Results 221 to 230 of about 1,005,453 (288)

AI-Based Predictive Maintenance Framework for Industrial Saw Blade Wear Monitoring Using Low-Cost Vibration Sensors. [PDF]

Sensors (Basel)
Alfaris H, Daoud O, Kneifel J, Suyyagh A.   +3 more
europepmc   +1 more source

AD-DETR: A Real-Time Transformer with Multi-Scale Alignment and Spatial-Spectral Fusion for Crop Disease Detection. [PDF]

Sensors (Basel)
Wang B, Zhou H, Wang Z, Chen R.
europepmc   +1 more source

Machine learning-driven renewable energy grid integration stability assessment: LIME interpretability and LLM intelligent analysis. [PDF]

Sci Rep
Xu R, Zheng Y, Meng Q, Yang Q.
europepmc   +1 more source

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Learning Attention in the Frequency Domain for Flexible Real Photograph Denoising

IEEE Transactions on Image Processing
Recent advancements in deep learning techniques have pushed forward the frontiers of real photograph denoising. However, due to the inherent pooling operations in the spatial domain, current CNN-based denoisers are biased towards focusing on low ...
Ruijun Ma, Long Qi, Leyuan Fang
exaly   +4 more sources

Contrastive Learning with Frequency Domain for Sequential Recommendation

Applied Soft Computing Journal, 2023
Liguo Zhang, Guisheng Yin, Yuxin Dong
exaly   +3 more sources

A Fault Diagnosis Method With Bitask-Based Time- and Frequency-Domain Feature Learning

IEEE Transactions on Instrumentation and Measurement, 2023
Deep-learning-based methods used for fault diagnosis show remarkable performance, and these methods primarily learn features based on the time or frequency domain. Generally, time- and frequency-domain features are important for identifying faults.
Ting Huang, Qiang Zhang, Handong Zheng   +2 more
exaly   +2 more sources

Joint Spatial and Frequency Domain Learning for Lightweight Spectral Image Demosaicing

IEEE Transactions on Image Processing
Conventional spectral image demosaicing algorithms rely on pixels’ spatial or spectral correlations for reconstruction. Due to the missing data in the multispectral filter array (MSFA), the estimation of spatial or spectral correlations is inaccurate ...
Tao Huang, Weisheng Dong, Guangming Shi   +2 more
exaly   +2 more sources

Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation

Proceedings of the 17th ACM Conference on Recommender Systems, 2023
Recently, contrastive learning for sequential recommendation has demonstrated its powerful ability to learn high-quality user representations. However, constructing augmented samples in the time domain poses challenges due to various reasons, such as ...
Yichi Zhang, Guisheng Yin, Yuxin Dong
semanticscholar   +2 more sources