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• Predicting Concrete Compressive Strength Using Deep Convolutional Neural Network Based on Image Characteristics
• Sanghyo Lee¹, Yonghan Ahn², Ha Young Kim^{3, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 1-17, 2020, DOI:10.32604/cmc.2020.011104
• Abstract In this study, we examined the efficacy of a deep convolutional neural network (DCNN) in recognizing concrete surface images and predicting the compressive strength of concrete. A digital single-lens reflex (DSLR) camera and microscope were simultaneously used to obtain concrete surface images used as the input data for the DCNN. Thereafter, training, validation, and testing of the DCNNs were performed based on the DSLR camera and microscope image data. Results of the analysis indicated that the DCNN employing DSLR image data achieved a relatively higher accuracy. The accuracy of the DSLR-derived image data was attributed to the relatively wider range… More
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• V-Shaped Monopole Antenna with Chichena Itzia Inspired Defected Ground Structure for UWB Applications
• Naeem Ahmad Jan¹, Saad Hassan Kiani², Fazal Muhammad², Daniyal Ali Sehrai², Amjad Iqbal³, Muhammad Tufail⁴, Sunghwan Kim^{5, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 19-32, 2020, DOI:10.32604/cmc.2020.011091
• Abstract Due to rapid growth in wireless communication technology, higher bandwidth requirement for advance telecommunication systems, capable of operating on two or higher bands with higher channel capacities and minimum distortion losses is desired. In this paper, a compact Ultra-Wideband (UWB) V-shaped monopole antenna is presented. UWB response is achieved by modifying the ground plane with Chichen Itzia inspired rectangular staircase shape. The proposed V-shaped is designed by incorporating a rectangle, and an inverted isosceles triangle using FR4 substrate. The size of the antenna is 25 mm×26 mm×1.6 mm. The proposed V-shaped monopole antenna produces bandwidth response of 3 GHz Industrial,… More
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• Numerical Control Measures of Stochastic Malaria Epidemic Model
• Muhammad Rafiq¹, Ali Ahmadian^{2, *}, Ali Raza³, Dumitru Baleanu⁴, Muhammad Sarwar Ahsan¹, Mohammad Hasan Abdul Sathar⁵
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 33-51, 2020, DOI:10.32604/cmc.2020.010893
• Abstract Nonlinear stochastic modeling has significant role in the all discipline of sciences. The essential control measuring features of modeling are positivity, boundedness and dynamical consistency. Unfortunately, the existing stochastic methods in literature do not restore aforesaid control measuring features, particularly for the stochastic models. Therefore, these gaps should be occupied up in literature, by constructing the control measuring features numerical method. We shall present a numerical control measures for stochastic malaria model in this manuscript. The results of the stochastic model are discussed in contrast of its equivalent deterministic model. If the basic reproduction number is less than one, then… More
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• Identifying and Verifying Vulnerabilities through PLC Network Protocol and Memory Structure Analysis
• Joo-Chan Lee¹, Hyun-Pyo Choi¹, Jang-Hoon Kim¹, Jun-Won Kim¹, Da-Un Jung¹, Ji-Ho Shin¹, Jung-Taek Seo^{1, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 53-67, 2020, DOI:10.32604/cmc.2020.011251
• Abstract Cyberattacks on the Industrial Control System (ICS) have recently been increasing, made more intelligent by advancing technologies. As such, cybersecurity for such systems is attracting attention. As a core element of control devices, the Programmable Logic Controller (PLC) in an ICS carries out on-site control over the ICS. A cyberattack on the PLC will cause damages on the overall ICS, with Stuxnet and Duqu as the most representative cases. Thus, cybersecurity for PLCs is considered essential, and many researchers carry out a variety of analyses on the vulnerabilities of PLCs as part of preemptive efforts against attacks. In this study,… More
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• New Optimal Newton-Householder Methods for Solving Nonlinear Equations and Their Dynamics
• Syahmi Afandi Sariman¹, Ishak Hashim^{1, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 69-85, 2020, DOI:10.32604/cmc.2020.010836
• Abstract The classical iterative methods for finding roots of nonlinear equations, like the Newton method, Halley method, and Chebyshev method, have been modified previously to achieve optimal convergence order. However, the Householder method has so far not been modified to become optimal. In this study, we shall develop two new optimal Newton-Householder methods without memory. The key idea in the development of the new methods is the avoidance of the need to evaluate the second derivative. The methods fulfill the Kung-Traub conjecture by achieving optimal convergence order four with three functional evaluations and order eight with four functional evaluations. The efficiency… More
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• Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data
• Bao Le Nguyen¹, E. Laxmi Lydia², Mohamed Elhoseny³, Irina V. Pustokhina⁴, Denis A. Pustokhin⁵, Mahmoud Mohamed Selim⁶, Gia Nhu Nguyen^{7, 8}, K. Shankar^{9, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 87-107, 2020, DOI:10.32604/cmc.2020.011599
• Abstract In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM… More
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• Computational Analysis of the Oscillatory Mixed Convection Flow along a Horizontal Circular Cylinder in Thermally Stratified Medium
• Zia Ullah¹, Muhammad Ashraf¹, Saqib Zia², Yuming Chu^{3, 4}, Ilyas Khan^{5, *}, Kottakkaran Sooppy Nisar⁶
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 109-123, 2020, DOI:10.32604/cmc.2020.011468
• Abstract The present work emphasizes the significance of oscillatory mixed convection stratified fluid and heat transfer characteristics at different stations of non-conducting horizontally circular cylinder in the presence of thermally stratified medium. To remove the difficulties in illustrating the coupled PDE’s, the finite-difference scheme with efficient primitive-variable formulation is proposed to transform dimensionless equations. The numerical simulations of coupled non-dimensional equations are computed in terms velocity of fluid, temperature and magnetic field which are computed to examine the fluctuating components of skin friction, heat transfer and current density for various emerging parameters. The governing parameters namely, thermally stratification parameter More
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• The Robust Regression Methods for Estimating of Finite Population Mean Based on SRSWOR in Case of Outliers
