In this paper, we introduce the class of autoregressive fractionally integrated moving average-generalized autoregressive conditional heteroskedasticity?(ARFIMA-GARCH) models with level shift type intervention that ar...In this paper, we introduce the class of autoregressive fractionally integrated moving average-generalized autoregressive conditional heteroskedasticity?(ARFIMA-GARCH) models with level shift type intervention that are capable of capturing three key features of time series: long range dependence, volatility?and level shift. The main concern is on detection of mean and volatility level shift in a fractionally integrated time series with volatility. We will denote such a time series as level shift autoregressive fractionally integrated moving average (LS-ARFIMA) and level shift generalized autoregressive conditional heteroskedasticity (LS-GARCH). Test statistics that are useful to examine if mean and volatility level shifts are present in an autoregressive fractionally integrated moving average-generalized autoregressive conditional heteroskedasticity (ARFIMA-GARCH) model are derived. Quasi maximum likelihood estimation of the model is also considered.展开更多
This paper introduces the class of seasonal fractionally integrated autoregressive<span style="font-family:Verdana;"> moving average</span><span style="font-family:Verdana;">-<...This paper introduces the class of seasonal fractionally integrated autoregressive<span style="font-family:Verdana;"> moving average</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">generalized conditional heteroskedastisticty (SARFIMA-</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">GARCH) models, with level shift type intervention that are capable of capturing simultaneously four key features of time series: seasonality, long range dependence, volatility and level shift. The main focus is on modeling seasonal level shift (SLS) in fractionally integrated and volatile processes. A natural extension of the seasonal level shift detection test of the mean for a realization of time series satisfying SLS-SARFIMA and SLS-GARCH models w</span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> derived. Test statistics that are useful to examine if seasonal level shift in a</span><span style="font-family:Verdana;">n</span><span style="font-family:Verdana;"> SARFIMA-GARCH model </span><span style="font-family:Verdana;">is</span><span style="font-family:Verdana;"> statistically plausible were established. Estimation of SLS-SARFIMA and SLS-GARCH parameters w</span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> also considered.</span>展开更多
Power consumption in test mode is much higher than that in normal mode,which is prone to causing circuit damage and reducing the yield of chips.To reduce the power dissipation efficiently,a modified linear feedback sh...Power consumption in test mode is much higher than that in normal mode,which is prone to causing circuit damage and reducing the yield of chips.To reduce the power dissipation efficiently,a modified linear feedback shift register(LFSR)is designed to decrease switching activity dramatically during the generation of address sequences for memory built-in self-test(MBIST).The address models are generated by a blend of two address generators with an optimized address partition and two distinct controlled clock signals.An address generator circuit for MBIST of 64 k×32 static random access memory(SRAM)is designed to illustrate the proposed scheme.Experimental results show that when the address bus size is 16 bits,compared with the traditional LFSR,the proposed LFSR can reduce the switching activity and dynamic power by 71.1%and 68.2%,respectively,with low area overhead.展开更多
针对钢管漏磁检测的特殊性,设计开发了一种带有干扰滤波的探伤监控系统,采用信号接收模块配合多串口卡,开辟了多个内存缓冲区,将数据进行多线程并行分析处理,极大地提高了数据处理的速度和检测的实时性。采用均值平移小波阈值去噪法对...针对钢管漏磁检测的特殊性,设计开发了一种带有干扰滤波的探伤监控系统,采用信号接收模块配合多串口卡,开辟了多个内存缓冲区,将数据进行多线程并行分析处理,极大地提高了数据处理的速度和检测的实时性。采用均值平移小波阈值去噪法对漏磁检测信号进行降噪处理,有效降低了测量噪音等干扰因素的影响,并消除伪吉布斯现象。软件部分利用Visual Studio C#进行开发,可以实现无人值守条件下的全自动流水线检测控制、自动钢管缺陷判断、报警和分析处理等功能。系统具有高的实用性和可靠性。展开更多
文摘In this paper, we introduce the class of autoregressive fractionally integrated moving average-generalized autoregressive conditional heteroskedasticity?(ARFIMA-GARCH) models with level shift type intervention that are capable of capturing three key features of time series: long range dependence, volatility?and level shift. The main concern is on detection of mean and volatility level shift in a fractionally integrated time series with volatility. We will denote such a time series as level shift autoregressive fractionally integrated moving average (LS-ARFIMA) and level shift generalized autoregressive conditional heteroskedasticity (LS-GARCH). Test statistics that are useful to examine if mean and volatility level shifts are present in an autoregressive fractionally integrated moving average-generalized autoregressive conditional heteroskedasticity (ARFIMA-GARCH) model are derived. Quasi maximum likelihood estimation of the model is also considered.
文摘This paper introduces the class of seasonal fractionally integrated autoregressive<span style="font-family:Verdana;"> moving average</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">generalized conditional heteroskedastisticty (SARFIMA-</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">GARCH) models, with level shift type intervention that are capable of capturing simultaneously four key features of time series: seasonality, long range dependence, volatility and level shift. The main focus is on modeling seasonal level shift (SLS) in fractionally integrated and volatile processes. A natural extension of the seasonal level shift detection test of the mean for a realization of time series satisfying SLS-SARFIMA and SLS-GARCH models w</span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> derived. Test statistics that are useful to examine if seasonal level shift in a</span><span style="font-family:Verdana;">n</span><span style="font-family:Verdana;"> SARFIMA-GARCH model </span><span style="font-family:Verdana;">is</span><span style="font-family:Verdana;"> statistically plausible were established. Estimation of SLS-SARFIMA and SLS-GARCH parameters w</span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> also considered.</span>
基金Foundation items:Fundamental Research Funds for the Central Universities(No.JUSRP51510)Primary Research&Development Plan of Jiangsu Province(No.BE2019003-2)。
文摘Power consumption in test mode is much higher than that in normal mode,which is prone to causing circuit damage and reducing the yield of chips.To reduce the power dissipation efficiently,a modified linear feedback shift register(LFSR)is designed to decrease switching activity dramatically during the generation of address sequences for memory built-in self-test(MBIST).The address models are generated by a blend of two address generators with an optimized address partition and two distinct controlled clock signals.An address generator circuit for MBIST of 64 k×32 static random access memory(SRAM)is designed to illustrate the proposed scheme.Experimental results show that when the address bus size is 16 bits,compared with the traditional LFSR,the proposed LFSR can reduce the switching activity and dynamic power by 71.1%and 68.2%,respectively,with low area overhead.
文摘针对钢管漏磁检测的特殊性,设计开发了一种带有干扰滤波的探伤监控系统,采用信号接收模块配合多串口卡,开辟了多个内存缓冲区,将数据进行多线程并行分析处理,极大地提高了数据处理的速度和检测的实时性。采用均值平移小波阈值去噪法对漏磁检测信号进行降噪处理,有效降低了测量噪音等干扰因素的影响,并消除伪吉布斯现象。软件部分利用Visual Studio C#进行开发,可以实现无人值守条件下的全自动流水线检测控制、自动钢管缺陷判断、报警和分析处理等功能。系统具有高的实用性和可靠性。
