Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

An Intelligent-Based Fault-Tolerant System for Solar-Fed Cascaded Multilevel Inverters

Abstract: This paper presents an intelligent-based fault-tolerant system for a solar photovoltaic (PV) inverter. Artificial neural network based controller is used to monitor, detect, and diagnose the faults in solar PV panels, battery, semiconductor switches, and inverters. The cascaded multilevel inverter is connected across the combination of solar PV panel and battery for dc–ac conversion. The major advantage of the proposed topology is that it can deliver power from source to the load even under fault and partial shaded conditions. The paper also reviews on various faults in the solar PV energy conversion process and provides suitable solutions for each circumstance. Simulations are undertaken in MATLAB 2016a/Simulink and experimental investigation is carried out for a 3 kWp solar PV system. The results have proven that the system is capable to deliver power in spite of faulty environments. The comparison and discussion of the results were made to show the effectiveness of the proposed system.

Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images

Abstract: Breast cancer is a commonly occurring disease in women all over the world. Mammogram is an efficient technique used for screening and identification of abnormalities over the breast region. Earlier identification of breast cancer enhances the prognosis of patients and is mainly based on the experience of the radiologist in interpretation of mammogram with quality of image. The advent of Deep Learning (DL) and Computer Vision techniques is widely used to perform breast cancer diagnosis. This paper presents a new Optimal Multi-Level Thresholding-based Segmentation with DL enabled Capsule Network (OMLTS-DLCN) breast cancer diagnosis model utilizing digital mammograms. The OMLTS-DLCN model involves an Adaptive Fuzzy based median filtering (AFF) technique as a pre-processing step to eradicate the noise that exists in the mammogram images. Besides, Optimal Kapur's based Multilevel Thresholding with Shell Game Optimization (SGO) algorithm (OKMT-SGO) is applied for breast cancer segmentation. In addition, the proposed model involves a CapsNet based feature extractor and Back-Propagation Neural Network (BPNN) classification model is employed to detect the existence of breast cancer. The diagnostic outcomes of the presented OMLTS-DLCN technique is examined by means of benchmark Mini-MIAS dataset and DDSM dataset. The experimental values obtained highlights the superior performance of the OMLTS-DLCN model with a higher accuracy of 98.50 and 97.55% on the Mini-MIAS dataset and DDSM dataset, respectively.

Performance analysis of evacuated tubes coupled solar still with double basin solar still and solid fins

Abstract: Experimental set up of double basin solar still with evacuated tubes has been fabricated by locally available materials and then carry out research work by use of solid fins Here 25 cm constant d