AI - Applications

Solar interfacial evaporation, as a novel practical freshwater production method, requires continuous research on how to improve the evaporation rates to increa

This study develops a method to predict and compensate for thermal deformation in machine tools, focusing on selecting temperature-sensitive points, e…

Upgrade looks to harness 'susbstantial' data from all-electronic toll collection systems

Cracks are the primary indicator informing the structural health of concrete structures. Frequent inspection is essential for maintenance, and automatic crack inspection offers a significant advantage, given its efficiency and accuracy. Previously, image-based crack detection systems have been utilized for individual images, yet these systems are not effective for large inspection areas. This paper thereby proposes an image-based crack detection system using a Deep Convolution Neural Network...

pure:unmask is designed to reduce frequency clashes between two tracks to create a clear, defined sound

The rapid expansion of AI-enabled Internet of Things (IoT) devices presents significant security challenges, impacting both privacy and organizational resources. The dynamic increase in big data generated by IoT devices poses a persistent problem, particularly in making decisions based on the continuously growing data. To address this challenge in a dynamic environment, this study introduces a specialized BERT-based Feed Forward Neural Network Framework (BEFNet) designed for IoT scenarios...

Room temperature semiconductor radiation detectors (RTSD) for X-ray and $$\gamma$$ -ray detection are vital tools for medical imaging, astrophysics and other applications. CdZnTe (CZT) has been the main RTSD for more than three decades with desired detection properties. In a typical pixelated configuration, CZT have electrodes on opposite ends. For advanced event reconstruction algorithms at sub-pixel level, detailed characterization of the RTSD is required in three dimensional (3D) space...

In Scientific Reports, Chinese researchers introduce a groundbreaking deep inverse convolutional neural network approach tailored for land cover remote sensing images. This novel method effectively addresses data imbalance, significantly improving classification accuracy and precision, with potential applications in urban planning, agriculture, and environmental monitoring.

This study identifies the feasibility of leveraging deep learning anomaly detection to identify event-driven traveling ionospheric disturbances (TIDs). DR. JIHYE PARK, FIONA LUHRMANN, DR. WENG-KEEN WONG, OREGON STATE UNIVERSITY Ionospheric responses to various geophysical events have been studied for decades, as they can play a key role in triggering events.

Individual muscle segmentation is the process of partitioning medical images into regions representing each muscle. It can be used to isolate spatially structured quantitative muscle characteristics, such as volume, geometry, and the level of fat infiltration. These features are pivotal to measuring the state of muscle functional health and in tracking the response of the body to musculoskeletal and neuromusculoskeletal disorders. The gold standard approach to perform muscle segmentation...

AI Accelerator is a type of hardware or software, designed for running AI algorithms and applications with high efficiency.

Repeatability of measurements from image analytics is difficult, due to the heterogeneity and complexity of cell samples, exact microscope stage positioning, and slide thickness. We present a method to define and use a reference focal plane that provides repeatable measurements with very high accuracy, by relying on control beads as reference material and a convolutional neural network focused on the control bead images. Previously we defined a reference effective focal plane (REFP) based on...