Online First
  • An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site
  • Abstract To meet the requirements of specifications, intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable construction. As one of the most important building materials in construction engineering, reinforcing bars (rebar) account for more than 30% of the cost in civil engineering. A significant amount of cutting waste is generated during the construction phase. Excessive cutting waste increases construction costs and generates a considerable amount of COCO2 emission. This study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to… More
  •   Views:45       Downloads:17        Download PDF
  • Unique Solution of Integral Equations via Intuitionistic Extended Fuzzy b-Metric-Like Spaces
  • Abstract In this manuscript, our goal is to introduce the notion of intuitionistic extended fuzzy b-metric-like spaces. We establish some fixed point theorems in this setting. Also, we plot some graphs of an example of obtained result for better understanding. We use the concepts of continuous triangular norms and continuous triangular conorms in an intuitionistic fuzzy metric-like space. Triangular norms are used to generalize with the probability distribution of triangle inequality in metric space conditions. Triangular conorms are known as dual operations of triangular norms. The obtained results boost the approaches of existing ones in the literature and are supported by… More
  •   Views:52       Downloads:19        Download PDF
  • An Intelligent Cluster Verification Model Using WSN to Avoid Close Proximity and Control Outbreak of Pandemic in a Massive Crowd
  • Abstract Assemblage at public places for religious or sports events has become an integral part of our lives. These gatherings pose a challenge at places where fast crowd verification with social distancing (SD) is required, especially during a pandemic. Presently, verification of crowds is carried out in the form of a queue that increases waiting time resulting in congestion, stampede, and the spread of diseases. This article proposes a cluster verification model (CVM) using a wireless sensor network (WSN), single cluster approach (SCA), and split cluster approach (SpCA) to solve the aforementioned problem for pandemic cases. We show that SD, cluster… More
  •   Views:34       Downloads:11        Download PDF
  • Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review
  • Abstract Hyperspectral image (HSI) classification has been one of the most important tasks in the remote sensing community over the last few decades. Due to the presence of highly correlated bands and limited training samples in HSI, discriminative feature extraction was challenging for traditional machine learning methods. Recently, deep learning based methods have been recognized as powerful feature extraction tool and have drawn a significant amount of attention in HSI classification. Among various deep learning models, convolutional neural networks (CNNs) have shown huge success and offered great potential to yield high performance in HSI classification. Motivated by this successful performance, this… More
  •   Views:41       Downloads:14        Download PDF
  • Research on Volt/Var Control of Distribution Networks Based on PPO Algorithm
  • Abstract In this paper, a model free volt/var control (VVC) algorithm is developed by using deep reinforcement learning (DRL). We transform the VVC problem of distribution networks into the network framework of PPO algorithm, in order to avoid directly solving a large-scale nonlinear optimization problem. We select photovoltaic inverters as agents to adjust system voltage in a distribution network, taking the reactive power output of inverters as action variables. An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment. OPENDSS is used to output system node voltage and network loss. This method realizes… More
  •   Views:69       Downloads:19        Download PDF
  • Application of Automated Guided Vehicles in Smart Automated Warehouse Systems: A Survey
  • Abstract Automated Guided Vehicles (AGVs) have been introduced into various applications, such as automated warehouse systems, flexible manufacturing systems, and container terminal systems. However, few publications have outlined problems in need of attention in AGV applications comprehensively. In this paper, several key issues and essential models are presented. First, the advantages and disadvantages of centralized and decentralized AGVs systems were compared; second, warehouse layout and operation optimization were introduced, including some omitted areas, such as AGVs fleet size and electrical energy management; third, AGVs scheduling algorithms in chessboardlike environments were analyzed; fourth, the classical route-planning algorithms for single AGV and multiple… More
  •   Views:53       Downloads:15        Download PDF
  • Peridynamic Shell Model Based on Micro-Beam Bond
  • Abstract Peridynamics (PD) is a non-local mechanics theory that overcomes the limitations of classical continuum mechanics (CCM) in predicting the initiation and propagation of cracks. However, the calculation efficiency of PD models is generally lower than that of the traditional finite element method (FEM). Structural idealization can greatly improve the calculation efficiency of PD models for complex structures. This study presents a PD shell model based on the micro-beam bond via the homogenization assumption. First, the deformations of each endpoint of the micro-beam bond are calculated through the interpolation method. Second, the micro-potential energy of the axial, torsional, and bending deformations… More
  •   Views:66       Downloads:19        Download PDF
  • Overview of 3D Human Pose Estimation
