Guodong Zhao¹, Yuewei Zhang¹, Yiqi Shi², Haiyan Lan^1,*, Qing Yang³

CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 421-436, 2019, DOI:10.32604/cmc.2019.03782

Abstract Climate change is the main factor affecting the country’s vulnerability, meanwhile, it is also a complicated and nonlinear dynamic system. In order to solve this complex problem, this paper first uses the analytic hierarchy process (AHP) and natural breakpoint method (NBM) to implement an AHP-NBM comprehensive evaluation model to assess the national vulnerability. By using ArcGIS, national vulnerability scores are classified and the country’s vulnerability is divided into three levels: fragile, vulnerable, and stable. Then, a BP neural network prediction model which is based on multivariate linear regression is used to predict the critical point of vulnerability. The function of… More >

Tao Shen^1,*, Yukari Nagai¹, Chan Gao²

CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 307-318, 2019, DOI:10.32604/cmc.2019.04876

Abstract Computer visualization has marvelous effects when it is applied in various fields, especially in architectural design. As an emerging force in the innovation industry, architects and design agencies have already demonstrated the value of architectural visual products in actual application projects. Based on the digital image technology, virtual presentation of future scenes simulates architecture design, architectural renderings and multimedia videos. Therefore, it can help design agencies transform the theoretical design concept into a lively and realistic visual which can provide the audience with a clearer understanding of the engineering and construction projects. However, it is challenging for designers to produce… More >

Yu Liu¹, Zhong Yang², Xiaoyong Yan³, Guangchi Liu⁴, Bo Hu^5,*

CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 79-100, 2019, DOI:10.32604/cmc.2019.03626

Abstract Node location estimation is not only the promise of the wireless network for target recognition, monitoring, tracking and many other applications, but also one of the hot topics in wireless network research. In this paper, the localization algorithm for wireless network with unevenly distributed nodes is discussed, and a novel multi-hop localization algorithm based on Elastic Net is proposed. The proposed approach is formulated as a regression problem, which is solved by Elastic Net. Unlike other previous localization approaches, the proposed approach overcomes the shortcomings of traditional approaches assume that nodes are distributed in regular areas without holes or obstacles,… More >

Ma Ning^1,*, Jianhe Guan¹, Pingzeng Liu², Ziqing Zhang³, Gregory M. P. O’Hare⁴

CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 215-227, 2019, DOI:10.32604/cmc.2019.03763

Abstract With the rapid development of China’s economy, the scale of the city has been continuously expanding, industrial enterprises have been increasing, the discharge of multiple pollutants has reached the top of the world, and the environmental problems become more and more serious. The air pollution problem is particularly prominent. Air quality has become a daily concern for people. In order to control air pollution, it is necessary to grasp the air quality situation in an all-round way. It is necessary to evaluate air quality. Accurate results of air quality evaluation can help people know more about air quality. In this… More >

Nianbin Wang¹, Ming He^1,2, Jianguo Sun^1,*, Hongbin Wang¹, Lianke Zhou¹, Ci Chu¹, Lei Chen³

CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 169-181, 2019, DOI:10.32604/cmc.2019.03709

Abstract Underwater target recognition is a key technology for underwater acoustic countermeasure. How to classify and recognize underwater targets according to the noise information of underwater targets has been a hot topic in the field of underwater acoustic signals. In this paper, the deep learning model is applied to underwater target recognition. Improved anti-noise Power-Normalized Cepstral Coefficients (ia-PNCC) is proposed, based on PNCC applied to underwater noises. Multitaper and normalized Gammatone filter banks are applied to improve the anti-noise capacity. The method is combined with a convolutional neural network in order to recognize the underwater target. Experiment results show that the… More >

Ying Li¹, Jianbo Li^1,*, Jianwei Chen¹, Minchao Lu¹, Caoyuan Li^2,3

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 571-587, 2018, DOI:10.32604/cmc.2018.02851

Abstract The explosive growth of mobile data demand is becoming an increasing burden on current cellular network. To address this issue, we propose a solution of opportunistic data offloading for alleviating overloaded cellular traffic. The principle behind it is to select a few important users as seeds for data sharing. The three critical steps are detailed as follows. We first explore individual interests of users by the construction of user profiles, on which an interest graph is built by Gaussian graphical modeling. We then apply the extreme value theory to threshold the encounter duration of user pairs. So, a contact graph… More >

Rooh ul Amin¹, Irum Inayat², Li Aijun¹, Shahaboddin Shamshirband^3,4,*, Timon Rabczuk⁵

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 365-388, 2018, DOI:10.32604/cmc.2018.03757

Abstract A bio-inspired global finite time control using global fast-terminal sliding mode controller and radial basis function network is presented in this article, to address the attitude tracking control problem of the three degree-of-freedom four-rotor hover system. The proposed controller provides convergence of system states in a pre-determined finite time and estimates the unmodeled dynamics of the four-rotor system. Dynamic model of the four-rotor system is derived with Newton’s force equations. The unknown dynamics of four-rotor systems are estimated using Radial basis function. The bio-inspired global fast terminal sliding mode controller is proposed to provide chattering free finite time error convergence… More >

Xiaodong Liu¹, Qi Liu^1,*

CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 179-194, 2018, DOI:10.32604/cmc.2018.04025

Abstract In a home energy management system (HEMS), appliances are becoming diversified and intelligent, so that certain simple maintenance work can be completed by appliances themselves. During the measurement, collection and transmission of electricity load data in a HEMS sensor network, however, problems can be caused on the data due to faulty sensing processes and/or lost links, etc. In order to ensure the quality of retrieved load data, different solutions have been presented, but suffered from low recognition rates and high complexity. In this paper, a validation and repair method is presented to detect potential failures and errors in a domestic… More >

Lingling Xia¹, Bo Song^2,3, Zhengjun Jing⁴, Yurong Song^5,*, Liang Zhang¹

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 123-144, 2018, DOI:10.32604/cmc.2018.03738

Abstract Considering dynamical disease spreading network consisting of moving individuals, a new double-layer network is constructed, one where the information dissemination process takes place and the other where the dynamics of disease spreading evolves. On the basis of Markov chains theory, a new model characterizing the coupled dynamics between information dissemination and disease spreading in populations of moving agents is established and corresponding state probability equations are formulated to describe the probability in each state of every node at each moment. Monte Carlo simulations are performed to characterize the interaction process between information and disease spreading and investigate factors that influence… More >

Rui Wang¹, Miaomiao Shen^1,*, Yanping Li¹, Samuel Gomes²

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 25-48, 2018, DOI:10.32604/cmc.2018.02408

Abstract Recently, sparse representation classification (SRC) and fisher discrimination dictionary learning (FDDL) methods have emerged as important methods for vehicle classification. In this paper, inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection, we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors. To improve the classification accuracy in complex scenes, we develop a new method, called multi-task joint sparse representation classification based on fisher discrimination dictionary learning, for… More >