CN110751196B - Oil-like drop attachment identification method in oil-water two-phase flow transparent pipe wall - Google Patents

Oil-like drop attachment identification method in oil-water two-phase flow transparent pipe wall Download PDF

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CN110751196B
CN110751196B CN201910966266.4A CN201910966266A CN110751196B CN 110751196 B CN110751196 B CN 110751196B CN 201910966266 A CN201910966266 A CN 201910966266A CN 110751196 B CN110751196 B CN 110751196B
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韩连福
丛垚
付长凤
刘超
刘兴斌
姜继玉
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Suzhou Hengyou New Energy Technology Co.,Ltd.
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Abstract

The invention belongs to the technical field of petroleum engineering and measurement, and particularly relates to a method for identifying oil-like drop attachments in a transparent pipe wall of an oil-water two-phase flow. 1. Carrying out initial image processing on the oil-water two-phase flow PTV image; 2. extracting effective characteristics; 3. randomly sampling A; 4. randomly selecting a sample attribute V; 5. recursively performing step 4 in each subspace; 6. repeating 3-5 times until t isolated trees are generated; 7. dividing A into an abnormal sample set D, a dense sample set C and a normal sample set B; 8. calculating the distance from the sample point to the clustering center in the union of the normal sample set B and the dense sample set C; 9. recalculating an oil drop edge clustering center and an oil drop attachment edge clustering center under the current classification result; 10. repeat 8 and 9 until one of the specified conditions is met; 11. and calculating the distance from each sample in the abnormal sample set D to the current oil drop edge cluster center and the current oil drop attachment edge cluster center respectively.

Description

Oil-like drop attachment identification method in oil-water two-phase flow transparent pipe wall
The technical field is as follows:
the invention belongs to the technical field of petroleum engineering and measurement, and particularly relates to a method for identifying oil-like drop attachments in a transparent pipe wall of an oil-water two-phase flow.
Background art:
the accurate measurement of the oil phase flow velocity of the oil-water two-phase flow is always a difficult problem in oil field logging, and the measuring method gradually tends to adopt an undisturbed and non-contact image analysis measuring method from a logging instrument with mechanical parts. The PTV is an effective method for researching the flow characteristics of the fluid, and characteristic points in the PTV image are extracted for matching, so that the flow velocity is measured. The method has high requirements on the shot multiphase flow image, and the quality of the image directly relates to the measurement precision of the oil-water two-phase flow parameters. In the process of measuring the oil phase flow velocity of the oil-water two-phase flow, after a plurality of experiments, oil drop attachment exists on the wall of the oil well, the main components of the oil drop attachment are oil and water stains adhered to the wall of the oil well due to unclean wall, the oil drop attachment is recorded while a multi-phase flow image is collected, and the oil drop attachment is used as interference information in the image and causes great interference on the measurement and analysis of the oil phase flow velocity of the oil-water two-phase flow.
PTV oil-water two-phase flow image pixels can be divided into 5 types: the pixel belongs to the background, the pixel belongs to the interior of oil drops, the pixel belongs to the edge of the oil drops, the pixel belongs to the interior of the pipe wall oil drop attachment and the pixel belongs to the edge of the pipe wall oil drop attachment. The problem of identifying the wall oil drop attachments can be converted into the problem of image clustering, so that the problem is solved by combining a clustering algorithm with an image processing method. The edge pixels in the image are involved in the image matching process, so that only the edge pixels need to be clustered.
The K-means algorithm is the most common clustering division algorithm, has the characteristics of simple principle, unsupervised performance and iterative optimization, and is valued by numerous scholars. Although the K-means algorithm has the advantages of simple implementation and high execution efficiency, the clustering effect of the K-means algorithm is greatly influenced by the selected initial central point and the outliers in the samples. The initial clustering center point selected randomly may cause the clustering result to fall into a local optimal solution, and the outliers in the sample data may cause the clustering center to deviate from the optimal position, so that the optimal clustering effect cannot be achieved, and therefore, the initial clustering center and the outliers in the data must be preprocessed before clustering.
The isolated forest algorithm is an unsupervised data anomaly detection algorithm, and compared with other algorithms, the method does not use distance or density for detection, so that large calculation amount during detection based on a distance and density method is avoided. In an isolated forest algorithm, the abnormal degree of a sample in data is judged based on a cutting idea, outlier data can be divided independently by cutting the outlier data for a few times because the outlier data is separated from other data points, dense data is just opposite, the abnormal degree of all samples can be obtained by utilizing the characteristic, and the linear time complexity is low. The isolated forest can judge the separating degree of the sample in the data, so that the traditional K-means algorithm is improved by adopting the isolated forest, the data are divided into three categories according to the abnormal score of the data under the algorithm, the high score is used as an outlier, and the clustering process is not involved; the low score is taken as a dense point, and the initial clustering center is selected; and taking other points as common normal data to participate in the iterative clustering process.
The invention content is as follows:
the invention aims to solve the problem of measurement accuracy reduction caused by serious interference of transparent tube wall oil drop attachments when PTV is adopted to measure the oil phase flow velocity of oil-water two-phase flow, and provides an image preprocessing method for the transparent tube wall oil drop attachments of the oil-water two-phase flow. The method solves the problem that when PTV is used for measuring the oil phase flow velocity of the oil-water two-phase flow, the attachment of transparent tube wall oil drops seriously interferes the measurement. Extracting effective characteristics of a plurality of oil-water two-phase flow images by adopting an image clustering method; the isolated forest algorithm is utilized to overcome the defect that the traditional K-means algorithm is greatly influenced by initial clustering center selection and outliers, the image clustering effect is enhanced, and the oil drop attachment identification accuracy is improved.
