Accelerating materials discovery using machine learning

Y Juan, Y Dai, Y Yang, J Zhang - Journal of Materials Science & …, 2021 - Elsevier
The discovery of new materials is one of the driving forces to promote the development of
modern society and technology innovation, the traditional materials research mainly …

Rebooting data-driven soft-sensors in process industries: A review of kernel methods

Y Liu, M **e - Journal of Process Control, 2020 - Elsevier
Soft-sensors usually assist in dealing with the unavailability of hardware sensors in process
industries, thus allowing for less fault occurrence and better control performance. However …

Modeling the SOFC by BP neural network algorithm

S Song, X **ong, X Wu, Z Xue - International Journal of Hydrogen Energy, 2021 - Elsevier
Solid oxide fuel cells (SOFCs) are complex systems in which electrochemistry,
thermophysics and ion conduction occur simultaneously. The coupling of the multi-physics …

Assessing credit risk of commercial customers using hybrid machine learning algorithms

MR Machado, S Karray - Expert Systems with Applications, 2022 - Elsevier
Given the large amount of customer data available to financial companies, the use of
traditional statistical approaches (eg, regressions) to predict customers' credit scores may …

A decision-theoretic rough set model with q-rung orthopair fuzzy information and its application in stock investment evaluation

G Tang, F Chiclana, P Liu - Applied Soft Computing, 2020 - Elsevier
Stock investment is characterized by high risk and massive profit, so it is necessary to
propose a scientific and accurate stock assessment and selection method for avoiding …

Forecasting agricultural commodity prices using model selection framework with time series features and forecast horizons

D Zhang, S Chen, L Liwen, Q **a - IEEE access, 2020 - ieeexplore.ieee.org
The fluctuations of agricultural commodity prices have a great impact on people's daily lives
as well as the inputs and outputs of agricultural production. An accurate forecast of …

Machine learning‐based model for lithium‐ion batteries in BMS of electric/hybrid electric aircraft

SR Hashemi… - … Journal of Energy …, 2021 - Wiley Online Library
Reliable operation and control of battery packs can lead to increasing applications of
batteries as energy sources for mobile power systems such as electric/hybrid electric aircraft …

Two-dimensional improved attribute reductions based on distance granulation and condition entropy in incomplete interval-valued decision systems

B Chen, X Zhang, Z Yuan - Information Sciences, 2024 - Elsevier
Attribute reductions rely on knowledge granulation and information measurement. Aiming at
incomplete interval-valued decision systems (IIVDSs), an attribute reduction (with the FSR …

Fuzzy probability theory

M Beer - Granular, Fuzzy, and Soft Computing, 2023 - Springer
Fuzzy probability theory is an extension to probability theory to dealing with a mix of
probabilistic uncertainty and non-probabilistic imprecision. It provides a theoretical basis to …

Machine-learning approaches for the discovery of electrolyte materials for solid-state lithium batteries

S Hu, C Huang - Batteries, 2023 - mdpi.com
Solid-state lithium batteries have attracted considerable research attention for their potential
advantages over conventional liquid electrolyte lithium batteries. The discovery of lithium …