IJSRD ||“Habitat Conclave 2016” Paper presentation on “Smart & Sustainable City”|| February 2016

Urban Infrastructure Issues and Solutions

Author(s):

Ashish Shakuniya; Shweta Prasad; Swati Bhute

Keywords:

Urban Infrastructure Issues, Urban Infrastructure Solutions

Abstract

In successful economies, cities are the engines of growth and infrastructure plays a significant part in the generation of economic progress and development. It also performs as the main driving force of urban activities. However in the era of intelligent technology, wherein new business models and creative thinking are required to design contemporary systems based on hard infrastructure working together with operational and digital technologies, the developing cities’ urbanisation is still getting manifested in the form of over-crowding, congestion, and inadequate infrastructure. Pollution, population, drinking water, sanitation, energy, mobility and transport, solid waste management, environmental degradation and many more such infrastructural issues still remain unaddressed. This following paper will present the current situation of the country’s infrastructure at both urban and rural level, and the urgent necessity of an advanced and smart infrastructure in carrying out our per diem activities. A secondary case study will help us understand and analyse our infrastructural issues at a much comprehensive level. The paper will conclude on our projection established on the alternatives provided by the government, private and individual institutions. Apart from better assembly and integration of components to manage continuous change in demand and supply; leadership, finance, policy support, up gradation and citizen engagement are also few essential factors which determine urban infrastructure change.

More about this article visit : http://www.ijsrd.com/C_Article.php?manuscript=HABTP005

Fault Diagnostics of Rolling Bearing based on Improve Time and Frequency Domain Features using Artificial Neural Networks : IJSRD.com

Paper Title :  Fault Diagnostics of Rolling Bearing based on Improve Time and Frequency Domain Features using Artificial Neural Networks

Author Name : Dr. Jigar Patel1 Vaishali Patel2 Amit Patel3 

Author Designation : 1Associate Professor 2Research Scholar 3Assistant Professor

Author College Name : 1KIRC, Kalol 2KSV, Gandhinagar 3CSPIT, Changa

Abstract - The neural network based approaches a feed forward neural network trained with Back Propagation technique was used for automatic diagnosis of defects in bearings. Vibration time domain signals were collected from a normal bearing and defective bearings under various speed conditions. The signals were processed to obtain various statistical parameters, which are good indicators of bearing condition, then best features are selected from graphical method and these inputs were used to train the neural network and the output represented the bearing states. The trained neural networks were used for the recognition of bearing states. The results showed that the trained neural networks were able to distinguish a normal bearing from defective bearings with 83.33 % reliability. Moreover, the network was able to classify the bearings into different states with success rates better than those achieved with the best among the state-of-the-art techniques.

Keyword - artificial neural networks (ANNs), condition monitoring, features extraction, Root mean square, Crest factor, Kurtosis, Skewness, Clearance factor, Impulse factor, shape factor, entropy, energy, upper bound, lower bound, central moment, signal distribution1, spectral skewness, spectral kurtosis, spectral energy, Periodogram. 

For Full Length Paper Visit http://www.ijsrd.com/Article.php?manuscript=IJSRDV1I4003