A5025801559- High voltage insulation and dielectric phenomena
- Power Transformer Diagnostics and Insulation
- EEG and Brain-Computer Interfaces
- X-ray Spectroscopy and Fluorescence Analysis
- Atomic and Molecular Physics
- Power Quality and Harmonics
- Machine Fault Diagnosis Techniques
- Muscle activation and electromyography studies
- Lightning and Electromagnetic Phenomena
- Blind Source Separation Techniques
- Neural dynamics and brain function
- Ion-surface interactions and analysis
- Image Enhancement Techniques
- Visual perception and processing mechanisms
- Thermal Analysis in Power Transmission
- Laser-induced spectroscopy and plasma
- Advanced Sensor and Energy Harvesting Materials
- Electrical Fault Detection and Protection
- Currency Recognition and Detection
- Electron and X-Ray Spectroscopy Techniques
- Retinal Development and Disorders
- Nuclear Physics and Applications
- Complex Systems and Time Series Analysis
- Power System Reliability and Maintenance
- ECG Monitoring and Analysis
National Institute of Technology Durgapur
2022-2025
Jadavpur University
1997-2024
Maulana Azad National Institute of Technology
2024
Allen Institute
2018-2023
Allen Institute for Brain Science
2018-2023
Birla Institute of Technology, Mesra
2021-2022
Techno India University
2019-2021
Indian Institute of Technology Bombay
2019-2021
Harvard University
2009-2021
Defence Research and Development Organisation
2021
Rolling bearing defects in induction motors are usually diagnosed using vibration signal analysis. For accurate detection of rolling defects, appropriate feature extraction from signals is necessary, failure which may lead to incorrect interpretation. Considering the above fact, this article presents an autocorrelation aided method for diagnosis defects. To end, healthy as well different faulty bearings were recorded accelerometers and respective done examine their self-similarity time...
In this paper, a novel approach for accurate sensing of incipient faults occurring in power transformers is proposed using dissolved gas analysis (DGA) technique. The Duval pentagon method popular technique often used to interpret transformer based on DGA data. However, one potential limitation conventional the presence rigid fault boundaries within which lead misinterpretations, leading poor detection accuracy. Considering issue, paper modification proposed, where instead rigidly separated...
Dissolved gas analysis (DGA) of insulating oils is one the most popular methods to detect incipient faults in power transformers. However, appropriate feature selection crucial for accurately detecting using DGA data. Another issue unavailability a balanced dataset, which can hamper fault classification accuracy. Considering these two issues, this article proposes novel and accurate framework ratios as features obtained from data The unbalanced was initially synthetic minority oversampling...
In this contribution, a method to segregate electroencephalogram (EEG) signals into focal (F) and non‐focal (NF) groups has been proposed, employing novel multifractal detrended fluctuation analysis (MFDFA)‐based feature sets. Manifestations in the fractal behaviour occurring due subtle morphological changes F NF EEG signals, can serve as an essential presurgical intervention for automated detection of structural epileptogenic area within human brain. Considering above‐said fact, present...
This letter presents a novel technique for classification of motor imagery (MI) electroencephalogram (EEG) signals employing multiplex weighted visibility graph (MWVG) algorithm. A (WVG) is an effective tool to map univariate time series into graphical representation while preserving its temporal characteristics. In this contribution, the concept WVG extended analyze multivariate EEG known as MWVG From transformed series, new method construction complex functional brain connectivity network...
Dissolved gas analysis (DGA) is a standard technique for detecting incipient faults in oil-immersed power transformers. However, fault sensing accuracy depends on feature selection and the machine learning (ML) algorithm used classification. To overcome these two issues, 50 features were extracted from DGA dataset of 2242 samples obtained local utilities. Two state-of-the-art deep algorithms, i.e., long-short-term memory (LSTM) bi-directional (bi-LSTM), to classify different types normal...
Dibenzyl disulfide (DBDS) is the most prevalent corrosive sulfur in transformer oil. It reacts with windings to produce copper sulfide (Cu2S) and gets deposited on insulating paper's surface, leading interturn faults within windings. Hence, this article proposes a deep neural network (DNN) predict DBDS content The parameters like interfacial tension (IFT), breakdown voltage (BDV), water (WC), oxygen, neutralization number (NN), color, furan content, specific gravity (SG) were used as...
