Prediction of Top-Coal Caving and Drawing Characteristics Using Artificial Neural Networks in Extremely Thick Coal Seam

2015 ◽  
Vol 743 ◽  
pp. 612-616 ◽  
Author(s):  
J.H. Yu ◽  
De Bing Mao
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coal Seam ◽  
Network Model ◽  
Mining Area ◽  
Software Training ◽  
Thick Coal Seam ◽  
Mining Depth ◽  
Training Samples ◽  
Artificial Neural

Based on the feature of large thickness and poor drawing characteristics in extremely thick coal seam top-coal caving method, combined with numerous practical examples analyses, the primarily six factors influence the drawing characteristics were found out which are mining depth, coal seam strength, joint crack development, parting thickness in top-coal, caving ratios, immediate roof filling coefficient. According to 45 typical top-coal caving in extremely thick coal seam samples, the prediction of top-coal caving and drawing characteristics based on artificial neural networks was established and training samples and testing samples was determined. Use SPSS statistical software training the network model. Then select No. 9 coal seam first mining area of Tiaohu mine as the application case. The drawing property was forecast according to the established network model. Application results show that the use of artificial neural networks for top-coal caving and drawing characteristic prediction is effective and feasible.

scholarly journals Design of Jetty Piles Using Artificial Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Yongjei Lee ◽  
Sungchil Lee ◽  
Hun-Kyun Bae
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stress Ratio ◽  
Fe Analysis ◽  
Design Time ◽  
Lateral Forces ◽  
Complex Modes ◽  
Training Samples ◽  
Parametric Studies ◽  
Artificial Neural

To overcome the complication of jetty pile design process, artificial neural networks (ANN) are adopted. To generate the training samples for training ANN, finite element (FE) analysis was performed 50 times for 50 different design cases. The trained ANN was verified with another FE analysis case and then used as a structural analyzer. The multilayer neural network (MBPNN) with two hidden layers was used for ANN. The framework of MBPNN was defined as the input with the lateral forces on the jetty structure and the type of piles and the output with the stress ratio of the piles. The results from the MBPNN agree well with those from FE analysis. Particularly for more complex modes with hundreds of different design cases, the MBPNN would possibly substitute parametric studies with FE analysis saving design time and cost.

Establish an Artificial Neural Networks Model to Make Quantitative Analysis about the Capillary Electrophoresis Spectrum

2012 ◽  
Vol 452-453 ◽  
pp. 1116-1120
Author(s):  
Hong Ping Li ◽  
Hong Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Capillary Electrophoresis ◽  
Artificial Neural Networks ◽  
Quantitative Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Network Training ◽  
Dissociation Degree ◽  
Artificial Neural

Simulating the overlapping capillary electrophoresis spectrogram under the dissimilar conditions by the computer system , Choosing the overlapping capillary electrophoresis spectrogram simulated under the different conditions , processing the data to compose a neural network training regulations, Applying the artificial neural networks method to make a quantitative analysis about the multi-component in the overlapping capillary electrophoresis spectrogram,Using: Radial direction primary function neural network model and multi-layered perceptron neural network model. The findings indicated that, along with the increasing of the capillary electrophoresis spectrogram noise level, the related components’ ability of the two kinds of the overlapping capillary electrophoresis spectrogram by neural network model quantitative analysis drop down. Along with the increasing of the capillary electrophoresis spectrogram’s total dissociation degree, the multi-layered perceptron neural network model to the related components’ ability of the overlapping capillary electrophoresis spectum by quantitative analysis raise up.

scholarly journals Integrating MNF and HHT Transformations into Artificial Neural Networks for Hyperspectral Image Classification

2020 ◽  
Vol 12 (14) ◽  
pp. 2327
Author(s):  
Ming-Der Yang ◽  
Kai-Hsiang Huang ◽  
Hui-Ping Tsai
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Critical Issue ◽  
Training Sample ◽  
Ann Model ◽  
Hilbert Huang Transform ◽  
Training Samples ◽  
Artificial Neural

