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  • OPEN ACCESS
    REVIEW
  • A Review of Electromagnetic Energy Regenerative Suspension System & Key Technologies
  • Changzhong Fu, Jiayu Lu*, Wenqing Ge, Cao Tan, Bo Li
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.023092
  • Abstract The active suspension has undoubtedly improved the performance of the vehicle, however, the trend of “lowcarbonization, intelligence, and informationization” in the automotive industry has put forward higher and more urgent requirements for the suspension system. The automotive industry and researchers favor active energy regeneration suspension technology with safety, comfort, and high energy regenerative efficiency. In this paper, we review the research progress of the structure form, optimization method, and control strategy of electromagnetic energy regenerative suspension. Specifically, comparing the pros and cons of the existing technology in solving the contradiction between dynamic performance and energy regeneration. In addition, the development… More
  •   Views:145       Downloads:43        Download PDF
  • OPEN ACCESS
    ARTICLE
  • A Deep Learning Approach to Mesh Segmentation
  • Abubakar Sulaiman Gezawa1, Qicong Wang1,2, Haruna Chiroma3, Yunqi Lei1,*
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021351
  • （This article belongs to this Special Issue: Advanced Machine Learning for Big Data Analytics in Natural Language Processing)
  • Abstract In the shape analysis community, decomposing a 3D shape into meaningful parts has become a topic of interest. 3D model segmentation is largely used in tasks such as shape deformation, shape partial matching, skeleton extraction, shape correspondence, shape annotation and texture mapping. Numerous approaches have attempted to provide better segmentation solutions; however, the majority of the previous techniques used handcrafted features, which are usually focused on a particular attribute of 3D objects and so are difficult to generalize. In this paper, we propose a three-stage approach for using Multi-view recurrent neural network to automatically segment a 3D shape into visually… More
  •   Views:319       Downloads:81        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Refined Sparse Representation Based Similar Category Image Retrieval
  • Xin Wang, Zhilin Zhu, Zhen Hua*
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021287
  • （This article belongs to this Special Issue: Data Acquisition and Electromagnetic Interference Detection by Internet of Things)
  • Abstract Given one specific image, it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images. However, traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances, ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image. Aiming to solve this problem above, we proposed in this paper one refined sparse representation based similar category image retrieval model. On the one hand, saliency detection and multi-level decomposition could contribute to taking salient and spatial… More
  •   Views:228       Downloads:76        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Computational Modeling of Reaction-Diffusion COVID-19 Model Having Isolated Compartment
  • Muhammad Shoaib Arif1,2,*, Kamaleldin Abodayeh1, Asad Ejaz2
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.022235
  • （This article belongs to this Special Issue: Recent Developments on Computational Biology-I)
  • Abstract Cases of COVID-19 and its variant omicron are raised all across the world. The most lethal form and effect of COVID-19 are the omicron version, which has been reported in tens of thousands of cases daily in numerous nations. Following WHO (World health organization) records on 30 December 2021, the cases of COVID-19 were found to be maximum for which boarding individuals were found 1,524,266, active, recovered, and discharge were found to be 82,402 and 34,258,778, respectively. While there were 160,989 active cases, 33,614,434 cured cases, 456,386 total deaths, and 605,885,769 total samples tested. So far, 1,438,322,742 individuals have been… More
  •   Views:218       Downloads:72        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Multi-Objective Redundancy Optimization of Continuous-Point Robot Milling Path in Shipbuilding
  • Jianjun Yao*, Chen Qian, Yikun Zhang, Geyang Yu
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021328
  • （This article belongs to this Special Issue: Computer Modeling in Ocean Engineering Structure and Mechanical Equipment)
  • Abstract The 6-DOF manipulator provides a new option for traditional shipbuilding for its advantages of vast working space, low power consumption, and excellent flexibility. However, the rotation of the end effector along the tool axis is functionally redundant when using a robotic arm for five-axis machining. In the process of ship construction, the performance of the parts’ protective coating needs to be machined to meet the Performance Standard of Protective Coatings (PSPC). The arbitrary redundancy configuration in path planning will result in drastic fluctuations in the robot joint angle, greatly reducing machining quality and efficiency. There have been some studies on… More
