CN111724394A - Matlab image analysis and self-optimization-based rapid characterization method for sludge morphology - Google Patents
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- 239000010802 sludge Substances 0.000 title claims abstract description 117
- 238000005457 optimization Methods 0.000 title claims abstract description 29
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- 238000010191 image analysis Methods 0.000 title claims abstract description 16
- 238000000034 method Methods 0.000 claims abstract description 29
- 230000000877 morphologic effect Effects 0.000 claims abstract description 15
- 238000004458 analytical method Methods 0.000 claims abstract description 12
- 238000012545 processing Methods 0.000 claims description 19
- 239000011159 matrix material Substances 0.000 claims description 13
- 238000004140 cleaning Methods 0.000 claims description 7
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- 239000012535 impurity Substances 0.000 claims description 4
- 239000006228 supernatant Substances 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
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- 238000006243 chemical reaction Methods 0.000 abstract description 3
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- 238000013461 design Methods 0.000 abstract description 2
- 238000013139 quantization Methods 0.000 abstract description 2
- 238000000605 extraction Methods 0.000 description 6
- 230000000903 blocking effect Effects 0.000 description 4
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- 230000011218 segmentation Effects 0.000 description 3
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- 230000005856 abnormality Effects 0.000 description 1
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- 238000012937 correction Methods 0.000 description 1
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- 238000013075 data extraction Methods 0.000 description 1
- 238000013501 data transformation Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
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- 230000003068 static effect Effects 0.000 description 1
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
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- G06T7/11—Region-based segmentation
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- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
- G06T5/30—Erosion or dilatation, e.g. thinning
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Abstract
The invention discloses a rapid characterization method of sludge morphology based on Matlab image analysis and self-optimization. The method comprises the following steps: (1) sampling sludge; (2) acquiring an image; (3) data conversion; (4) dividing the graph; (5) self-optimizing parameters; (6) and (5) morphological analysis. The method is convenient and efficient, has low instrument requirement, solves the problem of data quantization in the existing sludge morphological analysis process, and has important significance for risk pre-control of sludge bulking, filamentous bacterium growth and other problems in a sewage plant. Meanwhile, the method carries out self-optimization design aiming at the morphological characteristics of the activated sludge, can realize automatic adjustment of program parameters of different sludge forms and imaging conditions, greatly reduces the operation requirement in the actual application process, and has the value of cross-platform popularization.
Description
Technical Field
The invention relates to a method for rapidly characterizing the form of activated sludge based on image analysis, belonging to the field of biological treatment of wastewater.
Background
Activated sludge is the main contributor to the material removal function in biological wastewater treatment. The morphological structure of the activated sludge has important guiding significance for stable operation of the activated sludge and efficient removal of pollutants. In actual operation, the morphological structure of the sludge is generally analyzed by adopting methods of sedimentation performance measurement and visual observation, and a convenient and reliable method for characterizing the sludge morphology does not exist at present. Generally, with the popularization of hardware and corresponding software of personal computers and mobile phones, reliable image analysis software is developed, and the method has important significance for sewage plant workers and scientific workers to quantify the indexes of the activated sludge and further strengthen the stable operation of the activated sludge.
Disclosure of Invention
The invention aims to provide a method for rapidly analyzing sludge based on image matrix processing, which is used for performing data extraction and morphological structure analysis on a sludge image acquired by common optics through a series of program processes such as image matrix extraction, threshold data conversion, connected domain segmentation, parameter optimization, result analysis and the like.
The method mainly comprises the following steps of (1) sludge sampling, (2) image acquisition, (3) data conversion, (4) graph segmentation, (5) parameter self-optimization, and (6) morphological analysis and data derivation, and the like.
