Vol.128, No.1, 2021-Table of Contents

On the Cover


This work shows uncertainty qualification using isogeometric analysis with boundary element methods (IGABEM) based on model order reduction. The stochastic analysis is performed with Monte-Carlo simulation (MCs). IGABEM accelerates the sampling process by conducting numerical simulation directly from computer-aided design models without meshing. The Proper Orthogonal Decomposition (POD) and Radial Basis Functions (RBF) give fast approximation of system responses.

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  • Monte Carlo Simulation of Fractures Using Isogeometric Boundary Element Methods Based on POD-RBF
  • Abstract This paper presents a novel framework for stochastic analysis of linear elastic fracture problems. Monte Carlo simulation (MCs) is adopted to address the multi-dimensional uncertainties, whose computation cost is reduced by combination of Proper Orthogonal Decomposition (POD) and the Radial Basis Function (RBF). In order to avoid re-meshing and retain the geometric exactness, isogeometric boundary element method (IGABEM) is employed for simulation, in which the Non-Uniform Rational B-splines (NURBS) are employed for representing the crack surfaces and discretizing dual boundary integral equations. The stress intensity factors (SIFs) are extracted by M integral method. The numerical examples simulate several cracked structures… More
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  • A Pseudo-Spectral Scheme for Systems of Two-Point Boundary Value Problems with Left and Right Sided Fractional Derivatives and Related Integral Equations
  • Abstract We target here to solve numerically a class of nonlinear fractional two-point boundary value problems involving left- and right-sided fractional derivatives. The main ingredient of the proposed method is to recast the problem into an equivalent system of weakly singular integral equations. Then, a Legendre-based spectral collocation method is developed for solving the transformed system. Therefore, we can make good use of the advantages of the Gauss quadrature rule. We present the construction and analysis of the collocation method. These results can be indirectly applied to solve fractional optimal control problems by considering the corresponding Euler–Lagrange equations. Two numerical examples… More
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  • A Numerical Model for Simulating Two-Phase Flow with Adaptive Mesh Refinement
  • Abstract In this study, a numerical model for simulating two-phase flow is developed. The Cartesian grid with Adaptive Mesh Refinement (AMR) is adopted to reduce the computational cost. An explicit projection method is used for the time integration and the Finite Difference Method (FDM) is applied on a staggered grid for the discretization of spatial derivatives. The Volume of Fluid (VOF) method with Piecewise-Linear Interface Calculation (PLIC) is extended to the AMR grid to capture the gas-water interface accurately. A coarse-fine interface treatment method is developed to preserve the flux conservation at the interfaces. Several two-dimensional (2D) and three-dimensional (3D) benchmark… More
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  • Variable Importance Measure System Based on Advanced Random Forest
  • Abstract The variable importance measure (VIM) can be implemented to rank or select important variables, which can effectively reduce the variable dimension and shorten the computational time. Random forest (RF) is an ensemble learning method by constructing multiple decision trees. In order to improve the prediction accuracy of random forest, advanced random forest is presented by using Kriging models as the models of leaf nodes in all the decision trees. Referring to the Mean Decrease Accuracy (MDA) index based on Out-of-Bag (OOB) data, the single variable, group variables and correlated variables importance measures are proposed to establish a complete VIM system… More
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  • Parameters Calibration of the Combined Hardening Rule through Inverse Analysis for Nylock Nut Folding Simulation
  • Abstract Locking nuts are widely used in industry and any defects from their manufacturing may cause loosening of the connection during their service life. In this study, simulations of the folding process of a nut’s flange made from AISI 1040 steel are performed. Besides the bilinear isotropic hardening rule, Chaboche’s nonlinear kinematic hardening rule is employed with associated flow rule and Hill48 yield criterion to set a plasticity model. The bilinear isotropic hardening rule’s parameters are determined by means of a monotonic tensile test. The Chaboche’s parameters are determined by using a low cycle tension/compression test by applying curve fitting methods… More
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  • Optimal Control of Slurry Pressure during Shield Tunnelling Based on Random Forest and Particle Swarm Optimization
