The synthesis of new metal materials, including the ones that are obtained by 3D-printing methods, based on nonlinear dynamics and artificial intelligence approaches

2021 ◽  
pp. 67-71
Author(s):  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Acoustic Emission ◽  
3D Printing ◽  
Intelligent System ◽  
Neural Network Modeling ◽  
Metallic Materials ◽  
Fractal Characteristics ◽  
Metal Materials ◽  
Trained Neural Network

An intelligent system for forecasting the properties of metallic materials is developed. On the basis of a specially trained neural network, a model of a dynamic system for the synthesis of new materials, including by 3D printing, is obtained, which is capable of forecasting time series similar to the evolution of fractal characteristics of acoustic emission emitted during crystallization. Keywords: material, neural network, modeling, acoustic emission, forecasting, time series, fractal dimension, synthesis. [email protected]

Neural Network Modeling of the Fatigue Strength of Metal Materials at Low Temperatures

2021 ◽  
Vol 1037 ◽  
pp. 655-660
Author(s):  
Yury G. Kabaldin ◽  
Alexander A. Khlybov ◽  
Maksim S. Anosov ◽  
Dmitry A. Ryabov ◽  
Andrey V. Kiselev
Keyword(s):  
Neural Network ◽  
Fatigue Strength ◽  
Low Temperatures ◽  
Intelligent System ◽  
Fatigue Cracks ◽  
Neural Network Modeling ◽  
Metallic Materials ◽  
Test Conditions ◽  
Metal Materials ◽  
Cycles To Failure

An intelligent system has been developed to predict the fatigue strength of metallic materials over a wide temperature range. A neural network, properly trained, is a model of a dynamic system of fatigue failure of a part and is able to predict the values of the number of loading cycles to failure, as well as the onset of formation and growth rate of fatigue cracks for various test conditions, including at low temperatures.

Prediction of the fatigue strength of metals at low temperatures based on artificial intelligence

2021 ◽  
pp. 11-14
Author(s):  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Fatigue Strength ◽  
Low Temperatures ◽  
Intelligent System ◽  
Fatigue Cracks ◽  
Test Conditions ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Trained Neural Network

An intelligent system for predicting the fatigue strength of metals in a wide temperature range is developed using a specially trained neural network. The system makes it possible to predict the number of load cycles of a part to failure, as well as the start of formation and growth rate of fatigue cracks for different test conditions, including at low temperatures. Keywords: neural network, prediction of loading cycles, low temperatures, fatigue strength. [email protected]

scholarly journals Artificial Neural Network for Daily Low Stream Flow Rate Prediction of Perigiali Stream, Kavala City, NE Greece

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 578
Author(s):  
Thomas Papalaskaris ◽  
Theologos Panagiotidis
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Stream Flow ◽  
Low Flow ◽  
Urban Stream ◽  
Neural Network Modeling ◽  
Rate Data ◽  
Artificial Neural

Only a few scientific research studies with reference to extremely low stream flow conditions, have been conducted in Greece, so far. Forecasting future low stream flow rate values is a crucial and desicive task when conducting drought and watershed management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow indices, separating groundwater base flow and storm flow of storm hydrographs etc. Artificial Neural Network modeling simulation method generates artificial time series of simulated values of a random (hydrological in this specific case) variable. The present study produces artificial low stream flow time series of both a part of the past year (2016) as well as the present year (2017) considering the stream flow data observed during two different respecting interval period of the years 2016 and 2017. We compiled an Artificial Neural Network to simulate low stream flow rate data, acquired at a certain location of the partly regulated semi-urban stream which runs through the eastern exit of Kavala city, NE Greece, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The observed data were plotted against the predicted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the ANN model simulation procedure performance. Finally, we plot the recorded against the simulated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metrics and calculate the derived model statistics featuring the comparison between the recorded and the forecasted low stream flow rate data.

Research on Intelligent Forecasting of Financial Market Parameter Time Series Based on Deep Learning

2021 ◽  
Vol 7 (5) ◽  
pp. 4596-4607
Author(s):  
Enyang Zhu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Financial Markets ◽  
Intelligent System ◽  
System Modeling ◽  
Layer By Layer ◽  
Healthy Development ◽  
The Neural Network ◽  
Deep Integration

Objectives: Deep learning has become the most representative and potential intelligent system modeling technology in artificial intelligence. However, the complexity of financial markets goes far beyond all economic games. Methods: This paper is devoted to the feasibility and efficiency of the deep-integration neural network model as one of the main paradigms of in-depth learning in the intelligent prediction of financial time. A prediction model of stack self-coding neural network composed of bottom stack self-coding and top regression neurons is proposed. Results: Firstly, the self-encoder unsupervised learning mechanism is used to identify and learn the time series, and the layers of the neural network are learned greedy layer by layer. Then the stack self-encoder is extended to the SAEP model with supervised mechanism, and the parameters learned by SAE are used. Used to initialize the neural network, and finally use the supervised learning to fine-tune the weights. Conclusion: The research results show that the model provides effective financial planning and decision-making basis for financial forecasting, maintains the healthy development of financial markets, and maximizes the benefits of profit-making institutions.

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