Sona College of Technology

About: Sona College of Technology is a based out in . It is known for research contribution in the topics: Wireless sensor network & Cluster analysis. The organization has 867 authors who have published 1042 publications receiving 6868 citations. The organization is also known as: Sona College & Sona.

Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method.

Abstract: This paper presents a new approach for the optimization of drilling parameters on drilling Al/SiC metal matrix composite with multiple responses based on orthogonal array with grey relational analysis. Experiments are conducted on LM25-based aluminium alloy reinforced with green bonded silicon carbide of size 25 μm (10% volume fraction). Drilling tests are carried out using TiN coated HSS twist drills of 10 mm diameter under dry condition. In this study, drilling parameters namely cutting speed, feed and point angle are optimized with the considerations of multi responses such as surface roughness, cutting force and torque. A grey relational grade is obtained from the grey analysis. Based on the grey relational grade, optimum levels of parameters have been identified and significant contribution of parameters is determined by ANOVA. Confirmation test is conducted to validate the test result. Experimental results have shown that the responses in drilling process can be improved effectively through the new approach.

Investigations on leakage current and phase angle characteristics of porcelain and polymeric insulator under contaminated conditions

Abstract: This paper deals with the analysis of leakage current and phase angle characteristics of porcelain and silicone rubber insulator in order to develop a better diagnostic tool to identify the pollution severity of outdoor insulators In this work, laboratory based pollution performance tests are carried out on porcelain and silicone rubber insulator under ac voltage at different pollution levels and relative humidity conditions with sodium chloride as a contaminant Multi resolution signal decomposition (MRSD) using discrete wavelet transform (DWT) is employed to understand the time-frequency characteristics of the leakage current signal Fast Fourier transform (FFT) spectral analysis is adopted to calculate the phase angle values of the applied voltage and leakage current signals Reported results on porcelain and silicone rubber insulators show that the pollution severity of outdoor insulators could be identified from the DWT STD-MRA (standard deviation multi resolution analysis) distortion ratio pattern analysis of leakage current signals

Hybrid Tolerance Rough Set-Firefly based supervised feature selection for MRI brain tumor image classification

Abstract: Brain tumor is one of the most harmful diseases, and has affected majority of people including children in the world. The probability of survival can be enhanced if the tumor is detected at its premature stage. The intention of feature selection approach is to select a small subset of features which minimizes redundancy and maximizes relevance to the target such as the class labels in classification. Thus, the machine learning model receives a brief organization with high predictive accuracy using the selected prominent features. Therefore, currently, feature selection plays a significant role in machine learning and knowledge discovery. A novel hybrid supervised feature selection algorithm, called TRSFFQR (Tolerance Rough Set Firefly based Quick Reduct), is developed and applied for MRI brain images. The hybrid intelligent system aims to exploit the benefits of the basic models and at the same time, moderate their limitations. Different categories of features are extracted from the segmented MRI images, i.e., shape, intensity and texture based features. The features extracted from brain tumor Images are real values. Hence Tolerance Rough set is applied in this work. In this study, a hybridization of two techniques, Tolerance Rough Set (TRS) and Firefly Algorithm (FA) are used to select the imperative features of brain tumor. Performance of TRSFFQR is compared with Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CSA), Supervised Tolerance Rough Set-PSO based Relative Reduct (STRSPSO-RR) and Supervised Tolerance Rough Set-PSO based Quick Reduct (STRSPSO-QR).The experimental result shows the effectiveness of the proposed technique as well as improvements over the existing supervised feature selection algorithms.