Abstract
Hydrophobicity is an important parameter to characterize electrical properties of insulated materials. Therefore, it is an urgent task to develop online instruments to identify the hydrophobicity of insulated material's surface conveniently, quickly and accurately. For this purpose, a novel evaluation system with image processing and decision tree is proposed which is based on embedded platform. For obtaining satisfactory results, we first propose a mixed image segmentation method to overcome the complex conditions outside, concerning noncontrolled illumination, nonstandard surfaces and unfixed shooting angle. Then we adopt four new characteristic parameters to describe the image of each sample. Finally, a classification method based on MultiBoost decision tree is conducted which synthesizes the merits of both AdaBoost and Wagging algorithm. Results indicate the procedures can be applied in the DSP (Digital Signal Processor) platform perfectly and better results can be obtained than those did in our previous study or that of some other research.
polymeric insulators; hydrophobicity; image processing; characteristic parameters; multiboost decision tree
1 Introduction
With the increasing demands of power, the security problem of power grid is becoming more and
more critical. Insulator, as the key component of the system, is related to the safety of the
entire grid. In recent years, polymeric insulators have been widely used in power supply and
distribution systems because of good shatterproof nature, light weight, superior mechanical
property and low maintenance cost^{1}1. Yuan J, Zhang JR, Wu JF, Duan NX, Qiu YC and Guo J. Analysis of
hydrophobicity of composite insulators. Insulating Materials. 2002; 2:2022.
2. Hackam R. Outdoor HV composite polymeric insulators. IEEE Transactions on
Dielectrics and Electrical Insulation. 1999; 6(5):557585.
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^{}^{3}3. Gorur RS. Status assessment of composite insulators for outdoor HV
applications. In: Proceedings of the 5th International Conference on Properties and
Applications of Dielectric Materials. Seoul, Korea: Korean Instifute of Elecitrical and
Electronic Material Engineers; 1997. p. 3538.. Insulator
with hydrophobic surface has better electrical flashover characteristics than that with
hydrophilic surface or glass. However, the hydrophobicity of polymeric insulators in service
will degrade by many factors, such as pollution deposits, surface arcing, and aging. Therefore,
it is necessary to find an effective method for determining the hydrophobic level of insulated
material's surface.
According to the guide of IEC62073, three methods are given for the measurement of
hydrophobicity, i.e. contact angle method, surface tension method and spray method^{4}4. International Electrotechnical Commission. IEC62072 Guidance on the
measurement of wettability of insulator surfaces. Geneva; 2003.. The first two traditional laboratorial methods
of measuring contact angles and surface tension are not practical in the field because
requirements of welldefined experimental conditions can't be satisfied, such as fixed
illumination, optimal view of a single water drop, or smallflat, horizontal samples^{5}5. Berg M, Thottappillil R and Scuka V. Hydrophobicity estimation of HV
polymeric insulating materials: Development of a digital image processing method. IEEE
Transactions on Dielectrics and Electrical Insulation. 2001; 8(6):10981107.
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http://dx.doi.org/10.1109/94.971470...
.
Oppositely, the spray method is widely used because of its simple and low requirement of
equipments. As a pioneer work, the HC (Hydrophobicity Classification) method proposed by STRI
(Sweden Transmission Research Institute) offers a simple procedure for obtaining a collective
estimate of an insulating surface's hydrophobicity in the field which is regarded as the
authoritative standard^{6}6. Swedish Transmission Research Institute – STRI. Guide 92/1 Hydrophobicity
Classification Guide; 1992.
7. Hartings R. Hydrophobicity of composite insulators: measurement and influence
on flashover performance. In: Proceedings of the Stockholm Power Tech: International Symposium
on Electric Power Engineering. New York, USA: Royal Institute of Technology;
1995. p. 246251.^{}^{8}8. Gubanski S and Hartings R. Swedish research on the application of composite
insulators in outdoor insulation. Electrical Insulation Magazine. 1995; 11(5):2431.
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. In this method, six hydrophobic classes from
HC1 to HC6 are defined, according to the shape of waterdrops and the percentage of wet regions
on the hydrophobic surface. The defined HC1 performs the highest hydrophobic surface, where only
discrete and extremely circular waterdrops are formed. With the increase of HC, the
hydrophobicity declines gradually. When it approaches to HC4 or HC5, the insulator is becoming
hydrophilic, which in turn can be interpreted as a warning sign.
Traditional HC method has some subjective drawbacks which requires skillful technicians and
proper experimental time. Therefore, some objective measuring methods based on image processing
and feature extraction are proposed,^{5}5. Berg M, Thottappillil R and Scuka V. Hydrophobicity estimation of HV
polymeric insulating materials: Development of a digital image processing method. IEEE
Transactions on Dielectrics and Electrical Insulation. 2001; 8(6):10981107.
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^{,}^{9}9. Tokoro T, Nagao M and Kosaki M. Image analyses of hydrophobicity of silicon
rubber insulator. In: Annual Report Conference on Electrical Insulation and Dielectric
Phenomena. Texas, USA:Institute of Electrical and Electronics Engineers; 1992. p.
763766.
10. Altafim RAC, Santana AM, Murakami CR, Basso HC, Chierice GO and Neto SC.
Hydrophobicity of polyurethane resins. In: Proceedings of the International Conference on Solid
Dielectric. Toulouse, France: Institute of Electrical and Electronics Engineers; 2004. p. 59.
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11. Zhang R, Dong FM and Gao Z. Research on bead image recognition algorithm for