The technical scheme adopted by the invention is as follows: a method for identifying oil-like drop attachments in an oil-water two-phase flow transparent pipe wall comprises the following steps:
the method comprises the following steps: carrying out initial image processing on the collected multiple oil-water two-phase flow PTV images, wherein the processing comprises filtering, denoising and sharpening, and adopting a Canny operator to carry out oil drop and oil drop attachment edge detection;
step two: randomly selecting an image as a current frame image, and respectively extracting the color difference in the neighborhood of the edge pixels of oil drops and oil drop attachment, the gray value difference of the edge pixels of multi-frame images of the oil drops and the oil drop attachment and the motion displacement difference of the oil drops and the oil drop attachment as effective characteristics according to the detected edge;
step three: randomly sampling a characteristic data set A, wherein the characteristic data set A is a set of color difference characteristics in the neighborhood of oil drop and oil drop attachment edge pixels of the current frame image, multi-frame image edge pixel gray value difference characteristics of the oil drop and oil drop attachment, and motion displacement difference characteristics of the oil drop and the oil drop attachment, and A is { X { (X)1,X2,…,Xi}，XiThe sample is the ith sample in A, i is the edge pixel serial number, i is1, 2, …, and L is the number of edge pixels detected in the image; extracting N samples as a subsample set AsubN is the size of the extracted subsample set, and the isolated tree, lambda, is initially built1Is a sequence number of the isolated tree layer, λ1Has a value range of lambda 11,2, …, l, l is the height of the isolated tree, o is the node number, the value range of o is
Figure GDA0002593413730000031
In this step lambda1Is 1;
step four: randomly select a sample attribute V at AsubIn the V attribute value of (1)Generating a cutting point q, wherein the value of q is between the maximum value and the minimum value of the sample V attribute value, and under the V attribute, taking q as the cutting point AsubPartitioning into left node sample sets
Figure GDA0002593413730000032
And right node sample set
Figure GDA0002593413730000033
Randomly selecting left sample attributes
Figure GDA0002593413730000034
And right sample attributes
Figure GDA0002593413730000035
The current left sample attribute is denoted VleftThe current right sample attribute is denoted as VrightThe current left node sample set is denoted GleftThe current right node sample set is denoted GrightWherein λ is1Is the sequence number of the isolated tree layer, and omicron is the node sequence number. At this time, the process of the present invention,
Figure GDA0002593413730000041
Figure GDA0002593413730000042
step five: lambda [ alpha ]1Is increased by 1, at the current left node sample set GleftCurrent left sample property VleftRandomly generating a left cut point q in the valuesleft，qleftIs located at the current left sample attribute VleftBetween the maximum and minimum of the values, at the current left sample property VleftAt a value of qleftSet G of current left node samples for left cut pointleftPartitioning into left node subsamples
Figure GDA0002593413730000043
Right node subsample set
Figure GDA0002593413730000044
At the current right node sample set GrightCurrent right sample property VrightRandomly generating a right cut point q in the valueright，qrightIs located at the current right sample attribute VrightBetween the maximum and minimum of the values, at the current right sample property VrightAt a value of qrightSet of current right node samples G for right cut pointrightPartitioning into left node sample subsets
Figure GDA0002593413730000045
Right node sample subset
Figure GDA0002593413730000046
Randomly selecting left sample attributes
Figure GDA0002593413730000047
And right sample attributes
Figure GDA0002593413730000048
Updating current left sample attributes
Figure GDA0002593413730000049
Updating current right sample attributes
Figure GDA00025934137300000410
Current left node sample set
Figure GDA00025934137300000411
Updating a current right node sample set
Figure GDA00025934137300000412
Wherein tau is1Is the horizontal sequence number, tau, of the left node subsample set2Is the horizontal sequence number, tau, of the right node subsample set3For the left node sample subset horizontal sequence number, τ4Is the horizontal sequence number of the right node sample subset;
step six: repeating the step five until any one of the following conditions (1) is met and G on the current left node sample setleftAnd the current right node sample set GrightOnly one sample point in; (2) current left node sample set GleftAll samples in the same feature and the current right node sample set GrightAll the samples have the same characteristics; (3) the isolated tree reaches a limited height l;
step seven: repeating the third step and the sixth step until t isolated trees are generated, inputting each sample in the characteristic data set A into the t isolated trees, and calculating the abnormal score S of the sample;
step eight: dividing the characteristic data set A into an abnormal sample set D, a dense sample set C and a normal sample set B, and selecting an initial clustering center C of oil drop edges in the dense sample set C1Initial clustering center c of oil drop attachment edge2；
Step nine: calculating the distance d (X) between the sample point and the cluster center in the union of the normal sample set B and the dense sample set C,c′j) And clustering the union sample points of the normal sample set B and the dense sample set C to obtain the union sample serial number X of the normal sample set B and the dense sample set CThe first sample in the union of the normal sample set B and the dense sample set C, j is the serial number of the cluster center, and j is1, 2, C'1Is the current drop edge cluster center, c'2Clustering the edge of the current oil drop attachment;
step ten: recalculating the oil drop edge clustering center c' under the classification result of the step nine1Oil drop attachment edge clustering center c2；
Step eleven: and repeating the ninth step and the tenth step until one of the specified conditions is met, wherein the specified conditions comprise: (1) reaching the specified iteration times; (2) the change of the clustering target function value is less than a threshold value tr;
step twelve: calculating the current oil drop edge clustering center c 'of each sample in the abnormal sample set D'1And c 'is the edge clustering center of current oil drop attachment'2And dividing the samples into cluster classes to which the cluster centers closest to the samples belong according to the distance.
