Fibre channel storage area networks (FC-SAN) are effective solutions to address storage man- agement problems caused by very large volumes of data. But the expense of fibre channel devices limits FC-SAN applications...Fibre channel storage area networks (FC-SAN) are effective solutions to address storage man- agement problems caused by very large volumes of data. But the expense of fibre channel devices limits FC-SAN applications. The use of IP networks instead of fibre channel networks will reduce SAN cost, but will also reduce the performance. Therefore, small computer system interface (SCSI) devices were consid- ered to replace FC disks to reduce the SAN cost. A driver for the FC network adapter and the FC target, de- signed and implemented to support this structure, obeys the SCSI protocol and works in target mode with 200 MB/s bandwidth. The FC target architecture and implementation were compared with the FC initiator. The SCSI command transfer process in the FC layer was described. The performance test results show that the maximum I/O throughput reachs 167 MB/s for read requests and 196 MB/s for write requests (FC band- width is 200 MB/s), verifying that the FC target is very efficient. The modularization, efficiency, and low cost of the FC target will enable SAN and fibre channel to be more widely used in applications.展开更多
This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bil...This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bilinear, Natural and Nearest interpolation for missing data imputations. Performance indicators for these techniques were the root mean square error (RMSE), absolute mean error (AME), correlation coefficient and coefficient of determination ( R<sup>2</sup> ) adopted in this research. We randomly make 30% of total samples (total samples was 324) predictable from 70% remaining data. Although four interpolation methods seem good (producing <1 RMSE, AME) for imputations of air temperature data, but bilinear method was the most accurate with least errors for missing data imputations. RMSE for bilinear method remains <0.01 on all pressure levels except 1000 hPa where this value was 0.6. The low value of AME (<0.1) came at all pressure levels through bilinear imputations. Very strong correlation (>0.99) found between actual and predicted air temperature data through this method. The high value of the coefficient of determination (0.99) through bilinear interpolation method, tells us best fit to the surface. We have also found similar results for imputation with natural interpolation method in this research, but after investigating scatter plots over each month, imputations with this method seem to little obtuse in certain months than bilinear method.展开更多
In current study an attempt is carried out by filling missing data of geopotiential height over Pakistan and identifying the optimum method for interpolation. In last thirteen years geopotential height values over wer...In current study an attempt is carried out by filling missing data of geopotiential height over Pakistan and identifying the optimum method for interpolation. In last thirteen years geopotential height values over were missing over Pakistan. These gaps are tried to be filled by interpolation Techniques. The techniques for interpolations included Bilinear interpolations [BI], Nearest Neighbor [NN], Natural [NI] and Inverse distance weighting [IDW]. These imputations were judged on the basis of performance parameters which include Root Mean Square Error [RMSE], Mean Absolute Error [MAE], Correlation Coefficient [Corr] and Coefficient of Determination [R2]. The NN and IDW interpolation Imputations were not precise and accurate. The Natural Neighbors and Bilinear interpolations immaculately fitted to the data set. A good correlation was found for Natural Neighbor interpolation imputations and perfectly fit to the surface of geopotential height. The root mean square error [maximum and minimum] values were ranges from ±5.10 to ±2.28 m respectively. However mean absolute error was near to 1. The validation of imputation revealed that NN interpolation produced more accurate results than BI. It can be concluded that Natural Interpolation was the best suited interpolation technique for filling missing data sets from AQUA satellite for geopotential height.展开更多
基金Supported by the National High-Tech Research and Development Program (863) of China (No.2001AA111110)
文摘Fibre channel storage area networks (FC-SAN) are effective solutions to address storage man- agement problems caused by very large volumes of data. But the expense of fibre channel devices limits FC-SAN applications. The use of IP networks instead of fibre channel networks will reduce SAN cost, but will also reduce the performance. Therefore, small computer system interface (SCSI) devices were consid- ered to replace FC disks to reduce the SAN cost. A driver for the FC network adapter and the FC target, de- signed and implemented to support this structure, obeys the SCSI protocol and works in target mode with 200 MB/s bandwidth. The FC target architecture and implementation were compared with the FC initiator. The SCSI command transfer process in the FC layer was described. The performance test results show that the maximum I/O throughput reachs 167 MB/s for read requests and 196 MB/s for write requests (FC band- width is 200 MB/s), verifying that the FC target is very efficient. The modularization, efficiency, and low cost of the FC target will enable SAN and fibre channel to be more widely used in applications.
文摘This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bilinear, Natural and Nearest interpolation for missing data imputations. Performance indicators for these techniques were the root mean square error (RMSE), absolute mean error (AME), correlation coefficient and coefficient of determination ( R<sup>2</sup> ) adopted in this research. We randomly make 30% of total samples (total samples was 324) predictable from 70% remaining data. Although four interpolation methods seem good (producing <1 RMSE, AME) for imputations of air temperature data, but bilinear method was the most accurate with least errors for missing data imputations. RMSE for bilinear method remains <0.01 on all pressure levels except 1000 hPa where this value was 0.6. The low value of AME (<0.1) came at all pressure levels through bilinear imputations. Very strong correlation (>0.99) found between actual and predicted air temperature data through this method. The high value of the coefficient of determination (0.99) through bilinear interpolation method, tells us best fit to the surface. We have also found similar results for imputation with natural interpolation method in this research, but after investigating scatter plots over each month, imputations with this method seem to little obtuse in certain months than bilinear method.
文摘In current study an attempt is carried out by filling missing data of geopotiential height over Pakistan and identifying the optimum method for interpolation. In last thirteen years geopotential height values over were missing over Pakistan. These gaps are tried to be filled by interpolation Techniques. The techniques for interpolations included Bilinear interpolations [BI], Nearest Neighbor [NN], Natural [NI] and Inverse distance weighting [IDW]. These imputations were judged on the basis of performance parameters which include Root Mean Square Error [RMSE], Mean Absolute Error [MAE], Correlation Coefficient [Corr] and Coefficient of Determination [R2]. The NN and IDW interpolation Imputations were not precise and accurate. The Natural Neighbors and Bilinear interpolations immaculately fitted to the data set. A good correlation was found for Natural Neighbor interpolation imputations and perfectly fit to the surface of geopotential height. The root mean square error [maximum and minimum] values were ranges from ±5.10 to ±2.28 m respectively. However mean absolute error was near to 1. The validation of imputation revealed that NN interpolation produced more accurate results than BI. It can be concluded that Natural Interpolation was the best suited interpolation technique for filling missing data sets from AQUA satellite for geopotential height.
