http://nopr.niscair.res.in/handle/123456789/4981 Search the Channel search http://nopr.niscair.res.in/simple-search http://nopr.niscair.res.in/handle/123456789/5163 Title: Experimental analysis of heat pipe solar collector with different L/d_i ratio of heat pipe <br/> <br/>Authors: Sivaraman, B; Mohan, N Krishna <br/> <br/>Abstract: Heat pipe solar collector has better collecting efficiency compared to the conventional collectors. In this paper, experiments on the effect of L/dratio of heat pipe on heat pipe solar collector are presented. Two solar collectors with different L/d_i have been designed and fabricated. A heat pipe with stainless steel wick replaces the transport tubes of the solar collector. Copper and stainless steel were used as container and wick material and methanol was used as working fluid of heat pipe. Heat pipes are designed to have heat transport factor of around 194 W and 260 W of thermal energy. Experiments were conducted during summer season with a collector tilt angle of 13^o to the horizontal. The collector with L/d_i ratio of 52.63 was found to be more efficient than the collector with L/d_i ratio of 58.82. This improved efficiency is due to increase in heat transport factor of heat pipe, which increase with decrease in L/d_i ratio. <br/> <br/>Page(s): 698-701 http://nopr.niscair.res.in/handle/123456789/5160 Title: Advances in smart antenna system <br/> <br/>Authors: Kawitkar, Rameshwar; Wakde, D G <br/> <br/>Abstract: This paper presents brief account on smart antenna (SA) system. Smart or adaptive antenna arrays consist of an array of antenna elements with signal processing capability that optimizes the radiation and reception of a desired signal dynamically. SAs can place nulls in the direction of interferers via adaptive updating of weights linked to each antenna element. SAs thus cancel out most of the co-channel interference resulting in better quality of reception and lower dropped calls. SAs can also track the user within a cell via direction of arrival algorithms. <br/> <br/>Page(s): 660-665 http://nopr.niscair.res.in/handle/123456789/5158 Title: Design of adaptive neuro-fuzzy inference system for predicting surface roughness in turning operation <br/> <br/>Authors: Roy, Shibendu Shekhar <br/> <br/>Abstract: This paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting the surface roughness in turning operation for set of given cutting parameters, namely cutting speed, feed rate and depth of cut. Two different membership functions, triangular and bell shaped, were adopted during the training process of ANFIS in order to compare the prediction accuracy of surface roughness by the two membership functions. The comparison of ANFIS values with experimental data indicates that the adoption of both triangular and bell shaped membership functions in proposed system achieved satisfactory accuracy. The bell-shaped membership function in ANFIS achieves slightly higher prediction accuracy than triangular membership function. <br/> <br/>Page(s): 653-659 http://nopr.niscair.res.in/handle/123456789/5155 Title: Vehicular pollution modeling using artificial neural network technique: A review <br/> <br/>Authors: Sharma, N; Chaudhry, K K; Rao, C V Chalapati <br/> <br/>Abstract: Air quality models form one of the most important components of an urban air quality management plan. An effective air quality management system must be able to provide the authorities with information on the current and likely future trends, enabling them to make necessary assessments regarding the extent and type of the air pollution control management strategies to be implemented throughout the area. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Recently, statistical modeling tool such as artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, a review of the applications of ANN in vehicular pollution modeling under urban condition and basic features of ANN and modeling philosophy, including performance evaluation criteria for ANN based vehicular emission models have been described. <br/> <br/>Page(s): 637-647