CN114636736A - Electronic tongue white spirit detection method based on AIF-1DCNN - Google Patents
Electronic tongue white spirit detection method based on AIF-1DCNN Download PDFInfo
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Abstract
The invention discloses an electronic tongue white spirit detection method based on AIF-1 DCNN. The method comprises the following steps: utilizing an integrated electrode to obtain an original response data matrix X of the white spirit to carry out pretreatment to form a sample set E; extracting attention characteristics of the electrode with convolution kernel size of n to obtain a length k vector
Extracting attention characteristics of a square wave with a convolution kernel size of u from the sample set E to obtain a vector psi with the length of w; will vector
(Vector)Carrying out weighted fusion on the Ψ to obtain a vector Ψ, and fusing the Ψ to the original input to obtain λ; and training an AIF-1DCNN neural network for input lambda by adopting a self-adaptive momentum random optimization method, and performing classification detection on the test set by utilizing the trained AIF-1DCNN neural network model. The method solves the technical problem of low accuracy rate caused by neglecting the influence of each electrode and different square waves due to the fact that the existing deep learning algorithm only uses full-time domain information measured by the electronic tongue, and improves the efficiency and the accuracy rate of the annual analysis of the white spirit.
Description
Technical Field
The invention relates to the field of electronic tongues, in particular to an electronic tongue white spirit detection method based on AIF-1 DCNN.
Background
The electronic tongue has great potential in the field of food detection as a modern intelligent sensing instrument which is real-time, accurate, efficient, non-invasive and portable. Pattern recognition is a key part of electronic tongue systems, and common pattern recognition techniques can be divided into two major categories: machine learning and deep learning. The machine learning needs to manually extract the features, the process is complex and time-consuming, the full-time domain information measured by the electronic tongue is rarely used, the accuracy rate depends on the quality of the early-stage feature extraction, and a good classification effect cannot be obtained; in the deep learning field, a Convolutional Neural Network (CNN) has a powerful function in selecting and extracting features of high-dimensional data, and the existing deep learning algorithm applied to electronic tongue recognition only performs simple convolutional feature extraction analysis on the whole sample data and ignores the deep influence of each electrode and different pulse square wave voltages on the data, so that the efficiency and the accuracy rate need to be improved to overcome the existing defects.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an electronic tongue white spirit detection method based on AIF-1DCNN, which utilizes an integrated electrode in an electronic tongue to collect white spirit sample information and combines the AIF-1DCNN detection method to analyze data, thereby achieving the purpose of detecting the white spirit sample and improving the white spirit component analysis efficiency and accuracy.
In order to solve the problems of the prior art, the invention adopts the technical scheme that:
an electronic tongue white spirit detection method based on AIF-1DCNN comprises the following steps:
s1: preprocessing an original response data matrix X of the white spirit obtained by utilizing the integrated electrode to form a sample set E
Transversely splicing single data points of white spirit measured by 6 electrodes of the integrated electrode, vertically splicing the single data points according to the measurement times to form an original response data matrix X, and obtaining the original response data matrix X shown in formula (1)
Wherein, [ x ]i,1,xi,2,…,xi,j]Transversely splicing single data points of the white spirit measured by the plurality of electrodes to form sample data, wherein i is the number of the plurality of electrode measurement samples, and j is the number of sampling points;
further, the original response data matrix X is subjected to zero-averaging processing by using the following formula (2):
e is normalized data, XmaxMaximum value, X, for each column of the raw response data matrixminIs the minimum value of each column of the original response data matrix, and X is the original response data matrix.
S2: extracting attention characteristics of the electrode with convolution kernel size of n to obtain a length k vector by using a sample set E
Firstly, k convolution kernels with the kernel size of n are used for carrying out convolution on an original signal to obtain a characteristic F, and further, the F is subjected to global average pooling to obtain the FGPThen two 1-by-1 convolution activation extractions are carried out to obtain the attention characteristic with the length of k electrode channels
Attention is drawn as followsFormulas (3), (4), (5)
i, j represents the dimension of the input signal E, E ∈ E, Ei,jRepresents the number in e at row i and column j,
representing the number of the ith row and jth column of the l convolution kernel, blOffset representing the l-th convolution kernel
Where H, W is the dimension of the feature U, FcIs the number of the characteristic F channels, i, j is the value of the c channel with the position (i, j)
Where f (-) is the sigmoid activation function.
