AIMC Topic: Computer Simulation

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Computational analysis of controlled drug release from porous polymeric carrier with the aid of Mass transfer and Artificial Intelligence modeling.

Scientific reports
Controlled release of a desired drug from porous polymeric biomaterials was analyzed via computational method. The method is based on simulation of mass transfer and utilization of artificial intelligence (AI). This study explores the efficacy of thr...

Simplified internal models in human control of complex objects.

PLoS computational biology
Humans are skillful at manipulating objects that possess nonlinear underactuated dynamics, such as clothes or containers filled with liquids. Several studies suggested that humans implement a predictive model-based strategy to control such objects. H...

Multi-compartment neuron and population encoding powered spiking neural network for deep distributional reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Inspired by the brain's information processing using binary spikes, spiking neural networks (SNNs) offer significant reductions in energy consumption and are more adept at incorporating multi-scale biological characteristics. In SNNs, spiking neurons...

Addressing statistical challenges in the analysis of proteomics data with extremely small sample size: a simulation study.

BMC genomics
BACKGROUND: One of the most promising approaches for early and more precise disease prediction and diagnosis is through the inclusion of proteomics data augmented with clinical data. Clinical proteomics data is often characterized by its high dimensi...

The influence of neighbor selection on self-organized UAV swarm based on finite perception vision.

Bioinspiration & biomimetics
Recently, vision-based unmanned aerial vehicle (UAV) swarming has emerged as a promising alternative that can overcome the adaptability and scalability limitations of distributed and communication-based UAV swarm systems. While most vision-based cont...

NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation.

Science robotics
To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, such spatial awareness involves estimating the object's pose and shape. T...

Developing physics-informed neural networks for model predictive control of periodic counter-current chromatography.

Journal of chromatography. A
The applications of continuous manufacturing technology in biopharmaceuticals require advanced design, monitoring, and control due to its complexity. Traditional mechanistic models, which rely on numerical solutions, suffer from long computational ti...

Dynamic modelling and predictive position/force control of a plant-inspired growing robot.

Bioinspiration & biomimetics
This paper presents the development and control of a dynamic model for a plant-inspired growing robot, termed the 'vine-robot', using the Euler-Lagrangian method. The unique growth mechanism of the vine-robot enables it to navigate complex environmen...

MRI denoising with a non-blind deep complex-valued convolutional neural network.

NMR in biomedicine
MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal...

Deep learning-based near-field effect correction method for Controlled Source Electromagnetic Method and application.

PloS one
Addressing the impact of near-field effects in the Controlled Source Electromagnetic Method(CSEM) has long been a focal point in the realm of geophysical exploration. Therefore, we propose a deep learning-based near-field correction method for contro...