  •   Views:259       Downloads:86        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network
  • Tingshun Li1, Jiaohui Xu1,*, Zesan Liu2, Dadi Wang2, Wen Tan1
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020702
  • （This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
  • Abstract The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter, where this affects their capacity for power generation as well as their safety. Accurately identifying the icing of the blades of wind turbines in remote areas is thus important, and a general model is needed to this end. This paper proposes a universal model based on a Deep Neural Network (DNN) that uses data from the Supervisory Control and Data Acquisition (SCADA) system. Two datasets from SCADA are first preprocessed through undersampling, that is, they are labeled, normalized, and balanced. The… More
  •   Views:242       Downloads:83        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Predicting Carpark Prices Indices in Hong Kong Using AutoML
  • Rita Yi Man Li1, Lingxi Song2, Bo Li2,3, M. James C. Crabbe4,5,6, Xiao-Guang Yue7,*
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020930
  • （This article belongs to this Special Issue: Computer Modelling for Safer Built Environment and Smart Cities)
  • Abstract The aims of this study were threefold: 1) study the research gap in carpark and price index via big data and natural language processing, 2) examine the research gap of carpark indices, and 3) construct carpark price indices via repeat sales methods and predict carpark indices via the AutoML. By researching the keyword “carpark” in Google Scholar, the largest electronic academic database that covers Web of Science and Scopus indexed articles, this study obtained 999 articles and book chapters from 1910 to 2019. It confirmed that most carpark research threw light on multi-storey carparks, management and ventilation systems, and reinforced… More
  •   Views:546       Downloads:148        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Interpreting Randomly Wired Graph Models for Chinese NER
  • Jie Chen1, Jiabao Xu1, Xuefeng Xi1,*, Zhiming Cui1, Victor S. Sheng2
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020771
  • （This article belongs to this Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
  • Abstract Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing (NLP) tasks. However, most existing approaches only focus on improving the performance of models but ignore their interpretability. In this work, we propose a Randomly Wired Graph Neural Network (RWGNN) by using graph to model the structure of Neural Network, which could solve two major problems (word-boundary ambiguity and polysemy) of Chinese NER. Besides, we develop a pipeline to explain the RWGNN by using Saliency Map and Adversarial Attacks. Experimental results demonstrate that our approach can identify meaningful and reasonable interpretations for… More
  •   Views:239       Downloads:83        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition
  • Zhichen Hu1, Huali Ren2, Jielin Jiang1, Yan Cui4, Xiumian Hu3, Xiaolong Xu1,*
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.022268
  • Abstract An obviously challenging problem in named entity recognition is the construction of the kind data set of entities. Although some research has been conducted on entity database construction, the majority of them are directed at Wikipedia or the minority at structured entities such as people, locations and organizational nouns in the news. This paper focuses on the identification of scientific entities in carbonate platforms in English literature, using the example of carbonate platforms in sedimentology. Firstly, based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts, this paper designs… More
  •   Views:293       Downloads:100        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems
  • Mohamed Hassan Essai Ali1,*, Fahad Alraddady2, Mo’ath Y. Al-Thunaibat3, Shaima Elnazer2
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.022246
  • （This article belongs to this Special Issue: AI-Driven Engineering Applications)
  • Abstract For a 5G wireless communication system, a convolutional deep neural network (CNN) is employed to synthesize a robust channel state estimator (CSE). The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state information. Also, it utilizes pilots to offer more helpful information about the communication channel. The proposed CNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory (BiLSTM/LSTM) NNs-based CSEs. The CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based estimators. Using three different loss function-based classification layers and… More
  •   Views:304       Downloads:109        Download PDF
  • OPEN ACCESS
    ARTICLE
  • CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets
  • Zhiyun Yang1,#, Qi Liu1,#,*, Hao Wu1, Xiaodong Liu2, Yonghong Zhang3
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.022045
  • Abstract Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain. Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation, where radar echo maps were used to predict their consequent moment, so as to recognize potential severe convective weather events. However, these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation, due to the size limitation of convolution filter, lack of global feature, and less attention to… More
