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  • Data-Driven Structural Design Optimization for Petal-Shaped Auxetics Using Isogeometric Analysis
  • Abstract Focusing on the structural optimization of auxetic materials using data-driven methods, a back-propagation neural network (BPNN) based design framework is developed for petal-shaped auxetics using isogeometric analysis. Adopting a NURBS-based parametric modelling scheme with a small number of design variables, the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method, and demonstrated in this work to give high accuracy and efficiency. Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis, in contrast to the generally complex procedures of typical shape and size sensitivity approaches.
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  • A Numerical Study on Hydraulic Fracturing Problems via the Proper Generalized Decomposition Method
  • Abstract The hydraulic fracturing is a nonlinear, fluid-solid coupling and transient problem, in most cases it is always time-consuming to simulate this process numerically. In recent years, although many numerical methods were proposed to settle this problem, most of them still require a large amount of computer resources. Thus it is a high demand to develop more effificient numerical approaches to achieve the real-time monitoring of the fracture geometry during the hydraulic fracturing treatment. In this study, a reduced order modeling technique namely Proper Generalized Decomposition (PGD), is applied to accelerate the simulations of the transient, non-linear coupled system of hydraulic…
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  • Intelligent Spectrum Detection Model Based on Compressed Sensing in Cognitive Radio Network
  • Abstract In view of the uncertainty of the status of primary users in cognitive networks and the fact that the random detection strategy cannot guarantee cognitive users to accurately find available channels, this paper proposes a joint random detection strategy using the idle cognitive users in cognitive wireless networks. After adding idle cognitive users for detection, the compressed sensing model is employed to describe the number of available channels obtained by the cognitive base station to derive the detection performance of the cognitive network at this time. Both theoretical analysis and simulation results show that using idle cognitive users can reduce…
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  • Experimental Simulation and Numerical Modeling of Deformation and Damage Evolution of Pre-Holed Sandstones After Heat Treatment
  • Abstract The deformation and damage evolution of sandstone after heat treatment greatly influence the efficient and safe development of deep geothermal energy extraction. To investigate this issue, laboratory confined compression tests and numerical simulations were conducted on pre-holed sandstone specimens after heat treatment. The laboratory test results show that the failure modes are closely related to the heat treatment temperature, with increasing treatment temperature, the failure modes change from mixed and shear modes to a splitting mode. The cracks always initiate from the sidewalls of the hole and then propagate. The failure process inside the hole proceeds as follows: calm period,…
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  • Growing and Pruning Based Deep Neural Networks Modeling for Effective Parkinson’s Disease Diagnosis
  • Abstract Parkinson’s disease is a serious disease that causes death. Recently, a new dataset has been introduced on this disease. The aim of this study is to improve the predictive performance of the model designed for Parkinson’s disease diagnosis. By and large, original DNN models were designed by using specific or random number of neurons and layers. This study analyzed the effects of parameters, i.e., neuron number and activation function on the model performance based on growing and pruning approach. In other words, this study addressed the optimum hidden layer and neuron numbers and ideal activation and optimization functions in order…
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  • Analytical and Numerical Investigation for the DMBBM Equation
  • Abstract The nonlinear dispersive modified Benjamin-Bona-Mahony (DMBBM) equation is solved numerically using adaptive moving mesh PDEs (MMPDEs) method. Indeed, the exact solution of the DMBBM equation is obtained by using the extended Jacobian elliptic function expansion method. The current methods give a wider applicability for handling nonlinear wave equations in engineering and mathematical physics. The adaptive moving mesh method is compared with exact solution by numerical examples, where the explicit solutions are known. The numerical results verify the accuracy of the proposed method.
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  • Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG
  • Abstract The grading of hypoxic-ischemic encephalopathy (HIE) contributes to the clinical decision making for neonates with HIE. In this paper, an automated grading method based on electroencephalogram (EEG) data is proposed to describe the severity of HIE infants, namely mild asphyxia, moderate asphyxia and severe asphyxia. The automated grading method is based on a multi-class support vector machine (SVM) classifier, and the input features of SVM classifier include long-term features which are extracted by decomposing the EEG data into different 64 s epoch data and short-term features which are extracted by segmenting the 64 s epoch data into 8 s epoch…
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  • Mixed Noise Parameter Estimation Based on Variance Stable Transform
  • Abstract The ultimate goal of image denoising from video is to improve the given image, which can reduce noise interference to ensure image quality. Through denoising technology, image quality can have effectively optimized, signal-to-noise ratio can have increased, and the original mage information can have better reflected. As an important preprocessing method, people have made extensive research on image denoising algorithm. Video denoising needs to take into account the various level of noise. Therefore, the estimation of noise parameters is particularly important. This paper presents a noise estimation method based on variance stability transformation, which estimates the parameters of variance stability…
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  • Stress Analysis of Printed Circuit Board with Different Thickness and Composite Materials Under Shock Loading
  • Abstract In this study, the deformation and stress distribution of printed circuit board (PCB) with different thickness and composite materials under a shock loading were analyzed by the finite element analysis. The standard 8-layer PCB subjected to a shock loading 1500 g was evaluated first. Moreover, the finite element models of the PCB with different thickness by stacking various number of layers were discussed. In addition to changing thickness, the core material of PCB was replaced from woven E-glass/epoxy to woven carbon fiber/epoxy for structural enhancement. The non-linear material property of copper foil was considered in the analysis. The results indicated…
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  • Data Augmentation Technology Driven By Image Style Transfer in Self-Driving Car Based on End-to-End Learning
  • Abstract With the advent of deep learning, self-driving schemes based on deep learning are becoming more and more popular. Robust perception-action models should learn from data with different scenarios and real behaviors, while current end-to-end model learning is generally limited to training of massive data, innovation of deep network architecture, and learning in-situ model in a simulation environment. Therefore, we introduce a new image style transfer method into data augmentation, and improve the diversity of limited data by changing the texture, contrast ratio and color of the image, and then it is extended to the scenarios that the model has been…
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