The prediction of solar radiation is important for several applications in renewable energy research. There are a number of geographical variables which affect solar radiation prediction, the identification of these v...The prediction of solar radiation is important for several applications in renewable energy research. There are a number of geographical variables which affect solar radiation prediction, the identification of these variables for accurate solar radiation prediction is very important. This paper presents a hybrid method for the compression of solar radiation using predictive analysis. The prediction of minute wise solar radiation is performed by using different models of Artificial Neural Networks (ANN), namely Multi-layer perceptron neural network (MLPNN), Cascade feed forward back propagation (CFNN) and Elman back propagation (ELMNN). Root mean square error (RMSE) is used to evaluate the prediction accuracy of the three ANN models used. The information and knowledge gained from the present study could improve the accuracy of analysis concerning climate studies and help in congestion control.展开更多
Mobile ad hoc networks use many different routing protocols to route data packets among nodes. Various routing protocols have been developed, and their usage depends on the application and network architecture. This s...Mobile ad hoc networks use many different routing protocols to route data packets among nodes. Various routing protocols have been developed, and their usage depends on the application and network architecture. This study examined several different routing protocols, and evaluated the performance of three: the Ad Hoc On-Demand Distance Vector Protocol (AODV), the Destination-Sequenced Distance-Vector Routing (DSDV), and the Dynamic Source Routing (DSR). These three protocols were evaluated on a network with nodes ranging from 50 to 300, using performance metrics such as average delay, jitter, normal overhead, packet delivery ratio, and throughput. These performance metrics were measured by changing various parameters of the network: queue length, speed, and the number of source nodes. AODV performed well in high mobility and high density scenarios, whereas DSDV performed well when mobility and the node density were low. DSR performed well in low-mobility scenarios. All the simulations were performed in NS2 simulator.展开更多
文摘The prediction of solar radiation is important for several applications in renewable energy research. There are a number of geographical variables which affect solar radiation prediction, the identification of these variables for accurate solar radiation prediction is very important. This paper presents a hybrid method for the compression of solar radiation using predictive analysis. The prediction of minute wise solar radiation is performed by using different models of Artificial Neural Networks (ANN), namely Multi-layer perceptron neural network (MLPNN), Cascade feed forward back propagation (CFNN) and Elman back propagation (ELMNN). Root mean square error (RMSE) is used to evaluate the prediction accuracy of the three ANN models used. The information and knowledge gained from the present study could improve the accuracy of analysis concerning climate studies and help in congestion control.
文摘Mobile ad hoc networks use many different routing protocols to route data packets among nodes. Various routing protocols have been developed, and their usage depends on the application and network architecture. This study examined several different routing protocols, and evaluated the performance of three: the Ad Hoc On-Demand Distance Vector Protocol (AODV), the Destination-Sequenced Distance-Vector Routing (DSDV), and the Dynamic Source Routing (DSR). These three protocols were evaluated on a network with nodes ranging from 50 to 300, using performance metrics such as average delay, jitter, normal overhead, packet delivery ratio, and throughput. These performance metrics were measured by changing various parameters of the network: queue length, speed, and the number of source nodes. AODV performed well in high mobility and high density scenarios, whereas DSDV performed well when mobility and the node density were low. DSR performed well in low-mobility scenarios. All the simulations were performed in NS2 simulator.