  •   Views:246       Downloads:90        Download PDF
  • OPEN ACCESS
    ARTICLE
  • A Robust Data Hiding Reversible Technique for Improving the Security in e-Health Care System
  • Saima Kanwal1, Feng Tao1,*, Ahmad Almogren2, Ateeq Ur Rehman3, Rizwan Taj1, Ayman Radwan4
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020255
  • Abstract The authenticity and integrity of healthcare is the primary objective. Numerous reversible watermarking schemes have been developed to improve the primary objective but increasing the quantity of embedding data leads to covering image distortion and visual quality resulting in data security detection. A trade-off between robustness, imperceptibility, and embedded capacity is difficult to achieve with current algorithms due to limitations in their ability. Keeping this purpose insight, an improved reversibility watermarking methodology is proposed to maximize data embedding capacity and imperceptibility while maintaining data security as a primary concern. A key is generated by a random path with minimum bit… More
  •   Views:223       Downloads:94        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Self-Triggered Consensus Filtering over Asynchronous Communication Sensor Networks
  • Huiwen Xue1, Jiwei Wen1,*, Akshya Kumar Swain1, Xiaoli Luan1
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020127
  • （This article belongs to this Special Issue: Advanced on Modeling and State Estimation for Industrial Processes)
  • Abstract In this paper, a self-triggered consensus filtering is developed for a class of discrete-time distributed filtering systems. Different from existing event-triggered filtering, the self-triggered one does not require to continuously judge the trigger condition at each sampling instant and can save computational burden while achieving good state estimation. The triggering policy is presented for pre-computing the next execution time for measurements according to the filter’s own data and the latest released data of its neighbors at the current time. However, a challenging problem is that data will be asynchronously transmitted within the filtering network because each node self-triggers independently. Therefore,… More
  •   Views:280       Downloads:92        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Cherenkov Radiation: A Stochastic Differential Model Driven by Brownian Motions
  • Qingqing Li1,2, Zhiwen Duan1,2,*, Dandan Yang1,2
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.019249
  • （This article belongs to this Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics)
  • Abstract With the development of molecular imaging, Cherenkov optical imaging technology has been widely concerned. Most studies regard the partial boundary flux as a stochastic variable and reconstruct images based on the steadystate diffusion equation. In this paper, time-variable will be considered and the Cherenkov radiation emission process will be regarded as a stochastic process. Based on the original steady-state diffusion equation, we first propose a stochastic partial differential equation model. The numerical solution to the stochastic partial differential model is carried out by using the finite element method. When the time resolution is high enough, the numerical solution of the… More
  •   Views:218       Downloads:88        Download PDF
  • OPEN ACCESS
    ARTICLE
  • An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site
  • Zhaoxi Ma1, Qin Zhao1,*, Tianyou Cang1, Zongjian Li2, Yiyun Zhu1, Xinhong Hei3
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021216
  • （This article belongs to this Special Issue: Numerical Methods in Engineering Analysis, Data Analysis and Artificial Intelligence)
  • 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:309       Downloads:119        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Unique Solution of Integral Equations via Intuitionistic Extended Fuzzy b-Metric-Like Spaces
  • Naeem Saleem1, Khalil Javed2, Fahim Uddin3, Umar Ishtiaq4, Khalil Ahmed2, Thabet Abdeljawad5,6,*, Manar A. Alqudah7
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021031
  • 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:315       Downloads:138        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Research on Volt/Var Control of Distribution Networks Based on PPO Algorithm
  • Chao Zhu1, Lei Wang1, Dai Pan1, Zifei Wang2, Tao Wang2, Licheng Wang2,*, Chengjin Ye3
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021052
  • （This article belongs to this Special Issue: Artificial Intelligence in Renewable Energy and Storage Systems)
  • 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:274       Downloads:104        Download PDF
  • OPEN ACCESS
    REVIEW
  • Application of Automated Guided Vehicles in Smart Automated Warehouse Systems: A Survey
  • Zheng Zhang, Juan Chen*, Qing Guo
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021451
  • （This article belongs to this Special Issue: Computer Modeling for Smart Cities Applications)
