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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  • A Knowledge-Enhanced Dialogue Model Based on Multi-Hop Information with Graph Attention
  • Abstract With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, in the context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day. In order to improve the timeliness of customer service responses, many systems have begun to use customer service robots to respond to consumer questions, but the current customer service robots tend to respond to specific questions. For many questions that lack background knowledge, they can generate only responses that are biased towards generality and repetitiveness. To better promote the understanding of dialogue and generate more meaningful responses, this paper… More
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  • MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks
  • Abstract The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automatically segment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancing tumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, it is very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantages of DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks. We used dense blocks in the encoder part and residual blocks in the decoder part. The number… More
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  • A Contemporary Review on Drought Modeling Using Machine Learning Approaches
  • Abstract Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Its beginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughts in the last few decades. Predicting future droughts is vital for framing drought management plans to sustain natural resources. The data-driven modelling for forecasting the metrological time series prediction is becoming more powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques have demonstrated success in the drought prediction process and are becoming popular to predict the weather, especially the… More
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  • Multi-Disease Prediction Based on Deep Learning: A Survey
  • Abstract In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI's research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly improved. This article introduces some… More
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  • Multi-Material Topology Optimization of Structures Using an Ordered Ersatz Material Model
  • Abstract This paper proposes a new element-based multi-material topology optimization algorithm using a single variable for minimizing compliance subject to a mass constraint. A single variable based on the normalized elemental density is used to overcome the occurrence of meaningless design variables and save computational cost. Different from the traditional material penalization scheme, the algorithm is established on the ordered ersatz material model, which linearly interpolates Young's modulus for relaxed design variables. To achieve a multi-material design, the multiple floating projection constraints are adopted to gradually push elemental design variables to multiple discrete values. For the convergent element-based solution, the multiple… More
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  • Intelligent Segmentation and Measurement Model for Asphalt Road Cracks Based on Modified Mask R-CNN Algorithm
  • Abstract Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, cracks have been paid more attention, since cracks often cause major engineering and personnel safety incidents. Current manual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model based on the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The model proposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposal network (RPN), a region of interest (RoI) Align… More
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  • Stability Reliability of the Lateral Vibration of Footbridges Based on the IEVIE-SA Method
  • Abstract Research on the lateral vibrational stability of footbridges has attracted increasing attention in recent years. However, this stability contains a series of complex mechanisms, such as nonlinear vibration, random excitation, and random stability. The Lyapunov method is regarded as an effective tool for analyzing random vibrational stability; however, it is a qualitative method and can only provide a binary judgment for stability. This study proposes a new method, IEVIE–SA, which combines the energy method based on the comparison between the input energy and the variation of intrinsic energy (IEVIE) and the stochastic averaging (SA) method. The improved Nakamura model was… More
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  • Computational Analysis of Airflow in Upper Airway under Light and Heavy Breathing Conditions for a Realistic Patient Having Obstructive Sleep Apnea
  • Abstract Background: Obstructive sleep apnea is a sleeping disorder that has troubled a sizeable population. There is an active area of research on obstructive sleep apnea that intends to better understand airflow behaviors and therefore treat patients more effectively. This paper aims to investigate the airflow characteristics of the upper airway in an obstructive sleep apnea (OSA) patient under light and heavy breathing conditions by using Turbulent Kinetic Energy (TKE), an accurate method in expressing the flow concentration mechanisms of sleeping disorders. It is important to visualize the concentration of flow in the upper airway in order to identify the severity… More
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  • Deep Learning-Based Surrogate Model for Flight Load Analysis
  • Abstract Flight load computations (FLC) are generally expensive and time-consuming. This paper studies deep learning (DL)-based surrogate models of FLC to provide a reliable basis for the strength design of aircraft structures. We mainly analyze the influence of Mach number, overload, angle of attack, elevator deflection, altitude, and other factors on the loads of key monitoring components, based on which input and output variables are set. The data used to train and validate the DL surrogate models are derived using aircraft flight load simulation results based on wind tunnel test data. According to the FLC features, a deep neural network (DNN)… More
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  • Steganalysis of Low Embedding Rate CNV-QIM in Speech
  • Abstract To address the difficulty of detecting low embedding rate and high-concealment CNV-QIM (complementary neighbor vertices-quantization index modulation) steganography in low bit-rate speech codec, the code-word correlation model based on a BiLSTM (bi-directional long short-term memory) neural network is built to obtain the correlation features of the LPC codewords in speech codec in this paper. Then, softmax is used to classify and effectively detect low embedding rate CNV-QIM steganography in VoIP streams. The experimental results show that for speech steganography of short samples with low embedding rate, the BiLSTM method in this paper has a superior detection accuracy than state-of-the-art methods… More
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  • Numerical Implementation of a Unified Viscoplastic Model for Considering Solder Joint Response under Board-Level Temperature Cycling
