Vol.130, No.3, 2022-Table of Contents
  • Parameter Estimation Based on Censored Data under Partially Accelerated Life Testing for Hybrid Systems due to Unknown Failure Causes
  • Abstract In general, simple subsystems like series or parallel are integrated to produce a complex hybrid system. The reliability of a system is determined by the reliability of its constituent components. It is often extremely difficult or impossible to get specific information about the component that caused the system to fail. Unknown failure causes are instances in which the actual cause of system failure is unknown. On the other side, thanks to current advanced technology based on computers, automation, and simulation, products have become incredibly dependable and trustworthy, and as a result, obtaining failure data for testing such exceptionally reliable items… More
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  • Deep Learning-Based Cancer Detection-Recent Developments, Trend and Challenges
  • Abstract Cancer is one of the most critical diseases that has caused several deaths in today’s world. In most cases, doctors and practitioners are only able to diagnose cancer in its later stages. In the later stages, planning cancer treatment and increasing the patient’s survival rate becomes a very challenging task. Therefore, it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery planning. Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases, including cancer disease. However, manual interpretation of medical images is… More
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  • Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images
  • Abstract In telemedicine, the realization of reversible watermarking through information security is an emerging research field. However, adding watermarks hinders the distribution of pixels in the cover image because it creates distortions (which lead to an increase in the detection probability). In this article, we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high security. The proposed method selects two adjacent gray pixels whose least significant bit (LSB) is different from the relevant message bit and then calculates the distortion degree. We use the LSB pairing method to embed the secret matrix of patient record into… More
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  • Discussion of the Fluid Acceleration Quality of a Ducted Propulsion System on the Propulsive Performance
  • Abstract This paper focuses on the ducted propulsion with the accelerating nozzle, and discusses the influence of its fluid acceleration quality on its propulsive performances, including the hull efficiency, the relative rotative efficiency, the effective wake, and the thrust deduction factor. An actual ducted propulsion system is used as an example for computational analysis. The computational conditions are divided into four combinations, which are provided with different propeller pitches, cambers, and duct lengths. The method applied in this study is the Computational Fluid Dynamics (CFD) technology, and the contents of the calculation include the hull's viscous resistance, the wave-making resistance, the… More
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  • The Method of Fundamental Solutions for Two-Dimensional Elastostatic Problems with Stress Concentration and Highly Anisotropic Materials
  • Abstract The method of fundamental solutions (MFS) is a boundary-type and truly meshfree method, which is recognized as an efficient numerical tool for solving boundary value problems. The geometrical shape, boundary conditions, and applied loads can be easily modeled in the MFS. This capability makes the MFS particularly suitable for shape optimization, moving load, and inverse problems. However, it is observed that the standard MFS lead to inaccurate solutions for some elastostatic problems with stress concentration and/or highly anisotropic materials. In this work, by a numerical study, the important parameters, which have significant influence on the accuracy of the MFS for… More
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  • A Fast Approach for Predicting Aerodynamic Noise Sources of High-Speed Train Running in Tunnel
  • Abstract The aerodynamic noise of high-speed trains passing through a tunnel has gradually become an important issue. Numerical approaches for predicting the aerodynamic noise sources of high-speed trains running in tunnels are the key to alleviating aerodynamic noise issues. In this paper, two typical numerical methods are used to calculate the aerodynamic noise of high-speed trains. These are the static method combined with non-reflective boundary conditions and the dynamic mesh method combined with adaptive mesh. The fluctuating pressure, flow field and aerodynamic noise source are numerically simulated using the above methods. The results show that the fluctuating pressure, flow field structure… More
  •   Views:378       Downloads:319       Cited by:1        Download PDF
  • An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records
  • Abstract The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare. Despite the phenomenal advancement in the present healthcare services, the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients’ data. A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders. Various approaches and methods have been practiced by researchers to secure healthcare digital services. However, there are very… More
