AIMC Topic: Computer Simulation

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Meta-learning based blind image super-resolution approach to different degradations.

Neural networks : the official journal of the International Neural Network Society
Although recent studies on blind single image super-resolution (SISR) have achieved significant success, most of them typically require supervised training on synthetic low resolution (LR)-high resolution (HR) paired images. This leads to re-training...

Containment control for fractional-order networked system with intermittent sampled position communication.

Neural networks : the official journal of the International Neural Network Society
This paper investigates containment control for fractional-order networked systems. Two novel intermittent sampled position communication protocols, where controllers only need to keep working during communication width of every sampling period under...

Data-driven learning of structure augments quantitative prediction of biological responses.

PLoS computational biology
Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experimen...

Machine-Learning Assisted Screening of Correlated Covariates: Application to Clinical Data of Desipramine.

The AAPS journal
Stepwise covariate modeling (SCM) has a high computational burden and can select the wrong covariates. Machine learning (ML) has been proposed as a screening tool to improve the efficiency of covariate selection, but little is known about how to appl...

Deep-learning-based motion correction using multichannel MRI data: a study using simulated artifacts in the fastMRI dataset.

NMR in biomedicine
Deep learning presents a generalizable solution for motion correction requiring no pulse sequence modifications or additional hardware, but previous networks have all been applied to coil-combined data. Multichannel MRI data provide a degree of spati...

Reinforcement learning-guided control strategies for CAR T-cell activation and expansion.

Biotechnology and bioengineering
Reinforcement learning (RL), a subset of machine learning (ML), could optimize and control biomanufacturing processes, such as improved production of therapeutic cells. Here, the process of CAR T-cell activation by antigen-presenting beads and their ...

Adaptive sampling artificial-actual control for non-zero-sum games of constrained systems.

Neural networks : the official journal of the International Neural Network Society
Considering physical constraints encountered by actuators, this paper addresses the non-zero-sum game of continuous nonlinear systems with symmetric and asymmetric input constraints through aperiodic sampling artificial-actual control. Initially, the...

Heterogeneous coexisting attractors, large-scale amplitude control and finite-time synchronization of central cyclic memristive neural networks.

Neural networks : the official journal of the International Neural Network Society
Memristors are of great theoretical and practical significance for chaotic dynamics research of brain-like neural networks due to their excellent physical properties such as brain synapse-like memorability and nonlinearity, especially crucial for the...

Real-time driving risk prediction using a self-attention-based bidirectional long short-term memory network based on multi-source data.

Accident; analysis and prevention
Early warning of driving risks can effectively prevent collisions. However, numerous studies that predicted driving risks have suffered from the use of single data sources, insufficiently advanced models, and lack of time window analysis. To address ...

Fast synchronization control and application for encryption-decryption of coupled neural networks with intermittent random disturbance.

Neural networks : the official journal of the International Neural Network Society
In this paper, we design a new class of coupled neural networks with stochastically intermittent disturbances, in which the perturbation mechanism is different from other existed random neural networks. It is significant to construct the new models, ...