In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non linear chaotic model to analyze the time series of traffic flow is proposed. This model recons...In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non linear chaotic model to analyze the time series of traffic flow is proposed. This model reconstructs the time series of traffic flow in the phase space firstly, and the correlative information in the traffic flow is extracted richly, on the basis of it, a predicted equation for the reconstructed information is established by using chaotic theory, and for the purpose of obtaining the optimal predicted results, recognition and optimization to the model parameters are done by using genetic algorithm. Practical prediction research of urban traffic flow shows that this model has famous predicted precision, and it can provide exact reference for urban traffic programming and control.展开更多
This paper reports on the implementation of efficient burst assembly algorithms and traffic prediction. The ultimate goal is to propose a new burst assembly algorithm which is based on time-burst length （hybrid） thr...This paper reports on the implementation of efficient burst assembly algorithms and traffic prediction. The ultimate goal is to propose a new burst assembly algorithm which is based on time-burst length （hybrid） threshold with traffic prediction to reduce burst assembly delay in OBS （Optical Burst Switching） networks. Research has shown that traffic always change from time to time, hence, any measure that is put in place should be able to adapt to such changes. With our implemented burst assembly algorithm, the traffic rate is predicted and the predicted rate is used to dynamically adjust the burst assembly length. This work further investigates the impact of the proposed algorithm on traffic self similarity.展开更多
The rapid growth of streaming media applications on the Internet is proposing higher requirements on energy consumption and I/O performance of the storage systems.However,the optimized I/O requests from different init...The rapid growth of streaming media applications on the Internet is proposing higher requirements on energy consumption and I/O performance of the storage systems.However,the optimized I/O requests from different initiators will be mixed disorderly when they are reaching the storage system concurrently,which leads to increasing energy consumption.This paper proposes an energy-saving scheduling scheme based on I/O Stream(ES-IOS).The ES-IOS scheme can take the advantage of the I/O characteristics of streaming media and reorganize the mixed and disordered I/O requests into "streams".Technically,The ES-IOS scheme includes two main points,a priority-based weighted stream scheduling algorithm(PWSS) and a regression-fitting-based popularity prediction algorithm(RFPP).The PWSS algorithm can schedule the I/O streams in weighted queue based on priority to limit energy consumption.The priority of each stream is determined by its popularity.According to the I/O access records over a period,the RFPP algorithm can predict the popularity of each stream via regression fitting.Based on the popularities,the PWSS algorithm assigns more continuous service time to the hot streams and reversely less service time to the cold ones.Trace-driven experiments show that the ES-IOS scheme can reduce the energy consumption by 38%and enhance the I/O throughput by 27%approximately.展开更多
A high resolution speed and position identification algorithm, suitable for brushless DC drives, is presented in this paper. In particular, the algorithm is proposed for BLDC （brushless DC） machines that are charact...A high resolution speed and position identification algorithm, suitable for brushless DC drives, is presented in this paper. In particular, the algorithm is proposed for BLDC （brushless DC） machines that are characterized by an un-ideal trapezoidal emfs shape. The algorithm, which is developed basing upon the MRAS technique （model reference adaptive system） and the Popov＇s hyperstability criterion, guarantees the convergence of the estimated rotor speed and position signals to their corresponding actual values. The identification procedure can be performed starting from the knowledge of low resolution rotor position signals, phase currents and the BLDC emfs shape. The identification algorithm is properly tested on a BLDC drive controlled by a predictive algorithm, by performing a simulation study in the Matlab-Simulink environment. The corresponding results have highlighted the effectiveness of the proposed sensorless predictive control system, at both low and high speed operation.展开更多
文摘In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non linear chaotic model to analyze the time series of traffic flow is proposed. This model reconstructs the time series of traffic flow in the phase space firstly, and the correlative information in the traffic flow is extracted richly, on the basis of it, a predicted equation for the reconstructed information is established by using chaotic theory, and for the purpose of obtaining the optimal predicted results, recognition and optimization to the model parameters are done by using genetic algorithm. Practical prediction research of urban traffic flow shows that this model has famous predicted precision, and it can provide exact reference for urban traffic programming and control.
文摘This paper reports on the implementation of efficient burst assembly algorithms and traffic prediction. The ultimate goal is to propose a new burst assembly algorithm which is based on time-burst length （hybrid） threshold with traffic prediction to reduce burst assembly delay in OBS （Optical Burst Switching） networks. Research has shown that traffic always change from time to time, hence, any measure that is put in place should be able to adapt to such changes. With our implemented burst assembly algorithm, the traffic rate is predicted and the predicted rate is used to dynamically adjust the burst assembly length. This work further investigates the impact of the proposed algorithm on traffic self similarity.
基金Supported by the National High Technology Research and Development Programme of China(No.2011AA01A102)
文摘The rapid growth of streaming media applications on the Internet is proposing higher requirements on energy consumption and I/O performance of the storage systems.However,the optimized I/O requests from different initiators will be mixed disorderly when they are reaching the storage system concurrently,which leads to increasing energy consumption.This paper proposes an energy-saving scheduling scheme based on I/O Stream(ES-IOS).The ES-IOS scheme can take the advantage of the I/O characteristics of streaming media and reorganize the mixed and disordered I/O requests into "streams".Technically,The ES-IOS scheme includes two main points,a priority-based weighted stream scheduling algorithm(PWSS) and a regression-fitting-based popularity prediction algorithm(RFPP).The PWSS algorithm can schedule the I/O streams in weighted queue based on priority to limit energy consumption.The priority of each stream is determined by its popularity.According to the I/O access records over a period,the RFPP algorithm can predict the popularity of each stream via regression fitting.Based on the popularities,the PWSS algorithm assigns more continuous service time to the hot streams and reversely less service time to the cold ones.Trace-driven experiments show that the ES-IOS scheme can reduce the energy consumption by 38%and enhance the I/O throughput by 27%approximately.
文摘A high resolution speed and position identification algorithm, suitable for brushless DC drives, is presented in this paper. In particular, the algorithm is proposed for BLDC （brushless DC） machines that are characterized by an un-ideal trapezoidal emfs shape. The algorithm, which is developed basing upon the MRAS technique （model reference adaptive system） and the Popov＇s hyperstability criterion, guarantees the convergence of the estimated rotor speed and position signals to their corresponding actual values. The identification procedure can be performed starting from the knowledge of low resolution rotor position signals, phase currents and the BLDC emfs shape. The identification algorithm is properly tested on a BLDC drive controlled by a predictive algorithm, by performing a simulation study in the Matlab-Simulink environment. The corresponding results have highlighted the effectiveness of the proposed sensorless predictive control system, at both low and high speed operation.
