This software is a research prototype, solely developed for and published as part of the publication MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot ...

1 Department of Bachelor’s Degrees, Faculty of Applied Sciences, Denis Sassou Nguesso University, Brazzaville, Republic of the Congo. 2 Faculty of Science, Laboratory of Mathematics and Computer ...

Researchers report a machine learning approach to predict LPBF defects from up-skin and down-skin angles, suggesting there might be angle-aware process control for metal AM. Laser Powder Bed Fusion ...

Researchers from Stony Brook University, in collaboration with Ecosuite and Ecogy Energy, have developed a self-supervised machine-learning algorithm designed to identify physical anomalies in solar ...

Researchers from Stony Brook University, in collaboration with Ecosuite and Ecogy Energy, have developed a self-supervised machine learning algorithm designed to identify physical anomalies in solar ...

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as ...

Abstract: This work proposes the use of machine learning-based techniques for enhanced testability and performance calibration of an industrial 79-GHz power amplifier (PA) designed for an automotive ...

This Scientific Reports Collection welcomes original research on Machine learning methods for crystalline defects. Narrative review articles are also welcomed, to our sister journal Scientific Reviews ...

During the reconstruction and expansion of expressways, defects at the roadbed junction can compromise driving safety and significantly reduce the service life of the road. Based on engineering cases, ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Understanding the electronic, structural, and dynamical properties of highly oriented ...