  •   Views:478       Downloads:314        Download PDF
  • Deep Learning-Based Algorithm for Multi-Type Defects Detection in Solar Cells with Aerial EL Images for Photovoltaic Plants
  • Abstract Defects detection with Electroluminescence (EL) image for photovoltaic (PV) module has become a standard test procedure during the process of production, installation, and operation of solar modules. There are some typical defects types, such as crack, finger interruption, that can be recognized with high accuracy. However, due to the complexity of EL images and the limitation of the dataset, it is hard to label all types of defects during the inspection process. The unknown or unlabeled create significant difficulties in the practical application of the automatic defects detection technique. To address the problem, we proposed an evolutionary algorithm combined with… More
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  • A Novel Indoor Positioning Framework
  • Abstract Current positioning systems are primarily based on the Global Positioning System (GPS). Although the GPS is accurate within 10 m, it is mainly used for outdoor positioning services (Location-Based Service; LBS). However, since satellite signals cannot penetrate buildings, indoor positioning has always been a blind spot for satellite signals. As indoor positioning applications are extensive with high commercial values, they have created a competitive niche in the market. Existing indoor positioning technologies are unable to achieve less than 10 cm accuracy except for the Ultra Wide Band (UWB) technology. On the other hand, the Bluetooth protocol achieves an accuracy of… More
  •   Views:434       Downloads:378        Download PDF
  • Strengthened Initialization of Adaptive Cross-Generation Differential Evolution
  • Abstract Adaptive Cross-Generation Differential Evolution (ACGDE) is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms (EAs). However, its convergence and diversity are not satisfactory compared with the latest algorithms. In order to adapt to the current environment, ACGDE requires improvements in many aspects, such as its initialization and mutant operator. In this paper, an enhanced version is proposed, namely SIACGDE. It incorporates a strengthened initialization strategy and optimized parameters in contrast to its predecessor. These improvements make the direction of crossgeneration mutation more clearly and the ability of searching more efficiently. The experiments show… More
  •   Views:914       Downloads:517        Download PDF
  • Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models
  • Abstract This investigative study is focused on the impact of wavelet on traditional forecasting time-series models, which significantly shows the usage of wavelet algorithms. Wavelet Decomposition (WD) algorithm has been combined with various traditional forecasting time-series models, such as Least Square Support Vector Machine (LSSVM), Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS) and their effects are examined in terms of the statistical estimations. The WD has been used as a mathematical application in traditional forecast modelling to collect periodically measured parameters, which has yielded tremendous constructive outcomes. Further, it is observed that the wavelet combined models are classy… More
  •   Views:447       Downloads:421        Download PDF
  • Deep Neural Network with Strip Pooling for Image Classification of Yarn-Dyed Plaid Fabrics
  • Abstract Historically, yarn-dyed plaid fabrics (YDPFs) have enjoyed enduring popularity with many rich plaid patterns, but production data are still classified and searched only according to production parameters. The process does not satisfy the visual needs of sample order production, fabric design, and stock management. This study produced an image dataset for YDPFs, collected from 10,661 fabric samples. The authors believe that the dataset will have significant utility in further research into YDPFs. Convolutional neural networks, such as VGG, ResNet, and DenseNet, with different hyperparameter groups, seemed the most promising tools for the study. This paper reports on the authors’ exhaustive… More
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  • On Single Valued Neutrosophic Regularity Spaces
  • Abstract This article aims to present new terms of single-valued neutrosophic notions in the Šostak sense, known as single-valued neutrosophic regularity spaces. Concepts such as r-single-valued neutrosophic semi £-open, r-single-valued neutrosophic pre-£-open, r-single valued neutrosophic regular-£-open and r-single valued neutrosophic α£-open are defined and their properties are studied as well as the relationship between them. Moreover, we introduce the concept of r-single valued neutrosophic θ£-cluster point and r-single-valued neutrosophic γ £-cluster point, r-θ£-closed, and θ£-closure operators and study some of their properties. Also, we present and investigate the notions of r-single-valued neutrosophic θ£-connectedness and r-single valued neutrosophic δ£-connectedness and investigate relationship… More
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  • Dynamic Performance of Straddle Monorail Curved Girder Bridge
