scholarly journals Artificial neural networks for spatial distribution of fuel assemblies in reload of PWR reactors

2019 ◽  
Vol 7 (2B) ◽  
Author(s):  
Edyene Oliveira ◽  
Victor Castro ◽  
Carlos Velasquez ◽  
Claubia Pereira
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spatial Distribution ◽  
Nuclear Reactor ◽  
Average Power ◽  
Reactor Core ◽  
Fuel Assemblies ◽  
Power Peak ◽  
Artificial Neural ◽  
Better Than

An artificial neural network methodology is being developed in order to find an optimum spatial distribution of the fuel assemblies in a nuclear reactor core during reloading. The main bounding parameter of the modeling was the neutron multiplication factor, keff. The characteristics of the network are defined by the nuclear parameters: cycle, burnup, enrichment, fuel type, and average power peak of each element. As for the artificial neural network, the ANN Feedforward Multi_Layer_Perceptron with various layers and neurons were constructed. Three algorithms were used and tested: LM (Levenberg-Marquardt), SCG (Scaled Conjugate Gradient) and BayR (Bayesian Regularization). The artificial neural network has implemented using MATLAB 2015a version. As preliminary results, the spatial distribution of the fuel assemblies in the core using a neural network was slightly better than the standard core.

Artificial neural network-based seismic detector

1995 ◽  
Vol 85 (1) ◽  
pp. 308-319 ◽  
Author(s):  
Jin Wang ◽  
Ta-Liang Teng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Input Data ◽  
Seismic Event ◽  
Signal To Noise Ratio ◽  
Type B ◽  
Earthquake Detection ◽  
Seismic Event Detection ◽  
Artificial Neural ◽  
Better Than

Abstract An artificial neural network-based pattern classification system is applied to seismic event detection. We have designed two types of Artificial Neural Detector (AND) for real-time earthquake detection. Type A artificial neural detector (AND-A) uses the recursive STA/LTA time series as input data, and type B (AND-B) uses moving window spectrograms as input data to detect earthquake signals. The two AND's are trained under supervised learning by using a set of seismic recordings, and then the trained AND's are applied to another set of recordings for testing. Results show that the accuracy of the artificial neural network-based seismic detectors is better than that of the conventional algorithms solely based on the STA/LTA threshold. This is especially true for signals with either low signal-to-noise ratio or spikelike noises.

Use of an Artificial Neural Network for Data Analysis in Clinical Decision-Making: The Diagnosis of Acute Coronary Occlusion

1990 ◽  
Vol 2 (4) ◽  
pp. 480-489 ◽  
Author(s):  
William G. Baxt
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Artificial Neural Network ◽  
Coronary Occlusion ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Acute Coronary Occlusion ◽  
Artificial Neural ◽  
Better Than

A nonlinear artificial neural network trained by backpropagation was applied to the diagnosis of acute myocardial infarction (coronary occlusion) in patients presenting to the emergency department with acute anterior chest pain. Three-hundred and fifty-six patients were retrospectively studied, of which 236 did not have acute myocardial infarction and 120 did have infarction. The network was trained on a randomly chosen set of half of the patients who had not sustained acute myocardial infarction and half of the patients who had sustained infarction. It was then tested on a set consisting of the remaining patients to which it had not been exposed. The network correctly identified 92% of the patients with acute myocardial infarction and 96% of the patients without infarction. When all patients with the electrocardiographic evidence of infarction were removed from the cohort, the network correctly identified 80% of the patients with infarction. This is substantially better than the performance reported for either physicians or any other analytical approach.

scholarly journals The utilization of a GR4J model and wavelet-based artificial neural network for rainfall–runoff modelling

2018 ◽  
Vol 19 (5) ◽  
pp. 1295-1304
Author(s):  
C. Sezen ◽  
T. Partal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Potential Evapotranspiration ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Ann Model ◽  
The Usa ◽  
Streamflow Data ◽  
Artificial Neural ◽  
Better Than

Abstract Data-driven models and conceptual models have been utilized in an attempt to perform rainfall–runoff modelling. The aim of this study is comparing the performance of an artificial neural network (ANN) model, wavelet-based artificial neural network (WANN) model and GR4J lumped daily conceptual model for rainfall–runoff modelling of two rivers in the USA. It was obtained that the performance of the data-driven models (ANN, WANN) is better than the GR4J model especially when streamflow data the preceding day (Qt-1) and streamflow data the preceding two days (Qt-2) are used as input data in the ANN and WANN models for the simulation of low and high flows, in particular. On the other hand, when only precipitation and potential evapotranspiration data are used as input variables, the GR4J model performs better than the data-driven models.

Embedding Parameters Based Distinguish between Normal and Epileptic EEG Using Artificial Neural Network

2010 ◽  
Vol 20-23 ◽  
pp. 588-593
Author(s):  
Ye Yuan
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Delay Time ◽  
Probabilistic Neural Network ◽  
Embedding Dimension ◽  
Artificial Neural ◽  
Two Parameters ◽  
Electroencephalogram Eeg ◽  
Better Than

The embedding parameters of electroencephalogram (EEG) time series, i.e., the embedding dimension and delay time, are used together as the input features of artificial neural network for distinguishing between normal and epileptic EEG time series. Cao’s method and mutual information method are applied for computing the embedding dimension and delay time of normal and epileptic EEG time series, respectively. The probabilistic neural network (PNN) is used in this paper for distinguishing between normal and epileptic EEG time series. The results of the simulation show that the overall accuracy as high as 100% can be achieved by using the method proposed in this paper, and that the accuracy obtained based on the both parameters is better than that obtained based on each of the two parameters respectively.

Auto Covariance Combined with Artificial Neural Network for Predicting Protein-Protein Interactions

2013 ◽  
Vol 765-767 ◽  
pp. 1622-1624
Author(s):  
Juan Juan Li ◽  
Yue Hui Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Protein Interactions ◽  
Biological Data ◽  
Protein Protein Interactions ◽  
Artificial Neural Network Classifier ◽  
Artificial Neural ◽  
Biological Methods ◽  
Auto Covariance ◽  
Better Than

Proteins play biological function through the interactions in organisms. Proteins are major components of organisms, and they are of great significance. As an increasing number of high-throughput biological experiments are carried out, a large amount of biological data is produced. Bioinformatics is developed to study the relative data which turns out to be difficult to study using biological methods. The paper mainly studies how to apply the intelligent calculation methods to protein-protein interactions (PPIs) prediction. We proposed an approach, by combining auto covariance with artificial neural network classifier, to predict PPIs. Experiments show that our method performs better than related works with a 5% higher accuracy.

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