AIMC Topic: Algorithms

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Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution neural network.

Magnetic resonance in medicine
PURPOSE: To develop a deep learning-based method, dubbed Denoising CEST Network (DECENT), to fully exploit the spatiotemporal correlation prior to CEST image denoising.

Joint Fusion and Detection via Deep Learning in UAV-Borne Multispectral Sensing of Scatterable Landmine.

Sensors (Basel, Switzerland)
Compared with traditional mine detection methods, UAV-based measures are more suitable for the rapid detection of large areas of scatterable landmines, and a multispectral fusion strategy based on a deep learning model is proposed to facilitate mine ...

FCAN-XGBoost: A Novel Hybrid Model for EEG Emotion Recognition.

Sensors (Basel, Switzerland)
In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, existing methods often overlook the computational cost of EEG emotion recognition, and there is still ro...

Stacked ensemble machine learning for porosity and absolute permeability prediction of carbonate rock plugs.

Scientific reports
This study employs a stacked ensemble machine learning approach to predict carbonate rocks' porosity and absolute permeability with various pore-throat distributions and heterogeneity. Our dataset consists of 2D slices from 3D micro-CT images of four...

Research on the establishment of NDVI long-term data set based on a novel method.

Scientific reports
This study compares the relationship between different NDVI (Normalized Difference Vegetation Index), the NDVI of AVHRR (Advanced Very High Resolution Radiometer) (NDVIa), the NDVI of MODIS (Moderate Resolution Imaging Spectrometer) (NDVIm), and the ...

An investigation into the risk of population bias in deep learning autocontouring.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: To date, data used in the development of Deep Learning-based automatic contouring (DLC) algorithms have been largely sourced from single geographic populations. This study aimed to evaluate the risk of population-based bias by...

Observer-based state estimation for discrete-time semi-Markovian jump neural networks with round-robin protocol against cyber attacks.

Neural networks : the official journal of the International Neural Network Society
This paper investigates an observer-based state estimation issue for discrete-time semi-Markovian jump neural networks with Round-Robin protocol and cyber attacks. In order to avoid the network congestion and save the communication resources, the Rou...

Cooperative Game-Based Approximate Optimal Control of Modular Robot Manipulators for Human-Robot Collaboration.

IEEE transactions on cybernetics
Major challenges of controlling human-robot collaboration (HRC)-oriented modular robot manipulators (MRMs) include the estimation of human motion intention while cooperating with a robot and performance optimization. This article proposes a cooperati...

Novel LKF Method on H Synchronization of Switched Time-Delay Systems.

IEEE transactions on cybernetics
This article investigates H global asymptotic synchronization (GAS) of switched nonlinear systems with delay. By introducing mode-dependent double event-triggering mechanisms (DETMs), the communication resources in both system-controller (S-C) channe...

Nonfragile Output Feedback Tracking Control for Markov Jump Fuzzy Systems Based on Integral Reinforcement Learning Scheme.

IEEE transactions on cybernetics
In this article, a novel integral reinforcement learning (RL)-based nonfragile output feedback tracking control algorithm is proposed for uncertain Markov jump nonlinear systems presented by the Takagi-Sugeno fuzzy model. The problem of nonfragile co...