Further, in the second step, the extracted image features are as follows:
(1) oil drop and oil drop attachment edge pixel neighborhood interior color difference characteristics:
descriptor f of color difference characteristics in oil drop and oil drop attachment edge pixel neighborhoodi c1Is defined as follows
fi c1＝ωHσH+ωSσS+ωVσV
Wherein f isi c1Is a descriptor of the color difference characteristics in the neighborhood of the oil drop and oil drop attachment edge pixel of the ith edge pixel, omegaHIs a weight of the color difference on the H color component, and ωH<1，ωSIs a weight of the color difference on the S color component, and ωS<1，ωVIs a weight of the color difference on the V color component, and ωV<1，σHFor the color difference on the color component of H,
Figure GDA0002593413730000061
σSfor the color difference on the S color component,
Figure GDA0002593413730000062
σVfor the color difference on the V color component,
Figure GDA0002593413730000063
ii is the pixel neighborhood x coordinate, jj is the pixel neighborhood y coordinate, M1Is the maximum value of the x coordinate of the pixel neighborhood, M2Is the maximum value of the y-coordinate of the pixel neighborhood,
Figure GDA0002593413730000064
is the H color component value of the pixel at (ii, jj) within the neighborhood,
Figure GDA0002593413730000065
is the S color component value of the pixel at (ii, jj) within the neighborhood,
Figure GDA0002593413730000066
is the V color of the pixel at (ii, jj) in the neighborhoodComponent value, muHIs the average value, mu, of the H color components of the pixels in the neighborhoodSIs the average value, mu, of the S color components of the pixels in the neighborhoodVIs the average of the V color components of the pixels in the neighborhood;
(2) oil drops and oil drop attachment multi-frame image edge pixel gray value difference characteristics:
descriptor f of multi-frame image edge pixel gray value difference characteristic of oil drop and oil drop-like attachmenti c2The definition is as follows:
Figure GDA0002593413730000067
wherein f isi c2Is a descriptor of the gray value difference characteristic of oil drops of the ith edge pixel and oil drop attachment multi-frame image edge pixels, ncIs the number of frames selected for the frame,
Figure GDA0002593413730000071
is the gray value of the ith edge pixel in the current frame image, k is the serial number of the image frame number,
Figure GDA0002593413730000072
the gray value of the pixel at the position corresponding to the current frame image in the kth frame image is obtained;
(3) the oil drop and the oil drop attachment motion displacement difference characteristics are as follows:
the descriptor f of the motion displacement difference characteristici m1The definition is as follows:
Figure GDA0002593413730000073
wherein f isi m1Is a descriptor of the difference characteristic of the motion displacement of oil drops and oil drop attachment of the ith edge pixel, nαIn order to use the number of the query windows with different sizes, w is the serial number of the query window used, w is1, 2, …, nα，lwThe displacement obtained by adopting query windows with different sizes is calculated according to the following formula:
Figure GDA0002593413730000074
wherein u iswIs the displacement, v, of the pixel in the x direction obtained by cross-correlation calculation using the w-th query windowwIs the displacement of the pixel in the y direction obtained by cross-correlation calculation using the w-th query window:
Figure GDA0002593413730000075
wherein
Figure GDA0002593413730000076
The cross-correlation matrix under the w-th query window is adopted:
Figure GDA0002593413730000077
wherein
Figure GDA0002593413730000078
Is the coordinate of the x direction of the cross-correlation matrix,. phi.wAdopting a gray scale map f of an inquiry area covered by a w inquiry window for a current frame image'wA grey-scale map of the query region covered with the w-th query window is used for the next frame of image,
Figure GDA0002593413730000079
is fwThe average pixel value within the corresponding region,
Figure GDA00025934137300000710
is f'wThe average pixel value in the corresponding area, M is the x-direction pixel coordinate of the query area covered by the query window, and M is1, 2w，MwThe maximum value of the pixel coordinates in the x direction of the query area covered by the query window, N is the pixel coordinates in the y direction of the query area covered by the query window, and N is1, 2w，NwQuery region y-direction pixels covered by query windowThe maximum value of the coordinates.
Further, the three-step neutron sample set AsubExpressed as:
Figure GDA0002593413730000081
wherein
Figure GDA0002593413730000082
Is a color difference feature set in the neighborhood of the edge pixel, i' is a subsample set AsubThe inner edge pixel number, i' is1, 2, …, N is the decimated sub-sample set size,
Figure GDA0002593413730000083
as a subsample set AsubThe color difference feature in the edge pixel neighborhood of the ith' edge pixel,
Figure GDA0002593413730000084
for a multi-frame image edge pixel gray value difference feature set,
Figure GDA0002593413730000085
as a subsample set AsubThe multi-frame image edge pixel gray value difference characteristic of the ith' edge pixel,
Figure GDA0002593413730000086
in order to move the set of difference features of displacement,
Figure GDA0002593413730000087
as a subsample set AsubMotion displacement difference feature of the ith' edge pixel in the subsample set AsubInner ith' edge pixel sample
Figure GDA0002593413730000088
Further, the sample attribute in step four, V ∈ { F }c1,Fc2,Fm1And putting the samples with the V attribute value smaller than the cutting point q in the left node sample set
Figure GDA0002593413730000089
Putting samples with V attribute values larger than the cut point q into a right node sample set
Figure GDA00025934137300000810
Further, in the fifth step, the current left node sample set G is usedleftIn, the current left sample attribute VleftValue less than left cutting point qleftIs placed in the left node subsample set
Figure GDA00025934137300000811
In, the current left sample attribute VleftValue greater than left cutting point qleftIs placed in the right node subsample set
Figure GDA0002593413730000091
At the current right node sample set GrightIn, the current right sample attribute VrightValue less than right cutting point qrightIs placed in the left node sample subset
Figure GDA0002593413730000092
Attribute V of current right samplerightValue greater than right cut point qrightIs placed in the right node sample subset
Figure GDA0002593413730000093
Further, the function of the condition (3) l in the sixth step and the size N of the extracted subsample set is as follows:
l＝ceiling(log2N)
ceiling (log) in the formula2N) represents log2And N is rounded up.
Further, in the seventh step, the anomaly score S of the feature data set a is calculated according to the following method:
S＝[s(X1,N),s(X2,N),...,s(XL,N)]T
Figure GDA0002593413730000094
wherein s (X)iN) is a sample XiAbnormality score E (h (X) of (1)i) Is the average h (X) of t isolated treesi) Value h (X)i) For path length, i.e. in an orphan tree, from AsubTo XiThe number of edges experienced, (N) is the average of the path lengths given the decimated sub-sample set size N, defined as follows:
Figure GDA0002593413730000095
wherein H is the sum of the tones, and the calculation formula is as follows:
H(N-1)＝ln(N-1)+0.5772156649。
further, the abnormality score is larger than the specified value Tr in the eighth stepHIs marked as an outlier sample, and the outlier score is less than a specified value TrLThe samples of (2) are marked as dense samples, so that the original sample set A is divided into an abnormal sample set D, a dense sample set C and a normal sample set B:
Figure GDA0002593413730000101
wherein Xis1Is the sample of the dense sample set C, is1 is the sample number of the dense sample set C, Xis2Is the sample of the abnormal sample set D, is2 is the sample number of the abnormal sample set D, N*Represents a positive integer set, sgn is a step function;
at this time, oil drop edge cluster center c'1＝c1Oil droplet-like deposit edge cluster center c'2＝c2。
Further, the distance d (X) in the step nine,c′j) Is represented as follows:
d(X,c′j)＝||X,c′j||2
and clustering the union concentrated sample points of the normal sample set B and the dense sample set C according to the following formula:
Figure GDA0002593413730000102
wherein ζRepresents the first sample XThe category is the union concentrated sample number, zeta, of the normal sample set B and the dense sample set C∈{1,2}，ζζ 1, represents an oil drop edge class2, the oil drop attachment edge-like class is represented; n issubThe total number of samples in the union set of the normal sample set B and the dense sample set C,
Figure GDA0002593413730000103
represents d (X),c′j) The value of j at the minimum is taken.
