Stochastic partial differential equations (SPDEs) describe the dynamics of stochastic processes depending on space-time continuum. These equations have been widely used to model many applications in engineering and ma...Stochastic partial differential equations (SPDEs) describe the dynamics of stochastic processes depending on space-time continuum. These equations have been widely used to model many applications in engineering and mathematical sciences. In this paper we use three finite difference schemes in order to approximate the solution of stochastic parabolic partial differential equations. The conditions of the mean square convergence of the numerical solution are studied. Some case studies are discussed.展开更多
This study deal with seven points finite difference method to find the approximation solutions in the area of mean square calculus solutions for linear random parabolic partial differential equations. Several numerica...This study deal with seven points finite difference method to find the approximation solutions in the area of mean square calculus solutions for linear random parabolic partial differential equations. Several numerical examples are presented to show the ability and efficiency of this method.展开更多
目的探讨局灶性癫痫围发作期心率变异性变化特点。方法收集2014年9月至2019年9月在首都医科大学附属北京天坛医院癫痫中心进行术前评估并完成手术的癫痫患者102例,选择局灶性发作198次,手动测量相邻两个心电活动的RR间期,计算心率变异...目的探讨局灶性癫痫围发作期心率变异性变化特点。方法收集2014年9月至2019年9月在首都医科大学附属北京天坛医院癫痫中心进行术前评估并完成手术的癫痫患者102例,选择局灶性发作198次,手动测量相邻两个心电活动的RR间期,计算心率变异性时域参数-相邻正常心跳间期差值平方和的均方根(RMSSD),比较发作前60 s、发作期、终止后60 s RMSSD差异,并比较不同心率变化类型、不同发作类型、不同发作前状态以及不同致痫灶部位和侧别RMSSD差异。结果发作期和发作前60 s及终止后60 s RMSSD相比差异有统计学意义(P<0.001),提示发作期RMSSD降低;心率增快类型癫痫发作期RMSSD降低(P<0.001);复杂部分性癫痫发作期RMSSD降低(P<0.001);颞叶内侧癫痫发作期RMSSD降低(右颞叶内侧P<0.001;左颞叶内侧P<0.001);心率无变化(P=0.556)和心率减慢(P=0.983)类型癫痫发作、单纯部分性癫痫(P=0.869)、颞叶外侧癫痫(右颞叶外侧P=0.204;左颞叶外侧P=0.849)和颞叶外癫痫(右颞外P=0.188;左颞外P=0.068)发作期RMSSD无降低。发作期和发作前60 s RMSSD差值在睡眠期更明显(P=0.039)。结论心率增快类型癫痫发作、复杂部分性癫痫、颞叶内侧癫痫发作期易发生心率变异性下降,提示癫痫发作期副交感活性下降;睡眠期状态下发生的癫痫发作期心率变异性下降相比清醒期显著,提示睡眠期癫痫发作副交感活性下降更加明显。展开更多
In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and imple...In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression;but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.展开更多
文摘Stochastic partial differential equations (SPDEs) describe the dynamics of stochastic processes depending on space-time continuum. These equations have been widely used to model many applications in engineering and mathematical sciences. In this paper we use three finite difference schemes in order to approximate the solution of stochastic parabolic partial differential equations. The conditions of the mean square convergence of the numerical solution are studied. Some case studies are discussed.
文摘This study deal with seven points finite difference method to find the approximation solutions in the area of mean square calculus solutions for linear random parabolic partial differential equations. Several numerical examples are presented to show the ability and efficiency of this method.
文摘目的探讨局灶性癫痫围发作期心率变异性变化特点。方法收集2014年9月至2019年9月在首都医科大学附属北京天坛医院癫痫中心进行术前评估并完成手术的癫痫患者102例,选择局灶性发作198次,手动测量相邻两个心电活动的RR间期,计算心率变异性时域参数-相邻正常心跳间期差值平方和的均方根(RMSSD),比较发作前60 s、发作期、终止后60 s RMSSD差异,并比较不同心率变化类型、不同发作类型、不同发作前状态以及不同致痫灶部位和侧别RMSSD差异。结果发作期和发作前60 s及终止后60 s RMSSD相比差异有统计学意义(P<0.001),提示发作期RMSSD降低;心率增快类型癫痫发作期RMSSD降低(P<0.001);复杂部分性癫痫发作期RMSSD降低(P<0.001);颞叶内侧癫痫发作期RMSSD降低(右颞叶内侧P<0.001;左颞叶内侧P<0.001);心率无变化(P=0.556)和心率减慢(P=0.983)类型癫痫发作、单纯部分性癫痫(P=0.869)、颞叶外侧癫痫(右颞叶外侧P=0.204;左颞叶外侧P=0.849)和颞叶外癫痫(右颞外P=0.188;左颞外P=0.068)发作期RMSSD无降低。发作期和发作前60 s RMSSD差值在睡眠期更明显(P=0.039)。结论心率增快类型癫痫发作、复杂部分性癫痫、颞叶内侧癫痫发作期易发生心率变异性下降,提示癫痫发作期副交感活性下降;睡眠期状态下发生的癫痫发作期心率变异性下降相比清醒期显著,提示睡眠期癫痫发作副交感活性下降更加明显。
文摘In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression;but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.
