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Abstract Intelligent fault diagnosis is an important method in rotating machinery fault diagnosis and equipment health management. To deal with co-frequency vibration faults, a type of typical fault in rotating machinery...

Abstract By studying the effects of geometric precision on kinematic accuracy, an error mapping model has been established, based on the hypothesis that a motion pair and its installation surface are rigid. However, when...

Unmanned Aircraft Systems (UAS) can be useful tools for public safety agencies during crime or vehicle accident scene investigations if it can provide value to the resource-constrained agency. The speed of data collectio...

One of the most promising uses of aerial applications by a sUAS is on small farms where traditional crewed aerial applicators were not practical due to the limited size of the operations. This controlled field test aimed...

Abstract Ceramic cutting inserts are a type of cutting tool commonly used in high-speed metal cutting applications. However, the wear of these inserts caused by friction between the workpiece and cutting inserts limits t...

Quantumized Hall resistance (QHR) provides a traceable absolute reference for resistance measurement. However, the traditional transfer system based on super-conducting current comparator (CCC) relies on cryogenic liquid...

Abstract Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe ...

Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide. Over the past three decades, percutaneous coronary intervention (PCI) has evolved from a anatomical revascularization technique ...

Abstract Conventional coherent light-field manipulation techniques inherently conflict with the spatiotemporal incoherence of thermal radiation sources. While recent advances in thermophotonics have facilitated direction...

Pixel-level segmentation is critical for remote sensing applications, yet traditional supervised methods suffer from high annotation costs. While foundational vision models like CLIP and the Segment Anything Model (SAM) ...

Rainfall prediction efforts had been prevalent ever since the impact of climate change on occurrences of natural disasters globally. Implementation of machine and deep learning techniques on features that contribute to r...

Manual cocoa bean inspection is subjective and prone to error, significantly impacting chocolate production. This study presents an automated, high-throughput cocoa bean grading system leveraging YOLOv10, YOLOv11, and Vi...

Reliable beat-level arrhythmia detection from electrocardiogram (ECG) signals is essential for continuous patient monitoring. However, many existing deep learning models remain computationally expensive and are sensitive...

Reliable prognostics in predictive maintenance requires not only accurate remaining useful life (RUL) estimation but also robustness under distribution shift and support for decision-making under uncertainty. However, mo...

To address the difficulty of blasthole detection during the charging phase of drill-and-blast tunnelling, which is aggravated by dust interference and insufficient illumination, this study proposes an intelligent blastho...

An accurate intraoperative assessment of tissue perfusion in digestive surgery is critical to prevent complications such as anastomotic leakage, an event in which a surgical connection between organs fails to heal proper...

Electric Discharge Machining (EDM) is a non-traditional machining process that utilizes electric sparks between an electrode and a workpiece submerged in a dielectric fluid to ablate a material. It is commonly used in di...

Climate change and human impact are increasing the frequency and severity of flooding, making real-time, accurate simulation of mountainous flood inundation an urgent necessity. In this study, we developed a proxy model ...

Abstract Early and reliable smoke detection is critical for preventing fire-related disasters, safeguarding human lives, and ensuring infrastructure safety. Conventional sensor-based systems, however, are often constrain...

This study presents the application of an enhanced Proportional–Integral–Derivative (PID)-like Sliding Mode Controller (SMC), referred to as the Integral PID-like Sliding Mode (IPS) controller, for cruise control sys...

Background: Deep learning has transformed medical imaging by enabling earlier and more accurate disease diagnosis. Lesion and tumor segmentation, essential for analyzing and tracking morphological changes, is commonly do...

Abstract The current study employs Bayesian-regularization optimizer technique with artificial neural networks (BROT-ANNs) to investigate the significance of local thermal non-equilibrium influences on wax + sand-bas...

Abstract A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar (BRB) fault diagnosis of induction motors. Discrete Fourier transform (DFT) is the most popul...

This paper introduces CONV_GBERT, a hybrid deep learning framework for robust sentiment classification. The model integrates the bidirectional contextual understanding of BERT, the generative capabilities of GPT, and the...