A method for calculating the water absorption rate of superabsorbent polymers based on an image classification algorithm

Abstract

The present invention provides a method and system for calculating the water absorption rate of a water absorbent material from video data obtained by capturing a video of a specific water absorbent material absorbing water. According to the present invention, the water absorption video data is separated into frame image data, and an artificial neural network model is used to determine whether each frame image data is an image of water absorption in progress or an image of water already absorbed, and the water absorption start and end times are detected to calculate the water absorption rate of the water absorbent material.

Description

[Technical Field]

[0001] The present invention relates to a method for calculating the water absorption rate of a highly water-absorbent material. [Background technology]

[0002] Conventionally, the water absorption rate of a water absorbent material has been calculated, and the material has been used for applications that match the water absorption rate. Conventionally, to calculate such water absorption rate, a method has been used in which a sample of the water absorbent material is placed in the place where it is to be used, and then the water absorption time is measured manually using a timer.

[0003] However, this method is inefficient because it forces researchers to measure the water absorption time manually one by one.

[0004] Patent Document 1 discloses a method for measuring the change in mass of a water-absorbent material to roughly determine the time required for the amount of liquid absorption of a sample to reach its maximum amount of water absorption. However, this method has the problem that it can only be applied once the maximum amount of water absorption and the permeation rate coefficient of the sample are known.

[0005] The relevant prior art includes the following documents: [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Publication No. 2020-051997 Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention aims to provide a method and system for calculating the degree of water absorption of a water-absorbent material from an image of each frame of water-absorbent video data, which is different from conventional methods. [Means for solving the problem]

[0008] The present invention provides a method for generating an artificial neural network model for calculating the water absorption rate of a water-absorbent material, the method comprising: a training data preparation step including image data of a predetermined water-absorbent material and classification data labeled to distinguish whether the image is an image of the material before absorbing water or an image that has already absorbed water; a construction step of an artificial neural network that receives the predetermined image data and classifies the input image with a predetermined label; and a machine learning step that trains the artificial neural network by inputting the training data into the artificial neural network and updating parameters that constitute the artificial neural network.

[0009] In this case, the image data of the water-absorbent material is acquired by a video data acquisition step of acquiring water-absorption video data for a large number of water-absorbent materials, and a frame image data acquisition step of acquiring each frame image data constituting the video data. The water-absorption video data is characterized in that a predetermined amount of water is put into a container and stirred, while a predetermined amount of the water-absorbent material is added, and the process of the water-absorbent material absorbing water and solidifying is captured.

[0010] The present invention also provides a method for calculating the water absorption rate of a water-absorbent material, comprising: a water-absorbent material image data acquisition step of adding a water-absorbent material and acquiring image data of the water-absorbent material over a predetermined period of time; a frame image data acquisition step of separating the image data into frame image data to acquire frame image data; a frame image classification step of inputting the frame image data into an artificial neural network model to classify the frame images; and a water absorption rate calculation step of extracting water-absorption end frames and water-absorption start frames from the classified frame images and calculating the water absorption rate using these.

[0011] In this case, the water absorption rate in the water absorption rate calculation step may be calculated by the following Equation 1. [Formula 1] Water absorption speed = (water absorption end frame number - water absorption start frame number) / fps of video data

[0012] Furthermore, the present invention provides a water absorption rate calculation system for water absorbent materials, comprising: a video data acquisition unit that acquires water absorption video data of the water absorbent material; a frame image data acquisition unit that separates the water absorption video data into frame image data that make up the video data and acquires the frame image data; and a water absorption rate calculation unit that calculates the water absorption start and end points from the frame image data to calculate the water absorption rate of the water absorbent material.

[0013] The present invention further comprises a frame image discrimination unit that inputs each frame image data acquired by the frame image data acquisition unit in frame order and distinguishes whether it is an image during absorption, in which the absorbent material is absorbing water, or an image that has completed absorbing water, and is characterized in that the water absorption rate calculation unit calculates the water absorption start time from the frame number (start frame number) of the image that the frame image discrimination unit first discriminated as an image during absorption, and calculates the water absorption end time from the frame number (end frame number) of the image that the frame image discrimination unit first discriminated as an image that has completed absorbing water.

