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Self comparison performance analysis of H2O on multi spectral fluctuation pattern

机译：水在多光谱起伏图上的自比较性能分析

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摘要

This study aims to obtain the performance of a measured material (H2O) in a multi spectral fluctuation pattern by applying self-comparison method approach. This is done because the data obtained is quite large and also fluctuated. The fluctuation pattern used is HF (high fluctuation) and HHF (high high fluctuation). We propose several stages to exhibit the performance of this fluctuation design. It is started by forming of the data grouping stages that is useful for grouping each range of data. Then, we apply the coding of data set to provide the identity of data. Lastly, self comparison stage is able to analyze the fluctuation pattern activity in the data set obtained. Consequently, we divide this method into two parts, namely: ISC (inner self comparison) and OSC (outer self comparison). Furthermore, we analyze the data by comparing the two methods to the available sub of data sets. So it will indicate which of the sub data sets that have the expected standard values. Besides, there is 2D (dimension) graphics that represent the ISC result for each group. While, for the OSC result, there will be the comparison value to determine among the groups. Thus, we have found that the results obtained from the application of this method are the value of the amplitude percentage ratio that is approximately of 100% for grouping of 200 data sets.
机译：本研究旨在通过应用自比较方法来获得被测物质（H2O）在多光谱波动模式下的性能。这样做是因为获得的数据非常大并且也有波动。使用的波动模式为HF（高波动）和HHF（高波动）。我们提出了几个阶段来展示这种波动设计的性能。它是通过形成数据分组阶段开始的，该阶段对于将每个数据范围进行分组很有用。然后，我们对数据集进行编码以提供数据身份。最后，自我比较阶段能够分析获得的数据集中的波动模式活动。因此，我们将该方法分为两部分，即：ISC（内部自我比较）和OSC（外部自我比较）。此外，我们通过将两种方法与数据集的可用子集进行比较来分析数据。因此，它将指示哪些子数据集具有预期的标准值。此外，还有二维（维）图形代表每个组的ISC结果。同时，对于OSC结果，将在组之间确定比较值。因此，我们发现从该方法的应用获得的结果是振幅百分率的值，对于200个数据集的分组，大约为100％。

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