  • Abstract 3D human pose estimation is a major focus area in the field of computer vision, which plays an important role in practical applications. This article summarizes the framework and research progress related to the estimation of monocular RGB images and videos. An overall perspective of methods integrated with deep learning is introduced. Novel image-based and video-based inputs are proposed as the analysis framework. From this viewpoint, common problems are discussed. The diversity of human postures usually leads to problems such as occlusion and ambiguity, and the lack of training datasets often results in poor generalization ability of the model. Regression… More
  •   Views:50       Downloads:15        Download PDF
  • A Novel RFID Localization Approach to Smart Self-Service Borrowing and Returning System
  • Abstract The misreading problem of a passive ultra-high-frequency (UHF) radio frequency identification (RFID) tag is a frequent problem arising in the field of librarianship. Unfortunately, existing solutions are something inefficient, e.g., extra resource requirement, inaccuracy, and empiricism. To this end, under comprehensive analysis on the passive UHF RFID application in the librarianship scenario, a novel and judicious approach based on RFID localization is proposed to address such a misreading problem. Extensive simulation results show that the proposed approach can outperform the existing ones and can be an attractive candidate in practice. More
  •   Views:151       Downloads:46        Download PDF
  • A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network
  • Abstract Loosening detection; cascade deep convolutional neural network; object localization; saliency detection problem of bolts on axlebox covers. Firstly, an SSD network based on ResNet50 and CBAM module by improving bolt image features is proposed for locating bolts on axlebox covers. And then, the A2-PFN is proposed according to the slender features of the marker lines for extracting more accurate marker lines regions of the bolts. Finally, a rectangular approximation method is proposed to regularize the marker line regions as a way to calculate the angle of the marker line and plot all the angle values into an angle table, according… More
  •   Views:92       Downloads:39        Download PDF
  • A Hybrid BPNN-GARF-SVR Prediction Model Based on EEMD for Ship Motion
  • Abstract Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time series analysis method and many machine learning methods such as neural networks, support vector machines regression (SVR) have been widely used in ship motion predictions. However, these single models have certain limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition (EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction model optimized by genetic algorithm… More
  •   Views:122       Downloads:41        Download PDF
  • A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems
  • Abstract Task offloading is an important concept for edge computing and the Internet of Things (IoT) because computationintensive tasks must be offloaded to more resource-powerful remote devices. Task offloading has several advantages, including increased battery life, lower latency, and better application performance. A task offloading method determines whether sections of the full application should be run locally or offloaded for execution remotely. The offloading choice problem is influenced by several factors, including application properties, network conditions, hardware features, and mobility, influencing the offloading system’s operational environment. This study provides a thorough examination of current task offloading and resource allocation in edge… More
  •   Views:115       Downloads:37        Download PDF
  • A Multi-Scale Grasp Detector Based on Fully Matching Model
  • Abstract Robotic grasping is an essential problem at both the household and industrial levels, and unstructured objects have always been difficult for grippers. Parallel-plate grippers and algorithms, focusing on partial information of objects, are one of the widely used approaches. However, most works predict single-size grasp rectangles for fixed cameras and gripper sizes. In this paper, a multi-scale grasp detector is proposed to predict grasp rectangles with different sizes on RGB-D or RGB images in real-time for hand-eye cameras and various parallel-plate grippers. The detector extracts feature maps of multiple scales and conducts predictions on each scale independently. To guarantee independence… More
  •   Views:107       Downloads:37        Download PDF
  • Explainable Artificial Intelligence–A New Step towards the Trust in Medical Diagnosis with AI Frameworks: A Review
  • Abstract Machine learning (ML) has emerged as a critical enabling tool in the sciences and industry in recent years. Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks–thanks to advancements in technique, the availability of enormous databases, and improved computing power. Deep learning models are at the forefront of this advancement. However, because of their nested nonlinear structure, these strong models are termed as “black boxes,” as they provide no information about how they arrive at their conclusions. Such a lack of transparencies may be unacceptable in many applications, such as the medical domain. A… More
  •   Views:129       Downloads:42        Download PDF
  • Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges
  • Abstract The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power industry. However, with the convergence and increase in power data flow, the data dispatching network and the main station dispatching automation system have encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification of dispatching automation systems has been widely investigated. In this paper, we perform a comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss unresolved research issues and outline future directions in this… More
  •   Views:124       Downloads:37        Download PDF
  • Systematic Approach for Web Protection Runtime Tools’ Effectiveness Analysis
