Results 31 to 40 of about 262,721 (166)
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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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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Expressivity of Deep Neural Networks
, 2022 This review paper will appear as a book chapter in the book "Theory of Deep Learning" by Cambridge University ...
Ingo Gühring +2 more
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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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Prediction of Shear Strength of Ultra High Performance Reinforced Concrete Deep Beams without Stirrups by Neural Network [PDF]
Eurasian Journal of Science and Engineering, 2017 : Shear strength of ultra high performance reinforced concrete deep beams without stirrups predicted by neural network models. The neural network model based on 233 beams from literatures considering different parameters such as span to depth ratio ...
Sinan Abdulkhaleq Yaseen +2 more
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Intuitionistic Fuzzy Deep Neural Network
Mathematics, 2023 The concept of an intuitionistic fuzzy deep neural network (IFDNN) is introduced here as a demonstration of a combined use of artificial neural networks and intuitionistic fuzzy sets, aiming to benefit from the advantages of both methods.
Krassimir Atanassov +2 more
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Deep Neural Network or Dermatologist? [PDF]
, 2019 Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a
Kyle Young +4 more
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A deep learning-based method for predicting the low-cycle fatigue life of austenitic stainless steel
Materials Research Express, 2023 In modern engineering, predicting the fatigue life of materials is crucial for safety assessment. The relationship between fatigue life and its influencing factors is difficult to predict by traditional methods, and deep learning can achieve great power ...
Hongyan Duan +5 more
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Search for deep graph neural networks
Information Sciences, 2023 Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem. To further explore the benefits from structural diversity and depth of GNN architectures, we propose a GNN generation pipeline with a novel two-stage search space, which ...
Guosheng Feng +2 more
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