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Accurate failure tracing is essential for closed-loop control of CNC machine tools. To address poor interpretability, strong subjectivity, and complex modeling in existing methods, a meta-action failure tracing method is...

Abstract Object classification in decentralized environments is critically hindered by data heterogeneity and privacy constraints, which cause models to learn spurious, client-specific correlations rather than generaliza...

System-level remaining useful life (RUL) prediction is challenging because multisensor data fusion requires an integrated representation of multicomponent degradation, and the uncertainty of model parameters is often ove...

Farmer credit default identification in the context of inclusive finance is significantly hindered by sample scarcity, heterogeneous data sources, and the underutilization of “soft information” embedded in ...

Multimodal data integration is increasingly central to neuroscience and perception science, where heterogeneous signals such as behavioral responses, sensory inputs, electrophysiological recordings, neuroimaging measurem...

Abstract Air-conditioning load forecasting is important for building energy scheduling and control. The task is difficult because load data show clear periodic patterns, and sudden changes caused by events such as holida...

The field of remote sensing change detection (RSCD) has witnessed remarkable progress. Most existing RSCD methods currently follow the encoder-feature interaction-decoder architecture. However, these methods often treat ...

Abstract Facial Expression Recognition (FER) demonstrates significant value in practical scenarios such as intelligent human–computer interaction. However, conventional FER methods often struggle to balance performance...

BackgroundIn the field of geological exploration of coal mines, methods such as in-seam seismic exploration, the transient electromagnetic method, and audio-frequency electrical penetration offer unique technical advanta...

This paper proposes a multimodal perception-fusion-based control method for flexible exoskeleton robots to address challenges in human–robot collaboration and interaction accuracy. First, after analyzing the human–ro...

Abstract Early small fires in coal mines are of critical importance for mine safety, as most incidents begin as small fire sources. These fires essentially constitute a task of small-object detection. Detecting such targ...

Video snapshot compressive imaging is a computational imaging technique based on compressed sensing that acquires high-speed frames through a 2D compressed measurement, followed by an algorithmic reconstruction. Current ...

ABSTRACT With the advancement of satellite remote sensing technology, object detection based on high‐resolution remote sensing imagery has emerged as a prominent research focus in the field of computer vision. Although...

Abstract The rapid development of China’s financial markets, particularly the expansion of the ETF ecosystem, has made dynamic sector rotation strategies increasingly feasible and cost-effective for investors. At the s...

Convolutional neural networks (CNNs) and visual Transformers have become the dominant approaches for hyperspectral image (HSI) classification due to their powerful feature representation capabilities. However, CNNs are l...

Remote sensing-derived building footprint data has been widely applied in population mapping. However, nonresidential buildings typically occupy larger areas or volumes, and obtaining building footprint data with type in...

Understanding how semantic information flow reorganizes market topology remains an open challenge in financial network analysis. Prior work typically emphasizes price correlations or sentiment polarity, but rarely quanti...

With the proposal of the national dual-carbon strategic goal, distributed photovoltaic (PV) ushers in new development opportunities, and the management of voltage quality for photovoltaic power stations becomes a key cha...

To address issues such as missed detections and false positives in safety equipment wear detection tasks caused by complex backgrounds, severe occlusions, and small target sizes, as well as frequent ID switching and low ...

Abstract Long-range dependency processing is the core challenge of sequence modeling, and traditional sequence modeling algorithms suffer from information decay and low parallel computing efficiency in ultra long sequenc...

Fine-grained remote sensing ship detection plays an important role in maritime traffic management and the security guarantee system. Despite considerable research advances have been achieved via elaborately designed deep...

Time series analysis is essential for finance, transportation, energy, healthcare, and climate research. While time-frequency domain representation underpins effective time series modeling, its holistic utilization and i...

Abstract Deep learning-based image semantic segmentation techniques have made great strides in recent years. However, they still need large amounts of finely annotated image data, and generalizing the model from known cl...

Background: Neurosurgical training has historically been grounded in an apprenticeship model. This approach to developing competence relies on authentic learning opportunities presenting themselves in the operating thea...