determination of insulator levers. In: Proceedings of the International Conference on Computer
Science and Information Technology. Beijing, China: Institute of Electrical and Electronics
Engineers; 2009. p. 308311.
12. Berg M, Thottappillil R and Scuka V. A digital image processing method for
estimating the level of hydrophobicity of high voltage polymer. In: Proceedings of the Annual
Report Conference on Electrical Insulation and Dielectric Phenomena. Texas, USA: Institute of
Electrical and Electronics Engineers; 1999. p. 756762.
13. Zhao L, Li C, Xiong J, Zhang S, Yao J and Chen X. Online hydrophobicity
measurement for silicone rubber insulators on transmission lines. IEEE Transactions on Power
Delivery. 2009; 24(2):806813. http://dx.doi.org/10.1109/TPWRD.2008.2005654.
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14. Li Z, Liang X, Zhou Y, Tang J, Cui J and Liu Y. Influence of temperature on
the hydrophobicity of silicone rubber surfaces. In: Proceedings of the Annual Report Conference
on Electrical Insulation and Dielectric Phenomena. Colombia: Institute of Electrical and
Electronics Engineers; 2004. p. 679682.
15. Liang C, Yang WM and Liao QM. Water droplets segmentation for hydrophobicity
classification. In: Proceedings of the International Conference on Acoustics, Speech and Signal
Processing. Kyoto, Japan: Institute of Electrical and Electronics Engineers; 2012. p.
11811184.
16. Thomazini D, Gelfuso MV and Altafim RAC. Classification of polymers
insulators hydrophobicity based on digital image processing. Materials Research. 2012;
15(3):365371. http://dx.doi.org/10.1590/S151614392012005000032.
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17. Wang QD, Zhong ZF and Wang XP. Design and implementation of insulators
material hydrophobicity measure system by support vector machine decision tree learning. In:
Proceedings of the International Conference on Machine Learning and Cybernetics. Guangzhou,
China: Institute of Electrical and Electronics Engineers; 2005. p. 43284334.
18. Wang QD, Wen BX and Wang XP. Measuring insulating material hydrophobic level
by image recognition and classification. Electric Machines and Control. 2008;
12(1):9398.
19. Thomazini D, Gelfuso MV and Altafim RAC. Hydrophobicity classification of
polymeric materials based on fractal dimension. Materials Research. 2008; 11(4):415419.
http://dx.doi.org/10.1590/S151614392008000400006.
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20. Thomazini D, Gelfuso MV and Altafim RAC. Analysis of entropy and fractal
dimension descriptors to classify the hydrophobicity in polymeric insulators. In: Proceedings
of the International Symposium on Electrical Insulating. Mie, Japan; 2008. p.
279282.
21. Tokoro T, Iwasaki T and Kosaki M. Diagnosis of hydrophobic condition of
polymer materials using dielectric measurement and image analysis. In: Proceedings of the
Annual Report Conference on Electrical Insulation and Dielectric Phenomena. Colombia: Institute
of Electrical and Electronics Engineers; 2004. p. 627630.^{}^{22}22. Qi B, Tang LR and Zhang J. Research on measurement of hydrophobicity of
insulators. Proceedings of the Chinese Society for Electrical Engineering. 2008;
28(31):120124. such as
fractal dimension, circular factor, goniometric measurement using Hough transformation, scaled
entropy and histogram analysis, surface energy, and online hydrophobicity measurement
methodology. However, only one or two characteristic parameters are adopted for classification
in these methods which can't describe images comprehensively. Furthermore, researchers always
focus on the improvement of methods, and there is still no research on embedded instruments for
onsite measurement.
Therefore, an embedded system for measuring hydrophobicity named EIMHMS (Embedded Measuring
System of Insulator Material Hydrophobicity) is designed by misjudgingcost in this paper. The
methods used in EIMHMS are easily implemented, and this establishes the foundation for embedded
measuring instruments. In EIMHMS, a series of processing procedures are proposed for better
segmenting droplets which are suitable for embedded platform. Furthermore, in order to
synthesize the characteristic parameters mentioned above, four typical parameters are proposed
to depict the feature of each sample. Then a classifier based on MultiBoost decision tree^{23}23. Freund Y and Schapire RE. A decisiontheoretic generalization of online
learning and an application to boosting. Journal of Computer and System Sciences. 1997;
55(1):119139. http://dx.doi.org/10.1006/jcss.1997.1504.
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24. Breiman L. Bagging predictors. Machine Learning. 1996; 24(2):123140.
http://dx.doi.org/10.1007/BF00058655.
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25. Webb GI. MultiBoosting: a technique for combining boosting and wagging.
Machine Learning. 2000; 40(2):159196.
http://dx.doi.org/10.1023/A:1007659514849.
http://dx.doi.org/10.1023/A:100765951484...
26. Bernsen J. Dynamic thresholding of graylevel images. In: Proceedings of the
International Conference on Pattern Recognition. Paris, France; 1986. p.
12511255.^{}^{27}27. Wellner P. Adaptive thresholding for the digitaldesk. Cambridge: Xerox
Research Centre; 1993. Technical Report EPC93110, EuroPARC. is employed, and the generated "ifelse" rules can operate without
primary algorithm. In the end, promising results can be obtained in EIMHMS and all the
procedures can be applied in embedded platform perfectly.
2 Experimental Procedure
2.1 Equipment
In our experiments, a digital camera with 14 million pixels and 25X optical zoom (Sony, W315), a personal computer and a tiltable platform used for fixing samples are equipped. Furthermore, insulator specimens with different hydrophobic levels are needed, and each sample used in experiments is thin circular plate made of silicone rubber (SIR) with light red color, a thickness of 5mm and a diameter of 190mm. The waterdrop patterns are produced by an ordinary spray bottle containing distilled water.
2.2 Experiment
For simulating actual conditions in the field, before our tests, some principles should be followed (see Figure 1):