  • 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:309       Downloads:123        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Peridynamic Shell Model Based on Micro-Beam Bond
  • Guojun Zheng1,2, Zhaomin Yan1, Yang Xia1,2, Ping Hu1,2, Guozhe Shen1,2,*
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021415
  • （This article belongs to this Special Issue: Peridynamics and its Current Progress)
  • 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:287       Downloads:115        Download PDF
  • OPEN ACCESS
    REVIEW
  • Overview of 3D Human Pose Estimation
  • Jianchu Lin1,2, Shuang Li3, Hong Qin3,4, Hongchang Wang3, Ning Cui6, Qian Jiang7, Haifang Jian3,*, Gongming Wang5,*
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020857
  • （This article belongs to this Special Issue: Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
  • 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:291       Downloads:106        Download PDF
  • OPEN ACCESS
    ARTICLE
  • A Novel RFID Localization Approach to Smart Self-Service Borrowing and Returning System
  • Siguo Bi1, Cong Wang1, Jiajie Shen1, Wang Xiang1, Wei Ni2, Xin Wang1, Bochun Wu1,*, Yi Gong1,3,*
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.022298
  • （This article belongs to this Special Issue: Models of Computation: Specification, Implementation and Challenges)
  • 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:389       Downloads:141        Download PDF
  • OPEN ACCESS
    ARTICLE
  • A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network
  • Ji Wang1, Liming Li1,2,3, Shubin Zheng1,3, Shuguang Zhao2, Xiaodong Chai1,3, Lele Peng1,3, Weiwei Qi1,3, Qianqian Tong1
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.022143
  • 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:286       Downloads:122        Download PDF
  • OPEN ACCESS
    ARTICLE
  • A Hybrid BPNN-GARF-SVR Prediction Model Based on EEMD for Ship Motion
  • Hao Han, Wei Wang*
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021494
  • （This article belongs to this Special Issue: Models of Computation: Specification, Implementation and Challenges)
  • 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:336       Downloads:127        Download PDF
  • OPEN ACCESS
    REVIEW
  • A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems
  • Abednego Acheampong1, Yiwen Zhang1,*, Xiaolong Xu2, Daniel Appiah Kumah2
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021394
  • （This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)
  • 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:324       Downloads:122        Download PDF
  • OPEN ACCESS
    REVIEW
  • Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges
  • Liang Luo1, Xingmei Li1, Kaijiang Yang1, Mengyang Wei1, Jiong Chen1, Junqian Yang1, Liang Yao2,*
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021198
  • （This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)
  • 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:324       Downloads:118        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Analysis and Simulations of Open-Source Intelligence Process System Dynamics from User’s Perspective
  • Huan Liu1, Zhenyu Tang2, Ning Zhao2,*, Wei Qian1,*
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.018547
  • （This article belongs to this Special Issue: Application of Computer Modeling and Simulation in Social Complex System)
  • 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:317       Downloads:105        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models
  • Mohammad Sadegh Barkhordari1, Danial Jahed Armaghani2,*, Panagiotis G. Asteris3
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020840
  • （This article belongs to this Special Issue: Soft Computing Techniques in Materials Science and Engineering)
  • 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:539       Downloads:159        Download PDF
  • OPEN ACCESS
    ARTICLE
  • A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression
  • Hongfei Ma1,*, Wenqi Zhao2, Yurong Zhao1, Yu He1
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020498
  • （This article belongs to this Special Issue: Modeling of Fluids Flow in Unconventional Reservoirs)
  • 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:462       Downloads:174        Download PDF
  • OPEN ACCESS
    ARTICLE
  • An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder
  • Passent El-kafrawy1,2, Maie Aboghazalah2,*, Abdelmoty M. Ahmed3, Hanaa Torkey4, Ayman El-Sayed4
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021713
  • （This article belongs to this Special Issue: Artificial Intelligence of Things (AIoT): Emerging Trends and Challenges)
  • 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:546       Downloads:178        Download PDF
  • OPEN ACCESS
    ARTICLE
  • Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations
  • Tianyuan Jia1, Xiangyong Chen1,2,*, Xiurong Yao1,*, Feng Zhao1, Jianlong Qiu1
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020780
  • （This article belongs to this Special Issue: Modeling and Analysis of Autonomous Intelligence)
  • 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
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    ARTICLE
