Vol.128, No.3, 2021-Table of Contents

On the Cover


To date, intelligent algorithms for topology optimization have been extensively studied to reduce the cost of calculation. In the article, a feature pyramid network is built with physical constraints to accelerate the design of topology optimization. A model with physical constraints not only guarantees high precision but also has better physical performance than a model without physical constraints.

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  • An Improved Data-Driven Topology Optimization Method Using Feature Pyramid Networks with Physical Constraints
  • Abstract Deep learning for topology optimization has been extensively studied to reduce the cost of calculation in recent years. However, the loss function of the above method is mainly based on pixel-wise errors from the image perspective, which cannot embed the physical knowledge of topology optimization. Therefore, this paper presents an improved deep learning model to alleviate the above difficulty effectively. The feature pyramid network (FPN), a kind of deep learning model, is trained to learn the inherent physical law of topology optimization itself, of which the loss function is composed of pixel-wise errors and physical constraints. Since the calculation of… More
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  • Damage Assessment of Reinforced Concrete Structures through Damage Indices: A State-of-the-Art Review
  • Abstract Due to the developments of computer science and technology in recent years, computer models and numerical simulations for large and complicated structures can be done. Among the vast information and results obtained from the analysis and simulations, the damage performance is of great importance since this damage might cause enormous losses for society and humanity, notably in cases of severe damage occurring. One of the most effective tools to handle the results about the damage performance of the structure is the damage index (DI) together with the damage states, which are used to correlate the damage indices with the damage… More
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  • Review of Computational Techniques for the Analysis of Abnormal Patterns of ECG Signal Provoked by Cardiac Disease
  • Abstract The 12-lead ECG aids in the diagnosis of myocardial infarction and is helpful in the prediction of cardiovascular disease complications. It does, though, have certain drawbacks. For other electrocardiographic anomalies such as Left Bundle Branch Block and Left Ventricular Hypertrophy syndrome, the ECG signal with Myocardial Infarction is difficult to interpret. These diseases cause variations in the ST portion of the ECG signal. It reduces the clarity of ECG signals, making it more difficult to diagnose these diseases. As a result, the specialist is misled into making an erroneous diagnosis by using the incorrect therapeutic technique. Based on these concepts,… More
  •   Views:955       Downloads:642        Download PDF
  • Quantile Version of Mathai-Haubold Entropy of Order Statistics
  • Abstract Many researchers measure the uncertainty of a random variable using quantile-based entropy techniques. These techniques are useful in engineering applications and have some exceptional characteristics than their distribution function method. Considering order statistics, the key focus of this article is to propose new quantile-based Mathai-Haubold entropy and investigate its characteristics. The divergence measure of the Mathai-Haubold is also considered and some of its properties are established. Further, based on order statistics, we propose the residual entropy of the quantile-based Mathai-Haubold and some of its property results are proved. The performance of the proposed quantile-based Mathai-Haubold entropy is investigated by simulation… More
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  • Multi-Floor Indoor Trajectory Reconstruction Using Mobile Devices
  • Abstract An indoor trajectory is the path of an object moving through corridors and stairs inside a building. There are various types of technologies that can be used to reconstruct the path of a moving object and detect its position. GPS has been used for reconstruction in outdoor environments, but for indoor environments, mobile devices with embedded sensors are used. An accelerometer sensor and a magnetometer sensor are used to detect human movement and reconstruct the trajectory on a single floor. In an indoor environment, there are many activities that will create the trajectory similar to an outdoor environment, such as… More
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  • Discontinuous-Galerkin-Based Analysis of Traffic Flow Model Connected with Multi-Agent Traffic Model
  • Abstract As the number of automobiles continues to increase year after year, the associated problem of traffic congestion has become a serious societal issue. Initiatives to mitigate this problem have considered methods for optimizing traffic volumes in wide-area road networks, and traffic-flow simulation has become a focus of interest as a technique for advance characterization of such strategies. Classes of models commonly used for traffic-flow simulations include microscopic models based on discrete vehicle representations, macroscopic models that describe entire traffic-flow systems in terms of average vehicle densities and velocities, and mesoscopic models and hybrid (or multiscale) models incorporating both microscopic and… More
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  • Cellular Automata Simulations of Random Pitting Process on Steel Reinforcement Surface
  • Abstract The corrosion of reinforcement in the concrete will cause the effective cross-sectional area of reinforcement to be weakened and the performance of reinforcement to change and lead to the degradation of the bond behavior between reinforcement and concrete, which can seriously affect the mechanical properties of the structural elements. Therefore, it is of great practical significance to accurately simulate the corrosion morphology and the corrosion products of reinforcement. This paper improves the previous cellular automata models and establishes a new cellular automata model framework for simulating the random pitting corrosion process of reinforcement in concrete. This model defines the detailed… More
  •   Views:651       Downloads:450       Cited by:1        Download PDF
  • Global and Graph Encoded Local Discriminative Region Representation for Scene Recognition