Further, in the step ten, the oil drop edge cluster center c ″ is recalculated according to the following formula1Oil drop attachment edge clustering center c2：
Figure GDA0002593413730000104
Where j is the serial number of the cluster center, and j is1, 2. At this time, the current drop edge cluster center c'1′＝c″1Current oil drop attachment edge cluster center c'2＝c″2；
Further, the condition (2) clustering objective function in the step eleven is expressed as follows:
Figure GDA0002593413730000111
wherein the function of the coefficient
Figure GDA0002593413730000112
Lambda is the serial number of the clustering center,
Figure GDA0002593413730000113
represents d (X),c′λ) Taking the value of lambda at the minimum value;
further, in the step twelve, the samples in the abnormal sample set D are classified according to the following formula:
Figure GDA0002593413730000114
η is the sample number in the abnormal sample set D, XηIs the η th sample in the abnormal sample set D, ζηRepresents the η th sample XηClass to which it belongs, ζη∈{1,2}，ζηζ 1, represents an oil drop edge classηThe term "2" indicates an oil drop attachment edge-like substance.
The invention has the beneficial effects that: in order to solve the problem of measurement accuracy reduction caused by serious interference of transparent tube wall oil drop attachments when PTV is adopted to measure the oil phase flow velocity of the oil-water two-phase flow, a method for preprocessing an image of the oil drop attachments on the wall of the transparent tube of the oil-water two-phase flow is provided. The method solves the problem that when PTV is used for measuring the oil phase flow velocity of the oil-water two-phase flow, the attachment of transparent tube wall oil drops seriously interferes the measurement. Extracting effective characteristics of a plurality of oil-water two-phase flow images by adopting an image clustering method; the isolated forest algorithm is utilized to overcome the defect that the traditional K-means algorithm is greatly influenced by initial clustering center selection and outliers, the image clustering effect is enhanced, and the oil drop attachment identification accuracy is improved. Its main advantage is as follows:
(1) the invention adopts an image clustering method to extract a plurality of characteristics of the oil-water two-phase flow picture for clustering, thereby effectively identifying the edges of the wall oil drop attachments of the transparent pipe;
(2) the method adopts the improved K-means based on the isolated forest, and compared with other algorithms, the method does not use the distance or the density for detection, avoids large calculation amount when the detection is carried out based on the distance and density method, and has lower linear time complexity;
(3) the invention can effectively reduce the interference of the transparent tube wall oil drop attachment on PTV characteristic matching, thereby improving the measurement precision of the oil phase flow velocity of the oil-water two-phase flow.
Description of the drawings:
FIG. 1 is a diagram illustrating the result of image feature matching under the influence of oil drop attachment in the first embodiment;
FIG. 2 is a graph showing the result of the improved K-means image clustering identification and elimination of the oil-water two-phase flow picture with the oil drop-like attachment in the first embodiment;
fig. 3 is a measurement result diagram of the application of the identification and elimination of oil drop attachments of the transparent tube wall of the oil-water two-phase flow based on the traditional K-means and the identification and elimination of oil drop attachments of the transparent tube wall of the oil-water two-phase flow based on the improved K-means to the measurement of the oil phase flow velocity PTV of the oil-water two-phase flow of the vertical well in the first embodiment.
The specific implementation mode is as follows:
example one
Referring to the drawings, fig. 1 shows the image feature matching result under the influence of oil drop attachment, and it can be seen that many oil drop attachments are mistakenly matched as oil drops; FIG. 2 is the result of improved K-means image clustering identification and elimination of an oil-water two-phase flow picture with oil drop-like attachments; FIG. 3 is a measurement result obtained by applying recognition and elimination of oil drop attachments of transparent tube walls of oil-water two-phase flow based on traditional K-means and recognition and elimination of oil drop attachments of transparent tube walls of oil-water two-phase flow based on improved K-means to oil phase flow velocity PTV measurement of oil phase of oil-water two-phase flow of a vertical well respectively. The specific implementation mode is as follows:
firstly, oil drops are used as tracer particles, an LED backlight light source illuminates an area to be measured, and a high-speed camera is adopted to continuously shoot a plurality of vertical well oil-water two-phase flow pictures;
secondly, performing initial image processing on the collected multiple oil-water two-phase flow PTV images, wherein the initial image processing comprises filtering, denoising and sharpening, and performing oil drop and oil drop attachment edge detection by adopting a Canny operator;
randomly selecting an image as a current frame image, and respectively extracting color difference in the neighborhood of edge pixels of oil drops and oil drop attachment, gray value difference of multi-frame image edge pixels of the oil drops and the oil drop attachment and motion displacement difference of the oil drops and the oil drop attachment as effective characteristics according to the detected edge;
randomly sampling a characteristic data set A, wherein the characteristic data set A is a set of color difference characteristics in the neighborhood of oil drop and oil drop attachment edge pixels of image edge pixels, multi-frame image edge pixel gray value difference characteristics of oil drop and oil drop attachment, and motion displacement difference characteristics of oil drop and oil drop attachment, and A is { X ═ X { (X)1,X2,…,Xi}，XiThe sample is the ith sample in A, i is the edge pixel serial number, i is1, 2, …, and L is the number of edge pixels detected in the image; extracting N samples as a subsample set AsubN is the size of the extracted subsample set, and the isolated tree, lambda, is initially built1Is a sequence number of the isolated tree layer, λ1Has a value range of lambda 11,2, …, l, l is the height of the isolated tree, o is the node number, the value range of o is
Figure GDA0002593413730000136
In this step lambda1Is 1;
fifthly, randomly selecting a sample attribute V at AsubRandomly generating a cutting point q in the V attribute values, wherein the value of q is between the maximum value and the minimum value of the sample V attribute values, and under the V attribute, taking q as a cutting point to obtain an AsubPartitioning into left node sample sets
Figure GDA0002593413730000131
And right node sample set
Figure GDA0002593413730000132
Randomly selecting left sample attributes
Figure GDA0002593413730000133
And right sample attributes
Figure GDA0002593413730000134
The current left sample attribute is denoted VleftThe current right sample attribute is denoted as VrightThe current left node sample set is denoted GleftThe current right node sample set is denoted GrightWherein λ is1Is the number of the isolated tree layer, o%Is the node sequence number. At this time, the process of the present invention,
Figure GDA0002593413730000135
Figure GDA0002593413730000141
six, lambda1Is increased by 1, at the current left node sample set GleftCurrent left sample property VleftRandomly generating a left cut point q in the valuesleft，qleftIs located at the current left sample attribute VleftBetween the maximum and minimum of the values, at the current left sample property VleftAt a value of qleftSet G of current left node samples for left cut pointleftPartitioning into left node subsamples