S3: extracting the attention feature of the square wave with the convolution kernel size of u from the sample set E to obtain a vector psi with the length of w
The original signal is convolved by w convolution kernels with the kernel size u, then global average pooling and two 1 × 1 convolution activations are carried out to obtain the attention feature psi of the pulse square wave channel with the length w, the operation principle is the same as that of step S2,
wherein the relationship among k, n, w, u satisfies the formulas (6), (7)
w＝k×v (6)
k×n＝w×u (7)
Where v is the number of pulses per square wave for a single electrode, k is the number of electrodes, n is the length of data acquired per electrode, and u is the length of data acquired by a single electrode over the duration of a single square wave.
S4: will vector
Weighting and fusing the vector Ψ to obtain a vector Ψ
Further will be
Ψ A weighted fusion to yield T, taking into account
The pulse square waves corresponding to each electrode in psi are firstly aligned
Carrying out dimension transformation on phi to obtain phi and psi, wherein the dimension of phi is (k,1), the dimension of psi is (k, v), as shown in formulas (8) and (9), further carrying out multiplication operation on phi and psi by using a broadcasting mechanism to obtain weighted fusion attention tau, and the dimension of phi is (k, v)
Τ＝Φ×Ψ (10)
S5: fusing original input with vector gamma to obtain lambda
Firstly, fusing an original input e by a weighted fusion attention T to obtain lambda. Firstly, carrying out dimension transformation on weighted fusion attention T and an input signal e, wherein the dimension of T is changed from (k, v) to (w,1) to obtain T', the dimension of the input signal e is changed from (1, kxn) to (w, u) to obtain e, further, the attention and the input signal are fused by utilizing a broadcasting mechanism to obtain Λ, as shown in the following formulas (11) and (12), finally, carrying out dimension transformation on Λ again to obtain λ, and the dimension of λ is (kxn, 1)
Λ＝Γ×e (12)
S6: training an AIF-1DCNN neural network for input lambda by adopting a self-adaptive momentum random optimization method, and performing classification detection on a test set by utilizing the trained AIF-1DCNN neural network model
Wherein, the 1DCNN convolution neural network module in the AIF-1DCNN comprises five convolution layers, wherein the convolution kernel uses 1-dimensional convolution kernel with the sizes of (1 × 32 × 16, 1 × 32 × 8, 1 × 64 × 4, 1 × 64 × 2); in the convolution process, padding is set as 'SAME', the stride is 1, a Relu activation function is selected to increase the nonlinearity of the network, wherein, Flatten is carried out after the first pooling layer is inverted in an AIF-1DCNN neural network model, finally, a final label is generated through a Softmax classifier, the model selects cross entropy as a loss function, an Adam self-adaptive momentum random optimization algorithm is used to ensure that the loss function is converged to the minimum overall situation rapidly, and finally, the trained AIF-1DCNN neural network model is used for carrying out classification detection on the test set.
Has the advantages that:
compared with the prior art, the electronic tongue white spirit detection method based on the E-CPAF-1DCNN adopts the integrated electrodes in the electronic tongue to collect white spirit sample information, and utilizes the AIF-1DCNN neural network algorithm to analyze white spirit samples in the upper computer, so that the technical problems that the manual characteristic extraction process of the machine learning algorithm is complex, time is consumed, little full-time domain information measured by the electronic tongue is used, and the accuracy of the simple 1DCNN network is low are solved.
Drawings
FIG. 1 is a flow chart of the electronic tongue white spirit detection method based on AIF-1DCNN of the present invention;
FIG. 2 is a diagram of an AIF-1 DCNN-based network model according to an embodiment;
FIG. 3 is a block diagram of an attention module of the AIF-1DCNN in an embodiment;
FIG. 4 is a diagram of the 1DCNN module of the AIF-1DCNN in an implementation;
fig. 5 is a result graph of classification accuracy and iteration number of the electronic tongue data of white spirit in different years in an embodiment, where (a) is a relation between loss of the training set and the iteration number, and (b) is a relation between accuracy of the training set and the iteration number.
