AIMC Topic: Diffusion

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Speckle Noise Removal Model Based on Diffusion Equation and Convolutional Neural Network.

Computational intelligence and neuroscience
The image denoising model based on convolutional neural network (CNN) can achieve a good denoising effect. However, its robustness is poor, and it is not suitable for direct noise removal tasks. Differently, the image denoising method based on the di...

Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks.

The journal of physical chemistry. B
Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixture...

Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66.

Journal of chemical theory and computation
Modeling of diffusion of adsorbates through porous materials with atomistic molecular dynamics (MD) can be a challenging task if the flexibility of the adsorbent needs to be included. This is because potentials need to be developed that accurately ac...

Practical Exponential Stability of Impulsive Stochastic Reaction-Diffusion Systems With Delays.

IEEE transactions on cybernetics
This article studies the practical exponential stability of impulsive stochastic reaction-diffusion systems (ISRDSs) with delays. First, a direct approach and the Lyapunov method are developed to investigate the p th moment practical exponential stab...

Change in task conditions leads to changes in intermittency in intermittent feedback control employed by CNS in control of human stance.

Biological cybernetics
Event-driven intermittent feedback control is a form of feedback control in which the corrective control action is only initiated intermittently when the variables of interest exceed certain threshold criteria. It has been reported in the literature ...

Probing Diffusive Dynamics of Natural Tubule Nanoclays with Machine Learning.

ACS nano
Reproducibility of the experimental results and object of study itself is one of the basic principles in science. But what if the object characterized by technologically important properties is natural and cannot be artificially reproduced one-to-one...

Exploring Artificial Neural Networks Efficiency in Tiny Wearable Devices for Human Activity Recognition.

Sensors (Basel, Switzerland)
The increasing diffusion of tiny wearable devices and, at the same time, the advent of machine learning techniques that can perform sophisticated inference, represent a valuable opportunity for the development of pervasive computing applications. Mor...

Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning Model.

Computational intelligence and neuroscience
Approximate computing is an upsurging technique to accelerate the process through less computational effort while keeping admissible accuracy of error-tolerant applications such as multimedia and deep learning. Inheritance properties of the deep lear...

Signed random walk diffusion for effective representation learning in signed graphs.

PloS one
How can we model node representations to accurately infer the signs of missing edges in a signed social graph? Signed social graphs have attracted considerable attention to model trust relationships between people. Various representation learning met...

DigGCN: Learning Compact Graph Convolutional Networks via Diffusion Aggregation.

IEEE transactions on cybernetics
Recent interests in graph neural networks (GNNs) have received increasing concerns due to their superior ability in the network embedding field. The GNNs typically follow a message passing scheme and represent nodes by aggregating features from neigh...