  • Abstract Web applications represent one of the principal vehicles by which attackers gain access to an organization’s network or resources. Thus, different approaches to protect web applications have been proposed to date. Of them, the two major approaches areWeb Application Firewalls (WAF) and Runtime Application Self Protection (RASP). It is, thus, essential to understand the differences and relative effectiveness of both these approaches for effective decisionmaking regarding the security of web applications. Here we present a comparative study between WAF and RASP simulated settings, with the aim to compare their effectiveness and efficiency against different categories of attacks. For this, we… More
  •   Views:138       Downloads:41        Download PDF
  • Performance Analysis of an Artificial Intelligence Nanosystem with Biological Internet of Nano Things
  • Abstract Artificial intelligence (AI) has recently been used in nanomedical applications, in which implanted intelligent nanosystems inside the human body were used to diagnose and treat a variety of ailments with the help of the Internet of biological Nano Things (IoBNT). Biological circuit engineering or nanomaterial-based architectures can be used to approach the nanosystem. In nanomedical applications, the blood vascular medium serves as a communication channel, demonstrating a molecular communication system based on flow and diffusion. This paper presents a performance study of the channel capacity for flow-based-diffusive molecular communication nanosystems that takes into account the ligand-receptor binding mechanism. Unlike earlier… More
  •   Views:154       Downloads:66        Download PDF
  • Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors
  • Abstract Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown. Recently, Motor Current Signature Analysis (MCSA) is widely reported as a condition monitoring technique in the detection and identification of individual and multiple Induction Motor (IM) faults. However, checking the fault detection and classification with deep learning models and its comparison among themselves or conventional approaches is rarely reported in the literature. Therefore, in this work, we present the detection and identification of induction motor faults with MCSA and three Deep Learning (DL) models namely MLP, LSTM, and… More
  •   Views:322       Downloads:55        Download PDF
  • Analysis and Simulations of Open-Source Intelligence Process System Dynamics from User’s Perspective
  • Abstract In today’s society with advanced Internet, the amount of information increases dramatically with each passing day, which leads to increasingly complex processes of open-source intelligence. Therefore, it is more important to rationalize the operation mode and improve the operation efficiency of open-source intelligence under the premise of satisfying users’ needs. This paper focuses on the simulation study of the process system of opensource intelligence from the user’s perspective. First, the basic concept and development status of open-source intelligence are introduced in details. Second, six existing intelligence operation process models are summarized and their advantages and disadvantages are compared in focus.… More
  •   Views:138       Downloads:36        Download PDF
  • 6G-Enabled Internet of Things: Vision, Techniques, and Open Issues
  • Abstract There are changes in the development of wireless technology systems every decade. 6G (sixth generation) wireless networks improve on previous generations by increasing dependability, accelerating networks, increasing available bandwidth, decreasing latency, and increasing data transmission speed to standardize communication signals. The purpose of this article is to comprehend the current directions in 6G studies and their relationship to the Internet of Things (IoT). Also, this paper discusses the impacts of 6G on IoT, critical requirements and trends for 6G-enabled IoT, new service classes of 6G and IoT technologies, and current 6G-enabled IoT studies selected by the systematic literature review (SLR)… More
  •   Views:164       Downloads:46        Download PDF
  • Recent Progress on Aeroelasticity of High-Performance Morphing UAVs
  • Abstract The high-performance morphing aircraft has become a research focus all over the world. The morphing aircraft, unlike regular unmanned aerial vehicles (UAVs), has more complicated aerodynamic characteristics, making itmore difficultto conduct its design, model analysis, and experimentation. This paper reviews the recent process and the current status of aeroelastic issues, numerical simulations, and wind tunnel test of morphing aircrafts. The evaluation of aerodynamic characteristics, mechanism, and relevant unsteady dynamic aerodynamic modeling throughout the morphing process are the primary technological bottlenecks formorphing aircrafts. The unstable aerodynamic forces have a significant impact on the aircraft handling characteristics, control law design, and flight… More
  •   Views:127       Downloads:38        Download PDF
  • Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models
  • Abstract The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visual methods, which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise. As a result, a robust, reliable, and repeatable method of damage identification is required. Ensemble learning algorithms for identifying structural damage are evaluated in this article, which use deep convolutional neural networks, including simple averaging, integrated stacking, separate stacking, and hybrid weighted averaging ensemble and differential evolution (WAE-DE) ensemble models. Damage identification is carried out on three types of damage. The proposed algorithms are… More
  •   Views:296       Downloads:80        Download PDF
  • Water Quality Index Using Modified Random Forest Technique: Assessing Novel Input Features