  • Abstract In this work, a monorail vehicle-bridge coupling (VBC) model capable of accurately considering curve alignment and superelevation is established based on curvilinear moving coordinate system, to study the VBC vibration of straddle monorail curved girder bridge and the relevant factors influencing VBC. While taking Chongqing Jiao Xin line as an example, the VBC program is compiled using Fortran, where the reliability of algorithm and program is verified by the results of Chongqing monorail test. Moreover, the effects of curve radius, vehicle speed, and track irregularity on the corresponding vehicle and bridge vibrations are compared and analyzed. It is observed that… More
  •   Views:410       Downloads:312        Download PDF
  • Image Reconstruction for ECT under Compressed Sensing Framework Based on an Overcomplete Dictionary
  • Abstract Electrical capacitance tomography (ECT) has great application potential in multiphase process monitoring, and its visualization results are of great significance for studying the changes in two-phase flow in closed environments. In this paper, compressed sensing (CS) theory based on dictionary learning is introduced to the inverse problem of ECT, and the K-SVD algorithm is used to learn the overcomplete dictionary to establish a nonlinear mapping between observed capacitance and sparse space. Because the trained overcomplete dictionary has the property to match few features of interest in the reconstructed image of ECT, it is not necessary to rely on the sparsity… More
  •   Views:413       Downloads:356        Download PDF
  • Complex Network Formation and Analysis of Online Social Media Systems
  • Abstract To discover and identify the influential nodes in any complex network has been an important issue. It is a significant factor in order to control over the network. Through control on a network, any information can be spread and stopped in a short span of time. Both targets can be achieved, since network of information can be extended and as well destroyed. So, information spread and community formation have become one of the most crucial issues in the world of SNA (Social Network Analysis). In this work, the complex network of twitter social network has been formalized and results are… More
  •   Views:871       Downloads:513        Download PDF
  • A Novel Bidirectional Interaction Model and Electric Energy Measuring Scheme of EVs for V2G with Distorted Power Loads
  • Abstract With the increasing demand for petroleum resources and environmental issues, new energy electric vehicles are increasingly being used. However, the large number of electric vehicles connected to the grid has brought new challenges to the operation of the grid. Firstly, A novel bidirectional interaction model is established based on modulation theory with nonlinear loads. Then, the electric energy measuring scheme of EVs for V2G is derived under the conditions of distorted power loads. The scheme is composed of fundamental electric energy, fundamental-distorted electric energy, distorted-fundamental electric energy and distorted electric energy. And the characteristics of each electric energy are analyzed.… More
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  • Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations
  • Abstract This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm (M-AM-SGRPIA) for a class of single input single output (SISO) linear output error models with multi-threshold quantized observations. It proves the convergence of the designed algorithm. A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output (SIMO) or SISO nonlinear systems, and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system. The system input design is accomplished using the measurement technology of random repeatability test, and the probabilistic characteristic… More
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  • Action Recognition Based on CSI Signal Using Improved Deep Residual Network Model
  • Abstract In this paper, we propose an improved deep residual network model to recognize human actions. Action data is composed of channel state information signals, which are continuous fine-grained signals. We replaced the traditional identity connection with the shrinking threshold module. The module automatically adjusts the threshold of the action data signal, and filters out signals that are not related to the principal components. We use the attention mechanism to improve the memory of the network model to the action signal, so as to better recognize the action. To verify the validity of the experiment more accurately, we collected action data… More
  •   Views:400       Downloads:344        Download PDF
  • Localization of Mobile Robot Aided for Large-Scale Construction Based on Optimized Artificial Landmark Map in Ongoing Scene
  • Abstract The effectiveness of mobile robot aided for architectural construction depends strongly on its accurate localization ability. Localization of mobile robot is increasingly important for the printing of buildings in the construction scene. Although many available studies on the localization have been conducted, only a few studies have addressed the more challenging problem of localization for mobile robot in large-scale ongoing and featureless scenes. To realize the accurate localization of mobile robot in designated stations, we build an artificial landmark map and propose a novel nonlinear optimization algorithm based on graphs to reduce the uncertainty of the whole map. Then, the… More
  •   Views:417       Downloads:373        Download PDF