Detailed Description
As shown in fig. 2 and 3, the electronic tongue white spirit detection method based on AIF-1DCNN of the present invention includes the following steps:
s1: utilizing an integrated electrode to obtain an original response data matrix X of the white spirit to carry out pretreatment to form a sample set E;
transversely splicing single data points of white spirit measured by 6 electrodes of the integrated electrode, and vertically splicing the single data points according to the measurement times to form an original response data matrix X, as shown in formula (1)
Wherein, [ x ]i,1,xi,2,…,xi,j]Transversely splicing single data points of the white spirit measured by a plurality of electrodes to form sample data, wherein i is the number of the measured samples of the plurality of electrodes, and j is the number of sampling points;
further, the original response data matrix X is subjected to zero-averaging processing by using the following formula (2):
e is normalized data, XmaxMaximum value, X, for each column of the raw response data matrixminIs the minimum value of each column of the original response data matrix, and X is the original response data matrix.
S2: extracting attention characteristics of the electrode with convolution kernel size of n to obtain a length k vector by using a sample set E
Firstly, k convolution kernels with the kernel size of n are used for carrying out convolution on an original signal to obtain a characteristic F, and further, the F is subjected to global average pooling to obtain the FGPThen two 1-x 1 convolution activation extractions are carried out to obtain the length k electricityPolar tunnel attention feature
Attention is drawn to the following formulae (3), (4), (5)
i, j represents the dimension of the input signal E, E ∈ E, Ei,jRepresents the number in e at row i and column j,
representing the number of the ith row and jth column of the l convolution kernel, blOffset representing the l-th convolution kernel
Where H, W is the dimension of the feature U, FcThe number of the characteristic F channels, i, j is the value of the c channel at which the position is (i, j).
Where f (-) is the sigmoid activation function.
S3: extracting the attention feature of the square wave with the convolution kernel size of u from the sample set E to obtain a vector psi with the length of w
And (4) convolving the original signal by w convolution kernels with the kernel size u, then performing global average pooling and two 1 × 1 convolution activations to obtain the attention feature psi of the pulse square wave channel with the length w, wherein the operation principle is the same as that of the step S2.
Wherein the relationship among k, n, w, u satisfies the formulas (6), (7)
w＝k×v (6)
k×n＝w×u (7)
Where v is the number of pulses per electrode, k is the number of electrodes, n is the length of data acquired per electrode, and u is the length of data acquired by a single electrode over a single square wave duration.
S4: will vector
Weighting and fusing the vector Ψ to obtain a vector Ψ
Further will be
Ψ is weighted and fused to obtain the Ψ. In view of
The pulse square waves corresponding to each electrode in psi are firstly aligned
Carrying out dimension transformation on phi to obtain phi and psi, wherein the dimension of phi is (k,1), the dimension of psi is (k, v), as shown in formulas (8) and (9), further carrying out multiplication operation on phi and psi by using a broadcasting mechanism to obtain a weighted fusion attention tau, and the dimension of phi is (k, v),
Τ＝Φ×Ψ (10)
s5: fusing original input with vector gamma to obtain lambda
Firstly, carrying out weighted fusion on original input e to obtain lambda. Firstly, carrying out dimension transformation on a weighted fusion attention T and an input signal e, wherein the dimension of the T is changed from (k, v) to (w,1) to obtain T', the dimension of the input signal e is changed from (1, k × n) to (w, u) to obtain e, and further, fusing attention and the input signal by using a broadcasting mechanism to obtain Λ, which is shown in the following formulas (11) and (12). Finally, dimension transformation is carried out on the lambda again to obtain lambda, and the lambda dimension is (k multiplied by n, 1).