Figure GDA0002593413730000142
Right node subsample set
Figure GDA0002593413730000143
At the current right node sample set GrightCurrent right sample property VrightRandomly generating a right cut point q in the valueright，qrightIs located at the current right sample attribute VrightBetween the maximum and minimum of the values, at the current right sample property VrightAt a value of qrightSet of current right node samples G for right cut pointrightPartitioning into left node sample subsets
Figure GDA0002593413730000144
Right node sample subset
Figure GDA0002593413730000145
Randomly selecting left sample attributes
Figure GDA0002593413730000146
And right sample attributes
Figure GDA0002593413730000147
Updating the currentLeft sample Properties
Figure GDA0002593413730000148
Updating current right sample attributes
Figure GDA0002593413730000149
Current left node sample set
Figure GDA00025934137300001410
Updating a current right node sample set
Figure GDA00025934137300001411
Wherein tau is1Is the horizontal sequence number, tau, of the left node subsample set2Is the horizontal sequence number, tau, of the right node subsample set3For the left node sample subset horizontal sequence number, τ4Is the horizontal sequence number of the right node sample subset;
seventhly, repeating six steps until any one of the following conditions (1) is met and G on the current left node sample set is obtainedleftAnd the current right node sample set GrightOnly one sample point in; (2) current left node sample set GleftAll samples in the same feature and the current right node sample set GrightAll the samples have the same characteristics; (3) the isolated tree reaches a limited height l;
eighthly, repeating four to seven steps until t isolated trees are generated, traversing (inputting) each sample in the characteristic data set A to the t isolated trees, and calculating the abnormal score S of the sample;
ninthly, dividing the characteristic data set A into an abnormal sample set D, a dense sample set C and a normal sample set B, and selecting an initial clustering center C of oil drop edges in the dense sample set C1Initial clustering center c of oil drop attachment edge2；
Ten, calculating the distance d (X) between the sample point and the cluster center in the union of the normal sample set B and the dense sample set C,c′j) And clustering the union sample points of the normal sample set B and the dense sample set C to obtain the union sample serial number X of the normal sample set B and the dense sample set CIs the union of normal sample set B and dense sample set CThe first sample, j is the serial number of the cluster center, j is1, 2, c'1Is the current drop edge cluster center, c'2Clustering the edge of the current oil drop attachment;
eleven, recalculating the oil drop edge clustering center c' under the ten-step classification result1Oil drop attachment edge clustering center c2；
Twelve, repeating ten to eleven until one of the specified conditions is met, wherein the specified conditions comprise: (1) reaching the specified iteration times; (2) the change of the clustering target function value is less than a threshold value tr;
thirteen, calculating each sample in the abnormal sample set D to the current oil drop edge clustering center c'1And c 'is the edge clustering center of current oil drop attachment'2According to the distance, dividing the sample into cluster classes to which the cluster centers closest to the sample belong;
fourteen, processing the pixels identified as the oil drop attachment edges under the current clustering result, and removing the effect of the pixels in the PTV velocity measurement as shown in figure 2;
fifteen, identifying transparent tube wall oil drop attachments of the oil-water two-phase flow based on the traditional K-means and identifying transparent tube wall oil drop attachments of the oil-water two-phase flow based on the improved K-means are applied to the PTV measurement of the oil phase flow velocity of the oil-water two-phase flow of the vertical well respectively, the measurement result is shown in figure 3, under the working conditions of different water contents and different flow velocities, the PTV measurement precision based on the traditional K-means is 92.2%, the PTV measurement precision based on the improved K-means is 94.8%, and the PTV measurement precision based on the improved K-means is higher than that of the traditional K-means.

Claims (10)

1. A method for identifying oil-like drop attachments in an oil-water two-phase flow transparent pipe wall is characterized by comprising the following steps: the identification method comprises the following steps:
the method comprises the following steps: carrying out initial image processing on the collected multiple oil-water two-phase flow PTV images, wherein the processing comprises filtering, denoising and sharpening, and adopting a Canny operator to carry out oil drop and oil drop attachment edge detection;
step two: randomly selecting an image as a current frame image, and respectively extracting the color difference in the neighborhood of the edge pixels of oil drops and oil drop attachment, the gray value difference of the edge pixels of multi-frame images of the oil drops and the oil drop attachment and the motion displacement difference of the oil drops and the oil drop attachment as effective characteristics according to the detected edge;
step three: randomly sampling a characteristic data set A, wherein the characteristic data set A is a set of color difference characteristics in the neighborhood of oil drop and oil drop attachment edge pixels of the current frame image, multi-frame image edge pixel gray value difference characteristics of the oil drop and oil drop attachment, and motion displacement difference characteristics of the oil drop and the oil drop attachment, and A is { X { (X)1,X2,…,Xi}，XiThe sample is the ith sample in A, i is the edge pixel serial number, i is1, 2, …, and L is the number of edge pixels detected in the image; extracting N samples as a subsample set AsubN is the size of the extracted subsample set, and the isolated tree, lambda, is initially built1Is a sequence number of the isolated tree layer, λ1Has a value range of lambda11,2, …, l, l is the height of the isolated tree, o is the node number, the value range of o is
Figure FDA0002593413720000011
In this step lambda1Is 1;
step four: randomly select a sample attribute V at AsubRandomly generating a cutting point q in the V attribute values, wherein the value of q is between the maximum value and the minimum value of the sample V attribute values, and under the V attribute, taking q as a cutting point to obtain an AsubPartitioning into left node sample sets
Figure FDA0002593413720000012
And right node sample set
Figure FDA0002593413720000013
Randomly selecting left sample attributes
Figure FDA0002593413720000014
And right sample genusProperty of (2)
Figure FDA0002593413720000015
The current left sample attribute is denoted VleftThe current right sample attribute is denoted as VrightThe current left node sample set is denoted GleftThe current right node sample set is denoted GrightWherein λ is1Is the sequence number of the isolated tree layer, and omicron is the node sequence number; at this time, the process of the present invention,
Figure FDA0002593413720000021
Figure FDA0002593413720000022
step five: lambda [ alpha ]1Is increased by 1, at the current left node sample set GleftCurrent left sample property VleftRandomly generating a left cut point q in the valuesleft，qleftIs located at the current left sample attribute VleftBetween the maximum and minimum of the values, at the current left sample property VleftAt a value of qleftSet G of current left node samples for left cut pointleftPartitioning into left node subsamples
Figure FDA0002593413720000023
Right node subsample set
Figure FDA0002593413720000024