• Mir Subzar¹, Amer Ibrahim Al-Omari², Ayed R. A. Alanzi^{3, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 125-138, 2020, DOI:10.32604/cmc.2020.010230
• Abstract The ordinary least square (OLS) method is commonly used in regression analysis. But in the presence of outlier in the data, its results are unreliable. Hence, the robust regression methods have been suggested for a long time as alternatives to the OLS to solve the outliers problem. In the present study, new ratio type estimators of finite population mean are suggested using simple random sampling without replacement (SRSWOR) utilizing the supplementary information in Bowley’s coefficient of skewness with quartiles. For these proposed estimators, we have used the OLS, Huber-M, Mallows GM-estimate, Schweppe GM-estimate, and SIS GM-estimate methods for estimating the… More
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• Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning
• Muhammad Adnan Khan^{1, *}, Sagheer Abbas², Ayesha Atta^{2, 3}, Allah Ditta⁴, Hani Alquhayz⁵, Muhammad Farhan Khan⁶, Atta-ur-Rahman⁷, Rizwan Ali Naqvi⁸
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 139-151, 2020, DOI:10.32604/cmc.2020.011416
• Abstract The innovation in technologies related to health facilities today is increasingly helping to manage patients with different diseases. The most fatal of these is the issue of heart disease that cannot be detected from a naked eye, and attacks as soon as the human exceeds the allowed range of vital signs like pulse rate, body temperature, and blood pressure. The real challenge is to diagnose patients with more diagnostic accuracy and in a timely manner, followed by prescribing appropriate treatments and keeping prescription errors to a minimum. In developing countries, the domain of healthcare is progressing day by day using… More
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• A Key Recovery System Based on Password-Protected Secret Sharing in a Permissioned Blockchain
• Gyeong-Jin Ra¹, Chang-Hyun Roh¹, Im-Yeong Lee^{1, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 153-170, 2020, DOI:10.32604/cmc.2020.011293
• Abstract In today’s fourth industrial revolution, various blockchain technologies are being actively researched. A blockchain is a peer-to-peer data-sharing structure lacking central control. If a user wishes to access stored data, she/he must employ a private key to prove ownership of the data and create a transaction. If the private key is lost, blockchain data cannot be accessed. To solve such a problem, public blockchain users can recover the key using a wallet program. However, key recovery in a permissioned blockchain (PBC) has been but little studied. The PBC server is Honest-but-Curious (HBC), and should not be able to learn anything… More
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• Generalized Model of Blood Flow in a Vertical Tube with Suspension of Gold Nanomaterials: Applications in the Cancer Therapy
• Anees Imtiaz¹, Oi-Mean Foong², Aamina Aamina¹, Nabeel Khan¹, Farhad Ali^{3, 4, *}, Ilyas Khan⁵
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 171-192, 2020, DOI:10.32604/cmc.2020.011397
• Abstract Gold metallic nanoparticles are generally used within a lab as a tracer, to uncover on the presence of specific proteins or DNA in a sample, as well as for the recognition of various antibiotics. They are bio companionable and have properties to carry thermal energy to tumor cells by utilizing different clinical approaches. As the cancer cells are very smaller so for the infiltration, the properly sized nanoparticles have been injected in the blood. For this reason, gold nanoparticles are very effective. Keeping in mind the above applications, in the present work a generalized model of blood flow containing gold… More
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• Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic
• Ibrahim Arpaci¹, Shadi Alshehabi², Mostafa Al-Emran^{3, *}, Mahmoud Khasawneh⁴, Ibrahim Mahariq⁴, Thabet Abdeljawad^{5, 6, 7}, Aboul Ella Hassanien^{8, 9}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 193-204, 2020, DOI:10.32604/cmc.2020.011489
• （This article belongs to this Special Issue: Mathematical aspects of the Coronavirus Disease 2019 (COVID-19): Analysis and Control)
• Abstract People started posting textual tweets on Twitter as soon as the novel coronavirus (COVID-19) emerged. Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks. Therefore, this study aimed to analyze 43 million tweets collected between March 22 and March 30, 2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis. The results indicated that unigram terms were trended more frequently than bigram and trigram terms. A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic. The high-frequency… More
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• Simulation of Water-Soil-Structure Interactions Using Incompressible Smoothed Particle Hydrodynamics
• Abdelraheem M. Aly^{1, 2, *}, Mitsuteru Asai³, Ehab Mahmoud Mohamed^{4, 5}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 205-224, 2020, DOI:10.32604/cmc.2020.09227
• Abstract In the present work, an incompressible smoothed particle hydrodynamic (SPH) method is introduced to simulate water-soil-structure interactions. In the current calculation, the water is modelled as a Newtonian fluid. The soil is modelled in two different cases. In the first case, the granular material is considered as a fluid where a Bingham type constitutive model is proposed based on Mohr-Coulomb yield-stress criterion, and the viscosity is derived from the cohesion and friction angle. In addition, the fictitious suspension layers between water and soil depending on the concentration of soil are introduced. In the second case, Hooke’s law introduces elastic soil.… More
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• Dynamical Behavior and Sensitivity Analysis of a Delayed Coronavirus Epidemic Model
• Muhammad Naveed^{1, *}, Dumitru Baleanu^{2, 3, 4}, Muhammad Rafiq⁵, Ali Raza⁶, Atif Hassan Soori¹, Nauman Ahmed⁷
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 225-241, 2020, DOI:10.32604/cmc.2020.011534
• Abstract Mathematical delay modelling has a significant role in the different disciplines such as behavioural, social, physical, biological engineering, and bio-mathematical sciences. The present work describes mathematical formulation for the transmission mechanism of a novel coronavirus (COVID-19). Due to the unavailability of vaccines for the coronavirus worldwide, delay factors such as social distance, quarantine, travel restrictions, extended holidays, hospitalization, and isolation have contributed to controlling the coronavirus epidemic. We have analysed the reproduction number and its sensitivity to parameters. If, More