[0014] The frame image discrimination unit includes an artificial neural network unit that is pre-trained to receive input frame image data, distinguish it into an image in water absorption or an image that has already absorbed, and output the discrimination result. The artificial neural network unit receives input sample frame image data obtained by separating water absorption sample video data of a large number of sample absorbent materials into each sample frame image data, and data in which each sample frame image data is labeled as in water absorption or in the water absorption state, as training data, and is trained to match the labeled data with each sample frame image data. [Effects of the Invention]

[0015] According to the present invention, compared to the conventional method of manually measuring the water absorption rate of absorbent materials using a timer, by taking images of water absorption for absorbent materials with a wide range of water absorption rates, it is possible to apply an image classification algorithm and measure the water absorption rate more accurately.

[0016] The following drawings attached to this specification illustrate preferred embodiments of the present invention and serve to further understand the technical concepts of the present invention as well as the content of the invention, and therefore the present invention should not be interpreted as being limited to only the matters described in the drawings. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a diagram showing an outline of a system for calculating the water absorption rate of a water-absorbent material according to the present invention. [Figure 2] 10A to 10C are diagrams showing examples of frame-by-frame images of a water absorption video according to an embodiment of the present invention. [Figure 3] FIG. 10 is a diagram showing actual frame images of video data captured to prepare learning data. [Figure 4] FIG. 10 is a diagram showing the calculation output of the score per frame when video data is input to a prediction model trained according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] The present invention relates to a method and system for calculating the water absorption rate of a superabsorbent polymer (SAP) using an image classification algorithm. A conventional convolutional neural network (CNN) can be used as the image classification algorithm used in the present invention, and other image classification models that were publicly known before the filing date of the present invention can also be applied. It should be noted that the technical feature of the present invention is not the image classification model itself, but the use of an image classification model to calculate the water absorption rate from a water absorption video of a superabsorbent polymer.

[0019] 1. System for calculating the water absorption rate of water-absorbent material (superabsorbent polymer) according to the present invention

[0020] 1.1. Water absorption video data acquisition unit 10

[0021] The present invention includes a video data acquisition unit that acquires video data of the water absorption of the water absorbent material. The video data acquisition unit performs the video data acquisition step in the method for calculating the water absorption rate of the water absorbent material according to the present invention.

[0022] The water absorption video data of a water absorbent material refers to video data captured at a predetermined frame rate (fps) (number of frames per second) of the process of a predetermined water absorbent material absorbing water under predetermined conditions. The video data acquisition unit is a component that receives the video data and may be equipped with a normal video camera.

[0023] <Example of acquiring video data of water absorption of water-absorbing material>

[0024] An example of acquiring video data of water absorption by a water absorbent material is shown below. In the present invention, a 100 mL beaker was filled with 50 mL of 0.9 wt% salt water, and then magnetic stirring was performed at 600 rpm. While the salt water was rotating, 2 g of a highly water-absorbent resin (water absorbent material) was added, and the entire process of the salt water absorbing and solidifying was filmed to acquire video data. At this time, the video was filmed with a video camera so that the angle of the surface inside the beaker in the video was 30° to 60°.

[0025] 1.2. Frame image data acquisition unit 20

[0026] The frame image data acquisition unit is a component that separates the image data (water-absorbed image data) acquired by the image data acquisition unit or the input image data (water-absorbed image data) into image data on a frame-by-frame basis. It is well known to ordinary engineers that the task of separating predetermined image data into frame image data can be easily achieved using ordinary image processing software or image processing algorithms.

[0027] The frame image data acquisition unit performs a frame image data acquisition procedure in the method for generating an artificial neural network model and the method for calculating the water absorption rate of a water-absorbent material of the present invention.

[0028] 1.3. Water absorption rate calculation section 30

[0029] The water absorption rate calculation unit comprises an artificial neural network model 31 that receives the acquired frame image data, classifies the frame images, and outputs their scores; a water absorption progress determination unit 32 that calculates the water absorption start and end times from the output of the artificial neural network model; and a water absorption rate calculation unit 33 that calculates the water absorption rate from the water absorption start and end times.