  • Abstract The control of slurry pressure aiming to be consistent with the external water and earth pressure during shield tunnelling has great significance for face stability, especially in urban areas or underwater where the surrounding environment is very sensitive to the fluctuation of slurry pressure. In this study, an optimal control method for slurry pressure during shield tunnelling is developed, which is composed of an identifier and a controller. The established identifier based on the random forest (RF) can describe the complex non-linear relationship between slurry pressure and its influencing factors. The proposed controller based on particle swarm optimization (PSO) can… More
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  • Deep Learning Predicts Stress–Strain Relations of Granular Materials Based on Triaxial Testing Data
  • Abstract This study presents an AI-based constitutive modelling framework wherein the prediction model directly learns from triaxial testing data by combining discrete element modelling (DEM) and deep learning. A constitutive learning strategy is proposed based on the generally accepted frame-indifference assumption in constructing material constitutive models. The low-dimensional principal stress-strain sequence pairs, measured from discrete element modelling of triaxial testing, are used to train recurrent neural networks, and then the predicted principal stress sequence is augmented to other high-dimensional or general stress tensor via coordinate transformation. Through detailed hyperparameter investigations, it is found that long short-term memory (LSTM) and gated recurrent… More
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  • DEM Simulations of Resistance of Particle to Intruders during Quasistatic Penetrations
  • Abstract Based on the discrete element method and hydrostatics theory, an improved Archimedes principle is proposed to study the rules pertaining to resistance changes during the penetration process of an intruder into the particulate materials. The results illustrate the fact that the lateral contribution to the resistance is very small, while the tangential force of the lateral resistance originates from friction effects. Conversely, the resistance of particulate materials on the intruder mainly occurs at the bottom part of the intruding object. Correspondingly, the factors that determine the resistance of the bottom part of the intruding object and the rules pertaining to… More
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  • A Mortality Risk Assessment Approach on ICU Patients Clinical Medication Events Using Deep Learning
  • Abstract ICU patients are vulnerable to medications, especially infusion medications, and the rate and dosage of infusion drugs may worsen the condition. The mortality prediction model can monitor the real-time response of patients to drug treatment, evaluate doctors’ treatment plans to avoid severe situations such as inverse Drug-Drug Interactions (DDI), and facilitate the timely intervention and adjustment of doctor’s treatment plan. The treatment process of patients usually has a time-sequence relation (which usually has the missing data problem) in patients’ treatment history. The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network (RNN). However, sometimes, patients’ treatment can… More
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  • Modeling Dysentery Diarrhea Using Statistical Period Prevalence
  • Abstract Various epidemics have occurred throughout history, which has led to the investigation and understanding of their transmission dynamics. As a result, non-local operators are used for mathematical modeling in this study. Therefore, this research focuses on developing a dysentery diarrhea model with the use of a fractional operator using a one-parameter Mittag–Leffler kernel. The model consists of three classes of the human population, whereas the fourth one belongs to the pathogen population. The model carefully deals with the dimensional homogeneity among the parameters and the fractional operator. In addition, the model was validated by fitting the actual number of dysentery… More
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  • The Research of Automatic Classification of Ultrasound Thyroid Nodules
  • Abstract This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs) and discriminate them as benign or malignant. The system firstly uses variational level set active contour with gradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images. Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction method based on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, this paper introduces a weight factor to improve support vector machine, offering different classes of samples with different weights. Finally,… More
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  • A Deletable and Modifiable Blockchain Scheme Based on Record Verification Trees and the Multisignature Mechanism
  • Abstract As one of the most valuable technologies, blockchains have received extensive attention from researchers and industry circles and are widely applied in various scenarios. However, data on a blockchain cannot be deleted. As a result, it is impossible to clean invalid and sensitive data and correct erroneous data. This, to a certain extent, hinders the application of blockchains in supply chains and Internet of Things. To address this problem, this study presents a deletable and modifiable blockchain scheme (DMBlockChain) based on record verification trees (RVTrees) and the multisignature scheme. (1) In this scheme, an RVTree structure is designed and added… More
