Results 121 to 130 of about 154,885 (280)

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control

Applied Sciences
Remotely operated vehicles (ROVs) face challenges in achieving optimal trajectory tracking performance during underwater movement due to external disturbances and parameter uncertainties. To address this issue, this paper proposes a position and attitude
Gaosheng Luo, Dong Zhang, Wei Feng, Zhe Jiang, Xingchen Liu   +4 more
doaj   +1 more source

DESIGN AND REALIZATION OF A HAPTIC CRANE FORCE CONTROL FOR APPLICATION OF MATERIAL HANDLING BY USING ACTIVE FORCE CONTROL (AFC) [PDF]

, 2010
The final project highlights a method for controlling a haptic crane and hoist model based on an Active Force Control (AFC) strategy. It is a disturbance rejection control technique in which AFC is employed to control accurately and robustly the trolley ...
Ningrum, Endah Suryawati, Purnomo, Didik Setyo, Tamara, Mohamad Nasyir   +2 more
core  

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

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence‐Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study

Advanced Intelligent Systems, EarlyView.
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez, Estefania Ascencio, Jose M. Ageitos, Lucia Feijoo, Shan He, Amirreza Daghighi, Gerardo M. Casanola‐Martin, Cristian R. Munteanu, Santiago Rodriguez Yañez, Alejandro Pazos, Harbil Bediaga, Brenda Durand Ordiales, Raquel Esteban, Ana‐Maria Heres, Jorge Curiel Yuste, Lur Epelde, Bakhtiyor Rasulev, Tomas Gonzalez, Sonia Arrasate, Humberto González‐Diaz   +19 more
wiley   +1 more source

Real‐Time Sampling‐Based Model Predictive Control Based on Reverse Kullback–Leibler Divergence and Its Adaptive Acceleration

Advanced Intelligent Systems, EarlyView.
This study presents a new sampling‐based model predictive control minimizing reverse Kullback‐Leibler divergence to quickly find a local optimum. In addition, a modified Nesterov's acceleration method is introduced for faster convergence. The method is effective for real‐time simulations and real‐world operability improvement on a force‐driven mobile ...
Taisuke Kobayashi, Kota Fukumoto
wiley   +1 more source

Multimodal Locomotion of Soft Robots

Advanced Intelligent Systems, EarlyView.
This review comprehensively surveys recent advances in multimodal locomotion within soft robotics. Typical locomotion modes are summarized and categorized. Furthermore, the underlying mechanisms enabling multimodal locomotion are discussed and classified into three primary categories: active control‐based, reconfiguration‐based, and environment ...
Zihao Yuan, Huangwei Ji, Kai Huang, Feifei Chen, Guoying Gu   +4 more
wiley   +1 more source

Research of active disturbance rejection controller of direct torque control system of permanent magnet synchronous motor based on parameter optimizatio

Gong-kuang zidonghua, 2013
In view of problems of big torque fluctuation and easy to be influenced by load change existed in direct torque control system of permanent magnet synchronous motor based on PI controller, the paper designed an active disturbance rejection controller ...
YANG Jie, HUANG Ku
doaj