Digital twins for post-earthquake damage assessment of free-standing buildings including damage information

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Most of existing buildings are made of concrete, new buildings are being built with it and others are demolished when still in good structural condition. This drives increased demands for raw materials, greenhouse gas emissions, and an accumulation of demo ...

Purpose: In this work, we present a new machine learning method based on the transformer neural network to detect eye rubbing using a smartwatch in a real-life setting. In ophthalmology, the accurate detection and prevention of eye rubbing could reduce inc ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Current post-earthquake damage assessment methodologies are not only time-consuming but also subjective in nature and difficult to document. Recent advancements in artificial intelligence and technological devices make it possible to accomplish this task a ...

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved mod ...

Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional n ...

This dataset contains images (TIFF image data) of brain juvenile rats Wistar Han (P14) scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F5 ...

Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been most ...

Fiber reinforced polymer confined-recycled aggregate concrete filled steel tube (FRP-RACFST) not only proposes excellent load-bearing capacity and corrosion resistance, but is also an environmentally friendly structure due to the incorporation of recycled ...

Sustainable development has emerged as a paramount consideration in various fields of industry, including construction, to preserve the environment and its finite resources. Lightweight structures, such as fiber-polymer composite structures, address both s ...

Digital twins for post-earthquake damage assessment of free-standing buildings including damage information

Chattez avec Graph Search

Low-tech methods for the reuse of reinforced concrete structural elements

Eye-Rubbing Detection Using a Smartwatch: A Feasibility Study Demonstrated High Accuracy With Machine Learning

Aggregating Spatial and Photometric Context for Photometric Stereo

Automated post-earthquake damage assessment of stone masonry buildings integrating machine learning, computer vision, and physics-based modeling

Prediction rigidities for data-driven chemistry

Text as a Richer Source of Supervision in Semantic Segmentation Tasks

Data-driven models in fusion exhaust: AI methods and perspectives

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

Nissl_3, Raw images for Machine learning for histological annotation and quantification of cortical layers

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

Intelligent prediction framework for axial compressive capacity of FRP-RACFST columns

Energy dissipation in fiber-polymer composites adhesive joints under cyclic loading