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Weeks, Michael; Bayoumi, Magdy

doi:10.1023/a:1023648531542

Cited by 61 publications

(3 citation statements)

References 41 publications

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“…Although the WT method achieved good results in detecting power disturbances, it could also perform multi-resolution time-frequency analysis and successfully minimize the size of the disruption dataset collection; however, it exhibited some drawbacks, such as the reliance of its accuracy on the choice of mother wavelet, as well as the effectiveness in classification being, likewise, highly dependent on choosing features and associated classifiers [27]. Furthermore, the method was sensitive to noisy signal interference and had a high computational cost [28]. As a result, a novel signal processing method with a wide range of applications and anti-noise capabilities should be devised.…”

Section: Review Of Related Workmentioning

confidence: 99%

Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System

Syamsudin,

Chen,

Berutu

et al. 2024

Energies

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There is some risk of power quality disturbances at many stages of production, transformation, distribution, and energy consumption. The cornerstone for dealing with power quality problems is the characterization of power quality disturbances (PQDs). However, past research has focused on a narrow topic: noise disruption, overfitting, and training time. A new strategy is suggested to address this problem that combines efficient one-dimensional dataset compression with the convolutional neural network (CNN) classification algorithm. First, three types of compression algorithms: wavelet transform, autoencoder, and CNN, are proposed to be evaluated. According to the IEEE-1159 standard, the synthetic dataset was built with fourteen different PQD types. Furthermore, the PQD classification procedure integrated compressed data with the CNN classification algorithm. Finally, the suggested method demonstrates that combining CNN compression and classification methods can efficiently recognize PQDs. Even in noisy environments, PQD signal processing achieved up to 98.25% accuracy and managed the overfitting.

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Section: Review Of Related Workmentioning

confidence: 99%

Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System

Syamsudin,

Chen,

Berutu

et al. 2024

Energies

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“…DWT is a transformation used for frequency domain analysis of image [16,17]. DWT decomposes the image into four non-overlapping multi-resolution sub-bands: LL1 (Approximate or Low-Low sub-band), HL1 (Horizontal or high-Low sub-band), LH1 (Vertical or Low-high sub-band), and HH1 (Diagonal or High-high Sub band).…”

Section: Watermarking For Foveal and Visual Image Coding To Evaluate ...mentioning

confidence: 99%

Application of Computational Intelligence in Visual Quality Optimization Watermarking and Coding Tools to Improve the Medical IoT Platforms Using ECC Cybersecurity Based CoAP Protocol

Allali¹,

Morino²,

Oussous³

et al. 2023

Information Security and Privacy in the Digital World - Some Selected Topics

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To ensure copyright protection and authenticate ownership of media or entities, image watermarking techniques are utilized. This technique entails embedding hidden information about an owner in a specific entity to discover any potential ownership issues. In recent years, several authors have proposed various ways to watermarking. In computational intelligence contexts, however, there are not enough research and comparisons of watermarking approaches. Soft computing techniques are now being applied to help watermarking algorithms perform better. This chapter investigates soft computing-based image watermarking for a medical IoT platform that aims to combat the spread of COVID-19, by allowing a large number of people to simultaneously and securely access their private data, such as photos and QR codes in public places such as stadiums, supermarkets, and events with a large number of participants. Therefore, our platform is composed of QR Code, and RFID identification readers to ensure the validity of a health pass as well as an intelligent facial recognition system to verify the pass’s owner. The proposed system uses artificial intelligence, psychovisual coding, CoAP protocol, and security tools such as digital watermarking and ECC encryption to optimize the sending of data captured from citizens wishing to access a given space in terms of execution time, bandwidth, storage space, energy, and memory consumption.

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“…However, the wavelet transform method has its drawbacks. In [16], Michael Weeks and Magdy Bayoumi identified the drawbacks of various discrete wavelet transform (DWT) system architectures; in general, the excellent properties of the wavelet transform in one dimension cannot be extended to two dimensions or higher, and it exhibits a lack of adaptivity to other modal decomposition methods, e.g., EMD, LMD, VMD, SGMD, etc. Due to the complexity of the hydroacoustic environment, the one-dimensional feature vector extracted by the wavelet transform is often insufficient for providing optimal features; thus, the discovery of method with which to improve the selection of optimal features or multidimensional features has become a possible direction of development.…”

Section: The Wavelet Transformmentioning

confidence: 99%

Deep Learning-Based Classification of Raw Hydroacoustic Signal: A Review

Dong

2022

JMSE

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Underwater target recognition is a research component that is crucial to realizing crewless underwater detection missions and has significant prospects in both civil and military applications. This paper provides a comprehensive description of the current stage of deep-learning methods with respect to raw hydroacoustic data classification, focusing mainly on the variety and recognition of vessels and environmental noise from raw hydroacoustic data. This work not only aims to describe the latest research progress in this field but also summarizes three main elements of the current stage of development: feature extraction in the time and frequency domains, data enhancement by neural networks, and feature classification based on deep learning. In this paper, we analyze and discuss the process of hydroacoustic signal processing; demonstrate that the method of feature fusion can be used in the pre-processing stage in classification and recognition algorithms based on raw hydroacoustic data, which can significantly improve target recognition accuracy; show that data enhancement algorithms can be used to improve the efficiency of recognition in complex environments in terms of deep learning network structure; and further discuss the field’s future development directions.

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Untitled

Cited by 61 publications

References 41 publications

Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System

Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System

Application of Computational Intelligence in Visual Quality Optimization Watermarking and Coding Tools to Improve the Medical IoT Platforms Using ECC Cybersecurity Based CoAP Protocol

Deep Learning-Based Classification of Raw Hydroacoustic Signal: A Review

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