Λ＝Γ×e (12)
S6: training an AIF-1DCNN neural network for input lambda by adopting a self-adaptive momentum random optimization method, and performing classification detection on a test set by utilizing the trained AIF-1DCNN neural network model
Wherein, the 1DCNN convolution neural network module in the AIF-1DCNN comprises five convolution layers, wherein the convolution kernel uses 1-dimensional convolution kernel with the sizes of (1 × 32 × 16, 1 × 32 × 8, 1 × 64 × 4, 1 × 64 × 2); in the convolution process, padding is set to be 'SAME', the stride is 1, a Relu activation function is selected to increase the nonlinearity of the network, wherein, Flatten is carried out after the first pooling layer is inverted in an AIF-1DCNN neural network model, finally, a final label is generated through a Softmax classifier, the model selects cross entropy as a loss function, and an Adam self-adaptive momentum random optimization algorithm is used to ensure that the loss function is rapidly converged to the global minimum. And finally, carrying out classification detection on the test set by using the trained AIF-1DCNN neural network model.
The detection method of the present invention is described below with a specific embodiment, the environment of the operation of the present embodiment is the implementation environment of the experiment, which is realized by writing on a DellT t792 computer, Windows10, intel to strong 20-core processor, 64G run memory, 2 × 11GRTX2080Ti, Pycharm2019, python3.7, scinit-leann 0.21.3, tensoflow2.1.0, and kerras 2.3.1.
In the step S6, an Adam gradient descent optimization algorithm (lr being 1e-3) is used in the training process, the size of Batch _ size is set to 32, and the size of Epoch is 100; the ratio of training set, validation set and test set was 6:2: 2.
The data used in the experiment are five kinds of liquor data of different years of blending measured by an electronic tongue, and the data quantity collected by an integrated electrode is shown in table 1:
TABLE 1
In the experiment, 0.6 × 500 samples are used for sample training, as shown in fig. 5, the AIF-1DCNN model gradually converges with the increase of Epoch, so that the AIF-1DCNN model after the Epoch ═ 100 training is taken as the final model for completing the training, then 0.2 sample of the total number of samples is taken as the test set, and the results of the experimental data in the training set of the structure of the AIF-1DCNN model after completing the training are shown in fig. 3.
In order to verify the superiority of the AIF-1DCNN model in performance compared to the simple one-dimensional convolutional neural network model, a comparison experiment was performed, and table 3 shows the comparison result of the classification result of the AIF-1DCNN model compared to the conventional machine learning model.
TABLE 3AIF-1DCNN model compares the accuracy of a simple one-dimensional convolutional neural network
The table shows that the average accuracy of the AIF-1DCNN model is 96.8%, and the maximum difference of the accuracy is 1%; the average accuracy of the simple 1DCNN model is 93.4%, and the maximum difference of the accuracy is 6%.
The above description is only a few of the preferred embodiments of the present application and is not intended to limit the present application, which may be modified and varied by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (7)
1. An electronic tongue white spirit detection method based on AIF-1DCNN is characterized by comprising the following steps:
s1: utilizing an integrated electrode to obtain an original response data matrix X of the white spirit to carry out pretreatment to form a sample set E;
s2: extracting attention characteristics of the electrode with convolution kernel size of n to obtain a length k vector
S3: extracting attention features with convolution kernel size of u square waves from the sample set E to obtain a vector psi with length of w;
s4: will vector
Carrying out weighted fusion on the vector psi to obtain a vector T;
s5: fusing the vector T to the original input to obtain lambda;
s6: training an AIF-1DCNN neural network for input lambda by adopting a self-adaptive momentum random optimization method, and carrying out classification detection on a test set by utilizing a trained AIF-1DCNN neural network model;
and finally, generating a final label through a Softmax classifier, wherein the model selects cross entropy as a loss function, and ensures that the loss function is rapidly converged to the global minimum by using an Adam gradient descent optimization algorithm.