At the current right node sample set GrightCurrent right sample property VrightRandomly generating a right cut point q in the valueright，qrightIs located at the current right sample attribute VrightBetween the maximum and minimum of the values, at the current right sample property VrightAt a value of qrightSet of current right node samples G for right cut pointrightPartitioning into left node sample subsets
Figure FDA0002593413720000025
Right node sampleSubset of the book
Figure FDA0002593413720000026
Randomly selecting left sample attributes
Figure FDA0002593413720000027
And right sample attributes
Figure FDA0002593413720000028
Updating current left sample attributes
Figure FDA0002593413720000029
Updating current right sample attributes
Figure FDA00025934137200000210
Current left node sample set
Figure FDA00025934137200000211
Updating a current right node sample set
Figure FDA00025934137200000212
Wherein tau is1Is the horizontal sequence number, tau, of the left node subsample set2Is the horizontal sequence number, tau, of the right node subsample set3For the left node sample subset horizontal sequence number, τ4Is the horizontal sequence number of the right node sample subset;
step six: repeating the step five until any one of the following conditions (1) is met and G on the current left node sample setleftAnd the current right node sample set GrightOnly one sample point in; (2) current left node sample set GleftAll samples in the same feature and the current right node sample set GrightAll the samples have the same characteristics; (3) the isolated tree reaches a limited height l;
step seven: repeating the third step and the sixth step until t isolated trees are generated, inputting each sample in the characteristic data set A into the t isolated trees, and calculating the abnormal score S of the sample;
step eight: will specially beThe characteristic data set A is divided into an abnormal sample set D, a dense sample set C and a normal sample set B, and an oil drop edge initial clustering center C is selected from the dense sample set C1Initial clustering center c of oil drop attachment edge2；
Step nine: calculating the distance d (X) between the sample point and the cluster center in the union of the normal sample set B and the dense sample set C,c′j) And clustering the union sample points of the normal sample set B and the dense sample set C to obtain the union sample serial number X of the normal sample set B and the dense sample set CThe first sample in the union of the normal sample set B and the dense sample set C, j is the serial number of the cluster center, and j is1, 2, C'1Is the current drop edge cluster center, c'2Clustering the edge of the current oil drop attachment;
step ten: recalculating the oil drop edge clustering center c' under the classification result of the step nine1Oil drop attachment edge clustering center c2；
Step eleven: and repeating the ninth step and the tenth step until one of the specified conditions is met, wherein the specified conditions comprise: (1) reaching the specified iteration times; (2) the change of the clustering target function value is less than a threshold value tr;
step twelve: calculating the current oil drop edge clustering center c 'of each sample in the abnormal sample set D'1And c 'is the edge clustering center of current oil drop attachment'2And dividing the samples into cluster classes to which the cluster centers closest to the samples belong according to the distance.
2. The method for identifying the oil-like drop attachment in the oil-water two-phase flow transparent pipe wall according to claim 1, wherein the method comprises the following steps: in the second step, the extracted image features are as follows:
(1) oil drop and oil drop attachment edge pixel neighborhood interior color difference characteristics:
descriptor f of color difference characteristics in oil drop and oil drop attachment edge pixel neighborhoodi c1Is defined as follows
fi c1＝ωHσH+ωSσS+ωVσV
Wherein f isi c1Is a descriptor of the color difference characteristics in the neighborhood of the oil drop and oil drop attachment edge pixel of the ith edge pixel, omegaHIs a weight of the color difference on the H color component, and ωH<1，ωSIs a weight of the color difference on the S color component, and ωS<1，ωVIs a weight of the color difference on the V color component, and ωV<1，σHFor the color difference on the color component of H,
Figure FDA0002593413720000041
σSfor the color difference on the S color component,
Figure FDA0002593413720000042
σVfor the color difference on the V color component,
Figure FDA0002593413720000043
ii is the pixel neighborhood x coordinate, jj is the pixel neighborhood y coordinate, M1Is the maximum value of the x coordinate of the pixel neighborhood, M2Is the maximum value of the y-coordinate of the pixel neighborhood,
Figure FDA0002593413720000044
is the H color component value of the pixel at (ii, jj) within the neighborhood,
Figure FDA0002593413720000045
is the S color component value of the pixel at (ii, jj) within the neighborhood,
Figure FDA0002593413720000046
is the V color component value, μ, of the pixel at (ii, jj) within the neighborhoodHIs the average value, mu, of the H color components of the pixels in the neighborhoodSIs the average value, mu, of the S color components of the pixels in the neighborhoodVIs the average of the V color components of the pixels in the neighborhood;
(2) oil drops and oil drop attachment multi-frame image edge pixel gray value difference characteristics:
descriptor f of multi-frame image edge pixel gray value difference characteristic of oil drop and oil drop-like attachmenti c2The definition is as follows:
Figure FDA0002593413720000047
wherein f isi c2Is a descriptor of the gray value difference characteristic of oil drops of the ith edge pixel and oil drop attachment multi-frame image edge pixels, ncIs the number of frames selected for the frame,
Figure FDA0002593413720000051
is the gray value of the ith edge pixel in the current frame image, k is the serial number of the image frame number,
Figure FDA0002593413720000052
the gray value of the pixel at the position corresponding to the current frame image in the kth frame image is obtained;
(3) the oil drop and the oil drop attachment motion displacement difference characteristics are as follows:
the descriptor f of the motion displacement difference characteristici m1The definition is as follows:
Figure FDA0002593413720000053
wherein f isi m1Is a descriptor of the difference characteristic of the motion displacement of oil drops and oil drop attachment of the ith edge pixel, nαIn order to use the number of the query windows with different sizes, w is the serial number of the query window used, w is1, 2, …, nα，lwThe displacement obtained by adopting query windows with different sizes is calculated according to the following formula:
Figure FDA0002593413720000054
wherein u iswAdopts the w-th inquiry windowDisplacement, v, of pixel in x direction calculated by cross correlationwIs the displacement of the pixel in the y direction obtained by cross-correlation calculation using the w-th query window:
Figure FDA0002593413720000055
wherein
Figure FDA0002593413720000056
The cross-correlation matrix under the w-th query window is adopted:
Figure FDA0002593413720000057
wherein
Figure FDA0002593413720000058
Is the coordinate of the x direction of the cross-correlation matrix,. phi.wAdopting a gray scale map f of an inquiry area covered by a w inquiry window for a current frame image'wA grey-scale map of the query region covered with the w-th query window is used for the next frame of image,
Figure FDA0002593413720000059
is fwThe average pixel value within the corresponding region,
Figure FDA0002593413720000061
is f'wThe average pixel value in the corresponding area, M is the x-direction pixel coordinate of the query area covered by the query window, and M is1, 2w，MwThe maximum value of the pixel coordinates in the x direction of the query area covered by the query window, N is the pixel coordinates in the y direction of the query area covered by the query window, and N is1, 2w，NwAnd the maximum value of the pixel coordinate in the y direction of the query area covered by the query window.
