初至拾取是微地震数据处理的基本步骤及重要环节,在低信噪比情况下,传统的初至拾取方法性能不佳,无法满足实际需求。为此,提出一种新算法,该算法将时域微地震数据映射到Shearlet域,利用AIC(Akaike Information Criterion)模型对Shearle...初至拾取是微地震数据处理的基本步骤及重要环节,在低信噪比情况下,传统的初至拾取方法性能不佳,无法满足实际需求。为此,提出一种新算法,该算法将时域微地震数据映射到Shearlet域,利用AIC(Akaike Information Criterion)模型对Shearlet域各尺度层的数据实现初步识别,最小AIC值作为初至时刻。通过大量实验验证Shearlet-AIC算法在低至-13 d B信噪比下自动拾取的准确性,证实该算法优于传统初至拾取算法,解决了传统初至拾取算法在低信噪比时难以有效拾取微地震初至的难题。展开更多
In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likeliho...In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.展开更多
We used data from bottom trawl surveys to study the factors influencing the abundance of small yellow croaker, Larimichthys polyactis, in the southern Yellow Sea （SYS） and the East China Sea （ECS）. The resource de...We used data from bottom trawl surveys to study the factors influencing the abundance of small yellow croaker, Larimichthys polyactis, in the southern Yellow Sea （SYS） and the East China Sea （ECS）. The resource density index （RD1） was generally higher in summer and autumn than in spring and winter. RDIs were also significantly greater in the SYS than in the ECS in summer and autumn. The bottom water salinity and depth of spatial distribution of small yellow croaker was similar between the two areas in summer, but different in other seasons. Regression analysis suggested that environmental factors such as bottom water temperature, salinity, and depth influenced the RDIs in summer in these areas. Growth condition factor （GCF） in the two areas varied monthly and the croaker in the SYS grew more slowly than those in the ECS. This was likely due to the low bottom temperature of the Yellow Sea Cold Water Mass in summer and autumn or to higher human fishing pressure in the ECS. To ensure sustainable utilization of the croaker stocks in these regions, we recommend reducing the fishing intensity, increasing the cod-end mesh size, and improving the protection of juveniles.展开更多
The onset times of acoustic signals with spikes,heavy bodies and unclear takeoffs are difficult to be picked accurately by the automatic method at present.To deal with this problem,an improved joint method based on th...The onset times of acoustic signals with spikes,heavy bodies and unclear takeoffs are difficult to be picked accurately by the automatic method at present.To deal with this problem,an improved joint method based on the discrete wavelet transform(DWT),modified energy ratio(MER)and Akaike information criterion(AIC)pickers,has been proposed in this study.First,the DWT is used to decompose the signal into various components.Then,the joint application of MER and AIC pickers is carried out to pick the initial onset times of all selected components,where the minimum AIC position ahead of MER onset time is regarded as the initial onset time.Last,the average for initial onset times of all selected components is calculated as the final onset time of this signal.This improved joint method is tested and validated by the acoustic signals with different signal to noise ratios(SNRs)and waveforms.The results show that the improved joint method is not affected by the variations of SNR,and the onset times picked by this method are always accurate in different SNRs.Moreover,the onset times of all acoustic signals with spikes,heavy bodies and unclear takeoffs can be accurately picked by the improved joint method.Compared to some other methods including MER,AIC,DWT-MER and DWT-AIC,the improved joint method has better SNR stabilities and waveform adaptabilities.展开更多
This study investigates the volatility in daily stock returns for Total Nigeria Plc using nine variants of GARCH models:sGARCH,girGARCH,eGARCH,iGARCH,aGARCH,TGARCH,NGARCH,NAGARCH,and AVGARCH along with value at risk e...This study investigates the volatility in daily stock returns for Total Nigeria Plc using nine variants of GARCH models:sGARCH,girGARCH,eGARCH,iGARCH,aGARCH,TGARCH,NGARCH,NAGARCH,and AVGARCH along with value at risk estimation and backtesting.We use daily data for Total Nigeria Plc returns for the period January 2,2001 to May 8,2017,and conclude that eGARCH and sGARCH perform better for normal innovations while NGARCH performs better for student t innovations.This investigation of the volatility,VaR,and backtesting of the daily stock price of Total Nigeria Plc is important as most previous studies covering the Nigerian stock market have not paid much attention to the application of backtesting as a primary approach.We found from the results of the estimations that the persistence of the GARCH models are stable except for few cases for which iGARCH and eGARCH were unstable.Additionally,for student t innovation,the sGARCH and girGARCH models failed to converge;the mean reverting number of days for returns differed from model to model.From the analysis of VaR and its backtesting,this study recommends shareholders and investors continue their business with Total Nigeria Plc because possible losses may be overcome in the future by improvements in stock prices.Furthermore,risk was reflected by significant up and down movement in the stock price at a 99%confidence level,suggesting that high risk brings a high return.展开更多