The critical issue facing hyperspectral image (HSI) classification is the imbalance between dimensionality and the number of available training samples. This study attempted to solve the issue by proposing an integrating method using minimum noise fractions (MNF) and Hilbert–Huang transform (HHT) transformations into artificial neural networks (ANNs) for HSI classification tasks. MNF and HHT function as a feature extractor and image decomposer, respectively, to minimize influences of noises and dimensionality and to maximize training sample efficiency. Experimental results using two benchmark datasets, Indian Pine (IP) and Pavia University (PaviaU) hyperspectral images, are presented. With the intention of optimizing the number of essential neurons and training samples in the ANN, 1 to 1000 neurons and four proportions of training sample were tested, and the associated classification accuracies were evaluated. For the IP dataset, the results showed a remarkable classification accuracy of 99.81% with a 30% training sample from the MNF1–14+HHT-transformed image set using 500 neurons. Additionally, a high accuracy of 97.62% using only a 5% training sample was achieved for the MNF1–14+HHT-transformed images. For the PaviaU dataset, the highest classification accuracy was 98.70% with a 30% training sample from the MNF1–14+HHT-transformed image using 800 neurons. In general, the accuracy increased as the neurons increased, and as the training samples increased. However, the accuracy improvement curve became relatively flat when more than 200 neurons were used, which revealed that using more discriminative information from transformed images can reduce the number of neurons needed to adequately describe the data as well as reducing the complexity of the ANN model. Overall, the proposed method opens new avenues in the use of MNF and HHT transformations for HSI classification with outstanding accuracy performance using an ANN.

Neural network modeling of the implosion process in the tasks of optimization of oil production process

2021 ◽  
Author(s):  
A.R. Mukhutdinov ◽  
Z.R. Vakhidova ◽  
M.G. Efimov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Network Modeling ◽  
Water Hammer ◽  
Design Parameters ◽  
Neural Network Modeling ◽  
Artificial Neural

An increase in the productivity of oil wells is possible with the use of a promising technology based on implosion and a device for its implementation. It is known that the effectiveness of the technology depends on the design parameters of the device. Currently, a promising way to study processes is computer modeling based on modern information technologies. Therefore, solving forecasting problems using modern software based on artificial neural networks (ANNs) is an urgent task of scientific and practical interest. In this regard, the aim of the work is to develop a neural network model and its application to identify the features of the influence of the diameter and length of the implosion chamber of the device on the pressure of a water hammer during implosion. In the software environment, the following have been created and tested: a method for developing a neural network model; a method of conducting a computational experiment with it. The possibility of neural network modeling of the implosion process has been studied. The results of predicting the output parameter, in this case the pressure of the water hammer, on a pre-trained network, with a relative error of 3.5%, using the knowledge base are demonstrated. The results of applying the methodology for solving forecasting problems using software based on artificial neural networks are presented. It was found that the diameter and length of the implosion chamber significantly affect the pressure of the water hammer. The practical significance of the work lies in the ability to determine the required values of the diameter and length of the implosion chamber of the device at a given level of water hammer pressure.

The Estimation of the Critical Flashover Voltage of Insulators Using the Computational Intelligence

2020 ◽  
Vol 13 (4) ◽  
pp. 550-556
Author(s):  
Louiza Dehyadegari ◽  
Somayeh Khajehasani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Regression Coefficient ◽  
Economic Feasibility ◽  
Flashover Voltage ◽  
Artificial Neural

