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Probabilistic Damage Detection of a Steel Truss Bridge Model by Optimally Designed Bayesian Neural Network [PDF]
Sensors, 2018 Excellent pattern matching capability makes artificial neural networks (ANNs) a very promising approach for vibration-based structural health monitoring (SHM). The proper design of the network architecture with the suitable complexity is vital to the ANN-
Tao Yin, Hong-ping Zhu
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Frontiers in Energy Research, 2022 Knowledge-driven and data-driven methods are the two representative categories of intelligent technologies used in fault diagnosis in nuclear power plants.
Ben Qi +3 more
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Stochastic Control for Bayesian Neural Network Training [PDF]
Entropy, 2022 In this paper, we propose to leverage the Bayesian uncertainty information encoded in parameter distributions to inform the learning procedure for Bayesian models.
Ludwig Winkler +2 more
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Bayesian Deep Neural Network to Compensate for Current Transformer Saturation
IEEE Access, 2021 Current transformer saturation has a negative effect on the operation of IEDs, resulting in their malfunction. Here, we present a technique to compensate for saturated waveforms using Bayesian Deep Neural Network (BDNN) comprising Deep Neural Network ...
Sopheap Key +3 more
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Energy financial risk early warning model based on Bayesian network
Energy Reports, 2023 Oil is a global, non-renewable energy source, which plays a pivotal role in the development of the global economy and the strategic reserve system. With the expansion of crude oil futures trading scale, crude oil is no longer a pure energy commodity, but
Lin Wei, Hanyue Yu, Bin Li
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Доклады Белорусского государственного университета информатики и радиоэлектроники, 2021 At present, neural networks are increasingly used to solve many problems instead of traditional methods for solving them. This involves comparing the neural network and the traditional method for specific tasks.
V. S. Mukha
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An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing
Remote Sensing, 2021 In this paper, we present neural network methods for predicting uncertainty in atmospheric remote sensing. These include methods for solving the direct and the inverse problem in a Bayesian framework. In the first case, a method based on a neural network
Adrian Doicu +4 more
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Bayesian regularized NAR neural network based short-term prediction method of water consumption [PDF]
E3S Web of Conferences, 2019 With the continuous construction of urban water supply infrastructure, it is extremely urgent to change the management mode of water supply from traditional manual experience to modern and efficient means. The water consumption forecast is the premise of
Liu Jianyu, Zhao Linxue, Mao Yanlong
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Thermal Error Model of Linear Motor Feed System Based on Bayesian Neural Network
IEEE Access, 2021 The linear motor feed system has been in service in complex working conditions for a long time, thus causing the nonuniform distribution of the temperature field distribution.
Shengsen Liu +4 more
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Micromachines, 2023 Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and other fields, and signal integrity (SI) determines their performance.
Guangbao Shan +5 more
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