The technical scheme adopted by the invention is as follows:
a rapid characterization method of sludge morphology based on Matlab image analysis and self-optimization comprises the following steps:
s1: taking activated sludge, centrifuging, removing supernatant, adding distilled water for complementing, performing one-time cleaning, and repeating multiple times of cleaning to remove impurities in an activated sludge sample;
s2: dripping a sludge sample to the center of a glass slide, observing the sludge sample on the glass slide by using a microscope, searching an evenly dispersed activated sludge area, and shooting to obtain a sludge image;
s3: importing a shot sludge image in Matlab, carrying out graying treatment on the image, and then carrying out smoothing treatment through median filtering to remove noise points in the image to obtain a two-dimensional matrix of the grayscale image;
s4: integrally dividing a two-dimensional matrix of the gray level image into PaM rows, and dividing each row into PaN columns to form PaM multiplied by PaN image subregions;
s5: determining an initial threshold thresh for binarizing the segmented image by using a maximum inter-class variance method for each image subregion, and then performing binarization processing on the image subregions by using an optimal threshold Pa2 multiplied by thresh obtained after the initial threshold is corrected by a control coefficient Pa 2; performing connected domain analysis on each image subregion after binarization processing according to a preset lowest pixel threshold Pa1, and determining the position of a sludge floc boundary in the image subregion;
s6: according to the determined sludge floc boundary in each image subregion, segmenting the sludge floc image in the whole gray level image, extracting all the sludge floc images in the image, and counting noise pixels P and boundary distortion pixels Q in all the sludge floc images;
s7: setting an objective function as the weighted sum of normalized values of P and Q, and performing traversal optimization on the four parameters of Pan, PaM, Pa1 and Pa2 in respective parameter ranges to obtain the optimal parameter with the aim of minimizing the objective function;
s8: and obtaining all sludge floc images in the gray level image based on the optimal parameter values, and calculating to obtain morphological characteristic data of each sludge floc.
Preferably, in the sludge image, the density of activated sludge flocs is 10-50 per photo, and the number of photos is not less than 20.
Preferably, the microscope uses a 10 × eyepiece +40 × objective lens for observation and image capture.
Preferably, the optimized range of the parameter Pa1 is 4-6.
Preferably, the optimized range of the parameter Pa2 is 0.7-1.3.
Preferably, the range of the parameter PaM during optimization is 20-100.
Preferably, the range of the parameter Pan during optimization is 20-100.
Preferably, in the process of traversing and optimizing the parameters, the traversing step length is 1/10-1/100 of the parameter value interval.
Preferably, the morphological characteristic data comprises the number of sludge flocs, the perimeter of a contour, an equivalent diameter, roundness, a major-minor axis ratio and a fractal dimension.
Preferably, the graying of the image is performed by using an rgb2gray () function, the median filter is performed by using a filter2() function, and the initial threshold thresh is determined by using a graythresh () function.
The invention has the beneficial effects that:
the method is convenient and efficient, has low instrument requirement, solves the problem of data quantization in the existing sludge morphological analysis process, and has important significance for risk pre-control of sludge bulking, filamentous bacterium growth and other problems in a sewage plant. Meanwhile, the method carries out self-optimization design aiming at the morphological characteristics of the activated sludge, can realize automatic adjustment of program parameters of different sludge forms and imaging conditions, greatly reduces the operation requirement in the actual application process, and has the value of cross-platform popularization.
Drawings
FIG. 1 is a flow chart of the extraction of enriched phage from activated sludge.
Fig. 2 is a processing effect of the optimized image in the embodiment.
FIG. 3 is a comparison of image extraction effects of the embodiment with or without PaM and Pan control parameters.
Fig. 4 is a comparison of image extraction effects of the embodiment with or without the Pa2 control parameter.
Detailed Description
The invention is further illustrated by the following figures and specific examples.
A rapid characterization method of sludge morphology based on Matlab image analysis and self-optimization comprises the following main steps of (1) sludge sampling; (2) microscopic imaging; (3) converting image information; (4) processing image data; (5) self-optimizing parameters; (6) and (6) analyzing the data.
As shown in fig. 1, the specific implementation of the method is as follows:
s1: taking activated sludge, centrifuging, removing supernatant, adding distilled water for complementing, performing one-time cleaning, and repeating multiple times of cleaning to remove impurities in an activated sludge sample;
s2: and (3) dripping a sludge sample to the center of the glass slide, observing the sludge sample on the glass slide by using a microscope, searching an evenly dispersed activated sludge area, and shooting to obtain a sludge image.
S3: in Matlab, a shot sludge image is imported, graying processing is carried out on the image, smoothing processing is carried out through median filtering, noise points in the image are removed, and a two-dimensional matrix of a grayscale image is obtained.
S4: the two-dimensional matrix of the gray scale image is divided into PaM rows and each row is divided into PaN columns to form PaM × PaN image sub-regions.
S5: determining an initial threshold thresh for binarizing the segmented image by using a maximum inter-class variance method for each image subregion, and then performing binarization processing on the image subregions by using an optimal threshold Pa2 multiplied by thresh obtained after the initial threshold is corrected by a control coefficient Pa 2; and (4) performing connected domain analysis on each image subregion after binarization processing according to a preset lowest pixel threshold Pa1, and determining the position of the sludge floc boundary in the image subregion.
S6: and (3) segmenting the sludge floc image in the whole gray level image according to the determined sludge floc boundary in each image subregion, extracting all the sludge floc images in the image, and counting noise pixels P and boundary distortion pixels Q in all the sludge floc images.
