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Spark detection in distribution line videos remains challenging due to weak visual saliency, multi-scale variation, and complex environmental interference. To address these issues, this paper presents an enhanced Swin Tr...

The inherent non-stationarity, weak periodicity, and abrupt transient mutations of wind turbine gearbox vibration signals present substantial challenges to traditional diagnostic frameworks. To improve diagnostic accurac...

In recent years, electric bicycles have emerged as a novel and sustainable mode of transport, offering individuals and groups alike an active and environmental-friendly way to get around. It is evident that active transp...

This paper presents an inductorless design methodology for compact single-pole double-throw (SPDT) switches in Sub-6 GHz applications. To achieve compactness and meet key performance metrics below 5.0 GHz, the methodolog...

Time-series forecasting is fundamental to decision-making across numerous domains; however, systematic temporal delays in predictions remain a largely overlooked or unrecognised phenomenon. This phenomenon occurs when fo...

Dual-drive feed systems are widely adopted in high-end CNC machines because of their high precision, rigidity, and operational stability. Vibration suppression during machining is critical to maintaining feed accuracy an...

This paper presents an innovative methodology for modeling and predicting high-frequency harmonic losses in the rotating field of an electrical machine powered by a frequency converter. Conventional approaches are often ...

Machine learning has evolved into a potent tool for analysing patterns and making predictions from complex data. In this machine learning era, we employed neural network techniques to estimate the parameters of statistic...

Conventional adversarial perturbations on traffic sign images often induce noticeable color shifts and abnormal contrast compared to natural images, rendering them susceptible to detection, particularly by Vision Transfo...

We tackle the challenging problem of single-source domain generalization (DG) for medical image segmentation, where we train a network on one domain (e.g., CT) and directly apply it to a different domain (e.g., MR) witho...