  • Abstract An implicit integration scheme was developed for simulating the viscoplastic constitutive behavior of Sn3.0Ag0.5Cu solder and programmed into a user material subroutine of the finite element software ANSYS. The numerical procedure first solves the essential state variables by using a three-level iterative procedure, and updates the remaining stress and state variables accordingly. The numerical implementation was applied to consider the responses of solder joints in an electronic assembly under temperature cycling condition. The viscoplastic strain energy density accumulation over one temperature cycle was identified as a feasible parameter for evaluating the thermomechanical reliability of the solder joints. More
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  • A Computational Study on Lateral Flight Stability of the Cranefly in Hover
  • Abstract The dynamic flight stability of hovering insects includes the longitudinal and lateral motion. Research results have shown that for the majority of hovering insects the same longitudinal natural modes are identified and the hovering flight in longitudinal is unstable. However, in lateral, the modal structure for hovering insects could be different and the stability property of lateral disturbance motion is not as robust as that of longitudinal motion. The cranefly possesses larger aspect ratio and lower Reynolds number, and such differences in morphology and kinematics may make the lateral dynamic stability different. In this paper, the lateral flight stability of… More
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  • An Efficient Meshless Method for Hyperbolic Telegraph Equations in (1 + 1) Dimensions
  • Abstract Numerical solutions of the second-order one-dimensional hyperbolic telegraph equations are presented using the radial basis functions. The purpose of this paper is to propose a simple novel direct meshless scheme for solving hyperbolic telegraph equations. This is fulfilled by considering time variable as normal space variable. Under this scheme, there is no need to remove time-dependent variable during the whole solution process. Since the numerical solution accuracy depends on the condition of coefficient matrix derived from the radial basis function method. We propose a simple shifted domain method, which can avoid the full-coefficient interpolation matrix easily. Numerical experiments performed with… More
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  • High Order of Accuracy for Poisson Equation Obtained by Grouping of Repeated Richardson Extrapolation with Fourth Order Schemes
  • Abstract In this article, we improve the order of precision of the two-dimensional Poisson equation by combining extrapolation techniques with high order schemes. The high order solutions obtained traditionally generate non-sparse matrices and the calculation time is very high. We can obtain sparse matrices by applying compact schemes. In this article, we compare compact and exponential finite difference schemes of fourth order. The numerical solutions are calculated in quadruple precision (Real * 16 or extended precision) in FORTRAN language, and iteratively obtained until reaching the round-off error magnitude around 1.0E −32. This procedure is performed to ensure that there is no… More
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  • A Binomial Model Approach: Comparing the R0 Values of SARS-CoV-2 rRT-PCR Data from Laboratories across Northern Cyprus
  • Abstract Northern Cyprus has implemented relatively strict measures in the battle against the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The measures were introduced at the beginning of the COVID-19 pandemic, in order to prevent the spread of the disease. One of these measures was the use of two separate real-time reverse transcription polymerase chain reaction (rRT-PCR) tests for SARS-CoV-2 referred to as the double screening procedure, which was adopted following the re-opening of the sea, air and land borders for passengers after the first lockdown. The rRT-PCR double screening procedure involved reporting a negative rRT-PCR test which was… More
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  • Construction of Design Guidelines for Optimal Automotive Frame Shape Based on Statistical Approach and Mechanical Analysis
  • Abstract A body frame composed of thin sheet metal is a crucial structure that determines the safety performance of a vehicle. Designing a correct weight and high-performance automotive body is an emerging engineering problem. To improve the performance of the automotive frame, we attempt to reconstruct its design criteria based on statistical and mechanical approaches. At first, a fundamental study on the frame strength is conducted and a cross-sectional shape optimization problem is developed for designing the cross-sectional shape of an automobile frame having a very high mass efficiency for strength. Shape optimization is carried out using the nonlinear finite element… More
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  • Decision Making Algorithmic Approaches Based on Parameterization of Neutrosophic Set under Hypersoft Set Environment with Fuzzy, Intuitionistic Fuzzy and Neutrosophic Settings
  • Abstract Hypersoft set is an extension of soft set as it further partitions each attribute into its corresponding attribute-valued set. This structure is more flexible and useful as it addresses the limitation of soft set for dealing with the scenarios having disjoint attribute-valued sets corresponding to distinct attributes. The main purpose of this study is to make the existing literature regarding neutrosophic parameterized soft set in line with the need of multi-attribute approximate function. Firstly, we conceptualize the neutrosophic parameterized hypersoft sets under the settings of fuzzy set, intuitionistic fuzzy set and neutrosophic set along with some of their elementary properties… More
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  • An Improved Algorithm for the Detection of Fastening Targets Based on Machine Vision
  • Abstract Object detection plays an important role in the sorting process of mechanical fasteners. Although object detection has been studied for many years, it has always been an industrial problem. Edge-based model matching is only suitable for a small range of illumination changes, and the matching accuracy is low. The optical flow method and the difference method are sensitive to noise and light, and camshift tracking is less effective in complex backgrounds. In this paper, an improved target detection method based on YOLOv3-tiny is proposed. The redundant regression box generated by the prediction network is filtered by soft nonmaximum suppression (NMS)… More
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  • Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN
  • Abstract Accurate photovoltaic (PV) power prediction can effectively help the power sector to make rational energy planning and dispatching decisions, promote PV consumption, make full use of renewable energy and alleviate energy problems. To address this research objective, this paper proposes a prediction model based on kernel principal component analysis (KPCA), modified cuckoo search algorithm (MCS) and deep convolutional neural networks (DCNN). Firstly, KPCA is utilized to reduce the dimension of the feature, which aims to reduce the redundant input vectors. Then using MCS to optimize the parameters of DCNN. Finally, the photovoltaic power forecasting method of KPCA-MCS-DCNN is established. In… More
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