Application of Artificial Neural Networks to Lowcycle Fatigue and Creep Data Processing for Power Plant Materials

2019 ◽  
Author(s):  
K. Kim ◽  
B. Kong ◽  
J. Suk
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Plant ◽  
Data Processing ◽  
Creep Data ◽  
Plant Materials ◽  
Artificial Neural

Estimation of the energy production of a parabolic trough solar thermal power plant using analytical and artificial neural networks models

2021 ◽  
Vol 170 ◽  
pp. 620-638
Author(s):  
Anass Zaaoumi ◽  
Abdellah Bah ◽  
Mihaela Ciocan ◽  
Patrick Sebastian ◽  
Mugur C. Balan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Plant ◽  
Thermal Power Plant ◽  
Energy Production ◽  
Thermal Power ◽  
Solar Thermal ◽  
Parabolic Trough ◽  
Solar Thermal Power ◽  
Artificial Neural

Application of artificial neural networks to nuclear power plant transient diagnosis

2007 ◽  
Vol 92 (10) ◽  
pp. 1468-1472 ◽  
Author(s):  
T.V. Santosh ◽  
Gopika Vinod ◽  
R.K. Saraf ◽  
A.K. Ghosh ◽  
H.S. Kushwaha
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Artificial Neural

Neuromorphic Functional Modules of Spiking Neural Network

2021 ◽  
Vol 23 (6) ◽  
pp. 317-326
Author(s):  
E.A. Ryndin ◽  
◽  
N.V. Andreeva ◽  
V.V. Luchinin ◽  
K.S. Goncharov ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Plasticity ◽  
Data Processing ◽  
Circuit Design ◽  
Spiking Neural Networks ◽  
Spiking Neural Network ◽  
Wide Range ◽  
Artificial Neural

In the current era, design and development of artificial neural networks exploiting the architecture of the human brain have evolved rapidly. Artificial neural networks effectively solve a wide range of common for artificial intelligence tasks involving data classification and recognition, prediction, forecasting and adaptive control of object behavior. Biologically inspired underlying principles of ANN operation have certain advantages over the conventional von Neumann architecture including unsupervised learning, architectural flexibility and adaptability to environmental change and high performance under significantly reduced power consumption due to heavy parallel and asynchronous data processing. In this paper, we present the circuit design of main functional blocks (neurons and synapses) intended for hardware implementation of a perceptron-based feedforward spiking neural network. As the third generation of artificial neural networks, spiking neural networks perform data processing utilizing spikes, which are discrete events (or functions) that take place at points in time. Neurons in spiking neural networks initiate precisely timing spikes and communicate with each other via spikes transmitted through synaptic connections or synapses with adaptable scalable weight. One of the prospective approach to emulate the synaptic behavior in hardware implemented spiking neural networks is to use non-volatile memory devices with analog conduction modulation (or memristive structures). Here we propose a circuit design for functional analogues of memristive structure to mimic a synaptic plasticity, pre- and postsynaptic neurons which could be used for developing circuit design of spiking neural network architectures with different training algorithms including spike-timing dependent plasticity learning rule. Two different circuits of electronic synapse were developed. The first one is an analog synapse with photoresistive optocoupler used to ensure the tunable conductivity for synaptic plasticity emulation. While the second one is a digital synapse, in which the synaptic weight is stored in a digital code with its direct conversion into conductivity (without digital-to-analog converter andphotoresistive optocoupler). The results of the prototyping of developed circuits for electronic analogues of synapses, pre- and postsynaptic neurons and the study of transient processes are presented. The developed approach could provide a basis for ASIC design of spiking neural networks based on CMOS (complementary metal oxide semiconductor) design technology.

Analysis of ethanol–glucose mixtures by two microbial sensors: application of chemometrics and artificial neural networks for data processing

2001 ◽  
Vol 16 (9-12) ◽  
pp. 1001-1007 ◽  
Author(s):  
Alexei V Lobanov ◽  
Ivan A Borisov ◽  
Sherald H Gordon ◽  
Richard V Greene ◽  
Timothy D Leathers ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Processing ◽  
Microbial Sensors ◽  
Artificial Neural

Enhanced interpretation of magnetic survey data from archaeological sites using artificial neural networks

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. H45-H53 ◽  
Author(s):  
David. J. Bescoby ◽  
Gavin C. Cawley ◽  
P. Neil Chroston
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Processing ◽  
Survey Data ◽  
Source Parameters ◽  
Magnetic Data ◽  
Burial Depth ◽  
Training Procedure ◽  
Wide Range ◽  
Artificial Neural

The use of magnetic surveys for archaeological prospecting is a well-established and versatile technique, and a wide range of data processing routines are often applied to further enhance acquired data or derive source parameters. Of particular interest in this respect is the application of artificial neural networks (ANNs) to predict source parameters such as the burial depths of detected features of interest. Within this study, ANNs based upon a multilayer perceptron architecture are used to perform the nonlinear mapping between buried wall features detected within the magnetic data and their corresponding burial depth for surveys in the ancient city of Butrint in southern Albania, achieving a greater level of information from the survey data. Suitable network training examples and test data were generated using forward models based upon ground-truth observations. The training procedure adopts a supervised learning routine that is optimized using a conjugate gradient method, while the learning algorithm also prunes network elements to prevent overregularization by reducing model complexity. Data processing was further enhanced by introducing rotational invariance using Zernike moments and by utilizing the combined output of a number, or committee, of networks. When applied to a section of survey data from Butrint, the ANN routine successfully predicted the burial depth of a number of detected wall features, with an rms error on the order of [Formula: see text], and provided a coherent map of the buried building foundations. The neural network approach offered advantages in terms of efficiency and flexibility over more conventional data-inversion techniques within the context of the study, giving fast solutions for large, complex data sets while having high noise tolerance.

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