  • Abstract Scene recognition is a fundamental task in computer vision, which generally includes three vital stages, namely feature extraction, feature transformation and classification. Early research mainly focuses on feature extraction, but with the rise of Convolutional Neural Networks (CNNs), more and more feature transformation methods are proposed based on CNN features. In this work, a novel feature transformation algorithm called Graph Encoded Local Discriminative Region Representation (GEDRR) is proposed to find discriminative local representations for scene images and explore the relationship between the discriminative regions. In addition, we propose a method using the multi-head attention module to enhance and fuse convolutional… More
  •   Views:656       Downloads:449        Download PDF
  • Thermally Induced Vibration Analysis of Flexible Beams Based on Isogeometric Analysis
  • Abstract Spacecraft flexible appendages may experience thermally induced vibrations (TIV) under sudden heating loads, which in consequence will be unable to complete their intended missions. Isogeometric analysis (IGA) utilizes, in an isoparametric concept, the same high order and high continuity non-uniform rational B-splines (NURBS) to represent both the geometry and the physical field of the structure. Compared to the traditional Lagrange polynomial based finite element method where only C0-continuity across elements can be achieved, IGA is geometrically exact and naturally fulfills the C1-continuity requirement of Euler–Bernoulli (EB) beam elements, therefore, does not need extra rotational degrees-of-freedom. In this paper, we present… More
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  • Reliability Analysis of Piled Raft Foundation Using a Novel Hybrid Approach of ANN and Equilibrium Optimizer
  • Abstract In many civil engineering projects, Piled Raft Foundations (PRFs) are usually preferred where the incoming load from the superstructures is very high. In geotechnical engineering practice, the settlement of soil layers is a critical issue for the serviceability of the structures. Thus, assessment of risk associated with the structures corresponding to the maximum allowable settlement of soils needs to be carried out in the design phase. In this study, reliability analysis of PRF based on settlement criteria is performed using a high-performance hybrid soft computing model. The new approach is an integration of the artificial neural network (ANN) and a… More
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  • Improve the Accuracy of Fall Detection Based on Artificial Intelligence Algorithm
  • Abstract This work presents a fall detection system based on artificial intelligence. The system incorporates miniature wearable devices for fall detection. Fall detection is achieved by integrating a three-axis gyroscope and a three-axis accelerometer. The system gathers the differential data collected by the gyroscope and accelerometer, applies artificial intelligence algorithms for model training and constructs an effective model for fall detection. To provide easy wearing and effective position detection, it is designed as a small device attached to the user’s waist. Experiment results have shown that the accuracy of the proposed fall detection model is up to 98%, demonstrating the effectiveness… More
  •   Views:923       Downloads:656        Download PDF
  • Thermoelastic Structural Topology Optimization Based on Moving Morphable Components Framework
  • Abstract This study investigates structural topology optimization of thermoelastic structures considering two kinds of objectives of minimum structural compliance and elastic strain energy with a specified available volume constraint. To explicitly express the configuration evolution in the structural topology optimization under combination of mechanical and thermal load conditions, the moving morphable components (MMC) framework is adopted. Based on the characteristics of the MMC framework, the number of design variables can be reduced substantially. Corresponding optimization formulation in the MMC topology optimization framework and numerical solution procedures are developed for several numerical examples. Different optimization results are obtained with structural compliance and… More
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  • Functionally Graded Cellular Structure Design Using the Subdomain Level Set Method with Local Volume Constraints
  • Abstract Functional graded cellular structure (FGCS) usually shows superior mechanical behavior with low density and high stiffness. With the development of additive manufacturing, functional graded cellular structure gains its popularity in industries. In this paper, a novel approach for designing functionally graded cellular structure is proposed based on a subdomain parameterized level set method (PLSM) under local volume constraints (LVC). In this method, a subdomain level set function is defined, parameterized and updated on each subdomain independently making the proposed approach much faster and more cost-effective. Additionally, the microstructures on arbitrary two adjacent subdomains can be connected perfectly without any additional… More
  •   Views:830       Downloads:604       Cited by:1        Download PDF
  • A Step-Based Deep Learning Approach for Network Intrusion Detection
  • Abstract In the network security field, the network intrusion detection system (NIDS) is considered one of the critical issues in the detection accuracy and missed detection rate. In this paper, a method of two-step network intrusion detection on the basis of GoogLeNet Inception and deep convolutional neural networks (CNNs) models is proposed. The proposed method used the GoogLeNet Inception model to identify the network packets’ binary problem. Subsequently, the characteristics of the packets’ raw data and the traffic features are extracted. The CNNs model is also used to identify the multiclass intrusions by the network packets’ features. In the experimental results,… More
  •   Views:942       Downloads:683        Download PDF
  • Solution of Modified Bergman Minimal Blood Glucose-Insulin Model Using Caputo-Fabrizio Fractional Derivative
  • Abstract Diabetes is a burning issue in the whole world. It is the imbalance between body glucose and insulin. The study of this imbalance is very much needed from a research point of view. For this reason, Bergman gave an important model named-Bergman minimal model. In the present work, using Caputo-Fabrizio (CF) fractional derivative, we generalize Bergman’s minimal blood glucose-insulin model. Further, we modify the old model by including one more component known as diet D(t), which is also essential for the blood glucose model. We solve the modified model with the help of Sumudu transform and fixed-point iteration procedures. Also,… More
  •   Views:962       Downloads:684        Download PDF