An Intelligent Quadcopter Unbalance Classification Method Based on Stochastic Gradient Descent Logistic Regression

Al-Haddad, Luttfi A.; Jaber, Alaa Abdulhady

doi:10.1109/it-ela57378.2022.10107922

Cited by 19 publications

(2 citation statements)

References 17 publications

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“…This approach can yield more robust predictions, as it leverages the diverse perspectives of different models. Stochastic Gradient Descent (SGD) [ 24 , 25 ], a cornerstone of many learning algorithms due to its efficiency in handling large datasets, has been adapted for EEG data to optimize performance in real-time applications, enhancing the potential for on-the-fly analyses in clinical settings.…”

Section: Resultsmentioning

confidence: 99%

Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis

Alawee,

Basem,

Al-Haddad

2023

Journal of Electrical Bioimpedance

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Biomedical engineering stands at the forefront of medical innovation, with electroencephalography (EEG) signal analysis providing critical insights into neural functions. This paper delves into the utilization of EEG signals within the MILimbEEG dataset to explore their potential for machine learning-based task recognition and diagnosis. Capturing the brain’s electrical activity through electrodes 1 to 16, the signals are recorded in the time-domain in microvolts. An advanced feature extraction methodology harnessing Hjorth Parameters—namely Activity, Mobility, and Complexity—is employed to analyze the acquired signals. Through correlation analysis and examination of clustering behaviors, the study presents a comprehensive discussion on the emergent patterns within the data. The findings underscore the potential of integrating these features into machine learning algorithms for enhanced diagnostic precision and task recognition in biomedical applications. This exploration paves the way for future research where such signal processing techniques could revolutionize the efficiency and accuracy of biomedical engineering diagnostics.

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Section: Resultsmentioning

confidence: 99%

Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis

Alawee,

Basem,

Al-Haddad

2023

Journal of Electrical Bioimpedance

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“…Different methodologies in acquiring these vibration signals were discussed and presented for defect detection [35][36][37][38]. In this study, the ADXL335 accelerometer was selected based on its exceptional capabilities in detecting faults caused by vibration signals [39,40]. Any software designed for signal processing would functional to obtain the vibrational patterns.…”

Section: Methodsmentioning

confidence: 99%

Towards Condition Monitoring: Fabrication and Finite Element Analysis of A Helical Gear Transmission Rig for Fault Simulation

2024

jjmie

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Mechanical systems are mainly considered in heavy transmissions, and helix-angled gears are being mostly used in such fields of interest. As these mechanical components operate, they are heavily subjected to potential faults of operation. To avoid experimental faults, simulation-based analysis and test apparatus are now used for diagnosing purposes and hence, improving the dependability while enhancing their performance. For that, this paper presents an integrated approach of simulation analysis and the fabrication of a test rig specifically designed for fault inducing and seeding in helical gear transmissions. Computer-aided design and engineering techniques are both used to conduct vibration-based analysis. Six mode shapes are called out for in the modal simulation workbench where they were discussed accordingly. Bearing-mounted accelerometers were used to capture vibration signals of three operating axis in order to study the potentiality of utilizing them for condition monitoring. The current approach has demonstrated advancements in fault diagnosis methodologies and provided valuable insights in enhancing reliability and dependability of such applications.

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Vibration-current data fusion and gradient boosting classifier for enhanced stator fault diagnosis in three-phase permanent magnet synchronous motors

Al-Haddad,

Jaber,

Hamzah

et al. 2023

Electr Eng

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An Intelligent Quadcopter Unbalance Classification Method Based on Stochastic Gradient Descent Logistic Regression

Cited by 19 publications

References 17 publications

Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis

Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis

Towards Condition Monitoring: Fabrication and Finite Element Analysis of A Helical Gear Transmission Rig for Fault Simulation

Vibration-current data fusion and gradient boosting classifier for enhanced stator fault diagnosis in three-phase permanent magnet synchronous motors

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