S7: and setting an objective function as the weighted sum of the normalized values of P and Q, performing traversal optimization on the four parameters of Pan, PaM, Pa1 and Pa2 in respective parameter ranges by taking the four parameter values with the minimum objective function as the optimal parameter combination with the aim of minimizing the objective function.
S8: based on the optimum parameter value obtained in S7 described above, it was assigned to MATLAB, so that all sludge floc images in the gray-scale image were obtained in step S6. And then, calculating to obtain morphological characteristic data of each sludge floc, wherein the specific morphological characteristic data is adjusted as required.
In MATLAB, the implementation of the methods in the above different steps can be performed by using a self-contained function or a custom function, and can be selected according to actual needs as long as the corresponding functions can be implemented. The following is a further description of the implementation and effect of the present invention, taking the extraction and morphological characterization of phage particles as an example.
Examples
The purpose of this example is to extract phage particles from aerobic granular sludge with high efficiency, specifically according to the following steps:
step 1: sludge sampling
5mL of activated sludge is taken, centrifuged at 2000g/min for 20min, supernatant liquid is discarded by a liquid-transferring gun, distilled water is added to complement to 5mL, the centrifugal cleaning process is repeated for 3 times, impurities in the activated sludge sample are removed, and the activated sludge sample is used for the next step of microscopic imaging.
Step 2: microscopic imaging
About 1ml of sludge sample was pipetted with a rubber pipette, dropped to the center of the slide, and observed with a microscope (10 × eyepiece +40 × objective). And searching an active sludge area with good dispersion, shooting to obtain a plurality of sludge images, and performing subsequent treatment on each sludge image according to the same method to meet the statistical requirement.
And step 3: data transformation
In Matlab, the sludge image data stored in the specified path is imported using the imread () function, and an original image matrix Img _ exam (fig. 2a) in the format of 1536 × 2048 × 3unit8 is obtained.
The introduced sludge image is subjected to gradation processing using rgb2gray (), and an image matrix Im of 1536 × 2048 × unit8 is obtained. And performing median filtering by using a filter2() function to realize smoothing, removing noise generated in image processing and obtaining a two-dimensional matrix Im3 of the gray level image.
And the image is subjected to block analysis, so that the problem caused by the difference of imaging conditions of the image area is reduced. In the block analysis, the two-dimensional matrix Im3 of the grayscale image is divided into PaM rows and PaN columns, respectively, to form PaM × PaN n image sub-regions (the function control parameters are horizontal and vertical block values PaM, PaN). Then, for each image sub-region, an initial threshold thresh for binarizing the segmented image is determined using the maximum inter-class variance method, and the initial threshold thresh can be obtained by a graythresh () function. Since the initial threshold thresh is not necessarily the optimum threshold according to the characteristics of the sludge image, in this embodiment, the initial threshold is corrected by the control coefficient Pa2 to obtain the optimum threshold Pa2 × thresh, and Pa2 × thresh is used to replace the original thresh, thereby performing binarization processing on the image sub-region. In the binarization processing process, the gray level image is converted into a binary image by using a binarization function im2bw (), the lowest pixel threshold value adopted by binarization is a control parameter Pa1, when the gray level value of a pixel does not exceed Pa1, the gray level value of the pixel is assigned to be 0, and when the gray level value of the pixel exceeds Pa1, the gray level value of the pixel is assigned to be 255. Therefore, based on the binary image of each image subregion, connected domain analysis can be carried out, and the boundary position of each sludge floc in the image subregion is determined.
In the above steps, the four parameters of PaN, PaM, Pa1, and Pa2 are all used as parameters to be optimized, and the optimal values thereof need to be determined.
S6: according to the determined sludge floc boundary in each image subregion, segmenting the sludge floc image in the whole gray level image, extracting all the sludge floc images in the image, and counting noise pixels P and boundary distortion pixels Q in all the sludge floc images;
and 4, step 4: image data processing
For the whole gray level image, firstly utilizing a bwearopen () function to remove the background, utilizing a bwleabel function to label a sludge single connected domain, marking the boundary of the connected domain as the boundary of sludge flocs, and segmenting the single sludge floc image in the image matrix according to the boundary. And after the sub-regions of each image are segmented, obtaining the sludge floc image in the whole gray image.
