Results 181 to 190 of about 1,901,812 (345)

Data‐driven forecasting of ship motions in waves using machine learning and dynamic mode decomposition

International Journal of Adaptive Control and Signal Processing, EarlyView.
Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez, Mauro Gaggero, Andrea Serani   +2 more
wiley   +1 more source

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem. [PDF]

PeerJ Comput Sci, 2021
Escamilla Serna NJ, Seck-Tuoh-Mora JC, Medina-Marin J, Hernandez-Romero N, Barragan-Vite I, Corona Armenta JR.   +5 more
europepmc   +1 more source

Programación de operaciones en el taller de trabajo utilizando la meta - heurística de recocido simulado

Revista UIS Ingenierías, 2004
Se desarrolla una aplicación de la meta-heurística de recocido simulado a la programación enel taller de trabaja para dos configuracionesjlow-shop y job-shop. El desarrollo de los algoritmosse hace en el lenguaje de programación Java versión 1.3.
Gustavo Alfredo Bula
doaj  

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source

A Novel Approach to the Job Shop Scheduling Problem Based on the Deep Q-Network in a Cooperative Multi-Access Edge Computing Ecosystem. [PDF]

Sensors (Basel), 2021
Moon J, Yang M, Jeong J.
europepmc   +1 more source

Decentralized Job Shop Scheduling by Recursive Propagation Method.

, 2000
Rei HINO, Toshimichi MORIWAKI
openalex   +2 more sources

Optimized Time–Frequency Analysis for Induction Motor Fault Detection Using Hybrid Differential Evolution and Deep Learning Techniques

International Journal of Adaptive Control and Signal Processing, EarlyView.
Workflow of the parameter optimization process for ITSC fault detection, applying Differential Evolution optimization and the Smooth Pseudo Wigner‐Ville Distribution for signal processing. The optimized parameters are then used in the failure identification pipeline, which combines the signal processing with a YOLO‐based architecture for fault severity
Rafael Martini Silva, Avyner Lorran de Oliveira Vitor, Marcelo Favoretto Castoldi, Alessandro Goedtel, Wesley Angelino Souza   +4 more
wiley   +1 more source

Offline reinforcement learning for learning to dispatch for job shop scheduling. [PDF]

Mach Learn
Remmerden JV, Bukhsh Z, Zhang Y.
europepmc   +1 more source

Decentralized Job Shop Scheduling by Recursive Propagation Method. 2nd Report. Scheduling for Hierarchical Systems.

, 2000
Rei HINO, Kouichi IZUHARA, Toshimichi MORIWAKI   +2 more
openalex   +2 more sources

Microphysiological Glomerular Filtration Barriers: Current Insights, Innovations, and Future Applications

Advanced Biology, EarlyView.
The glomerular filtration barrier (GFB) is the first step of blood filtration by the kidneys. The concerning increase of kidney diseases makes the development of new models essential. In this context, microphysiological glomerular filtration barriers focus on closely reproducing the physiological architecture of the in vivo GFB: podocytes, glomerular ...
Manon Miran, Kieu Ngo, David Buob, Hanna Debiec, Pierre Ronco, Guillaume Perry   +5 more
wiley   +1 more source