Frame aggregation is a wireless link optimization mechanism that aims to reduce transmission overheads by sending multiple flames as the payload of a single MAC flame. It is considered as one of the most efficient met...Frame aggregation is a wireless link optimization mechanism that aims to reduce transmission overheads by sending multiple flames as the payload of a single MAC flame. It is considered as one of the most efficient methods to improve the wireless channel utilization and the throughput of wireless networks. The static assignment of frame aggregation parameters can result in delay penalties due to variations in traffic type. We propose a frame aggregation scheme which is based on dyn- amic pricing and queue scheduling for a multi- traffic scenario. The scheme adopts a dynamic differential pricing scheme for different types of traffic. Meanwhile, it polls buffer queues in accordance with the optimal aggregation wei- ght factors to maximise the network revenue. Simulation results indicate that the proposed frame aggregation scheme can effectively improve the network revenue and the average throughput, while guaranteeing the delay requirements of all types of traffic.展开更多
The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flo...The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flows through the connection points between the distribution systems and the basic grid as a function of the contracted amounts. The objective of this control is to avoid that these flows exceed some thresholds along the contracted values, avoiding monetary penalties to the utility or unnecessary amounts of contracted flows that overrates the costumers. This question highlights the necessity of forecast the flows in these connection points in sufficient time to permit the operator to take decisions to avoid flows beyond the contracted ones. In this context, this work presents the development of a neural network based load flow forecaster, being tested two time-series neural models： support vector machines and Bayesian inference applied to multilayered perceptron. The models are applied to real data from a Brazilian distribution utility.展开更多
基金the National Natural Science Foundation of Chinaunder Grants No.61072068,No.61201137the State Key Program of National Natural Science Foundation of China under Grant No.61231008the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MEST) under Grant No.2010-0018116
文摘Frame aggregation is a wireless link optimization mechanism that aims to reduce transmission overheads by sending multiple flames as the payload of a single MAC flame. It is considered as one of the most efficient methods to improve the wireless channel utilization and the throughput of wireless networks. The static assignment of frame aggregation parameters can result in delay penalties due to variations in traffic type. We propose a frame aggregation scheme which is based on dyn- amic pricing and queue scheduling for a multi- traffic scenario. The scheme adopts a dynamic differential pricing scheme for different types of traffic. Meanwhile, it polls buffer queues in accordance with the optimal aggregation wei- ght factors to maximise the network revenue. Simulation results indicate that the proposed frame aggregation scheme can effectively improve the network revenue and the average throughput, while guaranteeing the delay requirements of all types of traffic.
文摘The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flows through the connection points between the distribution systems and the basic grid as a function of the contracted amounts. The objective of this control is to avoid that these flows exceed some thresholds along the contracted values, avoiding monetary penalties to the utility or unnecessary amounts of contracted flows that overrates the costumers. This question highlights the necessity of forecast the flows in these connection points in sufficient time to permit the operator to take decisions to avoid flows beyond the contracted ones. In this context, this work presents the development of a neural network based load flow forecaster, being tested two time-series neural models： support vector machines and Bayesian inference applied to multilayered perceptron. The models are applied to real data from a Brazilian distribution utility.
