Analytical Modeling of a Multi-queue Nodes Network Router

This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.

Predicting the utilization factor of blasthole in rock roadways by random forest

The utilization factor of blasthole is a crucial indicator of the effectiveness of blasting in rock roadways.A significant value means that the explosive energy is fully utilized,the single-cycle advance is high,and the excavation rate is fast.A good blasting programme is a prerequisite for improving the utilization rate and predicting the utilization rate before blasting operations can verify the feasibility of the blasting programme.Firstly,a database of rock roadway blasting covering different geological and production conditions is estab-lished.Secondly,error analysis and the Gini coefficient method are used to weight the characteristic variables,quantify the importance of the variables and identify eight key indicators affecting the blasting hole utilization rate.Then,a random forest algorithm-based model for predicting utilization factor of blasthole is proposed,and the results of the model on the test set are:root mean square error(RMSE)is 0.0137,mean absolute error(MAE)is 0.0087,and coefficient of determination(R^(2))is 0.905.The performance of this method is com-pared with that of the neural network and support vector machine models on the test sets to verify the superiority of the random forest algorithm.Finally,to verify the generalization ability and practicality of the random forest prediction model,the model is applied to the rock roadway blasting construction of Gu Bei coal mine in Anhui Province.The results show that R2 is 0.913,so the model is reliable and accurate,which can meet the actual engineering requirements and lay the foundation for the promotion and application of this technology.

Congestion Management of Power System with Interline Power Flow Controller Using Disparity Line Utilization Factor and Multi-objective Differential Evolution

The restructuring of the electric power market has led to complex power transmission congestion problems.Additionally,scheduled power flows in the transmission line,as well as spontaneous power exchanges have also risen sharply in recent years.The proper placement of IPFC can improve the transmission line congestion problem to a great extent.This paper proposes a disparity line utilization factor(DLUF)for the optimal placement of IPFC to control the congestion in transmission lines.DLUF determines the difference between the percentages of Mega Volt Ampere utilization of each line connected to the same bus.The IPFC is placed in the lines with maximum DLUF.A multiobjective function consisting of reduction of active power loss,minimization of total voltage deviations,minimization of security margin and minimization of installed IPFC capacity is considered for the optimal tuning of IPFC using differential evolution algorithm.The proposed method is implemented for IEEE-30 bus test system under different loading conditions and the results are presented and analyzed to establish the effectiveness on the reduction of congestion.