Semiconductor Engineering

Google Details Five Generations Of TPU Training Supercomputers

Researchers from Google and University of California, Berkeley published a technical paper titled “Google’s Training ...
GitHub

Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural Networks

This paper is dedicated to the memory of my grandma. This is probably the only paper in my life that was written in a hospital the entire time. This is also at the same time the very first work in my ...
Scientific Research Publishing

Research on Structural Damage Identification Method Based on Convolutional Neural Network ()

1 College of Finance and Commerce, Guangzhou Railway Polytechnic, Guangzhou, China. 2 School of Intelligent Construction and Civil Engineering, Zhongyuan University of Technology, Zhengzhou, China. 3 ...
IEEE

Artificial Neural Network-Based Fault Detection and Classification for Enhanced Reliability in Electrical Power Transmission Networks

Abstract: The electrical power transmission networks (EPTNs), playing major role to transmit electrical power from location to remote location. The faults in the EPTN may degrade the performance of ...
IEEE

Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods

Abstract: Electric systems are getting more complex with time, and primitive protection methods such as traveling wave and impedance-based methods face limitations and shortcomings. This paper ...
Scientific Research Publishing

Leveraging Pima Dataset to Diabetes Prediction: Case Study of Deep Neural Network ()

Laboratory of Data Science and Digital Inclusion, Institut de Mathématiques et de Sciences Physiques, Université d’Abomey-Calavi, Abomey-Calavi, Bénin. Diabetes is a chronic disease. In 2019, it was ...
GitHub

Crack detection using one-class SVM

This demo shows how to detect the crack images using one-class SVM. In anomaly detection, normal images can be obtained a lot, while the anomaly images are not frequenctly obtained; we cannot get ...
acm.org

Deep Learning for AI

Yoshua Bengio, Yann LeCun, and Geoffrey Hinton are recipients of the 2018 ACM A.M. Turing Award for breakthroughs that have made deep neural networks a critical component of computing. Research on ...
Frontiers

Subject-Independent Functional Near-Infrared Spectroscopy-Based Brain–Computer Interfaces Based on Convolutional Neural Networks

Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in the field of brain–computer interfaces (BCIs) owing to their advantages such as non-invasiveness, user safety, ...
PNAS

A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping

Grasping objects is something primates do effortlessly, but how does our brain coordinate such a complex task? Multiple brain areas across the parietal and frontal cortices of macaque monkeys are ...
PNAS

Learning probabilistic neural representations with randomly connected circuits

We present a theory of neural circuits’ design and function, inspired by the random connectivity of real neural circuits and the mathematical power of random projections. Specifically, we introduce a ...