Yeshwantrao Chavan College of Engineering

About: Yeshwantrao Chavan College of Engineering is a based out in . It is known for research contribution in the topics: Inverter & Microstrip antenna. The organization has 632 authors who have published 586 publications receiving 4037 citations.

Wind electric power in the world and perspectives of its development in India

Abstract: The global market for wind power is expanding faster than any other source of renewable energy. From just 4,800 MW in 1995 raise to fifteen-fold to reach 73,904 MW at the end of 2006. Top five wind electric power generating countries at the end of 2006 were Germany, Spain, United States of America (USA), India and Denmark. Since 1980s, when the first commercial wind turbine was deployed, their capacity, efficiency and visual design have all improved a lot. A modern wind turbine annually produces 180 times more electricity at less than half the cost per unit (kWh) than its equivalent twenty years ago. The largest turbines being manufactured now are of rated power of 5 MW capacity and a rotor diameter of 126 m. Modern turbines are modular and quick to install, whilst wind farms vary in size from a few MW to several hundred MW. Keeping these factors in view, an attempt has been made in this paper to present current advances in wind turbine generator technology. Wind energy scenario in the world in general and in India in particular have been presented. Further the cost components of wind turbine electric generation system have been included.

Sliding Mode Control of Single-Phase Grid-Connected Quasi-Z-Source Inverter

Abstract: Quasi-Z-source inverters (qZSIs) are nowadays increasingly used owing to advantages such as single-stage operation, lower component rating, and continuous input current and common dc rail These benefits lead to investigate this converter for grid-connected applications This paper presents a grid-connected qZSI with both ac and dc side control Sliding mode control (SMC)-based controller for capacitor voltage regulation has been proposed to ensure a fast and dynamic response for wide variations in input voltage, output load, and reference controlled quantity A detailed mathematical model of the system is presented A stable and fast response of SMC has been demonstrated using simulation and is validated by experimental results

Cyber security analysis using vulnerability assessment and penetration testing

Abstract: In last twenty years, use of internet applications, web hacking activities have exaggerated speedily. Organizations facing very significant challenges in securing their web applications from rising cyber threats, as compromise with the protection issues don't seem to be reasonable. Vulnerability Assessment and Penetration Testing (VAPT) techniques help them to go looking out security loopholes. These security loopholes could also be utilized by attackers to launch attacks on technical assets. Thus it is necessary ascertain these vulnerabilities and install security patches. VAPT helps organization to determine whether their security arrangements are working properly. This paper aims to elucidate overview and various techniques used in vulnerability assessment and penetration testing (VAPT). Also focuses on making cyber security awareness and its importance at various level of an organization for adoption of required up to date security measures by the organization to stay protected from various cyber-attacks.

FCM Clustering Algorithms for Segmentation of Brain MR Images

Abstract: The study of brain disorders requires accurate tissue segmentation of magnetic resonance MR brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid CSF, Gray Matter GM, and White Matter WM, has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means FCM clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.