  • Abstract Water quality analysis is essential to understand the ecological status of aquatic life. Conventional water quality index (WQI) assessment methods are limited to features such as water acidic or basicity (pH), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH3-N), and suspended solids (SS). These features are often insufficient to represent the water quality of a heavy metal–polluted river. Therefore, this paper aims to explore and analyze novel input features in order to formulate an improved WQI. In this work, prospective insights on the feasibility of alternative water quality input variables as new discriminant features… More
  •   Views:250       Downloads:76        Download PDF
  • A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression
  • Abstract Accurate prediction of monthly oil and gas production is essential for oil enterprises to make reasonable production plans, avoid blind investment and realize sustainable development. Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise, and the application conditions are very demanding. With the rapid development of artificial intelligence technology, big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development. Based on the data-driven artificial intelligence algorithm Gradient Boosting Decision Tree (GBDT), this paper predicts the initial single-layer production by considering geological data, fluid PVT… More
  •   Views:260       Downloads:92        Download PDF
  • Regarding Deeper Properties of the Fractional Order Kundu-Eckhaus Equation and Massive Thirring Model
  • Abstract In this paper, the fractional natural decomposition method (FNDM) is employed to find the solution for the KunduEckhaus equation and coupled fractional differential equations describing the massive Thirring model. The massive Thirring model consists of a system of two nonlinear complex differential equations, and it plays a dynamic role in quantum field theory. The fractional derivative is considered in the Caputo sense, and the projected algorithm is a graceful mixture of Adomian decomposition scheme with natural transform technique. In order to illustrate and validate the efficiency of the future technique, we analyzed projected phenomena in terms of fractional order. Moreover,… More
  •   Views:276       Downloads:115        Download PDF
  • An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder
  • Abstract Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption of medical images is very important to secure patient information. Encrypting these images consumes a lot of time on edge computing; therefore, the use of an auto-encoder for compression before encoding will solve such a problem. In this paper, we use an auto-encoder to compress a medical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the vector was received and decrypted.… More
  •   Views:328       Downloads:93        Download PDF
  • Prediction of Photosynthetic Carbon Assimilation Rate of Individual Rice Leaves under Changes in Light Environment Using BLSTM-Augmented LSTM
  • Abstract A model to predict photosynthetic carbon assimilation rate (A) with high accuracy is important for forecasting crop yield and productivity. Long short-term memory (LSTM), a neural network suitable for time-series data, enables prediction with high accuracy but requires mesophyll variables. In addition, for practical use, it is desirable to have a technique that can predict A from easily available information. In this study, we propose a BLSTMaugmented LSTM (BALSTM) model, which utilizes bi-directional LSTM (BLSTM) to indirectly reproduce the mesophyll variables required for LSTM. The most significant feature of the proposed model is that its hybrid architecture uses only three… More
  •   Views:296       Downloads:90        Download PDF
  • An Intelligent Prediction Model for Target Protein Identification in Hepatic Carcinoma Using Novel Graph Theory and ANN Model
  • Abstract Hepatocellular carcinoma (HCC) is one major cause of cancer-related mortality around the world. However, at advanced stages of HCC, systematic treatment options are currently limited. As a result, new pharmacological targets must be discovered regularly, and then tailored medicines against HCC must be developed. In this research, we used biomarkers of HCC to collect the protein interaction network related to HCC. Initially, DC (Degree Centrality) was employed to assess the importance of each protein. Then an improved Graph Coloring algorithm was used to rank the target proteins according to the interaction with the primary target protein after assessing the top… More
  •   Views:234       Downloads:82        Download PDF
  • Performance Evaluation of Electromagnetic Shield Constructed from Open-Cell Metal Foam Based on Sphere Functions
  • Abstract This study evaluates the performance of a model of open-cell metal foams generated by sphere functions. To this end, an electromagnetic shield constructed from the model was inserted between two horn antennas in an electromagnetic wave propagation simulation. The foam-hole diameter in the electromagnetic shield model was varied as d = 2.5 and 5.0 mm, and the frequency of the electromagnetic waves was varied from 3 to 13 GHz. In the numerical experiments of shield effectiveness, the shields with foam holes of both diameters attenuated the electromagnetic waves across the studied frequency range. The shield effectiveness was enhanced at low… More
  •   Views:248       Downloads:87        Download PDF
  • Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations
