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Immersed boundary method based fluid–structure–thermal interaction solver with conjugate heat transfer
We present the development of a sharp-interface immersed boundary method based in-house fluid–structure interface (FSI) solver. In particular, we...
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Improving BFGO with Apical Dominance-Guided Gradient Descent for Enhanced Optimization
The Bamboo Forest Growth Optimization (BFGO) algorithm is a popular meta-heuristic for diverse optimization problems. However, its performance can be...
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Identification of material parameters in low-data limit: application to gradient-enhanced continua
Due to the growing trend towards miniaturization, small-scale manufacturing processes have become widely used in various engineering fields to...
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Investigation of Thermophysical Properties of Gradient Materials by the Method of Inverse Problems
Methods have been proposed for computational-experimental determination of thermophysical characteristics of gradient materials whose functional...
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A Modified Descent Spectral Conjugate Gradient Method for Unconstrained Optimization
Recently, Peyghami et al. (Optim Meth Soft 43:1–28, 2015) proposed a modified secant equation, which applied a new updating Yabe and Takano’s rule as...
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Accurate Detection and Diagnosis of Breast Cancer Using Scaled Conjugate Gradient Back Propagation Algorithm and Advanced Deep Learning Techniques
Development of breast cancer detection and its usage by different health care industries in their diagnostic center is a very much serious task for...
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The modified PRP conjugate gradient algorithm under a non-descent line search and its application in the Muskingum model and image restoration problems
In this paper, a modified Polak–Ribière–Polyak (PRP) method, which possesses the following desired properties for unconstrained optimization...
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Gradient descent based restoration method of track irregularity in asymmetric chord reference method
This paper proposes a gradient descent based restoration method of track irregularity. Based on the theory of asymmetric chord-reference method...
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Dependence Measure and Wolfe-Powell Criterion Based Two-stage Algorithm for Identification of Time Delay FIR Models
Time delay dynamic systems are widely existed due to sensors, actuators or other reasons. In this paper, a time delay FIR system is considered to...
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One-Bit Gridless DOA Estimation with Multiple Measurements Exploiting Accelerated Proximal Gradient Algorithm
With low hardware cost and power consumption, direction-of-arrival (DOA) estimation exploiting one-bit quantized array data has become an attractive...
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Estimation of Boundary Heat Flux with Conjugate Gradient Method by Experimental Transient Temperature Data
In this work, the estimation of heat flux for one-dimensional transient heat conduction problem has been done with the help of the search-based...
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An advanced intelligent MPPT control strategy based on the imperialist competitive algorithm and artificial neural networks
The development of a robust optimization control technique, which can handle numerous nonlinear system problems, is one of the most challenging...
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Regional gradient observability for fractional differential equations with Caputo time-fractional derivatives
We investigate the regional gradient observability of fractional sub-diffusion equations involving the Caputo derivative. The problem consists of...
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Lp quasi-norm minimization: algorithm and applications
Sparsity finds applications in diverse areas such as statistics, machine learning, and signal processing. Computations over sparse structures are...
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A Variational Framework for the Thermomechanics of Gradient-Extended Dissipative Solids – with Applications to Diffusion, Damage and Plasticity
The paper presents a versatile framework for solids which undergo nonisothermal processes with irreversibly changing microstructure at large strains....
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Elman and Feed-Forward Neural Networks with Different Training Algorithms for Solar Radiation Forecasting: A Comparison with a Case Study
In order to estimate daily solar radiation, this paper proposes Elman (ENN) and Feed forward backpropagation (FNN) neural networks. The time series...
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Stock Market Prediction Employing Discrete Wavelet Transform and Moving Average Gradient Descent
Stock market behavior is extremely volatile and complex in nature due to the randomness of the governing parameters. This makes attaining a high...
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FFN-XGB- design of a hybrid feed forward neural network and extreme gradient boosting model for early prediction of chronic kidney disease
The decrease in kidney function caused by chronic kidney disease (CKD) is progressive and permanent. Artificial Intelligent methods need to be...
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Gradient-Based Optimizer for Structural Optimization Problems
Meta-heuristic algorithms are stochastic search methods that have been used for quite a long time to solve complex, non-linear optimization problems...
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