A review of recent advances and applications of machine learning in tribology
In tribology, a considerable number of computational and experimental approaches to
understand the interfacial characteristics of material surfaces in motion and tribological …
understand the interfacial characteristics of material surfaces in motion and tribological …
Nanoengineering in biomedicine: current development and future perspectives
Recent advances in biomedicine largely rely on the development in nanoengineering. As
the access to unique properties in biomaterials is not readily available from traditional …
the access to unique properties in biomaterials is not readily available from traditional …
A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time series forecasting
S Zhang, Y Chen, W Zhang, R Feng - Information Sciences, 2021 - Elsevier
In the past decade, deep learning models have shown to be promising tools for time series
forecasting. However, owing to significant differences in the volatility characteristics among …
forecasting. However, owing to significant differences in the volatility characteristics among …
Data driven discovery of MOFs for hydrogen gas adsorption
Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-
effective storage materials is a challenge to its widespread use. Metal–organic frameworks …
effective storage materials is a challenge to its widespread use. Metal–organic frameworks …
Machine learning approach for accurate backmap** of coarse-grained models to all-atom models
Four different machine learning (ML) regression models: artificial neural network, k-nearest
neighbors, Gaussian process regression and random forest were built to backmap coarse …
neighbors, Gaussian process regression and random forest were built to backmap coarse …
[HTML][HTML] Order-up-to-level inventory optimization model using time-series demand forecasting with ensemble deep learning
Inventory control aims to meet customer demands at a given service level while minimizing
cost. As a result of market volatility, customer demand is generally changing, and ignoring …
cost. As a result of market volatility, customer demand is generally changing, and ignoring …
Predicting van der Waals heterostructures by a combined machine learning and density functional theory approach
Van der Waals (vdW) heterostructures are constructed by different two-dimensional (2D)
monolayers vertically stacked and weakly coupled by van der Waals interactions. VdW …
monolayers vertically stacked and weakly coupled by van der Waals interactions. VdW …
Monitoring sugar crystallization with deep neural networks
J Zhang, Y Meng, J Wu, J Qin, T Yao, S Yu - Journal of Food Engineering, 2020 - Elsevier
Human labor still play an important role in cane sugar crystallization process. Automation
control is essential to reduce human labor. An accurate image classification system is the …
control is essential to reduce human labor. An accurate image classification system is the …
Machine learnt image processing to predict weight and size of rice kernels
Accurate measurement of rice kernel sizes after milling is critical for rice milling operations.
The size and mass of the individual rice kernels are important parameters typically …
The size and mass of the individual rice kernels are important parameters typically …
Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts
New photocatalysts are traditionally identified through trial-and-error methods. Machine
learning has shown considerable promise for improving the efficiency of photocatalyst …
learning has shown considerable promise for improving the efficiency of photocatalyst …