Applications of Artificial Neural Networks and Dipole Sonic Anisotropy in Low-Porosity, Naturally Fractured, Complex Lithology Formations in the Southern Land Region of México

Kessler, Calvin; Mesa, Harold; Torne, Juan; Jimenez-Bueno, Oscar; Escamilla, Gelmunt; Correa-Perez, Idelfonso

doi:10.2118/103662-ms

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Data allocation for training was done randomly, with 70% dedicated to training, 15% for testing, and the remaining 15% for validation [19]. This systematic approach aims to contribute to the advancement of smart home automation, providing insights for diverse urban contexts and enhancing the e ciency of home-related processes through arti cial intelligence and neural network technologies.…”

Section: Model Architecturementioning

confidence: 99%

Smart Home AI Model (SHAM): Optimizing Switches and Sockets space in Residential Building during Planning/Construction through Multilayer Perceptron Estimation

Singh,

Saxena,

Singh

et al. 2024

Preprint

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This study presents the development and evaluation of a Multilayer Perceptron (MLP) model for estimating the optimal number of switches and sockets space in houses not designed by professional engineers. Utilizing data from 145 houses across diverse cities, the study employs the Back Propagation Levenberg-Marquardt (BPLM) technique for MLP model estimation The SHAM is AI based multilayer perceptron model which recommends optimum number of switches and sockets space by considering factors like topography, type of property, size of property, type of occupancy. This model predicts the most suitable combination of switches and sockets space for a newly planned house. Utilizing data from 145 houses across diverse cities, the MLP architecture selection was based on performance metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) from testing and training data. The statistical analysis identified the 26-9-1 MLP architecture with a minimum MAPE value of 0.2572. Subsequently, this selected neural network model was applied to predict the optimal number of switches and sockets space. The study demonstrates the efficacy of the developed Smart Home AI Model (SHAM), proving valuable for both unprofessional and professional civil engineers during the planning stage. The implementation of the AI-based SHAM model is discussed, highlighting its adaptability to get optimum number of switches and sockets space without a detailed designing of electrical circuits. Results from real-world simulations demonstrate the effectiveness of the model in achieving energy savings, efficient construction and user satisfaction compared to traditional, non-optimized setups.

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Section: Model Architecturementioning

confidence: 99%

Smart Home AI Model (SHAM): Optimizing Switches and Sockets space in Residential Building during Planning/Construction through Multilayer Perceptron Estimation

Singh,

Saxena,

Singh

et al. 2024

Preprint

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Predicting formation lithology from log data by using a neural network

Wang

Zhang

2008

Pet. Sci.

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In order to increase drilling speed in deep complicated formations in Kela-2 gas fi eld, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation (BP) model, an improved BP model was proposed, with main modifi cations of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas fi eld.

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Integration of Results from Recognition Algorithms Applied to the Uranium Deposits

Muhamediyev¹,

Amirgaliyev²,

Iskakov³

et al. 2014

J. Adv. Comput. Intell. Intell. Inform.

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Data interpretation of electric logging can be performed using self-learning systems such as artificial neural networks (ANNs). Preliminary research shows that by using ANN we can achieve 52-73% of coincidence of interpretable data and experimental results. Therefore, it is necessary to analyze the possibility of using other classification algorithms, and that of using several classification algorithms simultaneously through a unified system (referred to as anintegrator. These algorithms may improve the quality of recognition of individual species. The problem of developing a recognition system that combines several classification algorithms, also known as theintegrator, is formulated here. A simple algorithm is developed for the learning and recognition of an integrator for the post processing stage; this enhances the recognition accuracy by 1-3%.

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Applications of Artificial Neural Networks and Dipole Sonic Anisotropy in Low-Porosity, Naturally Fractured, Complex Lithology Formations in the Southern Land Region of México

Cited by 3 publications

References 6 publications

Smart Home AI Model (SHAM): Optimizing Switches and Sockets space in Residential Building during Planning/Construction through Multilayer Perceptron Estimation

Smart Home AI Model (SHAM): Optimizing Switches and Sockets space in Residential Building during Planning/Construction through Multilayer Perceptron Estimation

Predicting formation lithology from log data by using a neural network

Integration of Results from Recognition Algorithms Applied to the Uranium Deposits

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