[0030] (1) Image classification: Artificial neural network model 31

[0031] The present invention includes a frame image discrimination unit that inputs each frame image data acquired by the frame image data acquisition unit in frame order and distinguishes whether it is an image during absorption, in which the absorbent material is absorbing water, or an image that has already absorbed water, in which the absorption of water has been completed.

[0032] The image discrimination unit 31 may be configured with an artificial neural network model, and the artificial neural network model of the present invention is an example of the image classification algorithm used to calculate the water absorption rate of the superabsorbent polymer in the present invention described above. As the artificial neural network model, a normal convolutional neural network (CNN) can be used, and other image classification models that have been publicly known before the filing date of the present invention can also be applied.

[0033] The artificial neural network constituting the artificial neural network model of the present invention is machine-learned according to the learning procedure described below, and receives as input each frame image data constituting the water absorption video data of the water-absorbent material described above, distinguishes whether the image is an image in the process of absorbing water or an image that has already absorbed water, and outputs a score representing the probability that the image is an image in the process of absorbing water or the probability that the image has already absorbed water. The score output for each frame image is a value between 0 and 1. Figure 4 shows an example of the score for each frame image output by the artificial neural network model of the present invention trained according to the learning procedure described below.

[0034] (2) Artificial neural network training

[0035] The artificial neural network model 31 of the present invention comprises an artificial neural network that is machine-learned according to the following procedure: As the artificial neural network, a CNN is basically used.

[0036] First, as training data, a 100 mL beaker was filled with 50 mL of 0.9 wt% saltwater, and then magnetic stirring was performed at 600 rpm. While the saltwater was rotating, 2 g of superabsorbent resin (water-absorbing material) was added, and the entire process of the saltwater absorbing and solidifying was filmed to obtain video data. The video was filmed with a video camera so that the surface of the beaker was at an angle of 30° to 60° within the video.

[0037] Next, the video data was separated into frame image data to obtain the frame image data.

[0038] Such video data and frame image data for learning data can be acquired using the video data acquisition unit 10 and frame image data acquisition unit 20 described above.

[0039] Next, the frame image data is merged with labeling data that labels the frame image as either an image of water absorption or an image of water already absorbed. The labeling data can also be handled independently for each frame number without being merged with the frame image data.

[0040] Figure 2 shows an example of how each video data set is separated into frame image data and labeled for preparing learning data.

[0041] Next, an artificial neural network is configured to receive image data and classify the input image as a predetermined label. The artificial neural network may have a conventional CNN structure, and the data structure of a CNN as a data processing structure realized in a computer is well known to those skilled in the art.

[0042] Next, the above-mentioned training data is input into the artificial neural network configured as described above to proceed with machine learning, and the training of the artificial neural network is completed. In this machine learning step, the parameters of each layer constituting the artificial neural network are updated, and the artificial neural network model 31 of the present invention equipped with the trained artificial neural network receives frame image data obtained from any water-absorbent material video data and outputs a score, which is a discrimination value for whether the image corresponds to an image of the image before or after water absorption.

[0043] Examples of score output values ​​for the water absorption video data and frame image data of specified water absorbent materials A (sample 10) and B (sample 16) are shown in Figure 4. The closer the score is to 0, the more water absorption is progressing, and the closer the score is to 1, the more water absorption is complete.

[0044] (3) Water absorption progress judgment section 32

[0045] The water absorption progress determination unit 32 calculates the water absorption start time and water absorption end time based on the score of each frame image data output by the artificial neural network model 31.

[0046] In the example in Figure 4, the frame where the score stabilizes near 0 is selected as the frame where water absorption begins, and the frame where the score rises to 0.5 or higher is selected as the frame where water absorption ends.

[0047] (4) Water absorption rate calculation section 33

[0048] The water absorption rate calculation unit 30 includes a water absorption rate calculation unit 33 that calculates the water absorption rate from the water absorption start time and water absorption end time. The water absorption rate calculation unit 33 calculates the water absorption rate from the calculated water absorption start time and water absorption end time according to the following equation 2.

[0049] [Formula 2] Water absorption rate = water absorption end point - water absorption start point

[0050] The water absorption rate can also be calculated using the following formula 1.