  • Numerical Solutions of Fractional Variable Order Differential Equations via Using Shifted Legendre Polynomials
  • Kamal Shah1,2, Hafsa Naz2, Thabet Abdeljawad1,3,*, Aziz Khan1, Manar A. Alqudah4
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021483
  • （This article belongs to this Special Issue: Advanced Numerical Methods for Fractional Differential Equations)
  • 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
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  • Research on Leak Location Method of Water Supply Pipeline Based on MVMD
  • Qiansheng Fang, Haojie Wang, Chenlei Xie*, Jie Chen
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021131
  • （This article belongs to this Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
  • 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
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  • Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances, Challenges and Future Directions
  • Gulshan Kumar1,*, Hamed Alqahtani2
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020724
  • 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:411       Downloads:178        Download PDF
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  • Image Representations of Numerical Simulations for Training Neural Networks
  • Yiming Zhang1,*, Zhiran Gao1, Xueya Wang1, Qi Liu2
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.022088
  • 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
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  • Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches
  • Bao Rong Chang1, Hsiu-Fen Tsai2,*, Yu-Chieh Lin1
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020128
  • （This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
  • 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
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  • A Fractional Order Fast Repetitive Control Paradigm of Vienna Rectifier for Power Quality Improvement
  • Sue Wang1, Xintao Luo1,*, Saleem Riaz2, Haider Zaman3, Chaohong Zhou1, Pengfei Hao1
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.021850
  • （This article belongs to this Special Issue: Artificial Intelligence in Renewable Energy and Storage Systems)
  • 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:602       Downloads:217        Download PDF
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  • A Novel SE-CNN Attention Architecture for sEMG-Based Hand Gesture Recognition
  • Zhengyuan Xu1,2,#, Junxiao Yu1,#, Wentao Xiang1, Songsheng Zhu1, Mubashir Hussain3, Bin Liu1,*, Jianqing Li1,*
  • CMES-Computer Modeling in Engineering & Sciences, DOI: 10.32604/cmes.2022.020035
  • 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:555       Downloads:214        Download PDF
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  • Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique
  • Yang Yang1,2,*, Pengfei Zheng3,4, Fanru Zeng5, Peng Xin6, Guoxi He1, Kexi Liao1
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020220
  • （This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
  • 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
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  • An Efficient Differential Evolution for Truss Sizing Optimization Using AdaBoost Classifier
  • Tran-Hieu Nguyen*, Anh-Tuan Vu
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020819
  • （This article belongs to this Special Issue: New Trends in Structural Optimization)
  • 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
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  • A Hybrid Regional Model for Predicting Ground Deformation Induced by Large-Section Tunnel Excavation
  • Shengjun Deng1,2,3,*, Yang He1, Xiaonan Gong2, Jiajin Zhou2, Xiangdong Hu4
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020386
  • （This article belongs to this Special Issue: Mechanical Reliability of Advanced Materials and Structures for Harsh Applications)
  • 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:682       Downloads:252        Download PDF
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  • Three-stages Hyperspectral Image Compression Sensing with Band Selection
  • Jingbo Zhang, Yanjun Zhang, Xingjuan Cai*, Liping Xie*
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020426
  • （This article belongs to this Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)
  • 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:877       Downloads:302        Download PDF
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  • Cooperative Angles-Only Relative Navigation Algorithm for Multi-Spacecraft Formation in Close-Range
  • Sha Wang1,2, Chenglong He1, Baichun Gong2,*, Xin Ding2, Yanhua Yuan3
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.017470
  • 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:762       Downloads:309        Download PDF
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  • Analytical Models of Concrete Fatigue: A State-of-the-Art Review
  • Xiaoli Wei1, D. A. Makhloof1,2, Xiaodan Ren1,*
  • CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2022.020160
  • 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:894       Downloads:341        Download PDF
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