1
All the images should be taken outside,

2
Each sample should be placed with a suitable height H_{v} and angle α,

3
There is a horizontal distance of H_{h} = 2m3m between the camera and sample,

4
Any auxiliary spotlights are forbidden.
For obtaining samples with various HC, Thomazini et al.^{16}16. Thomazini D, Gelfuso MV and Altafim RAC. Classification of polymers
insulators hydrophobicity based on digital image processing. Materials Research. 2012;
15(3):365371. http://dx.doi.org/10.1590/S151614392012005000032.
http://dx.doi.org/10.1590/S151614392012...
^{,}^{19}19. Thomazini D, Gelfuso MV and Altafim RAC. Hydrophobicity classification of
polymeric materials based on fractal dimension. Materials Research. 2008; 11(4):415419.
http://dx.doi.org/10.1590/S151614392008000400006.
http://dx.doi.org/10.1590/S151614392008...
^{,}^{20}20. Thomazini D, Gelfuso MV and Altafim RAC. Analysis of entropy and fractal
dimension descriptors to classify the hydrophobicity in polymeric insulators. In: Proceedings
of the International Symposium on Electrical Insulating. Mie, Japan; 2008. p.
279282.
artificially change the hydrophobicity of specimen with spraying WIA (Water and Isopropyl
Alcohol) solution at different concentrations (from 0 to 100%). Although this method can make
different levels of hydrophobicity with only one or two specimens, the images obtained are
extremely standard which can't present the actual situations of the insulator's surface.
Therefore, we adopt another approach for obtaining samples with spraying water on insulators of
different HC. First, more than 140 insulators with different hydrophobicity (the number of
specimens with HC1HC6 is respectively 21, 22, 25, 28, 21, and 24) are provided by WHVRI (Wuhan
High Voltage Research Institute). Each specimen is labeled for a HC which has been defined by
various tests, such as DDT (Dynamic Drop Test) and STM (Surface Tension Method), and these
insulators can be used as the standard specimens. The experiment consists of the following
steps:
Step1. Place the tiltable platform at the height of 2m from the ground,
Step2. Fix the sample on the top of the platform and make it titled by 30° from the horizontal,
Step3. Spray preprepared distilled water on the surface of the fixed sample with spray bottle,
Step4. When the camera and droplets approach to the steady statue, photograph the spraying image with camera at the horizontal distance of 3m,
Step5. Repeat steps above until the images of all the samples are obtained.
After getting all the images, image processing and classification will be followed. For subsequent analysis, images are transferred to a personal computer with USB (Universal Serial Bus) interface. The main software tool for computation is the Matlab v7.11 and its image processing toolbox v7.1, which also provides a simple userfriendly environment for image analysis and GUI (Graphical User Interface) design. PC used for experiments is a Dell computer with a 3.0GHz CPU and 4GB ram. Furthermore, a DSP platform with TMSDM6446 processer is also set up for testing procedures. Most codes in experiments are programmed to DSP by CCSLink toolbox (Matlab Link for Code Composer Studio).
3 Image Processing Methods
More than 140 images are made during the course of experiments. In order to exclude the edge of insulator plate, only the central part of each collected image is used, i.e. a rectangular region of 200×200 pixels. Before Image processing, all the RGB images should be transformed into gray images to reduce the amount of calculation. Some original images with various HC are shown in Figure 2.
Image processing results. a1)a6) represent the original images from HC1HC6, b1)b6) represent the results of the adaptive threshold method from HC1HC6, c1)c6) represent the results of the adaptive threshold method from HC1HC6, d1)d6) represent the results of the COATS method from HC1HC6, e1)e6) represent the final results of mathematical morphology from HC1HC6. Average elapsed time is 0.922 seconds on PC and 2.3 seconds on DSP.
