AIMC Topic: Lithium

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Economic benefit analysis of lithium battery recycling based on machine learning algorithm.

PloS one
Lithium batteries, as an important energy storage device, are widely used in the fields of renewable vehicles and renewable energy. The related lithium battery recycling industry has also ushered in a golden period of development. However, the high c...

Efficient Spiking Neural Networks with Biologically Similar Lithium-Ion Memristor Neurons.

ACS applied materials & interfaces
Benefiting from the brain-inspired event-driven feature and asynchronous sparse coding approach, spiking neural networks (SNNs) are becoming a potentially energy-efficient replacement for conventional artificial neural networks. However, neuromorphic...

Machine learning for data-driven design of high-safety lithium metal anode.

STAR protocols
Here, we present a protocol for developing an inorganic-organic hybrid interphase layer using the self-assembled monolayers technique to enhance the surface of the lithium metal anode. We describe steps for extracting organic molecules from open-sour...

Deep learning neural network derivation and testing to distinguish acute poisonings.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was e...

Transfer learning based generalized framework for state of health estimation of Li-ion cells.

Scientific reports
Estimating the state of health (SOH) of batteries powering electronic devices in real-time while in use is a necessity. The applicability of most of the existing methods is limited to the datasets that are used to train the models. In this work, we p...

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Networks with Adaptive Bayesian Learning.

Sensors (Basel, Switzerland)
With smart electronic devices delving deeper into our everyday lives, predictive maintenance solutions are gaining more traction in the electronic manufacturing industry. It is imperative for the manufacturers to identify potential failures and predi...

Optimized design of battery pole control system based on dual-chip architecture.

PloS one
At present, the global demand for lithium batteries is still in a high growth state, and the traditional lithium battery pole mill control system is still dominated by ARM (Artificial Intelligence Enhanced Computing), DSP (Digital Signal Processing),...

State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy.

Sensors (Basel, Switzerland)
Nowadays, electric vehicles have gained great popularity due to their performance and efficiency. Investment in the development of this new technology is justified by increased consciousness of the environmental impacts caused by combustion vehicles ...

Quantitative analysis of lithium in brine by laser-induced breakdown spectroscopy based on convolutional neural network.

Analytica chimica acta
In this study, a simple and effective method for accurate determination of lithium in brine samples was developed by the combination of laser induced breakdown spectroscopy (LIBS) and convolutional neural network (CNN). Our results clearly demonstrat...

Unsupervised word embeddings capture latent knowledge from materials science literature.

Nature
The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the ma...