Results 51 to 60 of about 2,238,851 (367)
A new deep neural network for forecasting: Deep dendritic artificial neural network
Artificial Intelligence Review, 2023 Abstract Deep artificial neural networks have become a good alternative to classical forecasting methods in solving forecasting problems. Popular deep neural networks classically use additive aggregation functions in their cell structures.
Egrioglu, Erol, Bas, Eren
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Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning
Energies, 2022 As artificial intelligence technology has progressed, numerous businesses have used intelligent diagnostic technology. This study developed a deep LSTM neural network for a nuclear power plant to defect diagnostics.
Bing Liu +3 more
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, 2015 Proposed in the 1940s as a simplified model of the elementary computing unit in the human cortex, artificial neural networks (ANNs) have since been an active research area. Among the many evolutions of ANN, deep neural networks (DNNs) (Hinton, Osindero, and Teh 2006) stand out as a promising extension of the shallow ANN structure.
Rahul Khanna, Mariette Awad
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Comparisons of different deep learning-based methods on fault diagnosis for geared system
International Journal of Distributed Sensor Networks, 2019 The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system ...
Bing Han +3 more
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Tunnel Geology Prediction Using a Neural Network Based on Instrumented Drilling Test
Applied Sciences, 2020 Reliable geology prediction is of great importance in ensuring the stability and safety of tunnels and other underground engineering projects. This paper presents basic neural network and deep neural network models using a genetic algorithm (GA) to ...
Yuwei Fang +4 more
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Accurate prediction of protein structures and interactions using a 3-track neural network
Science, 2021 Deep learning takes on protein folding In 1972, Anfinsen won a Nobel prize for demonstrating a connection between a protein's amino acid sequence and its three-dimensional structure.
Baek M +31 more
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A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection [PDF]
European Conference on Computer Vision, 2016 A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection. The MS-CNN consists of a proposal sub-network and a detection sub-network.
Zhaowei Cai +3 more
semanticscholar +1 more source
Deep Sequential Neural Network
, 2014 Neural Networks sequentially build high-level features through their successive layers. We propose here a new neural network model where each layer is associated with a set of candidate mappings. When an input is processed, at each layer, one mapping among these candidates is selected according to a sequential decision process.
Denoyer, Ludovic, Gallinari, Patrick
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A research on underwater target recognition neural network for small samples
Xibei Gongye Daxue Xuebao, 2022 In the face of the challenges in the field of marine engineering applications in the new era, the goal of automation, high efficiency and accuracy can be achieved by using deep learning-based neural networks in hydroacoustic engineering.
WU Yanchen, WANG Yingmin
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Automatic diagnosis of the 12-lead ECG using a deep neural network [PDF]
Nature Communications, 2019 The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples.
Antônio H. Ribeiro +11 more
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