Nonlinear Control of the Permanent Magnet Synchronous Motor PMSM using Backstepping Method

Youssef, Chaou; Ziani, Said; Achour, Hafid Ben; Daoudia, Abdelkarim

doi:10.37394/23203.2022.17.7

Cited by 25 publications

(5 citation statements)

References 10 publications

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“…This paper emphasizes the indisputable benefits of wavelets in biomedical engineering. This mathematical tool is also referenced in contexts related to energy [17] and motor control [18,19] for battery-powered cars. Finally, yet importantly, the suggested technique may be implemented and optimized with the help of AI, in particular the Convolutional Neural Network algorithm, for describing the time scales picture of FECG and MECG [20].…”

Section: Results Of Simulationsmentioning

confidence: 99%

Contribution to Single-Channel Fetal Electrocardiogram Identification

Ziani¹

2022

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This paper presents the recognition of fetal ECG signals from a single-channel recording. The Continuous Wavelet Transform generates a two-dimensional representation of a single-dimension input containing fetal electrocardiogram waves, which are accurately located and isolated. By segmenting this 2D representation using Otsu's method, the continuous wavelet transform of each wave may be estimated. The temporal expression of the wave is then rebuilt by calculating the Inverse Continuous Wavelet Transform. The continuity between each sample allows us to follow the waves from one image to the next. Consequently, the complete profile can be automatically processed.

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Section: Results Of Simulationsmentioning

confidence: 99%

Contribution to Single-Channel Fetal Electrocardiogram Identification

Ziani¹

2022

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“…The results of this paper can be optimized by incorporating the learning methods and wavelet theory as reported in the references [10,11,12,13]. It is also noted that regularization methods can be effectively applied in the fields of control and energy, as indicated in the references [14,15,16,17,18].…”

Section: Ecg Recordingmentioning

confidence: 88%

Integration of Time-Frequency Analysis and Regularization Technique for Improved Identification of Fetal Electrocardiogram

Ziani,

El Ghmary,

Achmad Rizal

2023

Int. J. Onl. Eng.

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This research article presents a novel methodology for effectively extracting the fetal electrocardiogram (FECG) from a single-channel signal acquired on the maternal abdomen. The signal comprises a mixture of the FECG, maternal electrocardiogram (MECG), and ambient noise. The central concept involves projecting the signal into higher-dimensional spaces and leveraging the assumption of statistical independence among the constituent components to achieve their separation from the mixture. To accomplish this, singular value decomposition (SVD) is initially applied to the spectrogram, followed by an iterative application of independent component analysis (ICA) on the principal components. The SVD technique contributes to the enhanced separability of each individual component, while ICA facilitates the promotion of statistical independence between the fetal and maternal ECGs. Furthermore, we refine and customize the aforementioned approach specifically for ECG signals by incorporating knowledge of the frequency distribution of the MECG and other inherent ECG characteristics. The effectiveness of the proposed methodology is validated through comprehensive experimental studies, demonstrating its superior accuracy and performance compared to existing techniques.

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“…The undeniable value of wavelets in biomedical engineering is highlighted in this work. Additionally, it is mentioned that this mathematical tool can be applied to energy [37][38][39] and motor control [40,41] for electric vehicles.…”

Section: Discussionmentioning

confidence: 99%

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

Ziani

Farhaoui

Moutaib

2023

Big Data Min. Anal.

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This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and a dimension-reduction technique like Nonnegative Matrix Factorization (NMF). Due to the highly disproportionate frequency of the fetus's heart rate compared to the mother's, the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy. Furthermore, we can disentangle the various components of fetal ECG, which serve as inputs to the CNN model to optimize the actual FECG signal, denoted by FECGr, which is recovered using the SVD-ICA process. The findings demonstrate the efficiency of this innovative approach, which may be deployed in real-time.

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Nonlinear Control of the Permanent Magnet Synchronous Motor PMSM using Backstepping Method

Cited by 25 publications

References 10 publications

Contribution to Single-Channel Fetal Electrocardiogram Identification

Contribution to Single-Channel Fetal Electrocardiogram Identification

Integration of Time-Frequency Analysis and Regularization Technique for Improved Identification of Fetal Electrocardiogram

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

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