Cloud-based in-situ battery life prediction and classification using machine learning

Y Zhang, M Zhao - Energy Storage Materials, 2023 - Elsevier
In-situ battery life prediction and classification can advance lithium-ion battery prognostics
and health management. A novel physical features-driven moving-window battery life …

A review of surrogate-assisted evolutionary algorithms for expensive optimization problems

C He, Y Zhang, D Gong, X Ji - Expert Systems with Applications, 2023 - Elsevier
Many problems in real life can be seen as Expensive Optimization Problems (EOPs).
Compared with traditional optimization problems, the evaluation cost of candidate solutions …

A novel genetic LSTM model for wind power forecast

F Shahid, A Zameer, M Muneeb - Energy, 2021 - Elsevier
Variations of produced power in windmills may influence the appropriate integration in
power-driven grids which may disrupt the balance between electricity demand and its …

Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence

S Raschka, J Patterson, C Nolet - Information, 2020 - mdpi.com
Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …

Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate

J Zhou, Y Qiu, S Zhu, DJ Armaghani, C Li… - … Applications of Artificial …, 2021 - Elsevier
The advance rate (AR) of a tunnel boring machine (TBM) in hard rock condition is a key
parameter for the successful accomplishment of a tunneling project, and the proper and …

Effect of dataset size and train/test split ratios in QSAR/QSPR multiclass classification

A Rácz, D Bajusz, K Héberger - Molecules, 2021 - mdpi.com
Applied datasets can vary from a few hundred to thousands of samples in typical quantitative
structure-activity/property (QSAR/QSPR) relationships and classification. However, the size …

[HTML][HTML] 基于LSTM 循环神经网络的故障时间序列预测

王鑫, 吴际, 刘超, 杨海燕, 杜艳丽, 牛文生 - 2018 - html.rhhz.net
有效地预测使用阶段的故障数据对于合理制定可靠性计划以及开展可靠性维护活动等具有重要
的指导意义. 从复杂系统的历史故障数据出发, 提出了一种基于长短期记忆(LSTM) …

Latest research trends in fall detection and prevention using machine learning: A systematic review

S Usmani, A Saboor, M Haris, MA Khan, H Park - Sensors, 2021 - mdpi.com
Falls are unusual actions that cause a significant health risk among older people. The
growing percentage of people of old age requires urgent development of fall detection and …

Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states

AE Hramov, VA Maksimenko, AN Pisarchik - Physics Reports, 2021 - Elsevier
Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …

Data‐Driven Materials Innovation and Applications

Z Wang, Z Sun, H Yin, X Liu, J Wang, H Zhao… - Advanced …, 2022 - Wiley Online Library
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …