Abierdi¹, Yuzhou Xiang², Haiyi Zhong², Xin Gu², Hanlong Liu^{2, 3}, Wengang Zhang^{2, 3, *}

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 825-844, 2020, DOI:10.32604/cmc.2020.07946

Abstract Tunnel excavation is a complicated loading-unloading-reloading process characterized by decreased radial stresses and increased axial stresses. An approach that considers only loading, is generally used in tunnel model testing. However, this approach is incapable of characterizing the unloading effects induced by excavation on surrounding rocks and hence presents radial and tangential stress paths during the failure process that are different from the actual stress state of tunnels. This paper carried out a comparative analysis using laboratory model testing and particle flow code (PFC^2D)-based numerical simulation, and shed light upon the crack propagation process and, microscopic stress and force chain variations… More >

Yue Jia¹, Chun Li^{1, *}, Jinwu Jiang², Ning Wei³, Yang Chen⁴, Yongjie Jessica Zhang⁵

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 813-823, 2020, DOI:10.32604/cmc.2020.07801

Abstract The present work carries out molecular dynamics simulations to compute the thermal conductivity of the borophene nanoribbon and the borophene nanotube using the Muller-Plathe approach. We investigate the thermal conductivity of the armchair and zigzag borophenes, and show the strong anisotropic thermal conductivity property of borophene. We compare results of the borophene nanoribbon and the borophene nanotube, and find the thermal conductivity of the borophene is orientation dependent. The thermal conductivity of the borophene does not vary as changing the width of the borophene nanoribbon and the perimeter of the borophene nanotube. In addition, the thermal conductivity of the borophene… More >

Wuxiong Zhang^{1, 2}, Weidong Fang^{1, 2, *}, Qianqian Zhao^{1, 2}, Xiaohong Ji³, Guoqing Jia³

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 787-811, 2020, DOI:10.32604/cmc.2020.07620

Abstract Energy efficiency is very important for the Internet of Things (IoT), especially for front-end sensed terminal or node. It not only embodies the node’s life, but also reflects the lifetime of the network. Meanwhile, it is also a key indicator of green communications. Unfortunately, there is no article on systematic analysis and review for energy efficiency evaluation in IoT. In this paper, we systemically analyze the architecture of IoT, and point out its energy distribution, Furthermore, we summarized the energy consumption model in IoT, analyzed the pros and cons of improving energy efficiency, presented a state of the art the… More >

Guangyong Yang¹, Jianqiu Zeng¹, Mengke Yang^{2, *}, Yifei Wei³, Xiangqing Wang³, Zulfiqar Hussain Pathan⁴

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 769-785, 2020, DOI:10.32604/cmc.2020.07528

Abstract A vast amount of information has been produced in recent years, which brings a huge challenge to information management. The better usage of big data is of important theoretical and practical significance for effectively addressing and managing messages. In this paper, we propose a nine-rectangle-grid information model according to the information value and privacy, and then present information use policies based on the rough set theory. Recurrent neural networks were employed to classify OTT messages. The content of user interest is effectively incorporated into the classification process during the annotation of OTT messages, ending with a reliable trained classification model.… More >

Wenzheng Li¹, Yijun Gu^{1, *}

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 755-768, 2020, DOI:10.32604/cmc.2020.07984

Abstract As an unsupervised learning method, stochastic competitive learning is commonly used for community detection in social network analysis. Compared with the traditional community detection algorithms, it has the advantage of realizing the timeseries community detection by simulating the community formation process. In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set, the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization, parameter optimization and particle domination ability self-adaptive. The experiment result shows that each improved method improves the accuracy of the algorithm, and the… More >

Guang Sun^{1, 2, *}, Xiaoping Fan¹, Wangdong Jiang¹, Hangjun Zhou¹, Fenghua Li¹, Rong Yang¹

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 743-753, 2020, DOI:10.32604/cmc.2020.05564

Abstract Graph analysis can be done at scale by using Spark GraphX which loading data into memory and running graph analysis in parallel. In this way, we should take data out of graph databases and put it into memory. Considering the limitation of memory size, the premise of accelerating graph analytical process reduces the graph data to a suitable size without too much loss of similarity to the original graph. This paper presents our method of data cleaning on the software graph. We use SEQUITUR data compression algorithm to find out hot code path and store it as a whole paths… More >

Cui Li^{1, *}, Gang Wu¹, Lipeng Xing¹, Feng Zhu¹, Liang Zhao²

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 725-742, 2020, DOI:10.32604/cmc.2020.07188

Abstract The Vehicular Ad-hoc Network (VANET) is the fundamental of smart transportation system in the future, but the security of the communication between vehicles and vehicles, between vehicles and roadside infrastructures have become increasingly prominent. Certificateless aggregate signature protocol is used to address this security issue, but the existing schemes still have many drawbacks in terms of security and efficiency: First, many schemes are not secure, and signatures can be forged by the attacker; Second, even if some scheme are secure, many schemes use a large number of bilinear pairing operation, and the computation overhead is large. At the same time,… More >

Qiang Wei^{1, 2, *}, Guangmin Hu¹, Chao Shen³, Yunfei Yin^{4, 5}

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 705-724, 2020, DOI:10.32604/cmc.2020.07467

Abstract Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and monitoring. However, the existing methods are time-consuming for even moderate-scale networks. In this paper, we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance queries. The effectiveness of the proposed method is validated through synthetic and real-world networks. The experimental results show that our method is 10⁵ times faster than the existing methods and can solve the shortest-path… More >

Xiangmao Chang^{1, 2, *}, Yuan Qiu¹, Shangting Su¹, Deliang Yang³

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 691-703, 2020, DOI:10.32604/cmc.2020.07923

Abstract Wireless sensor networks are increasingly used in sensitive event monitoring. However, various abnormal data generated by sensors greatly decrease the accuracy of the event detection. Although many methods have been proposed to deal with the abnormal data, they generally detect and/or repair all abnormal data without further differentiate. Actually, besides the abnormal data caused by events, it is well known that sensor nodes prone to generate abnormal data due to factors such as sensor hardware drawbacks and random effects of external sources. Dealing with all abnormal data without differentiate will result in false detection or missed detection of the events.… More >

Junshan Tan¹, Rong Duan¹, Jiaohua Qin^{1, *}, Xuyu Xiang¹, Yun Tan¹

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 675-689, 2020, DOI:10.32604/cmc.2020.07730

Abstract Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system, making it more and more widely used in image retrieval. Multi-view data describes image information more comprehensively than traditional methods using a single-view. How to use hashing to combine multi-view data for image retrieval is still a challenge. In this paper, a multi-view fusion hashing method based on RKCCA (Random Kernel Canonical Correlation Analysis) is proposed. In order to describe image content more accurately, we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT (Bag-of-Words… More >