The method of determining the structures and parameters of radial basis function neural networks(RBFNNs) using improved genetic algorithms is proposed. Akaike′s information criterion (AIC) with generalization error t...The method of determining the structures and parameters of radial basis function neural networks(RBFNNs) using improved genetic algorithms is proposed. Akaike′s information criterion (AIC) with generalization error term is used as the best criterion of optimizing the structures and parameters of networks. It is shown from the simulation results that the method not only improves the approximation and generalization capability of RBFNNs ,but also obtain the optimal or suboptimal structures of networks.展开更多
Several types of mixed distribution are proposed and tested in order to determine the best model in describing daily rainfall amount in Peninsular Malaysia for the time period of 33 years. A mixed distribution is a mi...Several types of mixed distribution are proposed and tested in order to determine the best model in describing daily rainfall amount in Peninsular Malaysia for the time period of 33 years. A mixed distribution is a mixture of discrete and continuous daily rainfall which included the dry days. The mixed distributions tested in this study were exponential distribution, gamma distribution, weibull distribution and lognormal distribution. The model will be selected based on the Akaike Information Criterion (AIC). In general, the mixed lognormal distribution has been selected as the best model for most of the rain gauge stations in Peninsular Malaysia. However, these results are greatly influenced by the topographical, geographical and climatic changes of the rain gauge stations.展开更多
针对采用Hammerstein模型描述电磁脉冲效应时模型参数确定困难的问题,提出一种基于优化Hammerstein模型的非线性电磁脉冲效应建模仿真方法。该方法利用高阶累积量求解模型线性延迟阶数,采用赤池信息准则(Akaike Information Criterion,A...针对采用Hammerstein模型描述电磁脉冲效应时模型参数确定困难的问题,提出一种基于优化Hammerstein模型的非线性电磁脉冲效应建模仿真方法。该方法利用高阶累积量求解模型线性延迟阶数,采用赤池信息准则(Akaike Information Criterion,AIC)优化模型非线性阶次,两者共同确定出一个参数最少、精度较高的最优模型,并以瞬态抑制器的电磁脉冲效应建模为例验证了该方法。展开更多
文摘初至拾取是微地震数据处理的基本步骤及重要环节,在低信噪比情况下,传统的初至拾取方法性能不佳,无法满足实际需求。为此,提出一种新算法,该算法将时域微地震数据映射到Shearlet域,利用AIC(Akaike Information Criterion)模型对Shearlet域各尺度层的数据实现初步识别,最小AIC值作为初至时刻。通过大量实验验证Shearlet-AIC算法在低至-13 d B信噪比下自动拾取的准确性,证实该算法优于传统初至拾取算法,解决了传统初至拾取算法在低信噪比时难以有效拾取微地震初至的难题。
文摘In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.
基金Supported by the National Natural Science Foundation of China for Creative Research Groups (No. 40821004)the National Key Technology Research and Development Program (No. 2007BAD43B01)the Open Fund of Key Laboratory of Marine Ecology and Environmental Science, Institute of Oceanology, Chinese Academy of Sciences (No. KLMEES201001)
文摘We used data from bottom trawl surveys to study the factors influencing the abundance of small yellow croaker, Larimichthys polyactis, in the southern Yellow Sea （SYS） and the East China Sea （ECS）. The resource density index （RD1） was generally higher in summer and autumn than in spring and winter. RDIs were also significantly greater in the SYS than in the ECS in summer and autumn. The bottom water salinity and depth of spatial distribution of small yellow croaker was similar between the two areas in summer, but different in other seasons. Regression analysis suggested that environmental factors such as bottom water temperature, salinity, and depth influenced the RDIs in summer in these areas. Growth condition factor （GCF） in the two areas varied monthly and the croaker in the SYS grew more slowly than those in the ECS. This was likely due to the low bottom temperature of the Yellow Sea Cold Water Mass in summer and autumn or to higher human fishing pressure in the ECS. To ensure sustainable utilization of the croaker stocks in these regions, we recommend reducing the fishing intensity, increasing the cod-end mesh size, and improving the protection of juveniles.
