A precision detection technique for power disturbance in electrical system

Usman, Adil; Choudhry, M.A.

doi:10.1007/s00202-021-01343-0

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Moreover, the stability of these signals distinguishes sustained pain from transient changes, providing insights into the persistence of pain perception. To extract the fNIRS signal information related to intensity, dynamics, stability, complexity, and variation-like characteristics [ 30 ], we have carefully chosen features [ 31 , 32 ] such as Log Energy, Crest Factor, Shape Factor, Impulse Factor, Margin Factor, Mobility, Complexity, Mean Absolute Deviation of First Difference, Range, and Variation in First Difference as defined in Table 1 . These features are extracted from both

and

signals and fused at the feature level to create a fused feature vector.…”

Section: Materials and Methodsmentioning

confidence: 99%

Multilevel Pain Assessment with Functional Near-Infrared Spectroscopy: Evaluating ΔHBO2 and ΔHHB Measures for Comprehensive Analysis

Khan,

Sousani,

Hirachan

et al. 2024

Sensors

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Assessing pain in non-verbal patients is challenging, often depending on clinical judgment which can be unreliable due to fluctuations in vital signs caused by underlying medical conditions. To date, there is a notable absence of objective diagnostic tests to aid healthcare practitioners in pain assessment, especially affecting critically-ill or advanced dementia patients. Neurophysiological information, i.e., functional near-infrared spectroscopy (fNIRS) or electroencephalogram (EEG), unveils the brain’s active regions and patterns, revealing the neural mechanisms behind the experience and processing of pain. This study focuses on assessing pain via the analysis of fNIRS signals combined with machine learning, utilising multiple fNIRS measures including oxygenated haemoglobin (ΔHBO2) and deoxygenated haemoglobin (ΔHHB). Initially, a channel selection process filters out highly contaminated channels with high-frequency and high-amplitude artifacts from the 24-channel fNIRS data. The remaining channels are then preprocessed by applying a low-pass filter and common average referencing to remove cardio-respiratory artifacts and common gain noise, respectively. Subsequently, the preprocessed channels are averaged to create a single time series vector for both ΔHBO2 and ΔHHB measures. From each measure, ten statistical features are extracted and fusion occurs at the feature level, resulting in a fused feature vector. The most relevant features, selected using the Minimum Redundancy Maximum Relevance method, are passed to a Support Vector Machines classifier. Using leave-one-subject-out cross validation, the system achieved an accuracy of 68.51%±9.02% in a multi-class task (No Pain, Low Pain, and High Pain) using a fusion of ΔHBO2 and ΔHHB. These two measures collectively demonstrated superior performance compared to when they were used independently. This study contributes to the pursuit of an objective pain assessment and proposes a potential biomarker for human pain using fNIRS.

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and

signals and fused at the feature level to create a fused feature vector.…”

Section: Materials and Methodsmentioning

confidence: 99%

Multilevel Pain Assessment with Functional Near-Infrared Spectroscopy: Evaluating ΔHBO2 and ΔHHB Measures for Comprehensive Analysis

Khan,

Sousani,

Hirachan

et al. 2024

Sensors

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show abstract

“…A total of seventeen statistical features were extracted in this study namely, mean, standard deviation, skewness, kurtosis, Shannon energy, log energy, zero-crossing rate, total harmonic distortion, signal-to-noise distortion ratio, spurious-free dynamic range, peak to peak, RMS, energy, crest factor, shape factor, impulse factor, and margin factor. These feature have proved their capability in various detection problems in different applications [19,20]. Standard deviation [22] Skewness [22] Kurtosis [22] Shannon energy [23] Log energy [24] Zero crossing rate [25] Total harmonic distortion [26] Rms [27] Energy [23] Crest factor [28] Shape factor [29] Impulse factor [30] Margin factor [31]…”

Section: Time Domain Featuresmentioning

confidence: 99%

System Design for the Detection of Induction Motor Faults using Multi-Domain Feature Fusion

Usman

Aziz

Khan

2023

Preprint

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Accurate and early detection of the bearing faults would make it possible to solve the problem at a lower cost by replacing relevant parts. In this article, a machine learning technique has been proposed for the detection of inner-race and outer-race bearing faults of the motor. A test bench is developed for experimentation in which easy insertion and removal of the faulty and healthy bearing of an induction motor is possible. A vibration sensor was employed to diagnose the motor faults. A dataset of 3-dimensional vibration signals is acquired from the motor representing inner-race and outer-race bearing faults. Vibrational signal data is pre-processed using empirical mode decomposition (EMD) to decompose the raw signals into its integral components called Intrinsic Mode Functions (IMFs) and to find the region of interest. A new scheme for the selection of discriminated features in bearing fault detection application is presented here in which firstly, Multi-domain features were extracted from pre-processed, and were serially fused. Then, Principle Component Analysis (PCA) technique was employed for the reduction of feature vector dimension which resulted a set of only ten features. The proposed features have positive repercussions on the performance of various existing classifiers used in motor bearing fault detection applications. However, Support Vector Machine with a linear kernel (SVM-L) is proposed to recognize the healthy, inner-race fault and outer-race fault states of the motor bearing due to its linear basis. The results of extensive experimentation appreciate the performance of the proposed technique.

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A new deep learning method for the classification of power quality disturbances in hybrid power system

Eristi

Eri̇şti̇

2022

Electr Eng

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A precision detection technique for power disturbance in electrical system

Cited by 5 publications

References 23 publications

Multilevel Pain Assessment with Functional Near-Infrared Spectroscopy: Evaluating ΔHBO2 and ΔHHB Measures for Comprehensive Analysis

Multilevel Pain Assessment with Functional Near-Infrared Spectroscopy: Evaluating ΔHBO2 and ΔHHB Measures for Comprehensive Analysis

System Design for the Detection of Induction Motor Faults using Multi-Domain Feature Fusion

A new deep learning method for the classification of power quality disturbances in hybrid power system

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