[0051] [Formula 1] Water absorption speed = (water absorption end frame number - water absorption start frame number) / fps of video data

[0052] 2. Method for calculating the water absorption rate of the water-absorbent material (superabsorbent polymer) according to the present invention

[0053] The method for calculating the water absorption rate of a water-absorbent material according to the present invention comprises the following steps.

[0054] First, as explained in the above-mentioned embodiment section, the method includes a water-absorbent material image data acquisition step of putting a predetermined water-absorbent material into water and acquiring image data of the water-absorbent material for a predetermined period of time.

[0055] Next, the method includes a frame image data acquisition step of separating the video data into frame image data to acquire the frame image data, and a frame image classification step of inputting the acquired frame image data into an artificial neural network model that has been machine-learned using the above-mentioned method to classify the frame images.

[0056] The method also includes a water absorption rate calculation step of extracting the water absorption end frame and the water absorption start frame from the classified frame images and calculating the water absorption rate using these. The water absorption rate can be calculated using the above-mentioned formula 1 or formula 2.

[0057] Example 1

[0058] The method for measuring the water absorption rate of Samples 1 to 16 according to the water absorption rate calculation method of the present invention is as follows.

[0059] (i) Training of neural network models

[0060] A 100 ml beaker was filled with 50 ml of 0.9 wt % salt water, and then magnetic stirring was performed at 600 rpm. While the water was rotating, 2 g of a superabsorbent polymer sample was added, and video data was obtained of the entire process of the polymer absorbing water and solidifying. Video data was obtained for 16 types of superabsorbent polymer samples.

[0061] Next, the frames of the video data were separated to generate frame images, and the generated frame images were classified into two types, pre-freezing and post-freezing, and then labeled to obtain training data.

[0062] Next, a neural network model was trained using the labeled training data by applying CNN as an image classification algorithm.

[0063] (ii) Calculation of the water absorption rate for the sample (water-absorbing material)

[0064] For each of the superabsorbent resin samples 1 to 16, a 100 ml beaker was filled with 50 ml of 0.9 wt% salt water, and then magnetic stirring was performed at 600 rpm. While the water was rotating, 2 g of each of the samples 1 to 16 was added, and video data was obtained showing the entire process of the sample absorbing water and solidifying, which was then separated into frame images.

[0065] The frame images of each sample were sequentially input into the trained neural network model, and the frame numbers of the images showing the start and end of water absorption were extracted. The water absorption rate for each sample was calculated by performing a calculation according to Equation 2.

[0066] The calculated water absorption rates for Samples 1 to 16 are summarized in Table 1 below.

[0067] (Comparative Example 1)

[0068] In the comparative examples, similar to the above examples, 50 mL of 0.9 wt % salt water was filled into each 100 mL beaker for each of the superabsorbent resin samples 1 to 16, and then magnetic stirring was performed at 600 rpm. While the water was rotating, 2 g of each of the samples 1 to 16 was added and stirred.

[0069] However, in the comparative examples, the researcher personally measured the time using a timer during the process of water absorption and solidification. The timer was started when Samples 1 to 16 were placed in a beaker that was being stirred, and the timer was stopped when the water had been absorbed and completely solidified and no further stirring was performed. The water absorption rate was measured based on the time measured by the timer.

[0070] The difference between the measured values ​​of the water absorption rate according to the Examples and the Comparative Examples is shown in Table 1. The unit of the measured values ​​for each Example and Comparative Example is seconds.

[0071] [Table 1]

[0072] As is clear from the above examples and comparative examples, the water absorption rate measured according to the present invention shows an average difference of 0.38 seconds when compared with the conventional method of measuring the water absorption rate of a water-absorbent material manually using a timer. Considering that the conventional method is likely to have operational errors depending on the researcher, it is recognized that the method according to the present invention sufficiently guarantees accuracy.

[0073] Furthermore, the method of the present invention has the effect of automating the calculation process of the water absorption rate of the water-absorbent material, and has the effect of accurately calculating the water absorption rate for various samples using a standardized method.

[0074] (Example 2 / Comparative Example 2)

[0075] In Example 2, Sample 1 in Example 1 above was measured 10 times using the method according to the present invention and the conventional manual measurement method.

[0076] The table below shows the results of measurements made using each method.

