It is so much noticeable that initialization of architectural parameters has a great impact on whole learnability stream so that knowing mathematical properties of dataset results in providing neural network architecture a better expressivity and capacity. In this paper, five random samples of the Volve field dataset were taken. Then a training set was specified and the persistent homology of the dataset was calculated to show impact of data complexity on selection of multilayer perceptron regressor (MLPR) architecture. By using the proposed method that provides a well-rounded strategy to compute data complexity. Our method is a compound algorithm composed of the t-SNE method, alpha-complexity algorithm, and a persistence barcode reading method to extract the Betti number of a dataset. After that, MLPR were trained using that dataset using a single hidden layer with increased hidden neurons. Then, increased both hidden layers and hidden neurons. Our empirical analysis has shown that the training efficiency of MLPR severely depends on its architecture’s ability to express the homology of the dataset.
The physical and elastic characteristics of rocks determine rock strengths in general. Rock strength is frequently assessed using porosity well logs such as neutron and sonic logs. The essential criteria for estimating rock mechanic parameters in petroleum engineering research are uniaxial compressive strength and elastic modulus. Indirect estimation using well-log data is necessary to measure these variables. This study attempts to create a single regression model that can accurately forecast rock mechanic characteristics for the Harth Carbonate Formation in the Fauqi oil field. According to the findings of this study, petrophysical parameters are reliable indexes for determining rock mechanical properties having good performance p
... Show MoreA common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited g
... Show MoreNowadays, a very widespread of smartphones, especially Android smartphones, is observed. This is due to presence of many companies that produce Android based phones and provide them to consumers at reasonable prices with good specifications. The actual benefit of smartphones lies in creating communication between people through the exchange of messages, photos, videos, or other types of files. Usually, this communication is through the existence of an access point through which smartphones can connect to the Internet. However, the availability of the Internet is not guaranteed in all places and at all times, such as in crowded places, remote areas, natural disasters, or interruption of the Internet connection for any reason. To create a
... Show MoreObjective: this search aims to test the correlation between job complexity and psychological detachment then stats how the burnout can affect in this relationship and dose the burnout can contribute in development of this relationship. Theoretical framework: the research adopted some questions like how can psychological detachment can make the employee keeping away from work and isolates himself from work environment and how can the job complexity enhance this behavior for employee ,and how can the burnout increase the correlation between job complexity and psychological detachment ?, then trying to extraction some of recommendations may contributes in enhancing practicing and adopting these three variables (job complexity, psych
... Show MoreThe objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a
... Show MoreAfter the spread of the function of the scenographer in the modern theater, his vision has become present in most of the theatrical works and because the director is the master of the work and the owner of the vision that appears in front of the audience, the overlap between the visions of each of them was required. This research is an attempt to detect the overlap and disengagement in the work of each of them.
The research is divided into a methodological framework that included the research problem, importance, limits, and purpose, and then the definition of terms. In the theoretical framework, the research dealt with two theoretical sections that pave the way for raising ideas related to this subject: the first section (scenography
The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed
... Show MoreThe response of the combustor’s liner to the air-flow that passes through it is the key reason for the combustion chambers noise, hence the instabilities of those chambers that decreases the mechanical efficiency of such sections, by increased its mechanical vibrations, which increases the failure rate created during originating of the cracks spreading by the shakes producing by the series of high-level frequencies. Accordingly, any work debating the impact of the context of liners in the combustion chamber can provide grasping for the combustion noise generated by the undesirable vibrations, and benefits the industrial firms to design an ideal production procedure which increases the lifespan of the combustor. The goal of this work is
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