In the paper, we investigate a globally coupled linear system with finite subunits subject to temporal periodic force and with multiplicative dichotomous noise. It is shown that, the global coupling among the subunits...In the paper, we investigate a globally coupled linear system with finite subunits subject to temporal periodic force and with multiplicative dichotomous noise. It is shown that, the global coupling among the subunits can hugely enhance the phenomenon of SR for the amplitude of the average mean field as the functions of the transition rate of the noise and that as the function of the frequency of the signal respectively.展开更多
This paper aims to analyze, with a linear dynamic programming, the Algerian refining industry development by 2030 in the presence of uncertainties, both on the domestic demand and the exportation of the petroleum prod...This paper aims to analyze, with a linear dynamic programming, the Algerian refining industry development by 2030 in the presence of uncertainties, both on the domestic demand and the exportation of the petroleum products. Currently, the Algerian refining industry has to be adapted to meet demand progress both in terms of volume and also in terms of specifications, in a general context marked by a strong volatility of the oil markets. Commonly, refining operations planning models are based on a deterministic linear programming. However, because of the demand fluctuation, and other conditions for the market, many parameters should be considered as uncertain such as the demand and the exportation. The impact of such uncertainties on the development＇s pattern of refining capacities is analyzed with a stochastic model. Finally, the results of both deterministic and stochastic models are compared.展开更多
This paper studies the sensor selection problem for random field estimation in wireless sensor networks. The authors first prove that selecting a set of I sensors that minimize the estimation error under the D-optimal...This paper studies the sensor selection problem for random field estimation in wireless sensor networks. The authors first prove that selecting a set of I sensors that minimize the estimation error under the D-optimal criterion is NP-complete. The authors propose an iterative algorithm to pursue a suboptimal solution. Furthermore, in order to improve the bandwidth and energy efficiency of the wireless sensor networks, the authors propose a best linear unbiased estimator for a Gaussian random field with quantized measurements and study the corresponding sensor selection problem. In the case of unknown covariance matrix, the authors propose an estimator for the covariance matrix using measurements and also analyze the sensitivity of this estimator. Simulation results show the good performance of the proposed algorithms.展开更多
基金supported by the Ningbo's Supplement of National Natural Science Foundation of China under Grant No.10375009SRF for ROCS,SEM,and K.C.Wong Magna Fund in Ningbo University of China
文摘In the paper, we investigate a globally coupled linear system with finite subunits subject to temporal periodic force and with multiplicative dichotomous noise. It is shown that, the global coupling among the subunits can hugely enhance the phenomenon of SR for the amplitude of the average mean field as the functions of the transition rate of the noise and that as the function of the frequency of the signal respectively.
文摘This paper aims to analyze, with a linear dynamic programming, the Algerian refining industry development by 2030 in the presence of uncertainties, both on the domestic demand and the exportation of the petroleum products. Currently, the Algerian refining industry has to be adapted to meet demand progress both in terms of volume and also in terms of specifications, in a general context marked by a strong volatility of the oil markets. Commonly, refining operations planning models are based on a deterministic linear programming. However, because of the demand fluctuation, and other conditions for the market, many parameters should be considered as uncertain such as the demand and the exportation. The impact of such uncertainties on the development＇s pattern of refining capacities is analyzed with a stochastic model. Finally, the results of both deterministic and stochastic models are compared.
基金supported by the National Natural Science Foundation of China-Key Program under Grant No. 61032001the National Natural Science Foundation of China under Grant No.60828006
文摘This paper studies the sensor selection problem for random field estimation in wireless sensor networks. The authors first prove that selecting a set of I sensors that minimize the estimation error under the D-optimal criterion is NP-complete. The authors propose an iterative algorithm to pursue a suboptimal solution. Furthermore, in order to improve the bandwidth and energy efficiency of the wireless sensor networks, the authors propose a best linear unbiased estimator for a Gaussian random field with quantized measurements and study the corresponding sensor selection problem. In the case of unknown covariance matrix, the authors propose an estimator for the covariance matrix using measurements and also analyze the sensitivity of this estimator. Simulation results show the good performance of the proposed algorithms.