[0077] [Table 2]

[0078] As is clear from Table 2 above, the method according to the present invention has a much smaller difference between the maximum and minimum values ​​than the conventional method, and the standard deviation is also smaller, so it can be seen that the method according to the present invention is even superior in terms of repeatability accuracy. [Explanation of symbols]

[0079] The reference numerals and names of the components used in the drawings and description of the present invention are as follows: 10...Water absorption video data acquisition unit 20...Frame image data acquisition unit 30…Water absorption rate calculation section 31...Image classification unit (artificial neural network model) 32…Water absorption progress judgment unit 33...Water absorption rate calculation section

Claims

1. a step of preparing learning data including image data of a predetermined water-absorbing material and classification data in which the image is labeled according to whether it is an image in a state where water has been absorbed or an image that has already absorbed water; A step of constructing an artificial neural network that receives predetermined image data and classifies the input image as a predetermined label; a machine learning step of training the artificial neural network by inputting the training data into the artificial neural network and updating parameters constituting the artificial neural network; comprising The image data of the water absorbent material is An image data acquisition step of acquiring water absorption image data for a large number of water absorbent materials; a frame image data acquisition step of acquiring each frame image data constituting the video data; The frame image data is acquired using The water absorption image data is A method for generating an artificial neural network model, in which a predetermined amount of water is poured into a container and stirred while a predetermined amount of the water-absorbing material is added, and the process of the material absorbing water and solidifying is captured as video data.

2. a water-absorbent material image data acquisition step of adding a water-absorbent material and acquiring image data of the water-absorbent material for a predetermined time; a frame image data acquisition step of acquiring frame image data by separating the video data into frame image data; a frame image classification step of inputting the frame image data into an artificial neural network model to classify the frame images; a water absorption rate calculation step of extracting a water absorption end frame and a water absorption start frame from the classified frame images and calculating a water absorption rate using these frames; A method for calculating the water absorption rate of a water-absorbent material, comprising:

3. The artificial neural network model comprises:

3. The method for calculating the water absorption rate of a water-absorbent material according to claim 2, wherein the artificial neural network model is generated according to the method for generating an artificial neural network model according to claim 1.

4. 3. The method for calculating the water absorption rate of a water-absorbent material according to claim 2, wherein the water absorption rate in the water absorption rate calculation step is calculated by the following mathematical formula 1: [Formula 1] Water absorption speed = (water absorption end frame number - water absorption start frame number) / fps of video data

5. In a system for calculating the water absorption rate of a water-absorbent material, a video data acquisition unit for acquiring video data of the water-absorbing material; a frame image data acquisition unit that separates the water absorption video data into frame image data that constitute the video data and acquires the frame image data; a water absorption rate calculation unit that calculates the water absorption rate of the water absorbent material by calculating the water absorption start time and the water absorption end time from the frame image data; A water absorption rate calculation system for a water absorbent material, comprising:

6. a frame image discrimination unit that discriminates whether each frame image data acquired by the frame image data acquisition unit is an image during absorption, in which the image data is input in frame order, or an image after absorption, in which the image data has been completely absorbed; The water absorption rate calculation unit The water absorption rate calculation system for absorbent material described in claim 5, wherein the frame image discrimination unit calculates the start time of water absorption from the frame number (start frame number) of the image that was first discriminated as an image in the water absorption state, and calculates the end time of water absorption from the frame number (end frame number) of the image that was first discriminated as an image that had already absorbed water.

7. The water absorption rate calculation system for a water-absorbent material according to claim 5 , wherein the water absorption rate is calculated using the following formula 1: [Formula 1] Water absorption speed = (water absorption end frame number - water absorption start frame number) / fps of video data

8. The frame image determination unit The water absorption rate calculation system for absorbent materials as described in claim 6, further comprising an artificial neural network section that is pre-trained to receive input frame image data, distinguish it into an image in the middle of absorbing water or an image that has already absorbed water, and output the discrimination result.

9. The artificial neural network unit A water absorption rate calculation system for absorbent materials as described in claim 8, in which sample frame image data obtained by separating water absorption sample video data of a large number of sample absorbent materials into each sample frame image data and data labeled for each sample frame image data as being in an absorbed state or having absorbed water are input as learning data, and the system is trained to match the labeled data with each sample frame image data.

Images