Assia Maamar^1,*, Khelifa Benahmed²

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 15-39, 2019, DOI:10.32604/cmc.2019.06497

Abstract Recently, the radical digital transformation has deeply affected the traditional electricity grid and transformed it into an intelligent network (smart grid). This mutation is based on the progressive development of advanced technologies: advanced metering infrastructure (AMI) and smart meter which play a crucial role in the development of smart grid. AMI technologies have a promising potential in terms of improvement in energy efficiency, better demand management, and reduction in electricity costs. However the possibility of hacking smart meters and electricity theft is still among the most significant challenges facing electricity companies. In this regard, we propose a hybrid approach to… More >

Alanoud Alhussain¹, Heba Kurdi^1,*, Lina Altoaimy²

CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 805-815, 2019, DOI:10.32604/cmc.2019.05848

Abstract Edge devices in Internet of Things (IoT) applications can form peers to communicate in peer-to-peer (P2P) networks over P2P protocols. Using P2P networks ensures scalability and removes the need for centralized management. However, due to the open nature of P2P networks, they often suffer from the existence of malicious peers, especially malicious peers that unite in groups to raise each other's ratings. This compromises users' safety and makes them lose their confidence about the files or services they are receiving. To address these challenges, we propose a neural network-based algorithm, which uses the advantages of a machine learning algorithm to… More >

Dae-Young Kim¹, Seokhoon Kim^2,*

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 375-387, 2019, DOI:10.32604/cmc.2019.06077

Abstract Communication is important for providing intelligent services in connected vehicles. Vehicles must be able to communicate with different places and exchange information while driving. For service operation, connected vehicles frequently attempt to download large amounts of data. They can request data downloading to a road side unit (RSU), which provides infrastructure for connected vehicles. The RSU is a data bottleneck in a transportation system because data traffic is concentrated on the RSU. Therefore, it is not appropriate for a connected vehicle to always attempt a high speed download from the RSU. If the mobile network between a connected vehicle and… More >

Khader M. Hamdia², Hamid Ghasemi³, Xiaoying Zhuang^4,5, Naif Alajlan¹, Timon Rabczuk^1,2,*

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 79-87, 2019, DOI:10.32604/cmc.2019.05882

Abstract In this study, machine learning representation is introduced to evaluate the flexoelectricity effect in truncated pyramid nanostructure under compression. A Non-Uniform Rational B-spline (NURBS) based IGA formulation is employed to model the flexoelectricity. We investigate 2D system with an isotropic linear elastic material under plane strain conditions discretized by 45×30 grid of B-spline elements. Six input parameters are selected to construct a deep neural network (DNN) model. They are the Young's modulus, two dielectric permittivity constants, the longitudinal and transversal flexoelectric coefficients and the order of the shape function. The outputs of interest are the strain in the stress direction… More >

Xiaoying Zhuang^1,2,*, Shuai Zhou^3,4

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 57-77, 2019, DOI:10.32604/cmc.2019.04589

Abstract Advances in machine learning (ML) methods are important in industrial engineering and attract great attention in recent years. However, a comprehensive comparative study of the most advanced ML algorithms is lacking. Six integrated ML approaches for the crack repairing capacity of the bacteria-based self-healing concrete are proposed and compared. Six ML algorithms, including the Support Vector Regression (SVR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), Artificial Neural Network (ANN), Bayesian Ridge Regression (BRR) and Kernel Ridge Regression (KRR), are adopted for the relationship modeling to predict crack closure percentage (CCP). Particle Swarm Optimization (PSO) is used for the hyper-parameters… More >

Jixian Zhang¹, Ning Xie¹, Xuejie Zhang¹, Kun Yue¹, Weidong Li^2,*, Deepesh Kumar³

CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 123-135, 2018, DOI: 10.3970/cmc.2018.03728

Abstract Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training set, the prediction model can… More >

R. Agalya¹, R. Muthaiah^2,*, D. Muralidharan³

CMES-Computer Modeling in Engineering & Sciences, Vol.120, No.1, pp. 25-43, 2019, DOI:10.32604/cmes.2019.05616

Abstract In today’s modern design technology, post-silicon validation is an expensive and composite task. The major challenge involved in this method is that it has limited observability and controllability of internal signals. There will be an issue during execution how to address the useful set of signals and store it in the on-chip trace buffer. The existing approaches are restricted to particular debug set-up where all the components have equivalent prominence at all the time. Practically, the verification engineers will emphasis only on useful functional regions or components. Due to some constraints like clock gating, some of the regions can be… More >

Marco Götz^1,*, Ferenc Leichsenring¹, Thomas Kropp², Peter Müller², Tobias Falk², Wolfgang Graf¹, Michael Kaliske¹, Welf-Guntram Drossel²

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 387-423, 2018, DOI:10.31614/cmes.2018.04112

Abstract Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised. The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design. Multiple analysis methods are known and available to gain insight into existing models. In this contribution, selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process. The selection of introduced methods comprises techniques of machine learning and data mining, in which the utilization is aiming at a decreased numerical effort. The methods of choice are basically… More >

R. E. Jones^1,*, J. A. Templeton¹, C. M. Sanders¹, J. T. Ostien¹

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 309-342, 2018, DOI:10.31614/cmes.2018.04285

Abstract We use machine learning (ML) to infer stress and plastic flow rules using data from representative polycrystalline simulations. In particular, we use so-called deep (multilayer) neural networks (NN) to represent the two response functions. The ML process does not choose appropriate inputs or outputs, rather it is trained on selected inputs and output. Likewise, its discrimination of features is crucially connected to the chosen inputoutput map. Hence, we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties. In the context of the results of numerous simulations, we discuss the design, stability and accuracy of… More >

Xiang Yu¹, Zhihong Tian², Jing Qiu^2,*, Shen Su^2,*, Xiaoran Yan³

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 255-274, 2019, DOI:10.32604/cmc.2019.05821

Abstract With the development of Information technology and the popularization of Internet, whenever and wherever possible, people can connect to the Internet optionally. Meanwhile, the security of network traffic is threatened by various of online malicious behaviors. The aim of an intrusion detection system (IDS) is to detect the network behaviors which are diverse and malicious. Since a conventional firewall cannot detect most of the malicious behaviors, such as malicious network traffic or computer abuse, some advanced learning methods are introduced and integrated with intrusion detection approaches in order to improve the performance of detection approaches. However, there are very few… More >