3. An oil according to claim 1The method for identifying oil-like drop attachments in the wall of the water two-phase flow transparent pipe is characterized by comprising the following steps of: the three-step neutron sample set AsubExpressed as:
Figure FDA0002593413720000062
wherein
Figure FDA0002593413720000063
Is a color difference feature set in the neighborhood of the edge pixel, i' is a subsample set AsubThe inner edge pixel number, i' is1, 2, …, N is the decimated sub-sample set size,
Figure FDA0002593413720000069
as a subsample set AsubThe color difference feature in the edge pixel neighborhood of the ith' edge pixel,
Figure FDA0002593413720000064
for a multi-frame image edge pixel gray value difference feature set,
Figure FDA00025934137200000610
as a subsample set AsubThe multi-frame image edge pixel gray value difference characteristic of the ith' edge pixel,
Figure FDA0002593413720000065
in order to move the set of difference features of displacement,
Figure FDA00025934137200000611
as a subsample set AsubMotion displacement difference feature of the ith' edge pixel in the subsample set AsubInner ith' edge pixel sample
Figure FDA00025934137200000612
4. The method for identifying the oil-like drop attachments in the transparent tube wall of the oil-water two-phase flow according to claim 1, wherein the sample attribute V ∈ { F in the fourth step isc1,Fc2,Fm1And putting the samples with the V attribute value smaller than the cutting point q in the left node sample set
Figure FDA0002593413720000066
Putting samples with V attribute values larger than the cut point q into a right node sample set
Figure FDA0002593413720000067
Wherein
Figure FDA0002593413720000068
Is a color difference feature set in the neighborhood of the edge pixel, i' is a subsample set AsubThe inner edge pixel number, i' is1, 2, …, N is the decimated sub-sample set size,
Figure FDA0002593413720000077
as a subsample set AsubThe color difference feature in the edge pixel neighborhood of the ith' edge pixel,
Figure FDA0002593413720000071
for a multi-frame image edge pixel gray value difference feature set,
Figure FDA0002593413720000078
as a subsample set AsubThe multi-frame image edge pixel gray value difference characteristic of the ith' edge pixel,
Figure FDA0002593413720000072
in order to move the set of difference features of displacement,
Figure FDA0002593413720000079
as a subsample set AsubMotion displacement difference feature of the ith' edge pixel in the subsample set AsubInner ith' edge pixel sample
Figure FDA00025934137200000710
5. The method for identifying the oil-like drop attachment in the oil-water two-phase flow transparent pipe wall according to claim 1, wherein the method comprises the following steps: in the fifth step, the current left node sample set GleftIn, the current left sample attribute VleftValue less than left cutting point qleftIs placed in the left node subsample set
Figure FDA0002593413720000073
In, the current left sample attribute VleftValue greater than left cutting point qleftIs placed in the right node subsample set
Figure FDA0002593413720000074
At the current right node sample set GrightIn, the current right sample attribute VrightValue less than right cutting point qrightIs placed in the left node sample subset
Figure FDA0002593413720000075
Attribute V of current right samplerightValue greater than right cut point qrightIs placed in the right node sample subset
Figure FDA0002593413720000076
。
6. The method for identifying the oil-like drop attachment in the oil-water two-phase flow transparent pipe wall according to claim 1, wherein the method comprises the following steps: in the sixth step, the functional relation between the condition (3) l and the size N of the extracted subsample set is as follows:
l＝ceiling(log2N)
ceiling (log) in the formula2N) represents log2And N is rounded up.
7. The method for identifying the oil-like drop attachment in the oil-water two-phase flow transparent pipe wall according to claim 1, wherein the method comprises the following steps: in the seventh step, the abnormal score S of the feature data set A is calculated according to the following method:
S＝[s(X1,N),s(X2,N),...,s(XL,N)]T
Figure FDA0002593413720000081
wherein s (X)iN) is a sample XiAbnormality score E (h (X) of (1)i) Is the average h (X) of t isolated treesi) Value h (X)i) For path length, i.e. in an orphan tree, from AsubTo XiThe number of edges experienced, (N) is the average of the path lengths given the decimated sub-sample set size N, defined as follows:
Figure FDA0002593413720000082
wherein H is the sum of the tones, and the calculation formula is as follows:
H(N-1)＝ln(N-1)+0.5772156649。
8. the method for identifying the oil-like drop attachment in the oil-water two-phase flow transparent pipe wall according to claim 1, wherein the method comprises the following steps: step eight, the abnormal score is larger than the specified value TrHIs marked as an outlier sample, and the outlier score is less than a specified value TrLThe samples of (2) are marked as dense samples, so that the original sample set A is divided into an abnormal sample set D, a dense sample set C and a normal sample set B:
Figure FDA0002593413720000083
wherein Xis1Is the sample of the dense sample set C, is1 is the sample number of the dense sample set C, Xis2Is a sample of the abnormal sample set D, is2 is an abnormal sampleSample number of set D, N*Represents a positive integer set, sgn is a step function;
Figure FDA0002593413720000091
wherein s (X)iN) is a sample XiAbnormality score E (h (X) of (1)i) Is the average h (X) of t isolated treesi) Value h (X)i) For path length, i.e. in an orphan tree, from AsubTo XiThe number of edges experienced, (N) is the average of the path lengths given the decimated sub-sample set size N, defined as follows:
Figure FDA0002593413720000092
wherein H is the sum of the tones, and the calculation formula is as follows:
H(N-1)＝ln(N-1)+0.5772156649；
at this time, oil drop edge cluster center c'1＝c1Oil droplet-like deposit edge cluster center c'2＝c2。
9. The method for identifying the oil-like drop attachment in the oil-water two-phase flow transparent pipe wall according to claim 1, wherein the method comprises the following steps: distance d (X) in step nine,c′j) Is represented as follows:
d(X,c′j)＝||X,c′j||2
and clustering the union concentrated sample points of the normal sample set B and the dense sample set C according to the following formula:
Figure FDA0002593413720000093
wherein ζRepresents the first sample XThe category is the union concentrated sample number, zeta, of the normal sample set B and the dense sample set C∈{1,2}，ζζ 1, represents an oil drop edge class2, the oil drop attachment edge-like class is represented; n issubThe total number of samples in the union set of the normal sample set B and the dense sample set C,
Figure FDA0002593413720000094
represents d (X),c′j) The value of j at the minimum is taken.