It is difficult to recognize waterdrops from images because the color of insulators are various and the background of images is much complex. Furthermore, water transparency leads to smaller gray difference, and light reflection leads to fuzzy boundary. In order to extract intact droplets and operate on the DSP platform, simple and appropriate image processing methods should be proposed. Here we propose an adaptive threshold segmentation method based on canny operator (COATS) which can produce better results than single method. To reduce elapsed time, we introduce the integral image to replace the original image. In the end, binary image optimization based on mathematical morphology is conducted.
3.1 Adaptive threshold segmentation
An integral image is a tool that can be used whenever we have a function from pixels to real
numbers f(x,y), and we wish to compute the sum of this function over a
rectangular region of the image^{26}26. Bernsen J. Dynamic thresholding of graylevel images. In: Proceedings of the
International Conference on Pattern Recognition. Paris, France; 1986. p.
12511255.
27. Wellner P. Adaptive thresholding for the digitaldesk. Cambridge: Xerox
Research Centre; 1993. Technical Report EPC93110, EuroPARC.^{}^{28}28. Bradley D and Roth G. Adaptive thresholding using the integral image.
Journal of Graphics, GPU and Game Tools. 2007; 12(2):1321.
http://dx.doi.org/10.1080/2151237X.2007.10129236.
http://dx.doi.org/10.1080/2151237X.2007....
. If we
need to compute the sum over multiple overlapping rectangular windows, we can use an integral
image and achieve a constant number of operations per rectangle with only a linear amount of
preprocessing.
Where I(x,y) represents the integral image, f(x,y) represents the total pixels of a rectangular region. With the integral image, the sum of the function for any rectangle with upper left corner (x_{1},y_{1}), and lower right corner (x_{2},y_{2}) can be computed in constant time using the following equation
The main idea in adaptive threshold algorithm is that each pixel is compared to an average of
its surrounding pixels. If the value of the current pixel is t percent lower
than the average then it is set to black, otherwise it is set to white. With the integral
image, we compute the average of an s×s window of pixels centered around each
pixel, and the pseudocode is shown below^{28}28. Bradley D and Roth G. Adaptive thresholding using the integral image.
Journal of Graphics, GPU and Game Tools. 2007; 12(2):1321.
http://dx.doi.org/10.1080/2151237X.2007.10129236.
http://dx.doi.org/10.1080/2151237X.2007....
.
1: for i = 0 to w do
2: sum ← 0
3: for j = 0 to h do
4: sum ← sum+in[i, j]
5: if i = 0 then
6: intImg[i, j] ← sum
7: else
8: intImg[i, j] ← intImg[i−1, j]+sum
9: end if
10: end for
11: end for
12: for i = 0 to w do
13: for j = 0 to h do
14: x1 ← i−s/2 {border checking is not shown}
15: x2 ← i+s/2
16: y1 ← j−s/2
17: y2 ← j+s/2
18: count ← (x2−x1)×(y2−y1)
19: sum ← intImg[x2,y2]−intImg[x2,y1−1]−intImg[x1−1,y2]+intImg[x1−1,y1−1]
20: if (in[i, j]×count) <= (sum×(100−t)/100) then
21: out[i, j] ← 0
22: else
23: out[i, j] ← 255
24: end if
25: end for
26: end for
3.2 Improved canny operator
Canny edge detection algorithm^{29}29. Canny J. A computational approach to edge detection. IEEE Transactions on
Pattern Analysis and Machine Intelligence. 1986; PAMI8(6):679698.
http://dx.doi.org/10.1109/TPAMI.1986.4767851. PMid:21869365
http://dx.doi.org/10.1109/TPAMI.1986.476...
is one of
the most commonly used image processing algorithms on embedded platform with its easy
programming, excellent performance and the three criteria^{30}30. Gao J and Liu N. An improved adaptive threshold canny edge detection
algorithm. In: Proceedings of the International Conference on Computer Science and Electronic
Engineering. Hangzhou, China: Institute of Electrical and Electronics Engineers; 2012. p.
164168.. However, when applying Gaussian filter, it will cause the loss of
edge, and with the influence of shadow, it will sometimes provide false results. Therefore, an
improved canny operator based on droplets is proposed:

1
Conduct traditional canny operator and obtain the edge image E(i,j). Then label all the isolated lines as L_{1}, L_{2},…, L_{n}. If L_{i} is a closed curve, we consider L_{i} as the real edge; otherwise skip to step 2).

