Enhanced robust capacity estimation of lithium-ion batteries with unlabeled dataset and semi-supervised machine learning
M Ye, Q Wang, L Yan, M Wei, G Lian, K Zhao… - Expert Systems with …, 2024 - Elsevier
The capacity estimation is a crucial task in battery health and safety management. The
majority existing capacity estimation methods heavily rely on supervised learning with …
majority existing capacity estimation methods heavily rely on supervised learning with …
[HTML][HTML] Flooding and its relationship with land cover change, population growth, and road density
Bangladesh experiences frequent hydro-climatic disasters such as flooding. These disasters
are believed to be associated with land use changes and climate variability. However …
are believed to be associated with land use changes and climate variability. However …
[HTML][HTML] Data-driven prediction of tool wear using Bayesian regularized artificial neural networks
The prediction of wear in cutting tools is pivotal for boosting productivity and reducing
manufacturing costs. Although current data-driven models in machine learning and deep …
manufacturing costs. Although current data-driven models in machine learning and deep …
[HTML][HTML] Electricity demand forecasting based on feature extraction and optimized backpropagation neural network
As the global population is growing at a high rate, so is the electricity demand also
increasing at a faster rate. This exerts pressure on electricity-generating plants and …
increasing at a faster rate. This exerts pressure on electricity-generating plants and …
Corrosion effect on bond behavior between rebar and concrete using Bayesian regularized feed-forward neural network
Corrosion of reinforcement in concrete structures is essentially detrimental to the lifelong
service of the reinforced concrete structures. The diminishing of the bond strength is the …
service of the reinforced concrete structures. The diminishing of the bond strength is the …
Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning
This study presented a novel paradigm for forecasting 12-step-ahead monthly precipitation
at 126 California gauge stations. First, the satellite-based precipitation time series from …
at 126 California gauge stations. First, the satellite-based precipitation time series from …
[HTML][HTML] Efficient machine learning model to predict fineness, in a vertical raw meal of Morocco cement plant
F Belmajdoub, S Abderafi - Results in Engineering, 2023 - Elsevier
Soft sensor enables computing parameters that can be physically impossible to measure.
This work aims to develop a soft sensor for raw meal fineness in a vertical roller mill of a …
This work aims to develop a soft sensor for raw meal fineness in a vertical roller mill of a …
Predicting Temperature of Molten Steel in LF-Refining Process Using IF–ZCA–DNN Model
Z Xin, J Zhang, J Zhang, J Zheng, Y Jin… - Metallurgical and Materials …, 2023 - Springer
Controlling the temperature of molten steel in ladle furnace (LF)-refining process is one of
the main tasks to ensure that the steelmaking-continuous casting process runs smoothly. In …
the main tasks to ensure that the steelmaking-continuous casting process runs smoothly. In …
HGSmark: An efficient ECG watermarking scheme using hunger games search and Bayesian regularization BPNN
The extensive use of telemedicine has led to an exponential increase in the exchange of
medical data via insecure networks. For example, diagnosis in telemedicine practice often …
medical data via insecure networks. For example, diagnosis in telemedicine practice often …
Investigation of meta-heuristics algorithms in ANN streamflow forecasting
The deterministic approach, which utilizes the gradient information in the search process, is
prone to trapping at local minima, primarily due to the presence of saddle points and local …
prone to trapping at local minima, primarily due to the presence of saddle points and local …