Academic Articles & Research Papers

FIR-SDE: fast image restoration via mean-reverting stochastic differential equation

Abstract In computer vision, zero-shot image restoration—a technique enabling degraded image restoration without large-scale paired training data—has emerged as a pivotal technique for scenarios where data is limited or paired training data...

Using different computing systems to solve the automatic cloud classification problem according to MODIS satellite data by probabilistic neural network

The relevance of the research is caused by the necessity to develop algorithms and software to classify the cloud types based on single-layer cloud on the satellite images received from MODIS spectral radiometer used in Terra and Aqua remot...

Retrieval of carbon dioxide vertical concentration profiles from satellite data using artificial neural networks

Abstract. In this paper, we derive vertical distributions of carbon dioxide atmospheric concentration from satellite data using a retrieval algorithm based on an artificial neural network (ANN) technique. Sensitivity studies were made to se...

Surrogate Modeling of the Electric Field in the End-Winding Region of Pumped-Storage Generator Stators Based on Deep Neural Networks

The end-winding insulation structure of stator windings in pumped-storage generator units is complex, with pronounced electric field concentration under out-of-phase conditions, making them critical concerns in insulation design and conditi...

CG-DINO: Improved Network for Small Object Detection in Uncrewed Aerial Vehicle Imagery Based on DINO

Object detection in uncrewed aerial vehicle (UAV) acquired images remains a challenging task due to extreme scale variation, dense object layouts, and complex backgrounds, etc. Although convolutional neural networks (CNNs), transformers, an...

Formation of Cluster‐Structured Metallic Filaments in Organic Memristors for Wearable Neuromorphic Systems with Bio‐Mimetic Synaptic Weight Distributions

Abstract With increasing demand for wearable electronics capable of computing huge data, flexible neuromorphic systems mimicking brain functions have been receiving much attention. Despite considerable efforts in developing practical neural...

How structure determines correlations in neuronal networks.

Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, ...

neuralnet: Training of Neural Networks

Artificial neural networks are applied in many situations.neuralnet is built to train multi-layer perceptrons in the context of regression analyses, i.e. to approximate functional relationships between covariates and response variables.Thus...

Artificial neural networks

Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, ...

Identifying urban villages using graph neural network and transfer learning in Southern Jiangsu, China

China’s rapid urbanization has led to the emergence of urban villages (UVs), whose governance is crucial for urban quality and built environment management. However, conventional identification methods rely on high-resolution remote sensing...

Understanding the role of autoencoders for stiff dynamical systems using information theory

Using information theory, this study provides insights into how the construction of latent space of autoencoder (AE) using deep neural network (DNN) training finds a smooth (non-stiff) low-dimensional manifold in the stiff dynamical system....

Controllable diffusion framework for imbalanced Phi OTDR events classification

Abstract The application of the $$\Phi$$ -OTDR (Phase-Optical Time Domain Reflectometry) system in real-time monitoring of power grid infrastructure has been proven effective in identifying and classifying various anomalies, such as digging...

Nucleosome Linker Length and Distribution as Major Players in Epigenetic Regulation: Insights from Cryo-Electron Microscopy and Modeling of Retina Cell Maturation.

A Simple Baseline for Bayesian Uncertainty in Deep Learning

We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning. Stochastic Weight Averaging (SWA), which computes the first moment of stochastic gradient desc...

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estim...

Analysis of Customer Churn Classification for Sinarmas Syariah Lhokseumawe Insurance Services Using Deep Learning Tabnet and Explainable AI

Customer churn is a major challenge in the insurance industry because it directly affects customer retention, business sustainability, and company profitability. Early identification of customers at risk of churn is therefore essential for ...

Deep Learning Proactive Approach to Blackout Prevention in Smart Grids: An Early Warning System

Blackout events in smart grids can have significant impacts on individuals, communities and businesses, as they can disrupt the power supply and cause damage to the grid. In this paper, a new proactive approach to an early warning system fo...

High-fidelity single-frame computational super-resolution using signal-preserving denoising-enabled deconvolution

Abstract Computational super-resolution (SR) methods enable nanoscale imaging from single-frame wide-field or spinning-disk confocal images without hardware modifications, yet face limitations: statistical restoration suffers from noise and...

Deep Learning for PET Image Reconstruction

This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for the reconstruction of images in positron emission tomography (PET). Deep learning can be used either directly or as a component of conventio...

Development of Nuclear-thermal-mechanical Coupling Calculation Acceleration Method in Advanced Reactor Simulation Code FSAR

To meet the computational efficiency requirements for multi-physics simulations in the design and development of advanced reactor cores, a neural network-based acceleration method was investigated for multi-physics coupling calculations in ...