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In Fig. 3 and Fig. 4, apparent outliers (19, 34, 35, 36, 42, 43, 44 in Fig. 3 and 9,12,24,25 in Fig. 

4) are detected rightly. But point 30 in Fig. 3 and point 11 in Fig. 4 seem to be left out, though their RDD values are the closest ones to detected outliers (19 in Table 4 and 12 in Table 5). Did the algorithm fail? Actually, it would rather be said the two points do not appear so outstanding of the whole associated with the "pattern" we used than they are falsely undetected. And from following examples, we will see even in such case our algorithm really works. It is undoubtedly that pattern "curve" can not be just expressed by order of data values. It includes other obvious or hidden patterns, like angle order etc. One aspect, we can only use data value order and so simply detect outliers; another aspect, if we exploit all patterns contained by curve, any tiny outliers must be detected correctly. It shows the power of the פ algorithm. The פ algorithm treats outlier problem as pattern recognition problem, which is a view of holistic and makes both problems simplified. 

Following experiments furthermore indicate effect of the פ algorithm and IIR algorithm.

In Fig. 5, points 17, 31, 25 are confirmed different from others. If not being referred to a pattern, it is very difficult to achieve. What we should note is that this time RDD values of 17, 31 and 25 are not so big, which indicates that the deviation to the whole is small.

The last case is shown in Fig. 6, it is latent heat flux data of another day. The posterior half includes three outlier patches, respectively {35, 36}, {38, 39, 40, 41} and {43, 44, 45}, which makes the data in a very mess. RDD and IIR values are listed in Table 6. Only point 39 and point 43 are identified. Other points in outlier patches have big RDD values, but it seems they are masked. However, this mask effect is not same as others, because RDD values still reflect deviation correctly. In order to find out outliers completely, we might have two methods. One is to use the relationship among RDD values, RDDs" distribution and number N, which is our future work; another method is to do iterative calculation. After identifying outliers, remove them and calculate again until there is no outlier. In this way, we can get a serial of "pure" data. Such result to latent heat flux2 data is in Fig. 7. We can see a perfect 1-turn curve. Further study will work out a more effective method.