31 publications

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“…In 1974, the mathematician Meyer proved the existence of wavelet function and carried out an in-depth study in the theory. So far, wavelet transform theory has been widely used in signal analysis [ 16 ], image processing [ 17 ], fault diagnosis [ 18 ], and other engineering fields.…”

Section: Related Workmentioning

confidence: 99%

Text Feature Extraction for Public English Vocabulary Based on Wavelet Transform

Shi

2022

Computational and Mathematical Methods in Medicine

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Text interpretation of public English vocabulary is a critical task in the subject of natural language processing, which uses technology to allow humans and computers to communicate effectively using natural language. Text feature extraction is one of the most fundamental and crucial elements in allowing computers to effectively grasp and read text. This paper proposes a text feature extraction method based on wavelet analysis that performs fast discrete wavelet transform and inverse discrete wavelet transform on the feature vectors under the traditional TF-IDF vector space model to address the problem of low feature differentiation of high-dimensional data in text feature extraction. In particular, due to the design of the Mallat algorithm, there is frequency aliasing in the signal decomposition process. This phenomenon is a problem that cannot be ignored when using wavelet analysis for feature extraction. Therefore, this paper proposes an improved inverse discrete wavelet transform method, in which the signal is decomposed by Mallat algorithm to obtain wavelet coefficients at each scale and then reconstructed to the required wavelet space coefficients according to the reconstruction method, and the reconstructed coefficients are used to analyze the signal at that scale instead of the wavelet coefficients obtained at the corresponding scale. Experiments on the public English vocabulary dataset reveal that the wavelet transform-based strategy suggested in this research outperforms existing feature extraction methods while maintaining greater classification accuracy while reducing the dimensionality of the TF-IDF vector space model.

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Section: Related Workmentioning

confidence: 99%

Text Feature Extraction for Public English Vocabulary Based on Wavelet Transform

Shi

2022

Computational and Mathematical Methods in Medicine

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“…To diagnose the PV module fault type by images, this study employed the CDEM image (containing information of the fault state) based on the features generated by the Lorenz master and slave chaotic systems [21] in CSDM for fault recognition by the CNN. The CNN is a part that is defined in a deep learning network in modern times, e.g., facial feature recognition [22], biometric recognition embedded in an FPGA system [23] and high-tension cable fault diagnosis [24]. It is extensively used in signal processing and image classification.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Photovoltaic Module Fault Detection Based on a Convolutional Neural Network

Wang²,

Wei³

et al. 2021

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With the rapid development of solar energy, the photovoltaic (PV) module fault detection plays an important role in knowing how to enhance the reliability of the solar photovoltaic system and knowing the fault type when a system problem occurs. Therefore, this paper proposed the hybrid algorithm of chaos synchronization detection method (CSDM) with convolutional neural network (CNN) for studying PV module fault detection. Four common PV module states were discussed, including the normal PV module, module breakage, module contact defectiveness and module bypass diode failure. First of all, the defects in 16 pieces of 20W monocrystalline silicon PV modules were preprocessed, and there were four pieces of each fault state. When the signal generator delivered high frequency voltage to the PV module, the original signal was measured and captured by the NI PXI-5105 high-speed data acquisition system (DAS) and was calculated by CSDM, to establish the chaos dynamic error map as the image feature of fault diagnosis. Finally, the CNN was employed for diagnosing the fault state of the PV module. The findings show that after entering 400 random fault data (100 data for each fault) into the proposed method for recognition, the recognition accuracy rate of the proposed method was as high as 99.5%, which is better than the traditional ENN algorithm that had a recognition rate of 86.75%. In addition, the advantage of the proposed algorithm is that the mass original measured data can be reduced by CSDM, the subtle changes in the output signals are captured effectively and displayed in images, and the PV module fault state is accurately recognized by CNN.

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“…NNs support a range of technological fields including medical technology [1] as well as image processing [2], cloud computing [3], aerospace technology [4], meteorology [5], and especially in security-related technologies [6,7]. Moreover, several technologies and sciences such as chaos theory [8], frequency-domain transforms [9], genetic algorithms [10] and Digital Signal Processing (DSP) [1] are supporting neural networks as enablers.…”

Section: Introductionmentioning

confidence: 99%

The Dichotomy of Neural Networks and Cryptography: War and Peace

Zolfaghari

Koshiba

2022

ASI

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In recent years, neural networks and cryptographic schemes have come together in war and peace; a cross-impact that forms a dichotomy deserving a comprehensive review study. Neural networks can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by neural networks. On the other hand, neural networks and cryptographic algorithms can mutually support each other. Neural networks can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current surveys that take a comprehensive look at the many ways neural networks are currently interacting with cryptography. This survey aims to fill that niche by providing an overview on the state of the cross-impact between neural networks and cryptography systems. To this end, this paper will highlight the current areas where progress is being made as well as the aspects where there is room for future research to be conducted.

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Fault Diagnosis for Power Cables Based on Convolutional Neural Network With Chaotic System and Discrete Wavelet Transform

Cited by 33 publications

References 31 publications

Text Feature Extraction for Public English Vocabulary Based on Wavelet Transform

Text Feature Extraction for Public English Vocabulary Based on Wavelet Transform

Photovoltaic Module Fault Detection Based on a Convolutional Neural Network

The Dichotomy of Neural Networks and Cryptography: War and Peace

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