  • Abstract Fixed-time synchronization (FTS) of delayed memristor-based neural networks (MNNs) with discontinuous activations is studied in this paper. Both continuous and discontinuous activations are considered for MNNs. And the mixed delays which are closer to reality are taken into the system. Besides, two kinds of control schemes are proposed, including feedback and adaptive control strategies. Based on some lemmas, mathematical inequalities and the designed controllers, a few synchronization criteria are acquired. Moreover, the upper bound of settling time (ST) which is independent of the initial values is given. Finally, the feasibility of our theory is attested by simulation examples. More
  •   Views:252       Downloads:87        Download PDF
  • Prerequisite Relations among Knowledge Units: A Case Study of Computer Science Domain
  • Abstract The importance of prerequisites for education has recently become a promising research direction. This work proposes a statistical model for measuring dependencies in learning resources between knowledge units. Instructors are expected to present knowledge units in a semantically well-organized manner to facilitate students’ understanding of the material. The proposed model reveals how inner concepts of a knowledge unit are dependent on each other and on concepts not in the knowledge unit. To help understand the complexity of the inner concepts themselves, WordNet is included as an external knowledge base in this model. The goal is to develop a model that… More
  •   Views:239       Downloads:84        Download PDF
  • Numerical Solutions of Fractional Variable Order Differential Equations via Using Shifted Legendre Polynomials
  • Abstract In this manuscript, an algorithm for the computation of numerical solutions to some variable order fractional differential equations (FDEs) subject to the boundary and initial conditions is developed. We use shifted Legendre polynomials for the required numerical algorithm to develop some operational matrices. Further, operational matrices are constructed using variable order differentiation and integration. We are finding the operational matrices of variable order differentiation and integration by omitting the discretization of data. With the help of aforesaid matrices, considered FDEs are converted to algebraic equations of Sylvester type. Finally, the algebraic equations we get are solved with the help of… More
  •   Views:332       Downloads:132        Download PDF
  • Research on Leak Location Method of Water Supply Pipeline Based on MVMD
  • Abstract At present, the leakage rate of the water distribution network in China is still high, and the waste of water resources caused by water distribution network leakage is quite serious every year. Therefore, the location of pipeline leakage is of great significance for saving water resources and reducing economic losses. Acoustic emission technology is the most widely used pipeline leak location technology. The traditional non-stationary random signal de-noising method mainly relies on the estimation of noise parameters, ignoring periodic noise and components unrelated to pipeline leakage. Aiming at the above problems, this paper proposes a leak location method for water… More
  •   Views:275       Downloads:97        Download PDF
  • A Hybrid Local/Nonlocal Continuum Mechanics Modeling of Damage and Fracture in Concrete Structure at High Temperatures
  • Abstract This paper proposes a hybrid peridynamic and classical continuum mechanical model for the high-temperature damage and fracture analysis of concrete structures. In this model, we introduce the thermal expansion into peridynamics and then couple it with the thermoelasticity based on the Morphing method. In addition, a thermomechanical constitutive model of peridynamic bond is presented inspired by the classic Mazars model for the quasi-brittle damage evolution of concrete structures under high-temperature conditions. The validity and effectiveness of the proposed model are verified through two-dimensional numerical examples, in which the influence of temperature on the damage behavior of concrete structures is investigated.… More
  •   Views:284       Downloads:105        Download PDF
  • Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances, Challenges and Future Directions
  • Abstract Software-Defined Networking (SDN) enables flexibility in developing security tools that can effectively and efficiently analyze and detect malicious network traffic for detecting intrusions. Recently Machine Learning (ML) techniques have attracted lots of attention from researchers and industry for developing intrusion detection systems (IDSs) considering logically centralized control and global view of the network provided by SDN. Many IDSs have developed using advances in machine learning and deep learning. This study presents a comprehensive review of recent work of ML-based IDS in context to SDN. It presents a comprehensive study of the existing review papers in the field. It is followed… More
  •   Views:244       Downloads:96        Download PDF
  • Image Representations of Numerical Simulations for Training Neural Networks
  • Abstract A large amount of data can partly assure good fitting quality for the trained neural networks. When the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice, numerical simulations can provide a large amount of controlled high quality data. Once the neural networks are trained by such data, they can be used for predicting the properties/responses of the engineering objects instantly, saving the further computing efforts of simulation tools. Correspondingly, a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks… More
  •   Views:296       Downloads:114        Download PDF
  • Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches
  • Abstract Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up data searching. Next, exploiting a deep neural network to predict the approximate execution time of a job gives prioritized… More
  •   Views:306       Downloads:123        Download PDF
  • A Comparison of Shale Gas Fracturing Based on Deep and Shallow Shale Reservoirs in the United States and China
