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 >

Chen Shen^1,*, Youping Chen¹, Bing Chen¹, Jingming Xie¹

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 379-397, 2019, DOI:10.32604/cmc.2019.04883

Abstract Continuous material processing operations like printing and textiles manufacturing are conducted under highly variable conditions due to changes in the environment and/or in the materials being processed. As such, the processing parameters require robust real-time adjustment appropriate to the conditions of a nonlinear system. This paper addresses this issue by presenting a hybrid feedforward-feedback nonlinear model predictive controller for continuous material processing operations. The adaptive feedback control strategy of the controller augments the standard feedforward control to ensure improved robustness and compensation for environmental disturbances and/or parameter uncertainties. Thus, the controller can reduce the need for manual adjustments. The controller… 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 >

Ling Tan^1,*, Chong Li², Jingming Xia², Jun Cao³

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 275-288, 2019, DOI:10.32604/cmc.2019.03735

Abstract Due to the widespread use of the Internet, customer information is vulnerable to computer systems attack, which brings urgent need for the intrusion detection technology. Recently, network intrusion detection has been one of the most important technologies in network security detection. The accuracy of network intrusion detection has reached higher accuracy so far. However, these methods have very low efficiency in network intrusion detection, even the most popular SOM neural network method. In this paper, an efficient and fast network intrusion detection method was proposed. Firstly, the fundamental of the two different methods are introduced respectively. Then, the self-organizing feature… More >

Ning Cao^1,2, Shengfang Li¹, Keyong Shen¹, Sheng Bin³, Gengxin Sun^3,*, Dongjie Zhu⁴, Xiuli Han⁵, Guangsheng Cao⁵, Abraham Campbell⁶

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 227-241, 2019, DOI:10.32604/cmc.2019.06125

Abstract Monitoring, understanding and predicting Origin-destination (OD) flows in a city is an important problem for city planning and human activity. Taxi-GPS traces, acted as one kind of typical crowd sensed data, it can be used to mine the semantics of OD flows. In this paper, we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China. The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows. Then based on a novel complex network model, a semantics mining method of OD flows… 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 >