基金Project(2015CB060200) supported by the National Basic Research Program of ChinaProject(41772313) supported by the National Natural Science Foundation of ChinaProject(2018zzts736) supported by the Independent Innovation Exploration Project of Central South University,China
文摘The onset times of acoustic signals with spikes,heavy bodies and unclear takeoffs are difficult to be picked accurately by the automatic method at present.To deal with this problem,an improved joint method based on the discrete wavelet transform(DWT),modified energy ratio(MER)and Akaike information criterion(AIC)pickers,has been proposed in this study.First,the DWT is used to decompose the signal into various components.Then,the joint application of MER and AIC pickers is carried out to pick the initial onset times of all selected components,where the minimum AIC position ahead of MER onset time is regarded as the initial onset time.Last,the average for initial onset times of all selected components is calculated as the final onset time of this signal.This improved joint method is tested and validated by the acoustic signals with different signal to noise ratios(SNRs)and waveforms.The results show that the improved joint method is not affected by the variations of SNR,and the onset times picked by this method are always accurate in different SNRs.Moreover,the onset times of all acoustic signals with spikes,heavy bodies and unclear takeoffs can be accurately picked by the improved joint method.Compared to some other methods including MER,AIC,DWT-MER and DWT-AIC,the improved joint method has better SNR stabilities and waveform adaptabilities.
文摘This study investigates the volatility in daily stock returns for Total Nigeria Plc using nine variants of GARCH models:sGARCH,girGARCH,eGARCH,iGARCH,aGARCH,TGARCH,NGARCH,NAGARCH,and AVGARCH along with value at risk estimation and backtesting.We use daily data for Total Nigeria Plc returns for the period January 2,2001 to May 8,2017,and conclude that eGARCH and sGARCH perform better for normal innovations while NGARCH performs better for student t innovations.This investigation of the volatility,VaR,and backtesting of the daily stock price of Total Nigeria Plc is important as most previous studies covering the Nigerian stock market have not paid much attention to the application of backtesting as a primary approach.We found from the results of the estimations that the persistence of the GARCH models are stable except for few cases for which iGARCH and eGARCH were unstable.Additionally,for student t innovation,the sGARCH and girGARCH models failed to converge;the mean reverting number of days for returns differed from model to model.From the analysis of VaR and its backtesting,this study recommends shareholders and investors continue their business with Total Nigeria Plc because possible losses may be overcome in the future by improvements in stock prices.Furthermore,risk was reflected by significant up and down movement in the stock price at a 99%confidence level,suggesting that high risk brings a high return.
文摘The method of determining the structures and parameters of radial basis function neural networks(RBFNNs) using improved genetic algorithms is proposed. Akaike′s information criterion (AIC) with generalization error term is used as the best criterion of optimizing the structures and parameters of networks. It is shown from the simulation results that the method not only improves the approximation and generalization capability of RBFNNs ,but also obtain the optimal or suboptimal structures of networks.
文摘Several types of mixed distribution are proposed and tested in order to determine the best model in describing daily rainfall amount in Peninsular Malaysia for the time period of 33 years. A mixed distribution is a mixture of discrete and continuous daily rainfall which included the dry days. The mixed distributions tested in this study were exponential distribution, gamma distribution, weibull distribution and lognormal distribution. The model will be selected based on the Akaike Information Criterion (AIC). In general, the mixed lognormal distribution has been selected as the best model for most of the rain gauge stations in Peninsular Malaysia. However, these results are greatly influenced by the topographical, geographical and climatic changes of the rain gauge stations.
文摘针对采用Hammerstein模型描述电磁脉冲效应时模型参数确定困难的问题,提出一种基于优化Hammerstein模型的非线性电磁脉冲效应建模仿真方法。该方法利用高阶累积量求解模型线性延迟阶数,采用赤池信息准则(Akaike Information Criterion,AIC)优化模型非线性阶次,两者共同确定出一个参数最少、精度较高的最优模型,并以瞬态抑制器的电磁脉冲效应建模为例验证了该方法。