  • Abstract China began to build its national shale gas demonstration area in 2012. The central exploration, drilling, and development technologies for medium and shallow marine shale reservoirs with less than 3,500 m of buried depth in Changning-Weiyuan, Zhaotong, and other regions had matured. In this study, we macroscopically investigated the development history of shale gas in the United States and China and compared the physical and mechanical conditions of deep and shallow reservoirs. The comparative results revealed that the main reasons for the order-ofmagnitude difference between China’s annual shale gas output and the United States could be attributed to three aspects:… More
  •   Views:286       Downloads:113        Download PDF
  • Numerical Simulation Research on Static Aeroelastic Effect of the Transonic Aileron of a High Aspect Ratio Aircraf
  • Abstract The static aeroelastic effect of aircraft ailerons with high aspect ratio at transonic velocity is investigated in this paper by the CFD/CSD fluid-structure coupling numerical simulation. The influences of wing static aeroelasticity and the ‘scissor opening’ gap width between aileron control surface and the main wing surface on aileron efficiency are mainly explored. The main purpose of this paper is to provide technical support for the wind tunnel experimental model of aileron static aeroelasticity. The results indicate that the flight dynamic pressure has a great influence on the static aeroelastic effect of ailerons, and the greater the dynamic pressure, the… More
  •   Views:326       Downloads:118        Download PDF
  • On Soft Pre-Rough Approximation Space with Applications in Decision Making
  • Abstract A soft, rough set model is a distinctive mathematical model that can be used to relate a variety of real-life data. In the present work, we introduce new concepts of rough set based on soft pre-lower and soft pre-upper approximation space. These concepts are soft pre-rough equality, soft pre-rough inclusion, soft pre-rough belonging, soft predefinability, soft pre-internal lower, and soft pre-external lower. We study the properties of these concepts. Finally, we use the soft pre-rough approximation to illustrate the importance of our method in decision-making for Chikungunya medical illnesses. In reality, the impact factors of Chikungunya’s medical infection were determined.… More
  •   Views:374       Downloads:128        Download PDF
  • A Fractional Order Fast Repetitive Control Paradigm of Vienna Rectifier for Power Quality Improvement
  • Abstract Due to attractive features, including high efficiency, low device stress, and ability to boost voltage, a Vienna rectifier is commonly employed as a battery charger in an electric vehicle (EV). However, the 6k ± 1 harmonics in the acside current of the Vienna rectifier deteriorate the THD of the ac current, thus lowering the power factor. Therefore, the current closed-loop for suppressing 6k ± 1 harmonics is essential to meet the desired total harmonic distortion (THD). Fast repetitive control (FRC) is generally adopted; however, the deviation of power grid frequency causes delay link in the six frequency fast repetitive control… More
  •   Views:409       Downloads:143        Download PDF
  • A Novel SE-CNN Attention Architecture for sEMG-Based Hand Gesture Recognition
  • Abstract In this article, to reduce the complexity and improve the generalization ability of current gesture recognition systems, we propose a novel SE-CNN attention architecture for sEMG-based hand gesture recognition. The proposed algorithm introduces a temporal squeeze-and-excite block into a simple CNN architecture and then utilizes it to recalibrate the weights of the feature outputs from the convolutional layer. By enhancing important features while suppressing useless ones, the model realizes gesture recognition efficiently. The last procedure of the proposed algorithm is utilizing a simple attention mechanism to enhance the learned representations of sEMG signals to perform multi-channel sEMG-based gesture recognition tasks.… More
  •   Views:393       Downloads:142        Download PDF
  • Visual Object Tracking via Cascaded RPN Fusion and Coordinate Attention
  • Abstract Recently, Siamese-based trackers have achieved excellent performance in object tracking. However, the high speed and deformation of objects in the movement process make tracking difficult. Therefore, we have incorporated cascaded region-proposal-network (RPN) fusion and coordinate attention into Siamese trackers. The proposed network framework consists of three parts: a feature-extraction sub-network, coordinate attention block, and cascaded RPN block.We exploit the coordinate attention block, which can embed location information into channel attention, to establish long-term spatial location dependence while maintaining channel associations. Thus, the features of different layers are enhanced by the coordinate attention block. We then send these features separately into… More
  •   Views:535       Downloads:181        Download PDF
  • Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique
  • Abstract Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline leaks. In this study, a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real samples. This approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners. A total of 99 data were collected and split into training and test set with a 9:1 ratio. The… More
  •   Views:475       Downloads:160        Download PDF
  • Numerical Study for Magnetohydrodynamic (MHD) Unsteady Maxwell Nanofluid Flow Impinging on Heated Stretching Sheet
  • Abstract The numerous applications of Maxwell Nanofluid Stagnation Point Flow, such as those in production industries, the processing of polymers, compression, power generation, lubrication systems, food manufacturing and air conditioning, among other applications, require further research into the effects of various parameters on flow phenomena. In this paper, a study has been carried out for the heat and mass transfer of Maxwell nanofluid flow over the heated stretching sheet. A mathematical model with constitutive expressions is constructed in partial differential equations (PDEs) through obligatory basic conservation laws. A series of transformations are then used to take the system into an ordinary… More
