Results 161 to 170 of about 107,454 (343)

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Torque Ripple Suppression of Brushless DC Motor Drive System Based on Improved Harmonic Injection Active Disturbance Rejection Control. [PDF]

Sensors (Basel), 2022
He J, Yan C, Wang X.
europepmc   +1 more source

Active Disturbance Rejection Control of Shell Gasifier in IGCC Power Plants

, 2014
Chen Shihe, Jia Luo, Wu Le, Yuqiong Zhang, Donghai Li, Yali Xue   +5 more
openalex   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

Active Disturbance Rejection Control Design Using the Optimization Algorithm for a Hydraulic Quadruped Robot. [PDF]

Comput Intell Neurosci, 2021
Fan Y, Shao J, Sun G, Shao X.
europepmc   +1 more source

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker, Tamzid Ul Islam, Md. Robiul Islam   +2 more
wiley   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Frequency Regulation in Interconnected Power System Through Enhanced Beluga Whale Optimized Flatness-Based Active Disturbance Rejection Control [PDF]

Shahzad Ali, Yuanqing Xia, Zohaib Ahmad Khan, Abid Ali, Qamar Navid, Khursheed Aurangzeb, Muhammad Shahid Anwar   +6 more
openalex   +1 more source

Advanced Modified Direct Torque Control with Space Vector Modulation Based on Active Disturbance Rejection Control for Induction Motor Sensorless Drive

, 2020
Chalawane Haitam, Ahmed Essadki, Tamou Nasser   +2 more
openalex   +1 more source

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

Advanced Intelligent Systems, EarlyView.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee, Chanhong Hwang, Hyunseok Kang, Yechan Park, Hangwoo Jin, Tuan Anh Nguyen, Eunmi Choi, Dugki Min   +7 more
wiley   +1 more source