2
For an open curve L_{j}, we will conduct further diagnose. First, label the two endpoints a and b of L_{j}, and select n points between a and b, i.e. d_{1}, d_{2},…, d_{n}. Second, respectively calculate the tangent's oblique angle A_{1}, A_{2},…, A_{n} of each point in L_{j}. In the end, calculate the difference δ_{A} between the maximum A_{max} and minimum A_{min} of A_{i}. If δ_{A}> π, we consider L_{j} is real edge of droplets; otherwise L_{j} is the false edge produced by shadow and should be cast out.

3
With all the procedures above, results can be obtained, and denoted by EE(i,j).
3.3 COATS method
In order to obtain promising results, more details about waterdrops and edges should be applied. With results, we find that the canny operator is sensitive to noises and illumination, and the adaptive threshold method causes fuzzy periphery of each segmentation region. Therefore, we propose a mixed method combining the results of two methods. Consider the operational capability and programming complexity of DSP, the final results are obtained by adding these two images simply which are proved to be good enough.
3.4 Binary image optimization based on mathematical morphology
In order to remove noises and useless points which are still in results, we adopt a series of
morphological operations^{31}31. Haralick RM, Sternberg SR and Zhuang X. Image analysis using mathematical
morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1987;
PAMI9(4):532550. http://dx.doi.org/10.1109/TPAMI.1987.4767941. PMid:21869411
http://dx.doi.org/10.1109/TPAMI.1987.476...
:

1
Apply erosion and morphological reconstruction operations to remove small pixels and keep the original shape of the rest pixels,

2
Apply close operation to fill some narrow breaks in the droplets.

3
Eliminate the rest of small droplets and noises with opening operation.
3.5 Results and analysis
All the procedures above are applied on DSP platform, and the results in Figure 2 are operated on DSP platform and displayed in Matlab window. Furthermore, some results on LCD of DSP platform are shot by camera (see Figure 3).
Results on LCD of the DSP platform. a)d) represent the original image, threshold segmentation result, COAT result and final result.
The adaptive threshold method with integral image consumes less time than some other methods (tests in our other experiments) and performs better for the image with uneven illumination. Because of its easy implementation, it can operate on the embedded equipment perfectly. As shown in Figure 2b, although approximate shapes of droplets with different hydrophobicity are segmented, there are still some noises, conglutination and useless small droplets. Therefore, more information should be applied for further extraction, and we conduct an improved canny operator. However, although some accurate edges are obtained, there are still some redundant pixels (see Figure 2c), and with mathematical morphology operations, these pixels will be removed completely (see Figure 2d).
As shown in Figure 2d, all the procedures of image processing have been completed, and most droplets are extracted integrally. For the low hydrophobic images, such as HC5 and HC6, in which there is only one or two big water film parts, are easy to be segmented because of the strong contrast. However, it is difficult to recognize which region is wet. As shown in Figure 2b and 2c, there are more noises in the unwetted region than that of the wet region, and we can distinguish by using this criterion.
As the samples are labeled by experiments and experts, the errors due to images are derived from the inaccurate HC which may result in misjudgements in the end. However, these samples are tested for many methods, and the WHVRI has also conducted verification tests, and we consider that the errors can be ignored for classification. Furthermore, the small quantity of samples can result in inaccurate classification model which may reduce the accuracy of classifier (the analysis will be elaborated in Sec. 6).
4 Characteristic Parameters Extraction
For classifying different hydrophobic levels, some characteristic parameters should be given for depicting each image. While there are many attributions proposed by experts, such as fractal dimension, circular factor, the largest shape factor and so on. To synthesize the advantages above, this paper adopts four parameters improved by our previous work.
4.1 Characteristic parameters
Let N be the number of droplets recognized, S_{i}, C_{i}, (x_{i},y_{i}) be area, perimeter and center of bound rectangle of droplet i(0 ≤ i ≤ N)^{32}32. Peng KX, Wang QD and Wang XP. Spray image analysis based measurement of hydrophobic of insulator surfaces. Insulating Material. 2005; 1:4751..

1
Cover: Cover is the ratio of areas covered by water to areas not covered by water
Cover is one of most common parameters used for judging hydrophobicity of materials and is an important characteristic parameter which represents the overall hydrophobicity of material's surface.

2
Dis_uni: Dis_uni describes the uniformity of distribution in nine equal regions of a spraying image. The more evenly the waterdrops distribute, the bigger the dis_uni becomes.
Where
The computation of Dis_uni is essentially in calculating the shannon entropy of water distribution. The whole image is divided into nine zones c_{1}, c_{2},…,c_{k} (k=1, 2,…, 9), and we separately calculate the probability p_{i} = n(row, col)/N of c_{k} which means the probability of droplets falling into c_{k}, and then get its entropy. The bigger entropy means evenly distributed droplets and better hydrophobicity.