  •   Views:405       Downloads:150        Download PDF
  • An Efficient Differential Evolution for Truss Sizing Optimization Using AdaBoost Classifier
  • Abstract Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses that must be conducted. Building a surrogate model to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem. However, most existing surrogate models have been designed based on regression techniques. This paper proposes a novel method, called CaDE, which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution (DE) optimization. The proposed method is separated into two stages. During the first optimization stage, the… More
  •   Views:673       Downloads:212        Download PDF
  • An Efficient Computational Method for Differential Equations of Fractional Type
  • Abstract An effective solution method of fractional ordinary and partial differential equations is proposed in the present paper. The standard Adomian Decomposition Method (ADM) is modified via introducing a functional term involving both a variable and a parameter. A residual approach is then adopted to identify the optimal value of the embedded parameter within the frame of L2 norm. Numerical experiments on sample problems of open literature prove that the presented algorithm is quite accurate, more advantageous over the traditional ADM and straightforward to implement for the fractional ordinary and partial differential equations of the recent focus of mathematical models. Better… More
  •   Views:492       Downloads:177        Download PDF
  • A Hybrid Regional Model for Predicting Ground Deformation Induced by Large-Section Tunnel Excavation
  • Abstract Due to the large number of finite element mesh generated, it is difficult to use full-scale model to simulate largesection underground engineering, especially considering the coupling effect. A regional model is attempted to achieve this simulation. A variable boundary condition method for hybrid regional model is proposed to realize the numerical simulation of large-section tunnel construction. Accordingly, the balance of initial ground stress under asymmetric boundary conditions achieves by applying boundary conditions step by step with secondary development of Dynaflow scripts, which is the key issue of variable boundary condition method implementation. In this paper, Gongbei tunnel based on hybrid… More
  •   Views:503       Downloads:174        Download PDF
  • Disease Recognition of Apple Leaf Using Lightweight Multi-Scale Network with ECANet
  • Abstract To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks, a lightweight ResNet (LW-ResNet) model for apple disease recognition is proposed. Based on the deep residual network (ResNet18), the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features. By improving the identity mapping structure to reduce information loss. By introducing the efficient channel attention module (ECANet) to suppress noise from a complex background. The experimental results show that the average… More
  •   Views:662       Downloads:208        Download PDF
  • Three-stages Hyperspectral Image Compression Sensing with Band Selection
  • Abstract Compressed sensing (CS), as an efficient data transmission method, has achieved great success in the field of data transmission such as image, video and text. It can robustly recover signals from fewer Measurements, effectively alleviating the bandwidth pressure during data transmission. However, CS has many shortcomings in the transmission of hyperspectral image (HSI) data. This work aims to consider the application of CS in the transmission of hyperspectral image (HSI) data, and provides a feasible research scheme for CS of HSI data. HSI has rich spectral information and spatial information in bands, which can reflect the physical properties of the… More
  •   Views:658       Downloads:225        Download PDF
  • A Parallel Computing Schema Based on IGA
  • Abstract In this paper, a new computation scheme based on parallelization is proposed for Isogeometric analysis. The parallel computing is introduced to the whole progress of Isogeometric analysis. Firstly, with the help of the “tensorproduct” and “iso-parametric” feature, all the Gaussian integral points in particular element can be mapped to a global matrix using a transformation matrix that varies from element. Then the derivatives of Gauss integral points are computed in parallel, the results of which can be stored in a global matrix. And a middle layer is constructed to assemble the final stiffness matrices in parallel. The numerical example results… More
  •   Views:720       Downloads:259        Download PDF
  • Reducing the Range of Cancer Risk on BI-RADS 4 Subcategories via Mathematical Modelling
  • Abstract Breast Imaging Reporting and Data System, also known as BI-RADS is a universal system used by radiologists and doctors. It constructs a comprehensive language for the diagnosis of breast cancer. BI-RADS 4 category has a wide range of cancer risk since it is divided into 3 categories. Mathematical models play an important role in the diagnosis and treatment of cancer. In this study, data of 42 BI-RADS 4 patients taken from the Center for Breast Health, Near East University Hospital is utilized. Regarding the analysis, a mathematical model is constructed by dividing the population into 4 compartments. Sensitivity analysis is… More
  •   Views:632       Downloads:227        Download PDF
  • Cooperative Angles-Only Relative Navigation Algorithm for Multi-Spacecraft Formation in Close-Range