This article proposes a novel bearing fault detection framework for the real-time condition monitoring of induction motors based on difference visibility graph (DVG) theory. In this regard, vibration signals healthy as well different rolling defects were acquired from both fan-end and drive-end accelerometers. These data recorded three under four loading conditions. The time series converted to topological network using DVG. From transformed in domain, degree distribution (DD) was selected...
X-ray emissions due to the two-electron one-photon (TEOP) process in neon projectile and aluminum target have been successfully observed for beam energy window of 1.8-2.1 MeV. Experimental TEOP transition energies compared with theoretical predictions flexible atomic structure code (FAC) General-purpose Relativistic Atomic Structure (GRASP) package. Present results verified reported experimental values. Transition rates transitions also studied using said codes. The lines assigned when...
In this contribution, a novel technique for classification of electroencephalogram (EEG) signals has been presented employing generalised Stockwell ( S )‐transform technique. ‐transform is analysis any non‐stationary time series in joint time–frequency frame. work, epileptic seizure and seizure‐free EEG have taken from an available existing database applied individually on different sets signals. Selective features like standard deviation energy are evaluated the contour transformed...
Here, a technique for automated detection of epilepsy is proposed, based on novel set features derived from the multifractal spectrum electroencephalogram (EEG) signals. In fractal geometry, detrended fluctuation analysis (MDFA) to examine self-similarity non-linear, chaotic and noisy time series. EEG signals which are representatives complex human brain dynamics can be effectively characterised by MDFA. representing healthy, interictal seizure activities acquired an available dataset. The...
The spatial summation properties of visual signals were analyzed for geniculocortical afferents in the primary cortex (V1) anesthetized paralyzed macaque monkeys. Afferent input responses recorded extracellularly during cortical inactivation through superfusion with muscimol, allowing investigation lateral geniculate nucleus thalamus (LGN) cell absence feedback. Responses from afferent inputs classified as magno-, parvo-, or koniocellular based on anatomical organization within cortex,...
Abstract The clustering of neurons with similar response properties is a conspicuous feature neocortex. In primary visual cortex (V1), maps several like orientation preference are well described, but the functional architecture color, central to perception in trichromatic primates, not. Here we used two-photon calcium imaging macaques examine fine structure chromatic representation and found that responsive spatially uniform, stimuli form unambiguous clusters coincide blobs. Further, these...
Accurate detection of focal seizure area through EEG screening is important to remove the affected regions human brain, prior surgery. Considering aforesaid fact, in this paper, we propose a novel approach for automated and classification electroencephalography (EEG) signals. In contribution, procured non signals recorded from temporal lobe epileptic patients decomposed data into several brain rhythms obtain their time variation different neural oscillations. Then, selected random signal...
This article proposes a novel method for predicting frequency-domain spectroscopy (FDS) characteristics of oil–paper insulation at temperature, which is different from the measurement through estimating activation energy. The predicted FDS curves temperatures show very good agreement with each other when compared to experimentally measured characteristics. From characteristics, two moisture-sensitive parameters are proposed accurate estimation moisture content insulation. technique validated...
In this article, a deep learning framework for automated detection of partial discharge (PD) events employing signature high-frequency current transformer (HFCT) sensor is proposed. For contribution, one cycle PD signals captured using HFCT sensors were initially pre-processed and converted to RGB images recurrence plot (RP), which can capture nonlinearity dynamic fluctuations present in signals. The signal RP then fed proposed customized lightweight CNN model classification different...
Wettability of polymeric insulators is a prime indicator the insulator surface condition. New surfaces are hydrophobic in nature, where discrete water droplets formed on surface. But as insulation becomes aged, loses its hydrophobicity, leading to formation continuous channels, which subsequently leads dry band arcing and even flashover, thereby affecting long-term performance insulators. Therefore, accurate sensing wettability important for reliable diagnostics. Considering above fact, this...