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  • An Adversarial Smart Contract Honeypot in Ethereum
  • Abstract A smart contract honeypot is a special type of smart contract. This type of contract seems to have obvious vulnerabilities in contract design. If a user transfers a certain amount of funds to the contract, then the user can withdraw the funds in the contract. However, once users try to take advantage of this seemingly obvious vulnerability, they will fall into a real trap. Consequently, the user’s investment in the contract cannot be retrieved. The honeypot induces other accounts to launch funds, which seriously threatens the security of property on the blockchain. Detection methods for honeypots are available. However, studying… More
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  • Attribute-Based Keyword Search over the Encrypted Blockchain
  • Abstract To address privacy concerns, data in the blockchain should be encrypted in advance to avoid data access from all users in the blockchain. However, encrypted data cannot be directly retrieved, which hinders data sharing in the blockchain. Several works have been proposed to deal with this problem. However, the data retrieval in these schemes requires the participation of data owners and lacks finer-grained access control. In this paper, we propose an attribute-based keyword search scheme over the encrypted blockchain, which allows users to search encrypted files over the blockchain based on their attributes. In addition, we build a file chain… More
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  • Fatigue Topology Optimization Design Based on Distortion Energy Theory and Independent Continuous Mapping Method
  • Abstract Fatigue failure is a common failure mode under the action of cyclic loads in engineering applications, which often occurs with no obvious signal. The maximum structural stress is far below the allowable stress when the structures are damaged. Aiming at the lightweight structure, fatigue topology optimization design is investigated to avoid the occurrence of fatigue failure in the structural conceptual design beforehand. Firstly, the fatigue life is expressed by topology variables and the fatigue life filter function. The continuum fatigue optimization model is established with the independent continuous mapping (ICM) method. Secondly, fatigue life constraints are transformed to distortion energy… More
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  • Simulation of Elastic and Fatigue Properties of Epoxy/SiO2 Particle Composites through Molecular Dynamics
  • Abstract The influence of different nanoparticle sizes on the elastic modulus and the fatigue properties of epoxy/SiO2 nanocomposite is studied in this paper. Here, the cross-linked epoxy resins formed by the combination of DGEBA and 1,3-phenylenediamine are used as the matrix phase, and spherical SiO2 particles are used as the reinforcement phase. In order to simulate the elastic modulus and long-term performance of the composite material at room temperature, the simulated temperature is set to 298 K and the mass fraction of SiO2 particles is set to 28.9%. The applied strain rate is 109/s during the simulation of the elastic modulus.… More
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  • A Homogeneous Cloud Task Distribution Method Based on an Improved Leapfrog Algorithm
  • Abstract Cloud manufacturing is a new manufacturing model with crowd-sourcing characteristics, where a cloud alliance composed of multiple enterprises, completes tasks that a single enterprise cannot accomplish by itself. However, compared with heterogeneous cloud tasks, there are relatively few studies on cloud alliance formation for homogeneous tasks. To bridge this gap, a novel method is presented in this paper. First, a homogeneous cloud task distribution model under cloud environment was constructed, where services description, selection and combination were modeled. An improved leapfrog algorithm for cloud task distribution (ILA-CTD) was designed to solve the proposed model. Different from the current alternatives, the… More
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  • Multi-Layer Reconstruction Errors Autoencoding and Density Estimate for Network Anomaly Detection
  • Abstract Anomaly detection is an important method for intrusion detection. In recent years, unsupervised methods have been widely researched because they do not require labeling. For example, a nonlinear autoencoder can use reconstruction errors to attain the discrimination threshold. This method is not effective when the model complexity is high or the data contains noise. The method for detecting the density of compressed features in a hidden layer can be used to reduce the influence of noise on the selection of the threshold because the density of abnormal data in hidden layers is smaller than normal data. However, compressed features may… More
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