Let be an injective function. For a vertex labeling f, the induced edge labeling is defined by, or;then, the edge labels are distinct and are from . Then f is called a root square mean labeling of G. In this paper, we...Let be an injective function. For a vertex labeling f, the induced edge labeling is defined by, or;then, the edge labels are distinct and are from . Then f is called a root square mean labeling of G. In this paper, we prove root square mean labeling of some degree splitting graphs.展开更多
This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two win...This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).展开更多
Paris law can reflect the failure mechanism of materials and is usually used to be a method to predict fatigue life or residual fatigue life.But the variable which can represent the health of machine is hardly measure...Paris law can reflect the failure mechanism of materials and is usually used to be a method to predict fatigue life or residual fatigue life.But the variable which can represent the health of machine is hardly measured on line.To a degree,the difficulty of on-line application restricts the scope of application of Paris law.The relationship between characteristic values of vibration signals and the variable in the Paris equation which can describe the health of machine is investigated by taking ball bearings as investigative objects.Based on 6205 deep groove ball bearings as a living example,historical lives and vibration signals are analyzed.The feasibility of describing that variable in the Paris equation by the characteristic value of vibration signals is inspected.After that vibration signals decomposed by empirical mode decomposition（EMD）,root mean square（RMS） of intrinsic mode function（IMF） involving fault characteristic frequency has a consistent trend with the diameter of flaws.Based on the trend,two improved Paris models are proposed and the scope of application of them is inspected.These two Paris Models are validated by fatigue residual life data from tests of rolling element bearings and vibration signals monitored in the process of operation of rolling element bearings.It shows that the first improved Paris Model is simple and plain and it can be easily applied in actual conditions.The trend of the fatigue residual life predicted by the second improved Paris model is close to the actual conditions and the result of the prediction is slightly greater than the truth.In conclusion,after the appearance of detectable faults,these improved models based on RMS can predict residual fatigue life on line and a new approach to predict residual fatigue life of ball bearings on line without disturbing the machine running is provided.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
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
文摘Let be an injective function. For a vertex labeling f, the induced edge labeling is defined by, or;then, the edge labels are distinct and are from . Then f is called a root square mean labeling of G. In this paper, we prove root square mean labeling of some degree splitting graphs.
基金National Key Research and Development Program of the Ministry of Science(2018YFB1502801)Hubei Provincial Natural Science Foundation(2022CFD017)Innovation and Development Project of China Meteorological Administration(CXFZ2023J044)。
文摘This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).
基金supported by National Natural Science Foundation of China (Grant No. 50705096)National Science and Technology Major Project of China(Grant No. 2009zx04014-014)
文摘Paris law can reflect the failure mechanism of materials and is usually used to be a method to predict fatigue life or residual fatigue life.But the variable which can represent the health of machine is hardly measured on line.To a degree,the difficulty of on-line application restricts the scope of application of Paris law.The relationship between characteristic values of vibration signals and the variable in the Paris equation which can describe the health of machine is investigated by taking ball bearings as investigative objects.Based on 6205 deep groove ball bearings as a living example,historical lives and vibration signals are analyzed.The feasibility of describing that variable in the Paris equation by the characteristic value of vibration signals is inspected.After that vibration signals decomposed by empirical mode decomposition（EMD）,root mean square（RMS） of intrinsic mode function（IMF） involving fault characteristic frequency has a consistent trend with the diameter of flaws.Based on the trend,two improved Paris models are proposed and the scope of application of them is inspected.These two Paris Models are validated by fatigue residual life data from tests of rolling element bearings and vibration signals monitored in the process of operation of rolling element bearings.It shows that the first improved Paris Model is simple and plain and it can be easily applied in actual conditions.The trend of the fatigue residual life predicted by the second improved Paris model is close to the actual conditions and the result of the prediction is slightly greater than the truth.In conclusion,after the appearance of detectable faults,these improved models based on RMS can predict residual fatigue life on line and a new approach to predict residual fatigue life of ball bearings on line without disturbing the machine running is provided.
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.
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。
