数据压缩是减少网络数据流量、避免拥挤、提高控制系统性能的有效手段。针对数据压缩的问题,在分析了一系列现有有损压缩算法基础上,提出了一种新思想,改进了现有的旋转门(Swing Door Trending)算法,在每个存储数据的地方保存了两个有...数据压缩是减少网络数据流量、避免拥挤、提高控制系统性能的有效手段。针对数据压缩的问题,在分析了一系列现有有损压缩算法基础上,提出了一种新思想,改进了现有的旋转门(Swing Door Trending)算法,在每个存储数据的地方保存了两个有用的数据,并表明了该算法正确性和合理性。实验数据结果表明该算法确实可以在不增加压缩误差的前提下,有效地提高压缩比。展开更多
Process data compression and trending are essential for improving control system performances. Swing Door Trending (SDT) algorithm is well designed to adapt the process trend while retaining the merit of simplicity. B...Process data compression and trending are essential for improving control system performances. Swing Door Trending (SDT) algorithm is well designed to adapt the process trend while retaining the merit of simplicity. But it cannot handle outliers and adapt to the fluctuations of actual data. An Improved SDT (ISDT) algorithm is proposed in this paper. The effectiveness and applicability of the ISDT algorithm are demonstrated by computations on both synthetic and real process data. By applying an adaptive recording limit as well as outliers-detecting rules, a higher compression ratio is achieved and outliers are identified and eliminated. The fidelity of the algorithm is also improved. It can be used both in online and batch mode, and integrated into existing software packages without change.展开更多
文摘数据压缩是减少网络数据流量、避免拥挤、提高控制系统性能的有效手段。针对数据压缩的问题,在分析了一系列现有有损压缩算法基础上,提出了一种新思想,改进了现有的旋转门(Swing Door Trending)算法,在每个存储数据的地方保存了两个有用的数据,并表明了该算法正确性和合理性。实验数据结果表明该算法确实可以在不增加压缩误差的前提下,有效地提高压缩比。
基金The authors would like to acknowledge the support from Project“973”of the State Key Fundamental Research under grant G1998030415.
文摘Process data compression and trending are essential for improving control system performances. Swing Door Trending (SDT) algorithm is well designed to adapt the process trend while retaining the merit of simplicity. But it cannot handle outliers and adapt to the fluctuations of actual data. An Improved SDT (ISDT) algorithm is proposed in this paper. The effectiveness and applicability of the ISDT algorithm are demonstrated by computations on both synthetic and real process data. By applying an adaptive recording limit as well as outliers-detecting rules, a higher compression ratio is achieved and outliers are identified and eliminated. The fidelity of the algorithm is also improved. It can be used both in online and batch mode, and integrated into existing software packages without change.
