M. Rajalakshmi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.2, pp. 209-220, 2018, DOI:10.3970/cmes.2018.114.209

Abstract Amongst several biometric traits, Vein pattern biometric has drawn much attention among researchers and diverse users. It gains its importance due to its difficulty in reproduction and inherent security advantages. Many research papers have dealt with the topic of new generation biometric solutions such as iris and vein biometrics. However, most implementations have been based on small datasets due to the difficulties in obtaining samples. In this paper, a deeper study has been conducted on previously suggested methods based on Convolutional Neural Networks (CNN) using a larger dataset. Also, modifications are suggested for implementation using More >

Daojian Zeng^1,3, Yuan Dai^1,3, Feng Li^1,3, R. Simon Sherratt², Jin Wang^3,*

CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 121-136, 2018, DOI:10.3970/cmc.2018.055.121

Abstract Recently, many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction (DSRE). These approaches generally use a softmax classifier with cross-entropy loss, which inevitably brings the noise of artificial class NA into classification process. To address the shortcoming, the classifier with ranking loss is employed to DSRE. Uniformly randomly selecting a relation or heuristically selecting the highest score among all incorrect relations are two common methods for generating a negative class in the ranking loss function. However, the majority of the generated negative class can be easily discriminated from… More >

Igor Grešovnik^1,2, Tadej Kodelja¹, Robert Vertnik^2,3, Bojan Senčič^3,2,3, Božidar Šarler^1,2,4

CMC-Computers, Materials & Continua, Vol.30, No.1, pp. 19-38, 2012, DOI:10.3970/cmc.2012.030.019

Abstract Artificial neural networks (ANNs) are employed as an alternative to physical modeling for calculation of the relations between the production path process parameters (melting of scrap steel and alloying, continuous casting, hydrogen removal, reheating, rolling, and cooling on a cooling bed) and the final product mechanical properties (elongation, tensile strength, yield stress, hardness after rolling, necking) of steel semi products. They provide a much faster technique of response evaluation complementary to physical modeling. The Štore Steel company process path for production of steel bars is used as an example for demonstrating the approach. The applied… More >

V. Srinivas¹, Saptarshi Sasmal¹, K. Ramanjaneyulu²

Structural Durability & Health Monitoring, Vol.5, No.2, pp. 133-160, 2009, DOI:10.3970/sdhm.2009.005.133

Abstract Many advances have taken place in the area of structural damage detection and localization using several approaches. Availability of cost-effective computing memory and speed, improvement in sensor technology including remotely monitored sensors, advancements in the finite element method, adaptation of modal testing and development of non-linear system identification methods bring out immense technical advancements that have contributed to the advancement of modal-based damage detection methods. Advances in modal-based damage detection methods over the last 20-30 years have produced new techniques for examining vibration data for identification of structural damage. In this paper, studies carried out… More >

CMC-Computers, Materials & Continua, Vol.10, No.1, pp. 65-96, 2009, DOI:10.3970/cmc.2009.010.065

Abstract This work proposed an alternative formulation for the prediction of martensite start temperature (Ms) of Fe-Mn-Si shape memory alloys (SMAs) depending on the various compositions and heat treatment techniques by using Neural Network (NN) and genetic programming (GP) soft computing techniques. The training and testing patterns of the proposed NN and GP formulations are based on well established experimental results from the literature. The NN and GP based formulation results are compared with experimental results and found to be quite reliable with a very high correlation (R2=0.955 for GEP and 0.999 for NN). 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.… 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 More >

E. Abhilash¹, M.A. Joseph¹, Prasad Krishna¹

FDMP-Fluid Dynamics & Materials Processing, Vol.2, No.3, pp. 211-220, 2006, DOI:10.3970/fdmp.2006.002.211

Abstract Aluminium-Silicon alloys are in high de-mand as an engineering material for automotive,aerospace and other engineering applications. Mechanical properties of Al-Si alloys depend not only on chemical composition but also more importantly on microstructural features such as dendritic alpha-aluminiumphase and eutectic silicon particles. As an additive to Al-Si alloys, sodium improves mechanical properties byforming finer and fewer needles like microstructures.Thus, prediction of the macro and microstructures obtained at the end of the solidification is of great interest for the manufacturer of aluminium alloys. Neuralnetworks are sophisticated nonlinear regression routinesthat, when properly “trained”, allow for the identificationof More >