3
Area_uni: Area_uni describes the uniformity of areas covered with water.
Area_uni represents the size of droplets from the side, and is equivalently to calculate the area deviation of each droplet and the mean area. Bigger deviation indicates bigger Area_uni and worse evenness of distribution. To some extent, it also tells the difference between droplets and water films of some insulators with lower hydrophobic levels. Because the area of water film is bigger than uniform droplet a lot, the value of Area_uni is bigger when there are some water films.

4
Round_de: Round_de is the average round degree of all droplets.
4πS_{i}/C2 i is the formula of calculating roundness of irregular circle. In the equation above, 4π/C2 i is used for calculating the area of standard circle, and the roundness can be obtained with dividing by the real area S_{i}. Round_de represents the shape of droplets, and it is closer to ideal circle when Round_de approaches to 1.
4.2 Tests of geometrical independence
As we know, a good characteristic parameter should have the geometrical independence. Therefore, we make rotation and scale transformation of spraying images with HC1HC6, and observe the changes of Cover, Dis_uni, Area_uni, and Round_de in the case of geometric transformation.

1
Tests of Cover
Make rotation and scale transformation, and observe the change of Cover. As shown in Figure 4, 4a is the test of scale transformation, and 4b is the test of rotation transformation.
Tests of Cover with various hydrophobic levels. a) Scale transformation, b) Rotationtransformation.
As shown in Figure 4a, Cover has obvious change only when the image narrows down to 0.10.3 of the original image, and Cover is inaccurate when the rate approaches 0.1. As shown in Figure 4b, Cover almost has no change when making the rotation tests. So we can conclude that the parameter Cover has good geometrical independence.

2
Tests of Dis_uni
Make rotation and scale transformation, and observe the change of Dis_uni. As shown in Figure 5, 5a is the test of scale transformation, and 5b is the test of rotation transformation.
Tests of Dis_uni with Various Hydrophobic Levels. a) Scale Transformation, b) Rotation Transformation.
As shown in Figure 5a, Dis_uni has obvious change only when the image narrows down to 0.10.3 of the original image, and Dis_uni is inaccurate when the rate approaches 0.1. As shown in Figure 5b, Dis_uni almost has no change when making the rotation tests. So we can conclude that the parameter Dis_uni has good geometrical independence.

3
Tests of Area_uni
Make rotation and scale transformation, and observe the change of Area_uni. As shown in Figure 6, 6a is the test of scale transformation, and 6b is the test of rotation transformation.
Tests of Area_uni with Various Hydrophobic Levels. a) Scale Transformation, b) Rotation Transformation.
As shown in Figure 6a, Area_uni has obvious change only when the image narrows down to 0.10.3 of the original image, and Area_uni is inaccurate when the rate approaches 0.1. As shown in Figure 6b, Area_uni almost has no change when making the rotation tests. So we can conclude that the parameter Area_uni has good geometrical independence.

