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.

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.

Intelligent Traffic Sign Classifiers

2011 ◽  
pp. 956-962
Author(s):  
Raúl Vicen Bueno ◽  
Elena Torijano Gordo ◽  
Antonio García González ◽  
Manuel Rosa Zurera ◽  
Roberto Gil Pita
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Main Part ◽  
Point Of View ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Artificial Neural ◽  
The Brain

The Artificial Neural Networks (ANNs) are based on the behavior of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between different kinds of traffic signs. Moreover, this ANN learning must be done for traffic signs that are not in perfect conditions. So, the learning must be robust against several problems like rotation, translation or even vandalism. In order to achieve this objective, an intelligent extraction of information from the images is done. This stage is very important because it improves the performance of the ANN in this task.

Intelligent Radar Detectors

2011 ◽  
pp. 933-939
Author(s):  
Raúl Vicen Bueno ◽  
Manuel Rosa Zurera ◽  
María Pilar Jarabo Amores ◽  
Roberto Gil Pita ◽  
David de la Mata Moya
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Main Part ◽  
Point Of View ◽  
Communications Systems ◽  
Reflected Signal ◽  
Artificial Neural ◽  
The Brain

The Artificial Neural Networks (ANNs) are based on the behaviour of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between the presence or not of a reflected signal called target in a Radar environment dominated by clutter. The clutter involves all the signals reflected from other objects in a Radar environment that are not the desired target. Moreover, the noise is considered in this environment because it always exists in all the communications systems we can work with.

Artificial Neural Networks for Prediction

2011 ◽  
pp. 54-58
Author(s):  
Rafael Marti
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Human Capabilities ◽  
Design And Implementation ◽  
Starting Point ◽  
Input Signals ◽  
Artificial Neural ◽  
The Brain

The design and implementation of intelligent systems with human capabilities is the starting point to design Artificial Neural Networks (ANNs). The original idea takes after neuroscience theory on how neurons in the human brain cooperate to learn from a set of input signals to produce an answer. Because the power of the brain comes from the number of neurons and the multiple connections between them, the basic idea is that connecting a large number of simple elements in a specific way can form an intelligent system.

scholarly journals Information technologies for the modernization of the grain raw separation

2021 ◽  
Vol 937 (3) ◽  
pp. 032094
Author(s):  
V A Fedotov ◽  
S Yu Solovykh
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Measurement Systems ◽  
Grain Processing ◽  
Modern Development ◽  
Artificial Neural ◽  
Milling Properties

Abstract The article presents the basics of the functioning of information and measurement systems for optimizing the process of processing wheat grain. The quality of grain processing products is influenced by climatic factors and grinding technologies. The modern development of information technology makes it possible to modernize information and measurement systems for grain processing and developing algorithms for analyzing the grain physical characteristics. Trial grinding of wheat grains by different varieties was carried out at a laboratory mill. The obtained mathematical models made it possible to predict the quality of grain separation in separators. Digitalization of the grain processing industry includes the use of artificial neural networks for the analysis of images of grain mass by computer vision algorithms using the developed software. It is promising to increase the information content of granulometric analysis through the use of modern intelligent systems. To classify wheat by milling properties, it is proposed to use the grain hardness index. Computer vision and artificial neural networks were used to find and systematize grain grinding particles according to geometric properties. The error of the estimation for the hardness is no more than 3.5 %.

scholarly journals WSES project on decision support systems based on artificial neural networks in emergency surgery

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Andrey Litvin ◽  
Sergey Korenev ◽  
Sophiya Rumovskaya ◽  
Massimo Sartelli ◽  
Gianluca Baiocchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Support ◽  
Decision Support Systems ◽  
Emergency Surgery ◽  
Intelligent Systems ◽  
Medical Information ◽  
Support Systems ◽  
Emergency Setting ◽  
Artificial Neural

AbstractThe article is a scoping review of the literature on the use of decision support systems based on artificial neural networks in emergency surgery. The authors present modern literature data on the effectiveness of artificial neural networks for predicting, diagnosing and treating abdominal emergency conditions: acute appendicitis, acute pancreatitis, acute cholecystitis, perforated gastric or duodenal ulcer, acute intestinal obstruction, and strangulated hernia. The intelligent systems developed at present allow a surgeon in an emergency setting, not only to check his own diagnostic and prognostic assumptions, but also to use artificial intelligence in complex urgent clinical cases. The authors summarize the main limitations for the implementation of artificial neural networks in surgery and medicine in general. These limitations are the lack of transparency in the decision-making process; insufficient quality educational medical data; lack of qualified personnel; high cost of projects; and the complexity of secure storage of medical information data. The development and implementation of decision support systems based on artificial neural networks is a promising direction for improving the forecasting, diagnosis and treatment of emergency surgical diseases and their complications.

scholarly journals Neural Networks in Corporate Governance Systems

2021 ◽  
Vol 93 ◽  
pp. 03016
Author(s):  
Rizvan Turluev ◽  
Laura Hadjieva
Keyword(s):  
Neural Networks ◽  
Corporate Governance ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Complex Problems ◽  
Communication Processes ◽  
Governance Systems ◽  
Artificial Neural

Every year, the interest in solving more complex problems is growing, due to automation, the need for communication processes in intelligent systems. One of the promising directions for solving this problem is based on the use of artificial neural networks and neurocomputers, as the most progressive in relation to corporate governance problems.

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