10. The method for identifying the oil-like drop attachment in the oil-water two-phase flow transparent pipe wall according to claim 1, wherein the method comprises the following steps: in the step ten, the clustering center c' of the oil drop edges is recalculated according to the following formula1Oil drop attachment edge clustering center c2：
Figure FDA0002593413720000101
J is a serial number of a clustering center, and j is1 and 2; at this time, the current drop edge cluster center c'1＝c″1Current oil drop attachment edge cluster center c'2＝c″2(ii) a The condition (2) clustering objective function in the step eleven is represented as follows:
Figure FDA0002593413720000102
wherein the function of the coefficient
Figure FDA0002593413720000103
Lambda is the serial number of the clustering center,
Figure FDA0002593413720000104
represents d (X),c′λ) Taking the value of lambda at the minimum value; in the step twelve, the samples in the abnormal sample set D are classified according to the following formula:
Figure FDA0002593413720000105
whereinη is the sample number in the abnormal sample set D, XηIs the η th sample in the abnormal sample set D, ζηRepresents the η th sample XηClass to which it belongs, ζη∈{1,2}，ζηζ 1, represents an oil drop edge classη2, the oil drop attachment edge-like class is represented;
Figure FDA0002593413720000106
wherein ζRepresents the first sample XThe category is the union concentrated sample number, zeta, of the normal sample set B and the dense sample set C∈{1,2}，ζζ 1, represents an oil drop edge class2, the oil drop attachment edge-like class is represented; n issubThe total number of samples in the union set of the normal sample set B and the dense sample set C,
Figure FDA0002593413720000111
represents d (X),c′j) The value of j at the minimum is taken.
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Publication number Priority date Publication date Assignee Title
CN115861751B (en) * 2022-12-06 2024-04-16 常熟理工学院 Oil-water two-phase flow multi-oil drop identification method based on integrated characteristics
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108805865A (en) * 2018-05-22 2018-11-13 杭州智微信息科技有限公司 A kind of myeloplast localization method based on saturation degree cluster
CN109410586A (en) * 2018-12-13 2019-03-01 中南大学 A kind of Traffic State Detection Method based on multivariate data fusion
CN109766905A (en) * 2018-09-28 2019-05-17 中国人民解放军空军工程大学 Target cluster dividing method based on Self-Organizing Feature Maps
CN109871886A (en) * 2019-01-28 2019-06-11 平安科技（深圳）有限公司 Abnormal point ratio optimization method, apparatus and computer equipment based on spectral clustering
CN109948728A (en) * 2019-03-28 2019-06-28 第四范式（北京）技术有限公司 The method and apparatus of the training of abnormal transaction detection model and abnormal transaction detection
CN110046665A (en) * 2019-04-17 2019-07-23 成都信息工程大学 Based on isolated two abnormal classification point detecting method of forest, information data processing terminal

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170123931A1 (en) * 2011-08-12 2017-05-04 Nexenta Systems, Inc. Object Storage System with a Distributed Namespace and Snapshot and Cloning Features
CN108548817B (en) * 2018-03-26 2021-08-03 清华大学 Multiphase floating jet experiment generating device and oil drop bubble shadow image processing method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108805865A (en) * 2018-05-22 2018-11-13 杭州智微信息科技有限公司 A kind of myeloplast localization method based on saturation degree cluster
CN109766905A (en) * 2018-09-28 2019-05-17 中国人民解放军空军工程大学 Target cluster dividing method based on Self-Organizing Feature Maps
CN109410586A (en) * 2018-12-13 2019-03-01 中南大学 A kind of Traffic State Detection Method based on multivariate data fusion
CN109871886A (en) * 2019-01-28 2019-06-11 平安科技（深圳）有限公司 Abnormal point ratio optimization method, apparatus and computer equipment based on spectral clustering
CN109948728A (en) * 2019-03-28 2019-06-28 第四范式（北京）技术有限公司 The method and apparatus of the training of abnormal transaction detection model and abnormal transaction detection
CN110046665A (en) * 2019-04-17 2019-07-23 成都信息工程大学 Based on isolated two abnormal classification point detecting method of forest, information data processing terminal

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
A hybrid semi-supervised approach for financial fraud detection;Jin-Miao Liu 等;《2017 International Conference on Machine Learning and Cybernetics (ICMLC)》;20171116;第217-222页 *
Detection of Anomalous Vital Sign of Elderly Using Hybrid K-Means Clustering and Isolation Forest;Kurnianingsih 等;《TENCON IEEE Region 10 Conference Proceedings》;20181031;第913-918页 *
Flow Velocity Field Measurement of Vertical Upward Oil-Water Two-Phase Immiscible Flow Using the Improved DPIV Algorithm Based on ICP and MLS;Han, Lianfu 等;《APPLIED SCIENCES-BASEL》;20190831;第9卷(第16期);第1-22页 *
基于K-medoids聚类分析的多相流相态辨识方法研究;赵鸣 等;《天津理工大学学报》;20171231;第33卷(第6期);第55-59页 *
基于异常检测的K-means改进算法研究;薛晨杰 等;《软件导刊》;20190430;第18卷(第4期);第74-78页 *
基于目标轮廓的附着物定位与剔除方法;栗琳 等;《光电工程》;20120531;第39卷(第5期);第45-51页 *

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