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Found 5 papers, 1 papers with code
The State of Lithium-Ion Battery Health Prognostics in the CPS Era
Lithium-ion batteries (Li-ion) have revolutionized energy storage technology, becoming integral to our daily lives by powering a diverse range of devices and applications.
De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics
The usage of Lithium-ion (Li-ion) batteries has gained widespread popularity across various industries, from powering portable electronic devices to propelling electric vehicles and supporting energy storage systems.
TFBEST: Dual-Aspect Transformer with Learnable Positional Encoding for Failure Prediction
To overcome these challenges, in this work we propose a novel transformer architecture - a Temporal-fusion Bi-encoder Self-attention Transformer (TFBEST) for predicting failures in hard-drives.
Large-scale End-of-Life Prediction of Hard Disks in Distributed Datacenters
Past work suggests using LSTMs as an excellent approach to predicting remaining useful life.
AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence
This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks.
