In this paper, the glitching activity and process variations in the maximum power dissipation estimation of CMOS circuits are introduced. Given a circuit and the gate library, a new Genetic Algorithm (GA)-based techni...In this paper, the glitching activity and process variations in the maximum power dissipation estimation of CMOS circuits are introduced. Given a circuit and the gate library, a new Genetic Algorithm (GA)-based technique is developed to determine the maximum power dissipation from a statistical point of view. The simulation on 1SCAS-89 benchmarks shows that the ratio of the maximum power dissipation with glitching activity over the maximum power under zero-delay model ranges from 1.18 to 4.02. Compared with the traditional Monte Carlo-based technique, the new approach presented in this paper is more effective.展开更多
Surplus production models are the simplest analytical methods effective for fish stock assessment and fisheries management. In this paper, eight surplus production estimators(three estimation procedures) were tested o...Surplus production models are the simplest analytical methods effective for fish stock assessment and fisheries management. In this paper, eight surplus production estimators(three estimation procedures) were tested on Schaefer and Fox type simulated data in three simulated fisheries(declining, well-managed, and restoring fisheries) at two white noise levels. Monte Carlo simulation was conducted to verify the utility of moving averaging(MA), which was an important technique for reducing the effect of noise in data in these models. The relative estimation error(REE) of maximum sustainable yield(MSY) was used as an indicator for the analysis, and one-way ANOVA was applied to test the significance of the REE calculated at four levels of MA. Simulation results suggested that increasing the value of MA could significantly improve the performance of the surplus production model(low REE) in all cases when the white noise level was low(coefficient of variation(CV) = 0.02). However, when the white noise level increased(CV= 0.25), adding the value of MA could still significantly enhance the performance of most models. Our results indicated that the best model performance occurred frequently when MA was equal to 3; however, some exceptions were observed when MA was higher.展开更多
Passive source localization via a maximum likelihood （ML） estimator can achieve a high accuracy but involves high calculation burdens, especially when based on time-of-arrival and frequency-of-arrival measurements f...Passive source localization via a maximum likelihood （ML） estimator can achieve a high accuracy but involves high calculation burdens, especially when based on time-of-arrival and frequency-of-arrival measurements for its internal nonlinearity and nonconvex nature. In this paper, we use the Pincus theorem and Monte Carlo importance sampling （MCIS） to achieve an approximate global solution to the ML problem in a computationally efficient manner. The main contribution is that we construct a probability density function （PDF） of Gaussian distribution, which is called an important function for efficient sampling, to approximate the ML estimation related to complicated distributions. The improved performance of the proposed method is at- tributed to the optimal selection of the important function and also the guaranteed convergence to a global maximum. This process greatly reduces the amount of calculation, but an initial solution estimation is required resulting from Taylor series expansion. However, the MCIS method is robust to this prior knowledge for point sampling and correction of importance weights. Simulation results show that the proposed method can achieve the Cram6r-Rao lower bound at a moderate Gaussian noise level and outper- forms the existing methods.展开更多
We consider the periodic generalized autoregressive conditional heteroskedasticity(P-GARCH) process and propose a robust estimator by composite quantile regression. We study some useful properties about the P-GARCH mo...We consider the periodic generalized autoregressive conditional heteroskedasticity(P-GARCH) process and propose a robust estimator by composite quantile regression. We study some useful properties about the P-GARCH model. Under some mild conditions, we establish the asymptotic results of proposed estimator.The Monte Carlo simulation is presented to assess the performance of proposed estimator. Numerical study results show that our proposed estimation outperforms other existing methods for heavy tailed distributions.The proposed methodology is also illustrated by Va R on stock price data.展开更多
基金Supported by NSF of the United States under contract 5978 East Asia and Pacific Program 9602485
文摘In this paper, the glitching activity and process variations in the maximum power dissipation estimation of CMOS circuits are introduced. Given a circuit and the gate library, a new Genetic Algorithm (GA)-based technique is developed to determine the maximum power dissipation from a statistical point of view. The simulation on 1SCAS-89 benchmarks shows that the ratio of the maximum power dissipation with glitching activity over the maximum power under zero-delay model ranges from 1.18 to 4.02. Compared with the traditional Monte Carlo-based technique, the new approach presented in this paper is more effective.
基金supported by the special research fund of Ocean University of China (201022001)
文摘Surplus production models are the simplest analytical methods effective for fish stock assessment and fisheries management. In this paper, eight surplus production estimators(three estimation procedures) were tested on Schaefer and Fox type simulated data in three simulated fisheries(declining, well-managed, and restoring fisheries) at two white noise levels. Monte Carlo simulation was conducted to verify the utility of moving averaging(MA), which was an important technique for reducing the effect of noise in data in these models. The relative estimation error(REE) of maximum sustainable yield(MSY) was used as an indicator for the analysis, and one-way ANOVA was applied to test the significance of the REE calculated at four levels of MA. Simulation results suggested that increasing the value of MA could significantly improve the performance of the surplus production model(low REE) in all cases when the white noise level was low(coefficient of variation(CV) = 0.02). However, when the white noise level increased(CV= 0.25), adding the value of MA could still significantly enhance the performance of most models. Our results indicated that the best model performance occurred frequently when MA was equal to 3; however, some exceptions were observed when MA was higher.
基金Project supported by the National Natural Science Foundation of China （No. 61201381 ） and the China Postdoctoral Science Foundation （No. 2016M592989）
文摘Passive source localization via a maximum likelihood （ML） estimator can achieve a high accuracy but involves high calculation burdens, especially when based on time-of-arrival and frequency-of-arrival measurements for its internal nonlinearity and nonconvex nature. In this paper, we use the Pincus theorem and Monte Carlo importance sampling （MCIS） to achieve an approximate global solution to the ML problem in a computationally efficient manner. The main contribution is that we construct a probability density function （PDF） of Gaussian distribution, which is called an important function for efficient sampling, to approximate the ML estimation related to complicated distributions. The improved performance of the proposed method is at- tributed to the optimal selection of the important function and also the guaranteed convergence to a global maximum. This process greatly reduces the amount of calculation, but an initial solution estimation is required resulting from Taylor series expansion. However, the MCIS method is robust to this prior knowledge for point sampling and correction of importance weights. Simulation results show that the proposed method can achieve the Cram6r-Rao lower bound at a moderate Gaussian noise level and outper- forms the existing methods.
基金supported by National Natural Science Foundation of China(Grant No.11371354)Key Laboratory of Random Complex Structures and Data Science+2 种基金Chinese Academy of Sciences(Grant No.2008DP173182)National Center for Mathematics and Interdisciplinary SciencesChinese Academy of Sciences
文摘We consider the periodic generalized autoregressive conditional heteroskedasticity(P-GARCH) process and propose a robust estimator by composite quantile regression. We study some useful properties about the P-GARCH model. Under some mild conditions, we establish the asymptotic results of proposed estimator.The Monte Carlo simulation is presented to assess the performance of proposed estimator. Numerical study results show that our proposed estimation outperforms other existing methods for heavy tailed distributions.The proposed methodology is also illustrated by Va R on stock price data.