4
Tests of Round_de
Make rotation and scale transformation, and observe the change of Round_de. As shown in Figure 7, 7a is the test of scale transformation, and 7b is the test of rotation transformation.
Tests of Round_de with Various Hydrophobic Levels. a) Scale Transformation; b) Rotation Transformation.
As shown in Figure 7a, Round_de of HC5HC6 has obvious increase when the image narrows down to 0.10.3 of the original image, and Round_de of HC1HC4 has obvious decline when the rate approaches 0.10.2. As shown in Figure 7b, Round_de almost has no change when making the rotation tests. So we can conclude that the parameter Round_de has good geometrical independence.
The parameters above are selected from lots of attributions of the spraying image. They are all independent to the real size and angle of images that is convenient for classification. The four parameters of samples are shown in Figure 8, and we can find that Dis_uni has poor distinguish ability and on the contrary the other three parameters are better for classification.
5 Classification Based on MultiBoost Decision Tree
After getting attributions of all spraying images with different hydrophobic levels,
classification will be employed in the end. In our experiments, both the PLSR (Partial
LeastSquare Regression) method^{33}33. Wold S, KettanehWold N and Skagerberg B. Nonlinear PLS modeling.
Chemometrics and Intelligent Laboratory Systems. 1989; 7(12):5365.
http://dx.doi.org/10.1016/01697439(89)80111X.
http://dx.doi.org/10.1016/01697439(89)8...
based on
mathematical model and the decision tree method based on machine learning are carried out.
Compared with results, we conclude that there is no obvious mathematical relation between the
characteristic parameters and hydrophobic levels adopted in this paper. In order to develop
embedded equipment for measuring hydrophobicity, simple and easy methods for classification
should be adopted. The "ifelse" rules of decision tree are fit for running in MCU (Micro
Control Unit) with low operation speed.
The training and classification steps of decision tree induction are simple and fast which can
be applied to any domain of data distribution. However, simple classifier can't meet the needs
of error yet, and the committee learning algorithm is proposed for classification. Decision
committee learning has demonstrated spectacular success in reducing classification errors
generated by learned classifiers. These techniques develop a classifier in the form of a
committee of subsidiary classifier. The committee members are applied to a classification task
and their individual outputs are combined to create a single classification from the committee
as a whole. This combination of outputs is often performed by majority vote. Examples of these
techniques include classification ensembles formed by Bagging, AdaBoost, and Wagging^{23}23. Freund Y and Schapire RE. A decisiontheoretic generalization of online
learning and an application to boosting. Journal of Computer and System Sciences. 1997;
55(1):119139. http://dx.doi.org/10.1006/jcss.1997.1504.
http://dx.doi.org/10.1006/jcss.1997.1504...
^{,}^{24}24. Breiman L. Bagging predictors. Machine Learning. 1996; 24(2):123140.
http://dx.doi.org/10.1007/BF00058655.
http://dx.doi.org/10.1007/BF00058655...
.
5.1 MultiBoost decision tree
Two decision committee learning approaches, AdaBoost and Bagging, have received extensive
attention. Both AdaBoost and Bagging are generic techniques that can be employed with any base
classification techniques. They operate by resampling selectively from the training data to
generate derived training sets to which the base learner is applied. A number of studies
comparing AdaBoost and Bagging suggest that AdaBoost and Bagging have quite different
operational profiles. In general, it appears that Bagging is more consistent, and the frequency
to increase errors of the base learner is less than AdaBoost does. However, AdaBoost appears to
have greater average effects, and has substantially larger error reductions than Bagging does
on average. It is confirmed that AdaBoost reduces both bias and variance while Bagging and
Wagging have little effect on bias and greater effect on variance^{25}25. Webb GI. MultiBoosting: a technique for combining boosting and wagging.
Machine Learning. 2000; 40(2):159196.
http://dx.doi.org/10.1023/A:1007659514849.
http://dx.doi.org/10.1023/A:100765951484...
.MultiBoost (Combining Boosting and Wagging) is shown to
achieve most of AdaBoost’s superior bias reduction coupled with most of Bagging’s superior
variance reduction.
5.2 Result and analysis
Given the theories and experiments above, a MultiBoost tree based on C4.5 is adopted for our classification. Results of training and testing are provided by DSP platform with "ifelse" rules, and kfold cross validation is applied by Matlab (see Figure 9).
Errors (%) of AdaBoost and MultiBoost algorithm. a) Errors with training subset, b) Errors with testing subset, c) Errors with kfold cross validation method.
Firstly, because of the limited number of samples, we conducted kfold cross validation method to verify the rules of decision tree, which divides the full data set into k subsets. When modeling, only k1 subsets are used, and the remaining subset is used for validation data to verify the model. In this case, experiments will be repeated for k times, and there will be a predicted value in the end. The advantage of this approach is that it repeatedly uses random subsets for training and validation at the same time. Kfold cross validation is used for training and validation with the small data set, and it also can test the stability of model. Furthermore, training and testing experiments are also employed, which divide the full data set into two subsets, i.e. training data and testing data. As shown in Figures 9a and c, the error (%) of MultiBoost is less than AdaBoost algorithm. Because of small data set, only a few samples are applied for testing, and the error (%) is relatively large.
With results, we can conclude that both AdaBoost and MultiBoost methods can achieve a higher precision with the full data set, and the error of AdaBoost is 0%, which agrees with reference^{26}26. Bernsen J. Dynamic thresholding of graylevel images. In: Proceedings of the International Conference on Pattern Recognition. Paris, France; 1986. p. 12511255. published in our previous paper. However, the precision of AdaBoost is lower than that of MultiBoost with kfold cross validation and testing data, which proofs poor robustness and overfitting with full data set. This indicates MultiBoost algorithm is better.
6 Discussion
