College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Sliding mode control & Control theory. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

Active suspension systems for vehicles based on a sliding-mode controller in combination with inertial delay control

Abstract: In this paper a new active suspension scheme for vehicles with non-linearities is proposed. The proposed scheme makes the vehicle suspension system follow a modified skyhook model and is based on the sliding-mode control strategy augmented by the so-called inertial delay control which estimates and negates the effects of the unknown road profile and the uncertainties in the suspension system. The accuracy of estimation is analysed and it is shown that the estimation error is ultimately bounded. The means to improve the accuracy of estimation are outlined and the overall stability of the suspension system is proved. The efficacy of the method is illustrated by simulation for an automobile vehicle suspension system for various road profiles.

Sustainable rural electrification using rice husk biomass energy: a case study of Cambodia.

Abstract: The role of energy in overall social and economical developments is well recognized. However, most energy providers are reluctant to venture in rural regions of developing countries. These regions are generally characterized by: low-income populations, minimum access to electricity, rare access to financial services or loans. In this paper we present the potential of rice husk for rural electrification in Cambodia. Rice husk is locally abundant at almost no cost, with a production over 9.3 million tons paddy rice in 2014 for a total population of about 15 million people. The conversion of rice husk into electricity through gasification or thermally generated electricity is a well-known technology. Rice husk can contribute in a sustainable manner to grant access to electricity to Cambodian rural population and is more reliable and competitive with reference to other renewable energy sources of electricity. The present investigation focuses on the study of self-sustaining energy service model to provide grid quality power to rural populations without the need of subsidies. In Cambodia, many rice mills are operating in rural and semi-urban areas. Some of these mills are now using rice husk for electricity production for industries and rural households, generally for lighting at low cost. We believe that the power systems focusing on both power requirements for the industry such rice mills and power requirements for the people living in the neighboring villages/rural areas at affordable tariff could become the most appropriate solution for the sustainable rural electrification. Along with the potential of rice husk for rural electrification in Cambodia, we present a financially viable business model to provide the grid quality power to rural population without grant or subsidy.

Rough Sets in COVID-19 to Predict Symptomatic Cases

Abstract: Rough set theory is a new mathematical or set-theoretical practice to study inadequate knowledge. There are many use cases in the real world where there is a lack of crisp knowledge. In view of this, many Scientists have been attempted to address anomalies associated with imperfect knowledge for a long time. In recent times, computer and mathematics researchers have been trying to resolve this decisive issue, mainly in artificial intelligence province. The COVID-19 pandemic encroaches the harmony of the whole world. Many patients of COVID-19 have different symptoms, so it is very difficult to carry out the symptoms-based prediction COVID-19. However, the rough set theory approach help to minimize the number of attributes from the underlined decision table. This work defines the decision table having patients and symptoms of the COVID-19 in the rows and columns respectively. By studying data indiscernibility, elementary sets are specified for each attribute. Moreover, lower approximation, upper approximation, class of rough sets and accuracy of approximation are defined for different individual or group symptoms. This proposed work investigates whether particular symptoms belong to the decision set or not and also the accuracy of observations is calculated and analyzed. The probability of having COVID-19 is defined by considering the different sets of attributes. The main objective of this work is to minimize the number of symptoms of COVID-19 by rough set theory approach for better decision making. This symptoms-based prediction could help us while checking patients and decision-makers could be benefited while making policies and guidelines.