CMC-Vol. 53, No. 2, 2017

Open Access

ARTICLE

The Constitutive Relation of a Fabric Membrane Composite for a Stratospheric Airship Envelope Based on Invariant Theory
Junhui Meng^1,*, Mingyun Lv²
CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 73-89, 2017, DOI:10.3970/cmc.2017.053.073
Abstract The study of stratospheric airships has become the focus in many countries in recent years, because of its potential applications in many fields. Lightweight and high strength envelopes are the keys to the design of stratospheric airships, as it directly determines the endurance flight performance and loading deformation characteristics of the airship. A typical envelope of any stratospheric airship is a coated-fabric material which is composed of a fiber layer and several functional membrane layers. According to composite structure, nonlinearity and viscoelasticity are the two main characteristics of such envelope. Based on the analysis on More >

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ARTICLE

A Machine Learning Approach for MRI Brain Tumor Classification
Ravikumar Gurusamy¹, Dr Vijayan Subramaniam²
CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 91-108, 2017, DOI:10.3970/cmc.2017.053.091
Abstract A new method for the denoising, extraction and tumor detection on MRI images is presented in this paper. MRI images help physicians study and diagnose diseases or tumors present in the brain. This work is focused towards helping the radiologist and physician to have a second opinion on the diagnosis. The ambiguity of Magnetic Resonance (MR) image features is solved in a simpler manner. The MRI image acquired from the machine is subjected to analysis in the work. The real-time data is used for the analysis. Basic preprocessing is performed using various filters for noise More >

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ARTICLE

Influence of functionalization on the structural and mechanical properties of graphene
L.S. Melro^1,2, L.R. Jensen¹
CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 109-127, 2017, DOI:10.3970/cmc.2017.053.111
Abstract Molecular dynamics simulations were applied in order to calculate the Young’s modulus of graphene functionalized with carboxyl, hydroxyl, carbonyl, hydrogen, methyl, and ethyl groups. The influence of the grafting density with percentages of 3, 5, 7, and 10% and the type of distribution as a single cluster or several small clusters were also studied. The results show that the elastic modulus is dependent on the type of functional groups. The increasing coverage density also evidenced a decrease of the Young’s modulus, and the organization of functional groups as single cluster showed a lesser impact than More >

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ARTICLE

Effect of Rotation on the Propagation of Waves in Hollow Poroelastic Circular Cylinder with Magnetic Field
A.M. Farhan^{1, 2}
CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 129-156, 2017, DOI:10.3970/cmc.2017.053.133
Abstract Employing Biot’s theory of wave propagation in liquid saturated porous media, the effect of rotation and magnetic field on wave propagation in a hollow poroelastic circular of infinite extent are investigated. An exact closed form solution is presented. General frequency equations for propagation of poroelastic cylinder are obtained when the boundaries are stress free. The frequencies are calculated for poroelastic cylinder for different values of magnetic field and rotation. Numerical results are given and illustrated graphically. The results indicate that the effect of rotation, and magnetic field are very pronounced. Such a model would be More >

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ARTICLE

Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling
Susom Dutta¹, A. Ramach,ra Murthy², Dookie Kim³, Pijush Samui⁴
CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 157-174, 2017, DOI:10.3970/cmc.2017.053.167
Abstract In the present scenario, computational modeling has gained much importance for the prediction of the properties of concrete. This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete (SCC). Three models, namely, Extreme Learning Machine (ELM), Adaptive Neuro Fuzzy Inference System (ANFIS) and Multi Adaptive Regression Spline (MARS) have been employed in the present study for the prediction of compressive strength of self compacting concrete. The contents of cement (c), sand (s), coarse aggregate (a), fly ash (f), water/powder (w/p) ratio and superplasticizer (sp) dosage More >

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