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This paper deals with the state fault detection scheme for distribution flow networks subject to continuously varying conditions at boundaries. A robust PDE detection observer for transport flow systems is designed. Directly built on the nonlinear hyperbolic systems of balance laws model with anti-collocated setup, the PDE observer based on backstepping theory provide the on-line estimation of signals that are not measured. The stability of the error equation is proved. The estimation and the observability time are used for fault detection; an adaptive threshold is defined for the purpose. The performances of the observer and the fault detection method are validated on actual flow data collected from a real water distribution system (WDS) for leakage detection. The leak detection time corresponds to the first alarm activation, confirms the effectiveness of proposed approach.
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http://dx.doi.org/10.1016/j.isatra.2023.07.041 | DOI Listing |
This paper presents a novel multiscale signal processing framework for power quality disturbance (PQD) and cyber intrusion detection in smart grids, combining Non-Subsampled Contourlet Transform (NSCT), Split Augmented Lagrangian Shrinkage Algorithm (SALSA), and Morphological Component Analysis (MCA). A key innovation lies in an adaptive weighting mechanism within NSCT's directional sub bands, enabling dynamic energy redistribution and enhanced representation of both low-frequency anomalies (e.g.
View Article and Find Full Text PDFJ Appl Stat
January 2025
Indian Statistical Institute, Kolkata, India.
Image monitoring is an important research problem that has wide applications in various fields, including manufacturing industries, satellite imaging, medical diagnostics, and so forth. Traditional image monitoring control charts perform rather poorly when the changes occur at very small regions of the image, and when the changes of image intensity values are small in those regions. Their performances get worse if the images contain noise, and the changes occur near the edges of image objects.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Electrical Engineering and Mechatronics, Faculty of Engineering, University of Debrecen, Debrecen, Hungary.
Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans.
View Article and Find Full Text PDFSci Rep
September 2025
Computer Science and Engineering, Thapar Institute of Engineering and Technology, Street, Patiala, Punjab, 147004, India.
In recent years, electric vehicles (EVs) have become increasingly popular, driven by advancements in battery technology, growing environmental awareness, and the demand for sustainable transportation. Compared to internal combustion engines, EVs not only produce fewer emissions but also offer greater energy efficiency, leading to reduced operating costs. Despite these advantages, concerns about battery failures have been a significant safety issue for EVs.
View Article and Find Full Text PDFSci Rep
September 2025
Physics Department, Faculty of Science, Islamic University of Madinah, Madinah, 42351, Saudi Arabia.
Misalignment is among the most frequent mechanical faults in rotating electrical machines, often resulting in partial or complete motor failure over time. To tackle this issue, the present study proposes an innovative methodology for diagnosing misalignment faults in rotating electrical machines. The method integrates the dual-tree complex wavelet transform with a refined composite multiscale fluctuation dispersion entropy algorithm (DTCWT-RCMFDE) for feature extraction, combined with the least-squares support vector machines algorithm (LSSVM) for fault classification.
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