From the figures above, MultiBoost decision tree employed in classification is better than our previous work. Because the data set used in experiments is very small, we can't build a set of rules more accurately. Therefore, besides training and testing experiments, we also adopt a "kfold cross validation" method to verify the validity of the method. It is worth noting that once the rules of decision tree are established, we can only use "ifelse" rules to test new samples which can be implement easily for the embedded platforms.
Image processing is an effective method in classifying hydrophobic levels of insulators, and
there are many image processing methods based on spraying images are proposed, such as WTH +
EQU^{16}16. Thomazini D, Gelfuso MV and Altafim RAC. Classification of polymers
insulators hydrophobicity based on digital image processing. Materials Research. 2012;
15(3):365371. http://dx.doi.org/10.1590/S151614392012005000032.
http://dx.doi.org/10.1590/S151614392012...
(White TopHat + Histogram
Equalization), image segmentation with multithreshold,^{18}18. Wang QD, Wen BX and Wang XP. Measuring insulating material hydrophobic level
by image recognition and classification. Electric Machines and Control. 2008;
12(1):9398. etc. WTH+EQU is proposed by Thomaziniet al., and they
combine white tophat, histogram equalization and sobel operator to obtain edges of droplets. In
our experiments, we also adopt WTH+EQU method to test their and our samples, and we get the same
results with their images, but can't obtain satisfying results with our samples. It is because
the samples are created with spraying solutions produced by mixtures of isopropyl alcohol and
distilled water, and the solution presents a strong gray difference with background which is
easy for segmentation. Furthermore, we conduct simple canny operator with their samples, and
also get more accurate results which indicates the accuracy relies on their standard spraying
images. Image segmentation with multithreshold is applied in our previous work, and better
results of images with uniform illumination can be obtained. Because of the transparency of
droplets, there is little difference between backgrounds and droplets except edges. So we
conclude that traditional image segmentation is not a valid method. Compared with our previous
work, methods in this paper are more universally applied for uneven lighting images, but not for
all the images (e.g. dirty insulators) and some parameters should be set manually (e.g. the size
of structure element). Therefore, we will try to search for some adaptive methods in the
following works. Furthermore, we apply other image segmentation methods, such as spectral
clustering method, region growing algorithm, etc.^{34}34. von Luxburg U. A tutorial on spectral clustering. Statistics and Computing.
2007; 17(4):395416. http://dx.doi.org/10.1007/s112220079033z.
http://dx.doi.org/10.1007/s11222007903...
^{,}^{35}35. Sumuya, Gao C and Chai S. A note on spectral clustering method based on
normalized cut criterion. In: Proceedings of the Chinese Conference on Pattern Recognition.
Nanjing, China: Institute of Electrical and Electronics Engineers; 2009. p. 15.
http://dx.doi.org/10.1109/CCPR.2009.5343984http://dx.doi.org/
http://dx.doi.org/...
. But we
can't obtain better results.
Four characteristic parameters adopted in this paper have specific geometric significance and synthesis some frequentlyused characteristic parameters, such as circular factor, shape factor,^{9}9. Tokoro T, Nagao M and Kosaki M. Image analyses of hydrophobicity of silicon rubber insulator. In: Annual Report Conference on Electrical Insulation and Dielectric Phenomena. Texas, USA:Institute of Electrical and Electronics Engineers; 1992. p. 763766. cover rate, etc which can exclude the limitation of single parameter.
In classification, besides supervised and unsupervised clustering methods, we also applied mathematical regression method, such as PLS (Partial Least Squares), PCA (Principal Component Analysis) etc. But there is no satisfying nonlinear equation for prediction and we conclude that these four parameters have no obvious mathematical relation with HC levels.
Although AdaBoost algorithm get 0% error with full training data set, it is less accurate than
MultiBoost. It indicates that AdaBoost is easy to be over trained and has lower generalization.
Besides, SVM (support vector machine) applied in our previous work can also obtain good results.
In the next following study, we want to search for some factors which can be expressed with
equation like fractal dimension by Thomazini et al.^{19}19. Thomazini D, Gelfuso MV and Altafim RAC. Hydrophobicity classification of
polymeric materials based on fractal dimension. Materials Research. 2008; 11(4):415419.
http://dx.doi.org/10.1590/S151614392008000400006.
http://dx.doi.org/10.1590/S151614392008...
^{,}^{20}20. Thomazini D, Gelfuso MV and Altafim RAC. Analysis of entropy and fractal
dimension descriptors to classify the hydrophobicity in polymeric insulators. In: Proceedings
of the International Symposium on Electrical Insulating. Mie, Japan; 2008. p.
279282..
7 Conclusion
Measuring the hydrophobicity of insulated material's surface is important to supervise the quality of insulating material's production, and working insulators outdoors. In order to replace manual operation, we adopt image processing and pattern recognition method for classification.
We conduct many experiments with various analysis methods and finally decide to choose the abovementioned method, "combine the canny operator and adaptive threshold using the integral image". The testing results are essentially satisfactory compared with our previous work (AdaBoost Decision tree). But the algorithm used for image processing is still complex and is only effective for most images, so we will try to search for simple and more universal approaches and make them available on the embedded instrument.
We adopt four characteristic parameters to represent various hydrophobic levels which synthesize some merits proposed by other scholars. Given our previous work, we adopt a novel and simple method, MultiBoost decision tree, to improve the performance of classification. MultiBoost decision tree can be used to reduce errors by combining the advantages of AdaBoost and Bagging. Furthermore, when the training process is completed, we can obtain the rules of classification. Then we can apply the "ifelse" rules for testing without primary algorithm which lay a solid foundation for embedded implementation.
Acknowledgments
The authors acknowledge the assistance of WHVRI which provides SIR samples and the site for the experiments in this study. Furthermore, the authors also thank for the help of the 5th author, viceprofessor Wang. With his early theories, the authors can put forward improvement programs and obtain better results.
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Received
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Reviewed
23 Jan 2015