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• Network-Aided Intelligent Traffic Steering in 5G Mobile Networks
• Dae-Young Kim¹, Seokhoon Kim^{2, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 243-261, 2020, DOI:10.32604/cmc.2020.011253
• Abstract Recently, the fifth generation (5G) of mobile networks has been deployed and various ranges of mobile services have been provided. The 5G mobile network supports improved mobile broadband, ultra-low latency and densely deployed massive devices. It allows multiple radio access technologies and interworks them for services. 5G mobile systems employ traffic steering techniques to efficiently use multiple radio access technologies. However, conventional traffic steering techniques do not consider dynamic network conditions efficiently. In this paper, we propose a network aided traffic steering technique in 5G mobile network architecture. 5G mobile systems monitor network conditions and learn with network data. Through… More
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• A Structure Preserving Numerical Method for Solution of Stochastic Epidemic Model of Smoking Dynamics
• Ali Raza¹, Muhammad Rafiq², Nauman Ahmed³, Ilyas Khan^{4, *}, Kottakkaran Sooppy Nisar⁵, Zafar Iqbal³
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 263-278, 2020, DOI:10.32604/cmc.2020.011289
• Abstract In this manuscript, we consider a stochastic smoking epidemic model from behavioural sciences. Also, we develop a structure preserving numerical method to describe the dynamics of stochastic smoking epidemic model in a human population. The structural properties of a physical system include positivity, boundedness and dynamical consistency. These properties play a vital role in non-linear dynamics. The solution for nonlinear stochastic models necessitates the conservation of these properties. Unfortunately, the aforementioned properties of the model have not been restored in the existing stochastic methods. Therefore, it is essential to construct a structure preserving numerical method for a reliable analysis of… More
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• Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization
• Nana Zhang¹, Kun Zhu¹, Shi Ying^{1, *}, Xu Wang²
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 279-308, 2020, DOI:10.32604/cmc.2020.011001
• Abstract Software defect prediction plays an important role in software quality assurance. However, the performance of the prediction model is susceptible to the irrelevant and redundant features. In addition, previous studies mostly regard software defect prediction as a single objective optimization problem, and multi-objective software defect prediction has not been thoroughly investigated. For the above two reasons, we propose the following solutions in this paper: (1) we leverage an advanced deep neural network—Stacked Contractive AutoEncoder (SCAE) to extract the robust deep semantic features from the original defect features, which has stronger discrimination capacity for different classes (defective or non-defective). (2) we… More
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• Applying Stack Bidirectional LSTM Model to Intrusion Detection
• Ziyong Ran¹, Desheng Zheng^{1, *}, Yanling Lai¹, Lulu Tian²
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 309-320, 2020, DOI:10.32604/cmc.2020.010102
• Abstract Nowadays, Internet has become an indispensable part of daily life and is used in many fields. Due to the large amount of Internet traffic, computers are subject to various security threats, which may cause serious economic losses and even endanger national security. It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data. As machine learning technology matures, deep learning is widely used in various industries. Combining deep learning with network security and intrusion detection is the current trend. In this paper, the problem of data… More
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• A Cache Replacement Policy Based on Multi-Factors for Named Data Networking
• Meiju Yu¹, Ru Li^{1, *}, Yuwen Chen²
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 321-336, 2020, DOI:10.32604/cmc.2020.010831
• Abstract Named Data Networking (NDN) is one of the most excellent future Internet architectures and every router in NDN has the capacity of caching contents passing by. It greatly reduces network traffic and improves the speed of content distribution and retrieval. In order to make full use of the limited caching space in routers, it is an urgent challenge to make an efficient cache replacement policy. However, the existing cache replacement policies only consider very few factors that affect the cache performance. In this paper, we present a cache replacement policy based on multi-factors for NDN (CRPM), in which the content… More
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• An Improved Algorithm for Mining Correlation Item Pairs
• Tao Li¹, Yongzhen Ren^{1, *}, Yongjun Ren², Jinyue Xia³
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 337-354, 2020, DOI:10.32604/cmc.2020.06462
• Abstract Apriori algorithm is often used in traditional association rules mining, searching for the mode of higher frequency. Then the correlation rules are obtained by detected the correlation of the item sets, but this tends to ignore low-support high-correlation of association rules. In view of the above problems, some scholars put forward the positive correlation coefficient based on Phi correlation to avoid the embarrassment caused by Apriori algorithm. It can dig item sets with low-support but high-correlation. Although the algorithm has pruned the search space, it is not obvious that the performance of the running time based on the big data… More
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• An Opinion Spam Detection Method Based on Multi-Filters Convolutional Neural Network
• Ye Wang¹, Bixin Liu², Hongjia Wu¹, Shan Zhao¹, Zhiping Cai^{1, *}, Donghui Li^{3, *}, Cheang Chak Fong⁴
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 355-367, 2020, DOI:10.32604/cmc.2020.09835
• Abstract With the continuous development of e-commerce, consumers show increasing interest in posting comments on consumption experience and quality of commodities. Meanwhile, people make purchasing decisions relying on other comments much more than ever before. So the reliability of commodity comments has a significant impact on ensuring consumers’ equity and building a fair internet-trade-environment. However, some unscrupulous online-sellers write fake praiseful reviews for themselves and malicious comments for their business counterparts to maximize their profits. Those improper ways of self-profiting have severely ruined the entire online shopping industry. Aiming to detect and prevent these deceptive comments effectively, we construct a model… More
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• Frequent Itemset Mining of User’s Multi-Attribute under Local Differential Privacy
• Haijiang Liu¹, Lianwei Cui², Xuebin Ma^{1, *}, Celimuge Wu³
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 369-385, 2020, DOI:10.32604/cmc.2020.010987
• Abstract Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications. However, users’ personal privacy will be leaked in the mining process. In recent years, application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method. Local differential privacy means that users first perturb the original data and then send these data to the aggregator, preventing the aggregator from revealing the user’s private information. We propose a novel framework that implements frequent itemset mining under local differential privacy and is applicable to user’s… More
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• A Novel Beam Search to Improve Neural Machine Translation for English-Chinese
• Xinyue Lin¹, Jin Liu^{1, *}, Jianming Zhang², Se-Jung Lim³
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 387-404, 2020, DOI:10.32604/cmc.2020.010984
• Abstract Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train… More
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• The Identification of the Wind Parameters Based on the Interactive Multi-Models
• Lihua Zhu¹, Zhiqiang Wu¹, Lei Wang², Yu Wang^{1, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 405-418, 2020, DOI:10.32604/cmc.2020.010124
• Abstract The wind as a natural phenomenon would cause the derivation of the pesticide drops during the operation of agricultural unmanned aerial vehicles (UAV). In particular, the changeable wind makes it difficult for the precision agriculture. For accurate spraying of pesticide, it is necessary to estimate the real-time wind parameters to provide the correction reference for the UAV path. Most estimation algorithms are model based, and as such, serious errors can arise when the models fail to properly fit the physical wind motions. To address this problem, a robust estimation model is proposed in this paper. Considering the diversity of the… More
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• A Novel Design of Mechanical Switch for the High Overload Environment
• Yu Wang¹, Chen Liu¹, Lei Wang², Lihua Zhu^{1, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 419-432, 2020, DOI:10.32604/cmc.2020.010911
• Abstract The internal structure of the inertial measurement unit (IMU) in active state is easily damaged in the high overload environment. So that the IMU is usually required to be powered within the disappearance of the high overload. In this paper, a mechanical switch is designed to enable the IMU based on the analysis of the impact of high overload on the power-supply circuit. In which, parameters of mechanical switch are determined through theoretical calculation and data analysis. The innovation of the proposed structure lies in that the mechanical switch is triggered through the high overload process and could provide a… More
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• Multi-Directional Reconstruction Algorithm for Panoramic Camera
• Shi Qiu¹, Bin Li^{2, *}, Keyang Cheng³, Xiao Zhang², Guifang Duan⁴, Feng Li⁵
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 433-443, 2020, DOI:10.32604/cmc.2020.09708
• Abstract of view. It can be applied in virtual reality, smart homes and other fields as well. A multi-directional reconstruction algorithm for panoramic camera is proposed in this paper according to the imaging principle of dome camera, as the distortion inevitably exists in the captured panorama. First, parameters of a panoramic image are calculated. Then, a weighting operator with location information is introduced to solve the problem of rough edges by taking full advantage of pixels. Six directions of the mapping model are built, which include up, down, left, right, front and back, according to the correspondence between cylinder and spherical… More
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• Quantum Generative Model with Variable-Depth Circuit
• Yiming Huang^{1, *}, Hang Lei¹, Xiaoyu Li^{1, *}, Qingsheng Zhu², Wanghao Ren³, Xusheng Liu^{2, 4}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 445-458, 2020, DOI:10.32604/cmc.2020.010390
• Abstract In recent years, an increasing number of studies about quantum machine learning not only provide powerful tools for quantum chemistry and quantum physics but also improve the classical learning algorithm. The hybrid quantum-classical framework, which is constructed by a variational quantum circuit (VQC) and an optimizer, plays a key role in the latest quantum machine learning studies. Nevertheless, in these hybridframework-based quantum machine learning models, the VQC is mainly constructed with a fixed structure and this structure causes inflexibility problems. There are also few studies focused on comparing the performance of quantum generative models with different loss functions. In this… More
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• An Approach for Radar Quantitative Precipitation Estimation Based on Spatiotemporal Network
• Shengchun Wang¹, Xiaozhong Yu¹, Lianye Liu², Jingui Huang^{1, *}, Tsz Ho Wong³, Chengcheng Jiang¹
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 459-479, 2020, DOI:10.32604/cmc.2020.010627
• Abstract Radar quantitative precipitation estimation (QPE) is a key and challenging task for many designs and applications with meteorological purposes. Since the Z-R relation between radar and rain has a number of parameters on different areas, and the rainfall varies with seasons, the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation. This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model (ST-QPE), which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address these limitations. We report on… More
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• Auxiliary Diagnosis Based on the Knowledge Graph of TCM Syndrome
• Yonghong Xie^{1, 3}, Liangyuan Hu^{1, 3}, Xingxing Chen^{2, 3}, Jim Feng⁴, Dezheng Zhang^{1, 3, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 481-494, 2020, DOI:10.32604/cmc.2020.010297
• Abstract As one of the most valuable assets in China, traditional medicine has a long history and contains pieces of knowledge. The diagnosis and treatment of Traditional Chinese Medicine (TCM) has benefited from the natural language processing technology. This paper proposes a knowledge-based syndrome reasoning method in computerassisted diagnosis. This method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning path. According to this reasoning path, we could infer the path from the symptoms to the syndrome and get all possibilities… More
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• A Novel Method of Heart Failure Prediction Based on DPCNNXGBOOST Model
• Yuwen Chen^{1, 2, 3, *}, Xiaolin Qin^{1, 3}, Lige Zhang^{1, 3}, Bin Yi⁴
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 495-510, 2020, DOI:10.32604/cmc.2020.011278
• Abstract The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s diagnostic texts, and the text… More
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• Image Processing of Manganese Nodules Based on Background Gray Value Calculation
• Hade Mao^{1, 2}, Yuliang Liu^{1, 2, *}, Hongzhe Yan^{1, 2}, Cheng Qian³, Jing Xue⁴
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 511-527, 2020, DOI:10.32604/cmc.2020.09841
• Abstract To troubleshoot two problems arising from the segmentation of manganese nodule images-uneven illumination and morphological defects caused by white sand coverage, we propose, with reference to features of manganese nodules, a method called “background gray value calculation”. As the result of the image procession with the aid this method, the two problems above are solved eventually, together with acquisition of a segmentable image of manganese nodules. As a result, its comparison with other segmentation methods justifies its feasibility and stability. Judging from simulation results, it is indicated that this method is applicable to repair the target shape in the image,… More
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• Jointly Part-of-Speech Tagging and Semantic Role Labeling Using Auxiliary Deep Neural Network Model
• Yatian Shen¹, Yubo Mai², Xiajiong Shen², Wenke Ding^{2, *}, Mengjiao Guo³
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 529-541, 2020, DOI:10.32604/cmc.2020.011139
• Abstract Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic roles. At the same time, the predicate-argument structure in a sentence is important information for semantic role labeling task. In this work, we introduce the auxiliary deep neural network model, which models semantic dependency between part-of-speech and semantic roles and incorporates the information of predicate-argument into semantic role labeling. Based on the framework of joint learning, part-of-speech tagging is used as an auxiliary task to improve the result of the semantic role labeling. In addition, we introduce the argument recognition layer in the training process of… More
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• Accurate Multi-Scale Feature Fusion CNN for Time Series Classification in Smart Factory
• Xiaorui Shao¹, Chang Soo Kim^{1, *}, Dae Geun Kim²
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 543-561, 2020, DOI:10.32604/cmc.2020.011108
• Abstract Time series classification (TSC) has attracted various attention in the community of machine learning and data mining and has many successful applications such as fault detection and product identification in the process of building a smart factory. However, it is still challenging for the efficiency and accuracy of classification due to complexity, multi-dimension of time series. This paper presents a new approach for time series classification based on convolutional neural networks (CNN). The proposed method contains three parts: short-time gap feature extraction, multi-scale local feature learning, and global feature learning. In the process of short-time gap feature extraction, large kernel… More
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• Ore Image Segmentation Method Based on U-Net and Watershed
• Hui Li¹, Chengwei Pan^{2, 3}, Ziyi Chen¹, Aziguli Wulamu^{2, 3, *}, Alan Yang⁴
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 563-578, 2020, DOI:10.32604/cmc.2020.09806
• Abstract Ore image segmentation is a key step in an ore grain size analysis based on image processing. The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and over-segmentation. In this article, in order to solve the problem, an ore image segmentation method based on U-Net is proposed. We adjust the structure of U-Net to speed up the processing, and we modify the loss function to enhance the generalization of the model. After the collection of the ore image, we design the annotation standard and train the network… More
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• Automatic Terrain Debris Recognition Network Based on 3D Remote Sensing Data
• Xu Han^{1, #}, Huijun Yang^{1, 4, *}, Qiufeng Shen^{1, #}, Jiangtao Yang², Huihui Liang¹, Cancan Bao¹, Shuang Cang³
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 579-596, 2020, DOI:10.32604/cmc.2020.011262
• Abstract Although predecessors have made great contributions to the semantic segmentation of 3D indoor scenes, there still exist some challenges in the debris recognition of terrain data. Compared with hundreds of thousands of indoor point clouds, the amount of terrain point cloud is up to millions. Apart from that, terrain point cloud data obtained from remote sensing is measured in meters, but the indoor scene is measured in centimeters. In this case, the terrain debris obtained from remote sensing mapping only have dozens of points, which means that sufficient training information cannot be obtained only through the convolution of points. In… More
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• A Covert Communication Method Using Special Bitcoin Addresses Generated by Vanitygen
• Lejun Zhang^{1, 2}, Zhijie Zhang¹, Weizheng Wang³, Rasheed Waqas¹, Chunhui Zhao^{1, 4}, Seokhoon Kim⁵, Huiling Chen^{6, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 597-616, 2020, DOI:10.32604/cmc.2020.011554
• Abstract As an extension of the traditional encryption technology, information hiding has been increasingly used in the fields of communication and network media, and the covert communication technology has gradually developed. The blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance, which can effectively alleviate the disadvantages and problems of traditional covert communication. However, its combination with covert communication thus far has been mostly at the theoretical level. The BLOCCE method, as an early result of the combination of blockchain and covert communication technology, has the problems of low information embedding efficiency, the use… More
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• Semi-GSGCN: Social Robot Detection Research with Graph Neural Network
• Xiujuan Wang¹, Qianqian Zheng^{1, *}, Kangfeng Zheng², Yi Sui¹, Jiayue Zhang¹
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 617-638, 2020, DOI:10.32604/cmc.2020.011165
• Abstract Malicious social robots are the disseminators of malicious information on social networks, which seriously affect information security and network environments. Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks. Supervised classification based on manual feature extraction has been widely used in social robot detection. However, these methods not only involve the privacy of users but also ignore hidden feature information, especially the graph feature, and the label utilization rate of semi-supervised algorithms is low. Aiming at the problems of shallow feature extraction and low label utilization rate in existing social network robot detection… More
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• Identifying Game Processes Based on Private Working Sets
• Jinfeng Li¹, Li Feng^{1, *}, Longqing Zhang², Hongning Dai¹, Lei Yang¹, Liwei Tian¹
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 639-651, 2020, DOI:10.32604/cmc.2020.010309
• Abstract Fueled by the booming online games, there is an increasing demand for monitoring online games in various settings. One of the application scenarios is the monitor of computer games in school computer labs, for which an intelligent game recognition method is required. In this paper, a method to identify game processes in accordance with private working sets (i.e., the amount of memory occupied by a process but cannot be shared among other processes) is introduced. Results of the W test showed that the memory sizes occupied by the legitimate processes (e.g., the processes of common native windows applications) and game… More
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• Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks
• Fei Li^{1, *}, Jiayan Zhang¹, Edward Szczerbicki², Jiaqi Song¹, Ruxiang Li ¹, Renhong Diao¹
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 653-681, 2020, DOI:10.32604/cmc.2020.011264
• Abstract The increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated the intrusion detection system based on the in-vehicle system. We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior. In order to verify the accuracy and efficiency… More
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• Comprehensive Information Security Evaluation Model Based on Multi-Level Decomposition Feedback for IoT
• Jinxin Zuo^{1, 3}, Yueming Lu^{1, 3, *}, Hui Gao^{2, 3}, Ruohan Cao^{2, 3}, Ziyv Guo^{2, 3}, Jim Feng⁴
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 683-704, 2020, DOI:10.32604/cmc.2020.010793
• Abstract The development of the Internet of Things (IoT) calls for a comprehensive information security evaluation framework to quantitatively measure the safety score and risk (S&R) value of the network urgently. In this paper, we summarize the architecture and vulnerability in IoT and propose a comprehensive information security evaluation model based on multi-level decomposition feedback. The evaluation model provides an idea for information security evaluation of IoT and guides the security decision maker for dynamic protection. Firstly, we establish an overall evaluation indicator system that includes four primary indicators of threat information, asset, vulnerability, and management, respectively. It also includes eleven… More
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• Ultrasound Speckle Reduction Based on Histogram Curve Matching and Region Growing
• Jinrong Hu¹, Zhiqin Lei¹, Xiaoying Li^{2, *}, Yongqun He³, Jiliu Zhou¹
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 705-722, 2020, DOI:10.32604/cmc.2020.09878
• Abstract The quality of ultrasound scanning images is usually damaged by speckle noise. This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing algorithm. Unlike single statistical moment-based speckle reduction algorithms, this method adaptively smooths the speckle regions while preserving the margin and tissue structure to achieve high detectability. The criterion of a speckle region is defined by the similarity value obtained by matching the histogram of the current processing window and the reference window derived from the speckle region in advance. Then, according to the similarity value and tissue… More
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• A Distributed Privacy Preservation Approach for Big Data in Public Health Emergencies Using Smart Contract and SGX
• Jun Li^{1, 2}, Jieren Cheng^{2, *}, Naixue Xiong³, Lougao Zhan⁴, Yuan Zhang¹
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 723-741, 2020, DOI:10.32604/cmc.2020.011272
• （This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
• Abstract Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health. However, big data faces many ongoing serious challenges in the process of collection, storage, and use. Among them, data security and privacy problems have attracted extensive interest. In an effort to overcome this challenge, this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions (SGX). First of all, we define SGX as a trusted edge computing node, design data access module, data protection module, and data integrity check module, to achieve… More
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• An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic
• Peipeng Yu¹, Zhihua Xia^{1, *}, Jianwei Fei¹, Sunil Kumar Jha^{1, 2}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 743-760, 2020, DOI:10.32604/cmc.2020.011391
• （This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
• Abstract Coronaviruses are a well-known family of viruses that can infect humans or animals. Recently, the new coronavirus (COVID-19) has spread worldwide. All countries in the world are working hard to control the coronavirus disease. However, many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system, which leads to the mass spread of diseases. As a powerful tool, artificial intelligence (AI) has been successfully applied to solve various complex problems ranging from big data analysis to computer vision. In the process of epidemic control, many algorithms… More
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• Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine
• Linguo Li^{1, 2}, Lijuan Sun¹, Jian Guo¹, Shujing Li^{2, *}, Ping Jiang³
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 761-775, 2020, DOI:10.32604/cmc.2020.010158
• Abstract As an indispensable task in crop protection, the detection of crop diseases directly impacts the income of farmers. To address the problems of low crop-disease identification precision and detection abilities, a new method of detection is proposed based on improved genetic algorithm and extreme learning machine. Taking five different typical diseases with common crops as the objects, this method first preprocesses the images of crops and selects the optimal features for fusion. Then, it builds a model of crop disease identification for extreme learning machine, introduces the hill-climbing algorithm to improve the traditional genetic algorithm, optimizes the initial weights and… More
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• Multi-Level Feature-Based Ensemble Model for Target-Related Stance Detection
• Shi Li¹, Xinyan Cao^{1, *}, Yiting Nan²
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 777-788, 2020, DOI:10.32604/cmc.2020.010870
• Abstract Stance detection is the task of attitude identification toward a standpoint. Previous work of stance detection has focused on feature extraction but ignored the fact that irrelevant features exist as noise during higher-level abstracting. Moreover, because the target is not always mentioned in the text, most methods have ignored target information. In order to solve these problems, we propose a neural network ensemble method that combines the timing dependence bases on long short-term memory (LSTM) and the excellent extracting performance of convolutional neural networks (CNNs). The method can obtain multi-level features that consider both local and global features. We also… More
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• Identification of Parameters in 2D-FEM of Valve Piping System within NPP Utilizing Seismic Response
• Ruiyuan Xue¹, Shurong Yu^{1, *}, Xiheng Zhang¹
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 789-805, 2020, DOI:10.32604/cmc.2020.011340
• Abstract Nuclear power plants (NPP) contain plenty of valve piping systems (VPS’s) which are categorized into high anti-seismic grades. Tasks such as seismic qualification, health monitoring and damage diagnosis of VPS’s in its design and operation processes all depend on finite element method. However, in engineering practice, there is always deviations between the theoretical and the measured responses due to the inaccurate value of the structural parameters in the model. The structure parameters identification of VPS within NPP is still an unexplored domain to a large extent. In this paper, the initial 2Dfinite element model (FEM) for VPS with a DN80… More
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• Image Deblurring of Video Surveillance System in Rainy Environment
• Jinxing Niu^{1, *}, Yajie Jiang¹, Yayun Fu¹, Tao Zhang¹, Nicola Masini²
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 807-816, 2020, DOI:10.32604/cmc.2020.011044
• Abstract Video surveillance system is used in various fields such as transportation and social life. The bad weather can lead to the degradation of the video surveillance image quality. In rainy environment, the raindrops and the background are mixed, which lead to make the image degradation, so the removal of the raindrops has great significance for image restoration. In this article, after analyzing the inter-frame difference method in detecting and removing raindrops, a background difference method is proposed based on Gaussian model. In this method, the raindrop is regarded as a moving object relative to the background. The principle and procedure… More
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• Automated Chinese Essay Scoring Based on Deep Learning
• Shuai Yuan¹, Tingting He^{2, 3, *}, Huan Huang⁴, Rui Hou⁵, Meng Wang⁶
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 817-833, 2020, DOI:10.32604/cmc.2020.010471
• Abstract Writing is an important part of language learning and is considered the best approach to demonstrate the comprehensive language skills of students. Manually grading student essays is a time-consuming task; however, it is necessary. An automated essay scoring system can not only greatly improve the efficiency of essay scoring, but also provide more objective score. Therefore, many researchers have been exploring automated essay scoring techniques and tools. However, the technique of scoring Chinese essays is still limited, and its accuracy needs to be enhanced further. To improve the accuracy of the scoring model for a Chinese essay, we propose an… More
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• RFID Based Non-Preemptive Random Sleep Scheduling in WSN
• Tianle Zhang¹, Lihua Yin¹, Xiang Cui^{1, *}, Abhishek Behl², Fuqiang Dong³, Ziheng Cheng⁴, Kuo Ma⁴
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 835-845, 2020, DOI:10.32604/cmc.2020.06050
• Abstract In Wireless Sensor Network (WSN), because battery and energy supply are constraints, sleep scheduling is always needed to save energy while maintaining connectivity for packet delivery. Traditional schemes have to ensure high duty cycling to ensure enough percentage of active nodes and then derogate the energy efficiency. This paper proposes an RFID based non-preemptive random sleep scheduling scheme with stable low duty cycle. It employs delay tolerant network routing protocol to tackle the frequent disconnections. A low-power RFID based non-preemptive wakeup signal is used to confirm the availability of next-hop before sending packet. It eliminates energy consumption of repeated retransmission… More
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• Automated Dimensioning Method of Engineering Drawings for Mechanical Products Based on Curve Chain
• Gui Li^{1, *}, Peng Yang²
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 847-863, 2020, DOI:10.32604/cmc.2020.011398
• Abstract An automated method based on the curve chain was proposed for dimensioning of engineering drawings for the mechanical products. According to the internal relation between the features of 3D model feature and elements of 2D drawing, the curve chain was established to reflect the geometric topological structure between the elements. It divides the dimensions into the absolute dimensions within the cure chain and the relative dimensions between the curve chains. The parallel and lengthy relationship between the drawing elements of the constructed X and Y parallel matrix was solved to remove redundant elements in the curve chain and labeled the… More
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• The Optimization Study about Fault Self-Healing Restoration of Power Distribution Network Based on Multi-Agent Technology
• Fuquan Huang¹, Zijun Liu¹, Tinghuang Wang¹, Haitai Zhang^{2, *}, Tony Yip³
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 865-878, 2020, DOI:10.32604/cmc.2020.010724
• Abstract In order to quickly and accurately locate the fault location of the distribution network and increase the stability of the distribution network, a fault recovery method based on multi-objective optimization algorithm is proposed. The optimization of the power distribution network fault system based on multiagent technology realizes fast recovery of multi-objective fault, solve the problem of network learning and parameter adjustment in the later stage of particle swarm optimization algorithm falling into the local extreme value dilemma, and realize the multi-dimensional nonlinear optimization of the main grid and the auxiliary grid. The system proposed in this study takes power distribution… More
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• Device-Independent Quantum Key Distribution Protocol Based on Hyper-Entanglement
• Yan Chang^{1, *}, Shibin Zhang¹, Lili Yan¹, Xueyang Li¹, Tian Cao¹, Qirun Wang²
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 879-896, 2020, DOI:10.32604/cmc.2020.010042
• Abstract The secure key rate of quantum key distribution (QKD) is greatly reduced because of the untrusted devices. In this paper, to raise the secure key rate of QKD, a device-independent quantum key distribution (DIQKD) protocol is proposed based on hyper-entangled states and Bell inequalities. The security of the protocol is analyzed against the individual attack by an adversary only limited by the no-signaling condition. Based on the formalization of Clauser-Horne Shimony-Holt (CHSH) violation measurement on local correlation, the probability of a secure secret bit is obtained, which is produced by a pair of hyper-entangled particles. By analyzing the secure secret… More
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• Design and Implementation of PLC-Based Autonomous Construction System of Unmanned Vibratory Roller
• Weizu Huang¹, Dong Wang^{1, *}, Zuodong Xiao¹, Qiang Yao², Danjie Du³
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 897-912, 2020, DOI:10.32604/cmc.2020.09728
• Abstract The vibratory roller is a piece of vital construction machinery in the field of road construction. The unmanned vibratory roller efficiently utilizes the automated driving technology in the vehicle engineering field, which is innovative for the unmanned road construction. This paper develops and implements the autonomous construction system for the unmanned vibratory roller. Not only does the roller have the function of remote-controlled driving, but it also has the capability of autonomous road construction. The overall system design uses the Programmable Logic Controller (PLC) as the kernel controller. It establishes the communication network through multiple Input/Output (I/O) modules, Recommended Standard… More
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• Robust Core Tensor Dictionary Learning with Modified Gaussian Mixture Model for Multispectral Image Restoration
• Leilei Geng¹, Chaoran Cui¹, Qiang Guo¹, Sijie Niu², Guoqing Zhang³, Peng Fu^{4, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 913-928, 2020, DOI:10.32604/cmc.2020.09975
• Abstract The multispectral remote sensing image (MS-RSI) is degraded existing multispectral camera due to various hardware limitations. In this paper, we propose a novel core tensor dictionary learning approach with the robust modified Gaussian mixture model for MS-RSI restoration. First, the multispectral patch is modeled by three-order tensor and high-order singular value decomposition is applied to the tensor. Then the task of MS-RSI restoration is formulated as a minimum sparse core tensor estimation problem. To improve the accuracy of core tensor coding, the core tensor estimation based on the robust modified Gaussian mixture model is introduced into the proposed model by… More
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• Median Filtering Detection Based on Quaternion Convolutional Neural Network
• Jinwei Wang^{1, 2, 3, 4}, Qiye Ni³, Yang Zhang³, Xiangyang Luo^{2, *}, Yunqing Shi⁵, Jiangtao Zhai³, Sunil Kr Jha³
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 929-943, 2020, DOI:10.32604/cmc.2020.06569
• Abstract Median filtering is a nonlinear signal processing technique and has an advantage in the field of image anti-forensics. Therefore, more attention has been paid to the forensics research of median filtering. In this paper, a median filtering forensics method based on quaternion convolutional neural network (QCNN) is proposed. The median filtering residuals (MFR) are used to preprocess the images. Then the output of MFR is expanded to four channels and used as the input of QCNN. In QCNN, quaternion convolution is designed that can better mix the information of different channels than traditional methods. The quaternion pooling layer is designed… More
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• Research on Clothing Simulation Design Based on Three-Dimensional Image Analysis
• Wenyao Zhu^{1, 2}, Xue Li³, Young-Mi Shon^{4, *}
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 945-962, 2020, DOI:10.32604/cmc.2020.010089
• Abstract Traditional clothing design models based on adaptive meshes cannot reflect. To solve this problem, a clothing simulation design model based on 3D image analysis technology is established. The model uses feature extraction and description of image evaluation parameters, and establishes the mapping relationship between image features and simulation results by using the optimal parameter values, thereby obtaining a three-dimensional image simulation analysis environment. On the basis of this model, by obtaining the response results of clothing collision detection and the results of local adaptive processing of clothing meshes, the cutting form and actual cutting effect of clothing are determined to… More
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• A Novel GLS Consensus Algorithm for Alliance Chain in Edge Computing Environment
• Huijuan Wang^{1, *}, Jiang Yong¹, Qingwei Liu², Alan Yang³
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 963-976, 2020, DOI:10.32604/cmc.2020.09803
• Abstract Edge computing devices are widely deployed. An important issue that arises is in that these devices suffer from security attacks. To deal with it, we turn to the blockchain technologies. The note in the alliance chain need rules to limit write permissions. Alliance chain can provide security management functions, using these functions to meet the management between the members, certification, authorization, monitoring and auditing. This article mainly analyzes some requirements realization which applies to the alliance chain, and introduces a new consensus algorithm, generalized Legendre sequence (GLS) consensus algorithm, for alliance chain. GLS algorithms inherit the recognition and verification efficiency… More
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• An Improved DV-Hop Localization Algorithm Based on Selected Anchors
• Jing Wang^{1, *}, Anqi Hou¹, Yuanfei Tu¹, Hong Yu²
• CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 977-991, 2020, DOI:10.32604/cmc.2020.011003
• Abstract Wireless Sensor Network (WSN) based applications has been extraordinarily helpful in monitoring interested area. Only information of surrounding environment with meaningful geometric information is useful. How to design the localization algorithm that can effectively extract unknown node position has been a challenge in WSN. Among all localization technologies, the Distance Vector-Hop (DV-Hop) algorithm has been most popular because it simply utilizes the hop counts as connectivity measurements. This paper proposes an improved DV-Hop based algorithm, a centroid DV-hop localization with selected anchors and inverse distance weighting schemes (SIC-DV-Hop). We adopt an inverse distance weighting method for average distance amelioration to… More
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