H. Jiang^1,2, B. Yang¹, S. Liu³

CMC-Computers, Materials & Continua, Vol.38, No.2, pp. 61-77, 2013, DOI:10.3970/cmc.2013.038.061

Abstract Actin, fibrin and collagen fiber networks are typical hierarchical biological materials formed by bundling fibrils into fibers and branching/adjoining fibers into networks. The bundled fibrils interact with each other through weak van der Waals forces and, in some cases, additional spotted covalent crosslinks. In the present work, we apply Timoshenko's beam theory that takes into account the effect of transverse shear between fibrils in each bundle to study the overall mechanical behaviors of such fiber networks. Previous experimental studies suggested that these fibers are initially loose bundles. Based on the evidence, it is hypothesized that the fibers undergo transitions from… More >

Asif Husain¹, M. Guniganti², D. K. Sehgal², R. K. Pandey²

CMC-Computers, Materials & Continua, Vol.8, No.3, pp. 133-150, 2008, DOI:10.3970/cmc.2008.008.133

Abstract This paper describes an approach to identify the mechanical properties i.e. fracture and yield strength of steels. The study involves the FE simulation of shear punch test for various miniature specimens thickness ranging from 0.20mm to 0.80mm for four different steels using ABAQUS code. The experimental method of the miniature shear punch test is used to determine the material response under quasi-static loading. The load vs. displacement curves obtained from the FE simulation miniature disk specimens are compared with the experimental data obtained and found in good agreement. The resulting data from the load vs. displacement diagrams of different steels… More >

P. Le¹, N. Mai-Duy¹, T. Tran-Cong¹, G. Baker²

CMC-Computers, Materials & Continua, Vol.5, No.2, pp. 129-150, 2007, DOI:10.3970/cmc.2007.005.129

Abstract This paper presents a numerical simulation of the formation and evolution of strain localization in elasto-thermo-viscoplastic materials (adiabatic shear band) by the indirect/integral radial basis function network (IRBFN) method. The effects of strain and strain rate hardening, plastic heating, and thermal softening are considered. The IRBFN method is enhanced by a new coordinate mapping which helps capture the stiff spatial structure of the resultant band. The discrete IRBFN system is integrated in time by the implicit fifth-order Runge-Kutta method. The obtained results are compared with those of the Modified Smooth Particle Hydrodynamics (MSPH) method and Chebychev Pseudo-spectral (CPS) method. More >

N. Mai-Duy¹, A. Khennane², T. Tran-Cong³

CMC-Computers, Materials & Continua, Vol.5, No.1, pp. 63-78, 2007, DOI:10.3970/cmc.2007.005.063

Abstract This paper reports a meshless method, which is based on radial-basis-function networks (RBFNs), for the static analysis of moderately-thick laminated composite plates using the first-order shear deformation theory. Integrated RBFNs are employed to represent the field variables, and the governing equations are discretized by means of point collocation. The use of integration rather than conventional differentiation to construct the RBF approximations significantly stabilizes the solution and enhances the quality of approximation. The proposed method is verified through the solution of rectangular and non-rectangular composite plates. Numerical results obtained show that the method achieves a very high degree of accuracy and… More >

Ramana M. Pidaparti^1,2, Evan J. Neblett²

CMC-Computers, Materials & Continua, Vol.5, No.1, pp. 1-10, 2007, DOI:10.3970/cmc.2007.005.001

Abstract A neural network (NN) model is developed for the analysis and prediction of the mapping between degradation of chemical elements and electrochemical parameters during the corrosion process. The input parameters to the neural network model are alloy composition, electrochemical parameters, and corrosion time. The output parameters are the degradation of chemical elements in AA 2024-T3 material. The NN is trained with the data obtained from Energy Dispersive X-ray Spectrometry (EDS) on corroded specimens. A very good performance of the neural network is achieved after training and validation with the experimental data. After validating the NN model, simulations were carried out… More >

Dun Li¹, Hong Wu¹, Jinzhu Gao², Zhuoyun Liu¹, Lun Li¹, Zhiyun Zheng^1,*

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 301-321, 2019, DOI:10.32604/cmc.2019.05953

Abstract With the rapid development of the semantic web and the ever-growing size of uncertain data, representing and reasoning uncertain information has become a great challenge for the semantic web application developers. In this paper, we present a novel reasoning framework based on the representation of fuzzy PR-OWL. Firstly, the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning, incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory, and introduces fuzzy PR-OWL, an Ontology language based on OWL2. Fuzzy PR-OWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic interpretation. Secondly,… More >

Xianyu Wu¹, Chao Luo¹, Qian Zhang², Jiliu Zhou¹, Hao Yang^{1, 3, *}, Yulian Li¹

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 289-300, 2019, DOI:10.32604/cmc.2019.05990

Abstract Words are the most indispensable information in human life. It is very important to analyze and understand the meaning of words. Compared with the general visual elements, the text conveys rich and high-level moral information, which enables the computer to better understand the semantic content of the text. With the rapid development of computer technology, great achievements have been made in text information detection and recognition. However, when dealing with text characters in natural scene images, there are still some limitations in the detection and recognition of natural scene images. Because natural scene image has more interference and complexity than… More >

Yueping Wang^1,*, Xuan Zhang², Yixuan Zhang³

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 171-184, 2019, DOI:10.32604/cmc.2019.06204

Abstract Full-duplex (FD) has been recognized as a promising technology for future 5G networks to improve the spectrum efficiency. However, the biggest practical impediments of realizing full-duplex communications are the presence of self-interference, especially in complex cellular networks. With the current development of self-interference cancellation techniques, full-duplex has been considered to be more suitable for device-to-device (D2D) and small cell communications which have small transmission range and low transmit power. In this paper, we consider the full-duplex D2D communications in multi-tier wireless networks and present an analytical model which jointly considers mode selection, resource allocation, and power control. Specifically, we consider… More >

Kehua Yang^1,*, Chaowei She¹, Wei Zhang¹, Jiqing Yao², Shaosong Long¹

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 155-169, 2019, DOI:10.32604/cmc.2019.05901

Abstract In recent years, multi-label learning has received a lot of attention. However, most of the existing methods only consider global label correlation or local label correlation. In fact, on the one hand, both global and local label correlations can appear in real-world situation at same time. On the other hand, we should not be limited to pairwise labels while ignoring the high-order label correlation. In this paper, we propose a novel and effective method called GLLCBN for multi-label learning. Firstly, we obtain the global label correlation by exploiting label semantic similarity. Then, we analyze the pairwise labels in the label… More >

Di Shang^1,*, Mohammed Ghriga¹

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 1-10, 2019, DOI:10.32604/cmc.2019.08217

Abstract In this research, we aim to identify and investigate the impacts of key influencers on community formations and developments. We assess the impacts of key influencers by analyzing the activities and structure of the social media presence of a local community. Results of our analysis show that key influencers play important roles in connecting the community, transferring information, and improving overall sentiment of the community members. Our findings suggest that community practitioners can apply social network analysis to identify value-added influencers and discover strategies for improving the community and keeping leadership roles. More >