And counting the noise and the boundary distortion in the whole sludge floc image, and deriving a quality function P, Q, wherein P represents the number of pixels belonging to the noise in the image, and Q represents the number of pixels belonging to the distorted boundary in the image. The boundary distortion can be determined according to the comparison of the image characteristics of the segmented graph and the actual sludge, and the boundary position which does not accord with the image characteristics of the sludge floc is taken as a distortion boundary. For example, the general sludge floc pixel frequency curve is in an inverted V shape, the typical boundary distortion image distribution is in an L shape, and the two are obviously inconsistent, so that the distortion can be judged.
And 5: parameter self-optimization
And (3) optimizing parameters aiming at the control parameters Pa1, Pa2, PaM and Pan adopted in the steps 1-3, wherein P and Q are targets to be optimized, and the aim is to control noise points and boundary distortion as much as possible. Since the values of P and Q are not in the same dimension, both need to be normalized first. Then, the objective function is set as the weighted sum of the normalized values of P and Q, the weighted values of both are 0.5 and 0.5 in this embodiment, i.e. the weights of both are equivalent.
Then, value range ranges of the four parameters are set, the range when the parameter Pa1 is optimized is 4-6, the range when the parameter Pa2 is optimized is 0.7-1.3, the range when the parameter PaM is optimized is 20-100, and the range when the parameter PaN is optimized is 20-100. With the aim of minimizing the objective function, traversal optimization is carried out on the parameters of Pan, PaM, Pa1 and Pa2 within respective parameter ranges, and in the process of traversal optimization of the parameters, the traversal step length is 1/10-1/100 of the parameter value range. And taking a group of parameters with the minimum objective function as the optimal parameters. Finally, the optimal values of the four parameters of Pa1, Pa2, Pan and PaM are respectively calculated to be 4.2, 0.9, 80 and 80.
Step 6: data analysis
And (4) inputting the optimized optimal parameter values in the step (5) into the MATLAB again, and processing according to the step (3) and the step (4) again to obtain the optimal result of image extraction, which is shown in fig. 2. b). And (3) deriving the quantity of the activated sludge, the perimeter of the contour, the equivalent diameter, the roundness, the major-minor axis ratio and the fractal dimension by using a statistical function static ().
Among the four parameters, PaM and PAN are blocking parameters and are respectively used for controlling the size of a pixel block after the image algorithm is divided. Pa1 is a noise control parameter. Pa2 is a binary segmentation threshold control parameter used to fine tune the image segmentation threshold. P and Q are quality functions used for counting noise and boundary distortion.
In order to visually display the functions of the control parameters, a corresponding comparison is set in the embodiment.
Referring to fig. 3, a) is a result of extracting sludge floc images without image blocking, that is, PaN and PaM both take values of 1, and b) is a result of extracting sludge floc images with image blocking in this embodiment, that is, PaN and PaM both take values of 80. It is clear from the figure that the top of the image has a distinct black anomaly due to the microscope image having a strong light source in its center and weak light intensity in its periphery. If the blocking processing is not carried out, or a position of the image far from the center is caused to have a large error in the binarization process, the image is distorted due to the light source background error.
Referring to fig. 4, a) is a result of extracting sludge floc images without correcting the initial threshold thresh of the image obtained by the graythresh () function, that is, Pa2 takes a value of 1, and b) is a result of extracting sludge floc images with correcting the initial threshold thresh of the image obtained by the graythresh () function by increasing Pa2 in this embodiment, that is, Pa2 takes an optimal value of 0.9. It can be clearly seen from the figure that more obvious black block abnormalities appear around the image of the extracted sludge flocs, but the extracted sludge flocs are closer to the real form by the correction of Pa2, and noise and distortion are greatly controlled.
The above-described embodiments are merely preferred embodiments of the present invention, which should not be construed as limiting the invention. Various changes and modifications may be made by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present invention. Therefore, the technical scheme obtained by adopting the mode of equivalent replacement or equivalent transformation is within the protection scope of the invention.