  • Abstract As to solve the collaborative relative navigation problem for near-circular orbiting small satellites in close-range under GNSS denied environment, a novel consensus constrained relative navigation algorithm based on the lever arm effect of the sensor offset from the spacecraft center of mass is proposed. Firstly, the orbital propagation model for the relative motion of multi-spacecraft is established based on Hill-Clohessy-Wiltshire dynamics and the line-of-sight measurement under sensor offset condition is modeled in Local Vertical Local Horizontal frame. Secondly, the consensus constraint model for the relative orbit state is constructed by introducing the geometry constraint between the spacecraft, based on which… More
  •   Views:601       Downloads:235        Download PDF
  • Analytical Models of Concrete Fatigue: A State-of-the-Art Review
  • Abstract Fatigue failure phenomena of the concrete structures under long-term low amplitude loading have attracted more attention. Some structures, such as wind power towers, offshore platforms, and high-speed railways, may resist millions of cycles loading during their intended lives. Over the past century, analytical methods for concrete fatigue are emerging. It is concluded that models for the concrete fatigue calculation can fall into four categories: the empirical model relying on fatigue tests, fatigue crack growth model in fracture mechanics, fatigue damage evolution model based on damage mechanics and advanced machine learning model. In this paper, a detailed review of fatigue computing… More
  •   Views:665       Downloads:244        Download PDF
  • A CFD-DEM-Wear Coupling Method for Stone Chip Resistance of Automotive Coatings with a Rigid Connection Particle Method for Non-Spherical Particles
  • Abstract The stone chip resistance performance of automotive coatings has attracted increasing attention in academic and industrial communities. Even though traditional gravelometer tests can be used to evaluate stone chip resistance of automotive coatings, such experiment-based methods suffer from poor repeatability and high cost. The main purpose of this work is to develop a CFD-DEM-wear coupling method to accurately and efficiently simulate stone chip behavior of automotive coatings in a gravelometer test. To achieve this end, an approach coupling an unresolved computational fluid dynamics (CFD) method and a discrete element method (DEM) are employed to account for interactions between fluids and… More
  •   Views:641       Downloads:232        Download PDF
  • Implementation of OpenMP Parallelization of Rate-Dependent Ceramic Peridynamic Model
  • Abstract A rate-dependent peridynamic ceramic model, considering the brittle tensile response, compressive plastic softening and strain-rate dependence, can accurately represent the dynamic response and crack propagation of ceramic materials. However, it also considers the strain-rate dependence and damage accumulation caused by compressive plastic softening during the compression stage, requiring more computational resources for the bond force evaluation and damage evolution. Herein, the OpenMP parallel optimization of the rate-dependent peridynamic ceramic model is investigated. Also, the modules that compute the interactions between material points and update damage index are vectorized and parallelized. Moreover, the numerical examples are carried out to simulate the… More
  •   Views:766       Downloads:280        Download PDF
  • Dense-Structured Network Based Bearing Remaining Useful Life Prediction System
  • Abstract This work is focused on developing an effective method for bearing remaining useful life predictions. The method is useful in accurately predicting the remaining useful life of bearings so that machine damage, production outage, and human accidents caused by unexpected bearing failure can be prevented. This study uses the bearing dataset provided by FEMTO-ST Institute, Besançon, France. This study starts with the exploration of neural networks, based on which the biaxial vibration signals are modeled and analyzed. This paper introduces pre-processing of bearing vibration signals, neural network model training and adjustment of training data. The model is trained by optimizing… More
  •   Views:958       Downloads:315        Download PDF
  • Efficient UAV-Based MEC Using GPU-Based PSO and Voronoi Diagrams
  • Abstract Mobile-Edge Computing (MEC) displaces cloud services as closely as possible to the end user. This enables the edge servers to execute the offloaded tasks that are requested by the users, which in turn decreases the energy consumption and the turnaround time delay. However, as a result of a hostile environment or in catastrophic zones with no network, it could be difficult to deploy such edge servers. Unmanned Aerial Vehicles (UAVs) can be employed in such scenarios. The edge servers mounted on these UAVs assist with task offloading. For the majority of IoT applications, the execution times of tasks are often… More
  •   Views:944       Downloads:315        Download PDF
  • Dynamic Meta-Modeling Method to Assess Stochastic Flutter Behavior in Turbomachinery
  • Abstract With increasing design demands of turbomachinery, stochastic flutter behavior has become more prominent and even appears a hazard to reliability and safety. Stochastic flutter assessment is an effective measure to quantify the failure risk and improve aeroelastic stability. However, for complex turbomachinery with multiple dynamic influencing factors (i.e., aeroengine compressor with time-variant loads), the stochastic flutter assessment is hard to be achieved effectively, since large deviations and inefficient computing will be incurred no matter considering influencing factors at a certain instant or the whole time domain. To improve the assessing efficiency and accuracy of stochastic flutter behavior, a dynamic meta-modeling… More
  •   Views:911       Downloads:332        Download PDF