AIMC Topic: Computer Simulation
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Efficient learning representation of noise-reduced foam effects with convolutional denoising networks.
PloS one
Oct 10, 2022
This study proposes a neural network framework for modeling the foam effects found in liquid simulation without noise. The position and advection of the foam particles are calculated using the existing screen projection method, and the noise problem ...
Designing all-pay auctions using deep learning and multi-agent simulation.
Scientific reports
Oct 8, 2022
We propose a multi-agent learning approach for designing crowdsourcing contests and All-Pay auctions. Prizes in contests incentivise contestants to expend effort on their entries, with different prize allocations resulting in different incentives and...
Prediction Model and Data Simulation of Sports Performance Based on the Artificial Intelligence Algorithm.
Computational intelligence and neuroscience
Oct 7, 2022
There is still a certain deviation between the current artificial intelligence technology and the traditional learning mode, which makes it unable to be effectively applied in teaching and learning. Therefore, an effective method needs to be proposed...
Trajectory Optimization in Terms of Energy and Performance of an Industrial Robot in the Manufacturing Industry.
Sensors (Basel, Switzerland)
Oct 5, 2022
Currently, the high demand for new products in the automotive sector requires large investments in factories. The automotive industry is characterized by high automatization, largely achieved by manipulator robots capable of multitasking. This work p...
Adaptive neural network control for uncertain dual switching nonlinear systems.
Scientific reports
Oct 5, 2022
Dual switching system is a special hybrid system that contains both deterministic and stochastic switching subsystems. Due to its complex switching mechanism, few studies have been conducted for dual switching systems, especially for systems with unc...
Reinforcement Learning Control of Robotic Knee With Human-in-the-Loop by Flexible Policy Iteration.
IEEE transactions on neural networks and learning systems
Oct 5, 2022
We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees, such as stability and optimality at system level. Existing approximate/adaptive dynamic...
Graph-Based Bayesian Optimization for Large-Scale Objective-Based Experimental Design.
IEEE transactions on neural networks and learning systems
Oct 5, 2022
Design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been d...
Spherical Formation Tracking Control of Nonlinear Second-Order Agents With Adaptive Neural Flow Estimate.
IEEE transactions on neural networks and learning systems
Oct 5, 2022
This article addresses the spherical formation tracking control problem of nonlinear second-order vehicles moving in flowfields under both undirected networks and directed, strongly connected networks. Different from the previous adaptive estimate of...
Adaptive Observation-Based Efficient Reinforcement Learning for Uncertain Systems.
IEEE transactions on neural networks and learning systems
Oct 5, 2022
This article develops an adaptive observation-based efficient reinforcement learning (RL) approach for systems with uncertain drift dynamics. A novel concurrent learning adaptive extended observer (CL-AEO) is first designed to jointly estimate the sy...
Data-Driven Designs of Fault Detection Systems via Neural Network-Aided Learning.
IEEE transactions on neural networks and learning systems
Oct 5, 2022
With the aid of neural networks, this article develops two data-driven designs of fault detection (FD) for dynamic systems. The first neural network is constructed for generating residual signals in the so-called finite impulse response (FIR) filter-...
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