Results 211 to 220 of about 393,474 (272)

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

Fixed-Time Active Disturbance Rejection Temperature-Pressure Decoupling Control for a High-Flow Air Intake System. [PDF]

Entropy (Basel)
Zhang L, Zhang H, Shi D, Dan Z, Wang X, Zhai C, Xiao G, Xu Z.   +7 more
europepmc   +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

Comparison of preprocessing techniques for effective cognitive analysis using electroencephalography (EEG). [PDF]

MethodsX
N K, V K.
europepmc   +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

Low Noise Feed-Through Compensation Circuit Design for Resonant MEMS Pressure Sensor. [PDF]

Micromachines (Basel)
Liao J, Li P, Miao J, Liang R, Gao Z, Zhuang X.   +5 more
europepmc   +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

Recent progress of nanomaterials for diagnosis and treatment of rejection in heart transplantation. [PDF]

Front Bioeng Biotechnol
Li G, Wang C, Ai X, Yang Y, Yu H, Wu P, Zhao W, Tian J, Jiang S.   +8 more
europepmc   +1 more source

Design Challenges to Expand the Functionality of Drones: Deformable Rotorcraft and Nature‐Inspired Flapping Drones

Advanced Intelligent Systems, EarlyView.
This review systematically analyzes deformable drones, including extendable, foldable, and tilting configurations, along with nature‐inspired flapping rotorcraft. By classifying deformation principles and structural mechanisms, design trade‐offs, functional capabilities, and future development potential are highlighted.
Ju‐Hee Lee, Sumin Jung, Jinwoo Jang, Yeji Han, Minchae Kang, Jihun Seong, Suyeon Seo, Eunsol Park, Haesol Kwak, Min‐Woo Han   +9 more
wiley   +1 more source

Quantum-enhanced artificial potential field method for robust drone swarm shape formation. [PDF]

Sci Rep
Almaameri I, Blázovics L.
europepmc   +1 more source