Design of Current Mode Sigmoid Function and Hyperbolic Tangent Function

Datta, Debanjana; Agarwal, Sweta; Kumar, Vikash; Raj, Mayank; Ray, Bimalendu; Banerjee, Ayan

doi:10.1007/978-981-32-9767-8_5

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The ltered design variables may have a large number of intermediate values at the solid boundary. To make the boundary sharper, the hyperbolic tangent projection is used to reduce these physically insigni cant regions (Datta et al, 2019):…”

Section: Methodsmentioning

confidence: 99%

Topology optimization of heat sinks in half-open space: a study on design domain

Yu,

Liu,

2024

Preprint

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Efficient heat sinks are the key components of heat dissipation devices. Although topology optimization can theoretically design high-performance heat sinks, screening a design domain in an open space is still a challenge. This work studies the topology optimization of three-dimensional (3D) heat sinks for LED chips using the variable density method, without considering air convection to save computing resources. A theoretical analysis proposes that the design domain should be approximately a hemisphere. Topology optimization is performed in three shaped design domains, namely cylindrical, conical frustum, and inverted conical frustum. By 3D printing three topology-optimized heat sinks, their actual heat dissipation effects on LED chips are compared. The experiment and simulation results consistently show that the heat sink in the conical frustum design domain has the best heat dissipation effect. Equipped with such a heat sink, a 5-W LED chip is 5.1°C cooler than that with the traditional finned heat sink. Our results show that a well-selected design domain can effectively balance the heat conduction and heat exchange, eventually leading to a better heat sink.

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Section: Methodsmentioning

confidence: 99%

Topology optimization of heat sinks in half-open space: a study on design domain

Yu,

Liu,

2024

Preprint

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“…Furthermore, the effectiveness of different transferring functions in predicting pore pressure was investigated. This analysis involved testing the tangential sigmoidal function, logarithmic sigmoidal function, and linear function [28][29][30], each offering distinct mathematical properties that can impact the model's predictive capabilities. Additionally, the influence of the number of layers in the ANN model architecture was explored, ranging from single-layer configurations to models with two or three layers.…”

Section: Ann Model Learningmentioning

confidence: 99%

A New Empirical Correlation for Pore Pressure Prediction Based on Artificial Neural Networks Applied to a Real Case Study

Mahmoud,

Alzayer,

Panagopoulos

et al. 2024

Processes

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Pore pressure prediction is a critical parameter in petroleum engineering and is essential for safe drilling operations and wellbore stability. However, traditional methods for pore pressure prediction, such as empirical correlations, require selecting appropriate input parameters and may not capture the complex relationships between these parameters and the pore pressure. In contrast, artificial neural networks (ANNs) can learn complex relationships between inputs and outputs from data. This paper presents a new empirical correlation for predicting pore pressure using ANNs. The proposed method uses 42 datasets of well log data, including temperature, porosity, and water saturation, to train ANNs for pore pressure prediction. The trained model, with the Bayesian regularization backpropagation function, predicts the pore pressure with an average absolute percentage error (AAPE) and correlation coefficient (R) of 4.22% and 0.875, respectively. The trained ANN is then used to develop a new empirical correlation that relates pore pressure to the input parameters considering the weights and biases of the optimized ANN model. To validate the proposed correlation, it is applied to a blind dataset, where the model successfully predicts the pore pressure with an AAPE of 5.44% and R of 0.957. The results show that the proposed correlation provides accurate and reliable predictions of pore pressure. The proposed method provides a robust and accurate approach for predicting pore pressure in petroleum engineering applications, which can be used to improve drilling safety and wellbore stability.

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“…Each neuron's duty is to calculate the net layer's weight and pass data through a function called the transfer function. Sigmoid Tangent is regarded as one of the most useful functions in this case and has been widely used by experts [56][57][58][59][60][61]. Thus, the abovementioned function was used as the transfer function.…”

Section: Architecture Of the Networkmentioning

confidence: 99%

Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects

et al. 2020

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The cost, time and scope of a construction project are key parameters for its success. Thus, predicting these indices is indispensable. Correct and accurate prediction of cost throughout the progress of a project gives project managers the chance to identify projects that need revision in their schedules in order to result in the maximum benefit. The aim of this study is to minimize the shortcomings of the Earned Value Management (EVM) method using an Artificial Neural Network (ANN) and multiple regression analysis in order to predict project cost indices more precisely. A total of 50 road construction projects in Fars Province, Iran, were selected for analysis in this research. An ANN model was used to predict the projects’ cost performance indices, thereby creating a more accurate symmetry between the predicted and actual cost by considering factors that influence project success. The input data of the ANN model were analysed in MATLAB software. A multiple regression model was also used as another analytical tool to validate the outcome of the ANN. The results showed that the ANN model resulted in a lower Mean Squared Error (MSE) and a greater correlation coefficient than both the traditional EVM model and the multiple regression model.

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Design of Current Mode Sigmoid Function and Hyperbolic Tangent Function

Cited by 5 publications

References 13 publications

Topology optimization of heat sinks in half-open space: a study on design domain

Topology optimization of heat sinks in half-open space: a study on design domain

A New Empirical Correlation for Pore Pressure Prediction Based on Artificial Neural Networks Applied to a Real Case Study

Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects

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