NEURAL NETWORK-BASED APPROACH TO RESISTIVITY LOGS EXPRESS SIMULATION IN REALISTIC MODELS OF COMPLEX TERRIGENOUS SEDIMENTS

2021 ◽  
pp. 70-78
Author(s):  
A. M. Petrov ◽  
◽  
K. N. Danilovskiy ◽  
K. V. Sukhorukova ◽  
A. R. Leonenko ◽  
...  
Keyword(s):  
Neural Network ◽  
Drilling Fluid ◽  
Thin Layers ◽  
Network Algorithms ◽  
Fluid Displacement ◽  
Geological Conditions ◽  
Simulation Based ◽  
Resistivity Logs ◽  
Actual Target ◽  
Logging Tool

The article proposes a new algorithmic approach to resistivity logs simulation based on convolutional neural networks wich allows constructing algorithms for solving forward problems for specific logging tools in detailed models of near-wellbore space with thin layers, accounting for radial resistivity changes, borehole wall irregularities and drilling fluid displacement by the logging tool. Experimental algorithms for expressmodeling for three common Russian galvanic and induсtion logging methods in two-dimensional models of the near-wellbore space have been implemented based on the proposed approach. Logs simulation using the developed neural network algorithms is multi pletimes faster than using numerical solvers. The proposed solutions open up possibilities to use more sophisticated basic geoelectric models of the near-wellbore space. The use of models adequate in complexity to the actual target geological objects will increase the reliability of interpretation results of resistivity logs measured in complex geological conditions.

The future of simulation based medical education: Adaptive simulation utilizing a deep multitask neural network

2021 ◽  
Author(s):  
Aaron J. Ruberto ◽  
Dirk Rodenburg ◽  
Kyle Ross ◽  
Pritam Sarkar ◽  
Paul C. Hungler ◽  
...  
Keyword(s):  
Neural Network ◽  
Medical Education ◽  
Adaptive Simulation ◽  
Simulation Based ◽  
The Future

scholarly journals Management of the Torch Structure with the New Methodological Approaches to Regulation Based on Neural Network Algorithms

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1909
Author(s):  
Konstantin Osintsev ◽  
Sergei Aliukov ◽  
Yuri Prikhodko
Keyword(s):  
Neural Network ◽  
Coal Dust ◽  
Combustion Process ◽  
Analytical Calculation ◽  
Continuous Distribution ◽  
Initial Section ◽  
Distribution Functions ◽  
Thermophysical Characteristics ◽  
Network Algorithms ◽  
Furnace Volume

A method for evaluating the thermophysical characteristics of the torch is developed. Mathematically the temperature at the end of the zone of active combustion based on continuous distribution functions of particles of solid fuels, in particular coal dust. The particles have different average sizes, which are usually grouped and expressed as a fraction of the total mass of the fuel. The authors suggest taking into account the sequential nature of the entry into the chemical reactions of combustion of particles of different masses. In addition, for the application of the developed methodology, it is necessary to divide the furnace volume into zones and sections. In particular, the initial section of the torch, the zone of intense burning and the zone of afterburning. In this case, taking into account all the thermophysical characteristics of the torch, it is possible to make a thermal balance of the zone of intense burning. Then determines the rate of expiration of the fuel-air mixture, the time of combustion of particles of different masses and the temperature at the end of the zone of intensive combustion. The temperature of the torch, the speed of flame propagation, and the degree of particle burnout must be controlled. The authors propose an algorithm for controlling the thermophysical properties of the torch based on neural network algorithms. The system collects data for a certain time, transmits the information to the server. The data is processed and a forecast is made using neural network algorithms regarding the combustion modes. This allows to increase the reliability and efficiency of the combustion process. The authors present experimental data and compare them with the data of the analytical calculation. In addition, data for certain modes are given, taking into account the system’s operation based on neural network algorithms.

Dynamic statistical evaluation of safety emergency management in coal enterprises based on neural network algorithms

2020 ◽  
Vol 39 (4) ◽  
pp. 5521-5534
Author(s):  
Ying Liu ◽  
Zhongqi Fan ◽  
Hongliang Qi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Management ◽  
Coal Mine ◽  
Evaluation System ◽  
Statistical Evaluation ◽  
Specific Situation ◽  
Network Algorithms ◽  
Management Capability ◽  
Coal Enterprises

By establishing the evaluation system of emergency management capability for coal mine enterprises, we can identify the problems and shortcomings in coal mine emergency management, improve and improve its emergency management capability for coal mine emergencies. In this paper, the authors analyze the dynamic statistical evaluation of safety emergency management in coal enterprises based on neural network algorithms. Neural networks can form any form of topological structure through neurons, so they can directly simulate fuzzy reasoning in structure, that is to say, the equivalent structure of neural networks and fuzzy systems can be formed. This paper constructs the index system based on accident causes, and verifies the scientific rationality of the system. On this basis, according to the specific situation of coal mine emergency management, we design the evaluation criteria of coal mine emergency management capability evaluation index. Because coal mine accidents have the characteristics of complexity, variability and sudden dynamic, it is necessary to adjust and improve the accidents dynamically at any time. The model combines qualitative and quantitative indicators, and can make an overall evaluation of coal mine emergency management capability. It has the characteristics of clear results and strong fitting of simulation results.

Construction of first-class university course based on artificial intelligence and neural network algorithm

2020 ◽  
pp. 1-12
Author(s):  
Yingli Duan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Vocational Training ◽  
Network Algorithms ◽  
Teaching Efficiency ◽  
Neural Network Algorithm ◽  
Solid Foundation ◽  
University Level ◽  
University Course

Curriculum is the basis of vocational training, its development level and teaching efficiency determine the realization of vocational training objectives, as well as the quality and level of major vocational academic training. Therefore, the development of curriculum is an important issue. And affect the school’s teaching capacity building. The analysis of the latest developments in the main courses shows that there are some deviations or irrationalities in the curriculum in some colleges and universities, and the general problems of understanding the latest courses, such as lack of solid foundation in curriculum setting, unclear direction of objectives, unclear reform ideas, inadequate and systematic construction measures, lack of attention to the quality of education. This paper explains the rules for the establishment of first-level courses, clarifies the ideas and priorities of architecture, and explores strategies for building university-level courses using knowledge of artificial intelligence and neural network algorithms in order to gain experience from them.

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