Results 51 to 60 of about 418,704 (219)

Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

Journal of Marine Science and Engineering, 2020
Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the ...
Dohan Oh, Julia Race, Selda Oterkus, Bonguk Koo   +3 more
doaj   +1 more source

A Review of Optical Neural Networks

Applied Sciences, 2022
With the continuous miniaturization of conventional integrated circuits, obstacles such as excessive cost, increased resistance to electronic motion, and increased energy consumption are gradually slowing down the development of electrical computing and ...
Danni Zhang, Zhongwei Tan
doaj   +1 more source

Identifying Quantum Phase Transitions using Artificial Neural Networks on Experimental Data

, 2018
Machine learning techniques such as artificial neural networks are currently revolutionizing many technological areas and have also proven successful in quantum physics applications.
Asteria, Luca, Becker, Christoph, Fläschner, Nick, Käming, Niklas, Rem, Benno S., Sengstock, Klaus, Tarnowski, Matthias, Weitenberg, Christof   +7 more
core   +1 more source

Fault diagnosis of high-speed train wheelset bearing based on a lightweight neural network

工程科学学报, 2021
Deep learning is gaining attention in the field of mechanical equipment fault diagnosis. With the help of deep learning techniques, deep neural networks (DNNs) have great potential for machinery fault diagnosis.
Fei-yue DENG, Hao DING, Hao-yang LÜ, Ru-jiang HAO, Yong-qiang LIU   +4 more
doaj   +1 more source

Detecting Adversarial Examples through Nonlinear Dimensionality Reduction [PDF]

, 2019
Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combining non-linear dimensionality reduction and density estimation techniques.
Bacciu, Davide, Biggio, Battista, Crecchi, Francesco   +2 more
core   +1 more source

Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review [PDF]

, 2022
Wen Jiang, Yihui Ren, Ying Liu, Jiaxu Leng   +3 more
openalex   +1 more source

MANAGEMENT OF ARTIFICIAL NEURAL NETWORKS FOR RECOGNITION MAPPING OF HIGH-DEFINITION IMAGES

Надежность и качество сложных систем, 2022
Background. Scientific article reveals the problem of analyzing, recognizing and managing highdefinition images with a minimum error due to the previous frame-by-frame recognition of a complex of lowdefinition images.
N.D. Koshelev, A. Alhatem, K.S. Novikov, A.D. Tsuprik, N.K. Yurkov   +4 more
doaj   +1 more source

ADVANCED NEURAL NETWORKS AND DEEP LEARNING TECHNIQUES IN FINANCIAL MARKET PREDICTION [PDF]

Analele Universităţii Constantin Brâncuşi din Târgu Jiu : Seria Economie
This study investigates the role of artificial neural networks (ANN) and deep learning (DL) in the financial sector, focusing on their theoretical applications.
ENE CEZAR CATALIN
doaj  

Practical recommendations for gradient-based training of deep architectures [PDF]

, 2012
Learning algorithms related to artificial neural networks and in particular for Deep Learning may seem to involve many bells and whistles, called hyper-parameters.
Bengio, Yoshua
core  

Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability

Nature Communications
Deep neural networks have revolutionized several domains, including autonomous driving, cancer detection, and drug design, and are the foundation for massive artificial intelligence models.
Zhenjia Chen, Zhenyuan Lin, Ji Yang, Cong Chen, Di Liu, Liuting Shan, Yuanyuan Hu, Tailiang Guo, Huipeng Chen   +8 more
doaj   +1 more source