Background: Electric insulation is generally a vital factor in both the technical and economic feasibility of complex power and electronic systems. Several researches focus on the behavior of insulators under polluted conditions. That they are mathematical and physical models of insulators, experiments and simulation programs. Also experiments on critical flashover voltage are timeconsuming and have more limitations such as high cost and need for especial equipment’s. Objective: This paper focused on optimized predicting of critical flashover voltage of Polluted insulators based on artificial intelligence. Methods: Fuzzy logic and artificial neural networks are used in order to have the best estimation of the critical flashover. Results: In this way the correlation index (regression coefficient) improved about 2% toward previous works with same experimental data sets. Additionally, with using the properties of nonlinear artificial neural networks we can have the perfect (R=100%) prediction of the critical flashover voltage on experimental dataset. Conclusion: In this paper two methods for the estimation of critical flashover voltage of polluted insulators using fuzzy logic and neural networks was presented. the regression coefficient R achieved by the optimal parameters is 98.4% while in previous work is 96.7%. In neural network model we have regression coefficient 100% and in previous neural network model it was 99%. our test set is the same as previous works and achieved from experiments. These results show that fuzzy proposed methods are powerful and useful tools lead to a more accurate, generalized and objective estimation of the critical flashover voltage.

scholarly journals Predicting Methane Concentration in Longwall Regions Using Artificial Neural Networks

2019 ◽  
Vol 16 (8) ◽  
pp. 1406 ◽  
Author(s):  
Tutak ◽  
Brodny
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Methane Concentration ◽  
Mining Industry ◽  
Mining Area ◽  
Work Safety ◽  
Mining Operations ◽  
Mining Hazards ◽  
Artificial Neural

Methane, which is released during mining exploitation, represents a serious threat to this process. This is because the gas may ignite or cause an explosion. Both of these phenomena are extremely dangerous. High levels of methane concentration in mine headings disrupt mining operations and cause the risk of fire or explosion. Therefore, it is necessary to monitor and predict its concentration in the areas of ongoing mining exploitation. The paper presents the results of tests performed to improve work safety. The article presents the methodology of using artificial neural networks for predicting methane concentration values in one mining area. The objective of the paper is to develop an effective method for forecasting methane concentration in the mining industry. The application of neural networks for this purpose represents one of the first attempts in this respect. The method developed makes use of direct methane concentration values measured by a system of sensors located in the exploitation area. The forecasting model was built on the basis of a Multilayer Perceptron (MLP) network. The corresponding calculations were performed using a three-layered network with non-linear activation functions. The results obtained in the form of methane concentration prediction demonstrated minor errors in relation to the recorded values of this concentration. This offers an opportunity for a broader application of intelligent systems for effective prediction of mining hazards.

NEURAL NETWORK MODEL DEVELOPMENT FOR DISEASE RATE MONITORING AND PREDICTION OF ANTIEPIDEMIC MEASURE EFFECTIVENESS

2020 ◽  
Vol 2020 (10) ◽  
pp. 42-50
Author(s):  
Nataliya Sukhanova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Model Development ◽  
Computing System ◽  
Disease Rate ◽  
Artificial Neural ◽  
Visualization Of Data

There is developed a neural network model for disease rate prediction and assessment of antiepidemic measure effectiveness. As basis of the development there were adopted the existing automated information systems which are used for monitoring and visualization of data on Moscow population disease rate. Under conditions of the emergence and propagation of new dangerous infectious and virus diseases the information processing must be carried out in real time, a prediction for future is required. It is necessary to create, update and adjust rapidly a set of anti-epidemic measures offered. The investigation purpose consists in the prediction of infection spreading and the assessment of anti-epidemic measures based on data on the population disease rate. There is offered a neural network model realized on the basis of the modular computing system and artificial neural networks. A modular computing system includes modules of different types connected between each other with a switch network. In the modular computing system there are included modules of artificial neural networks with the special switch structure. Switchboards allow connecting and disconnecting single modules and elements of neural networks. A neural network model changes dynamically its structure and adapted to a current epidemic situation.

scholarly journals Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Petr Maca ◽  
Pavel Pech
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Standardized Precipitation Index ◽  
Model Performance ◽  
Drought Indices ◽  
Artificial Neural

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

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