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Found 12 papers, 6 papers with code
A Fuzzy Reinforcement LSTM-based Long-term Prediction Model for Fault Conditions in Nuclear Power Plants
Early fault detection and timely maintenance scheduling can significantly mitigate operational risks in NPPs and enhance the reliability of operator decision-making.
A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module
This article focuses on the faults of important mechanical components such as pumps, valves, and pipelines in the reactor coolant system, main steam system, condensate system, and main feedwater system of nuclear power plants (NPPs).
Research on fault diagnosis of nuclear power first-second circuit based on hierarchical multi-granularity classification network
The safe and reliable operation of complex electromechanical systems in nuclear power plants is crucial for the safe production of nuclear power plants and their nuclear power unit.
Research on an intelligent fault diagnosis method for nuclear power plants based on ETCN-SSA combined algorithm
This paper proposes a novel intelligent fault diagnosis method for NPPs that combines enhanced temporal convolutional network (ETCN) with sparrow search algorithm (SSA).
High Performance Three-Terminal Thyristor RAM with a P+/P/N/P/N/N+ Doping Profile on a Silicon-Photonic CMOS Platform
3T TRAM with doping profile (P+PNPNN+) is experimentally demonstrated on a silicon photonic platform.
Efficiently improving key weather variables forecasting by performing the guided iterative prediction in latent space
Weather forecasting refers to learning evolutionary patterns of some key upper-air and surface variables which is of great significance.
Diagnosing and Re-learning for Balanced Multimodal Learning
In this way, the over-emphasizing of scarcely informative modalities is avoided.
Expressive and Generalizable Low-rank Adaptation for Large Models via Slow Cascaded Learning
Efficient fine-tuning plays a fundamental role in modern large models, with low-rank adaptation emerging as a particularly promising approach.
Spiking-PhysFormer: Camera-Based Remote Photoplethysmography with Parallel Spike-driven Transformer
Additionally, the power consumption of the transformer block is reduced by a factor of 12. 2, while maintaining decent performance as PhysFormer and other ANN-based models.
SAM-Deblur: Let Segment Anything Boost Image Deblurring
Image deblurring is a critical task in the field of image restoration, aiming to eliminate blurring artifacts.
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
The rapidly growing body of research in adversarial machine learning has demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarially generated images.