Claims (10)
1. A rapid characterization method for sludge morphology based on Matlab image analysis and self-optimization is characterized by comprising the following steps:
s1: taking activated sludge, centrifuging, removing supernatant, adding distilled water for complementing, performing one-time cleaning, and repeating multiple times of cleaning to remove impurities in an activated sludge sample;
s2: dripping a sludge sample to the center of a glass slide, observing the sludge sample on the glass slide by using a microscope, searching an evenly dispersed activated sludge area, and shooting to obtain a sludge image;
s3: importing a shot sludge image in Matlab, carrying out graying treatment on the image, and then carrying out smoothing treatment through median filtering to remove noise points in the image to obtain a two-dimensional matrix of the grayscale image;
s4: integrally dividing a two-dimensional matrix of the gray level image into PaM rows, and dividing each row into PaN columns to form PaM multiplied by PaN image subregions;
s5: determining an initial threshold thresh for binarizing the segmented image by using a maximum inter-class variance method for each image subregion, and then performing binarization processing on the image subregions by using an optimal threshold Pa2 multiplied by thresh obtained after the initial threshold is corrected by a control coefficient Pa 2; performing connected domain analysis on each image subregion after binarization processing according to a preset lowest pixel threshold Pa1, and determining the position of a sludge floc boundary in the image subregion;
s6: according to the determined sludge floc boundary in each image subregion, segmenting the sludge floc image in the whole gray level image, extracting all the sludge floc images in the image, and counting noise pixels P and boundary distortion pixels Q in all the sludge floc images;
s7: setting an objective function as the weighted sum of normalized values of P and Q, and performing traversal optimization on the four parameters of Pan, PaM, Pa1 and Pa2 in respective parameter ranges to obtain the optimal parameter with the aim of minimizing the objective function;
s8: and obtaining all sludge floc images in the gray level image based on the optimal parameter values, and calculating to obtain morphological characteristic data of each sludge floc.
2. The Matlab image analysis and self-optimization-based sludge morphology rapid characterization method according to claim 1, wherein in the sludge image, the activated sludge floc density is 10-50 per photo, and the number of photos is not less than 20.
3. The Matlab image analysis and self-optimization-based sludge morphology rapid characterization method of claim 1, wherein the microscope adopts a 10X eyepiece +40X objective lens for observation and image shooting.
4. The Matlab image analysis and self-optimization-based sludge morphology rapid characterization method according to claim 1, wherein the parameter Pa1 is in the range of 4-6 during optimization.
5. The Matlab image analysis and self-optimization-based sludge morphology rapid characterization method according to claim 1, wherein the parameter Pa2 is in the range of 0.7-1.3 during optimization.
6. The Matlab image analysis and self-optimization-based sludge morphology rapid characterization method according to claim 1, wherein the parameter PaM is optimized within a range of 20-100.
7. The Matlab image analysis and self-optimization-based sludge morphology rapid characterization method according to claim 1, wherein the parameter Pan is optimized within a range of 20-100.
8. The Matlab image analysis and self-optimization-based sludge morphology rapid characterization method of claim 1, wherein in the process of traversing and optimizing the parameters, the traversing step is 1/10-1/100 of a parameter value interval.
9. The Matlab image analysis and self-optimization-based sludge morphology rapid characterization method according to claim 1, wherein the morphology feature data comprises sludge floc number, contour perimeter, equivalent diameter, roundness, major-minor axis ratio, and fractal dimension.
10. The Matlab image analysis and self-optimization-based sludge morphology rapid characterization method of claim 1, wherein the graying processing of the image adopts rgb2gray () function, the median filtering adopts filter2() function, and the determination method of the initial threshold thresh adopts graythresh () function.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112767362A (en) * | 2021-01-22 | 2021-05-07 | 沈阳化工大学 | Sludge bulking prediction method based on activated sludge phase difference microscopic image |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1793839A (en) * | 2005-12-12 | 2006-06-28 | 苏州科技学院 | Automatic monitoring device of active sludge multiparameter |
| US20100225715A1 (en) * | 2009-03-04 | 2010-09-09 | Hewlett-Packard Development Company, L.P. | Automatic cleaning in a liquid ink printing system |
| CN106442526A (en) * | 2016-08-29 | 2017-02-22 | 青岛理工大学 | Activated sludge floc analysis method based on MATLAB |
-
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1793839A (en) * | 2005-12-12 | 2006-06-28 | 苏州科技学院 | Automatic monitoring device of active sludge multiparameter |
| US20100225715A1 (en) * | 2009-03-04 | 2010-09-09 | Hewlett-Packard Development Company, L.P. | Automatic cleaning in a liquid ink printing system |
| CN106442526A (en) * | 2016-08-29 | 2017-02-22 | 青岛理工大学 | Activated sludge floc analysis method based on MATLAB |
Non-Patent Citations (1)
| Title |
|---|
| MUHAMMAD BURHAN KHAN等: "《Illumination Compensated Segmentation of Microscopic Images of Activated Sludge Flocs》", 《 2015 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112767362A (en) * | 2021-01-22 | 2021-05-07 | 沈阳化工大学 | Sludge bulking prediction method based on activated sludge phase difference microscopic image |
| CN112767362B (en) * | 2021-01-22 | 2024-07-02 | 沈阳化工大学 | Activated sludge phase contrast microscopic image-based sludge expansion prediction method |
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