2. The method for detecting electronic tongue white spirit based on AIF-1DCNN as claimed in claim 1, wherein step S1 specifically comprises: transversely splicing single data points of white spirit measured by 6 electrodes of the integrated electrode, and vertically splicing the single data points according to the measurement times to form an original response data matrix X, as shown in formula (1)
Wherein, [ x ]1，1，x1，2，...，x1，j]Transversely splicing single data points of the white spirit measured by the plurality of electrodes to form sample data, wherein i is the number of the measured samples of the plurality of electrodes, and j is the number of sampling points;
further, the original response data matrix X is subjected to zero-averaging processing by using the following formula (2):
e is normalized data, XmaxMaximum value, X, for each column of the raw response data matrixminIs the minimum value of each column of the original response data matrix, and X is the original response data matrix.
3. The method for detecting electronic tongue white spirit based on AIF-1DCNN as claimed in claim 1, wherein in step S2, k convolution kernels with kernel size n are first used to convolve original signals to obtain feature F, and further global average pooling of F is performed to obtain FGPThen two 1-by-1 convolution activation extractions are carried out to obtain the attention characteristic with the length of k electrode channels
Attention is drawn to the following formulae (3), (4), (5)
i, j represents the dimension of the input signal E, E ∈ E, Ei,jRepresents the number in e at row i and column j,
representing the number of the ith row and jth column of the l convolution kernel, blOffset representing the l-th convolution kernel
Where H, W is the dimension of the feature U, FcIs the number of the characteristic F channels, i, j is the value of the c channel with the position (i, j)
Where f (-) is the sigmoid activation function.
4. The method for detecting AIF-1 DCNN-based electronic tongue white spirit according to claim 1, wherein in step S3, the original signal is convolved with w convolution kernels with kernel size u, and then global average pooling and two 1 × 1 convolution activations are performed to obtain the attention feature Ψ of the pulse square wave channel with length w,
wherein the relationship among k, n, w, u satisfies the formulas (6), (7)
w＝k×v (6)
k×n＝w×u (7)
Where v is the number of pulses per electrode, k is the number of electrodes, n is the length of data acquired per electrode, and u is the length of data acquired by a single electrode over a single square wave duration.
5. The method for detecting electronic tongue white spirit based on AIF-1DCNN as claimed in claim 1, wherein step S4 is further performed
Ψ A weighted fusion to yield T, taking into account
The pulse square waves corresponding to each electrode in psi are firstly aligned
Carrying out dimension transformation on phi to obtain phi and psi, wherein the dimension size of phi is (k,1), the dimension size of psi is (k, v), as shown in formulas (8) and (9), further carrying out multiplication operation on phi and psi by using a broadcasting mechanism to obtain a weighted fusion attention tau, and the dimension size of tau is (k, v).
Τ＝Φ×Ψ (10)
6. The method for detecting electronic tongue white spirit based on AIF-1DCNN according to claim 1, wherein in step S5, a weighted fusion attention t is first fused to an original input e to obtain λ, the weighted fusion attention t and the input signal e are first dimension-transformed, the dimension of t is changed from (k, v) to (w,1) to obtain t', the dimension of the input signal e is changed from (1, kxn) to (w, u) to obtain e, the attention and the input signal are further fused by using a broadcasting mechanism to obtain Λ, as shown in the following formulas (11) and (12), finally, the Λ is dimension-transformed again to obtain λ, the dimension of λ is (kxn, 1),
Λ＝Γ×e (12)。
7. the method according to claim 1, wherein in step S6, the 1DCNN convolutional neural network module in the AIF-1DCNN includes five convolutional layers, wherein the convolutional kernel uses 1-dimensional convolutional kernels having sizes of 1 × 32 × 16, 1 × 32 × 8, 1 × 64 × 4, 1 × 64 × 2; in the convolution process, padding is set as 'SAME', the stride is 1, a Relu activation function is selected to increase the nonlinearity of the network, wherein, Flatten is carried out after the first pooling layer is inverted in an AIF-1DCNN neural network model, finally, a final label is generated through a Softmax classifier, the model selects cross entropy as a loss function, an Adam self-adaptive momentum random optimization algorithm is used to ensure that the loss function is converged to the minimum overall situation rapidly, and finally, the trained AIF-1DCNN neural network model is used for carrying out classification detection on the test set.
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| CN113313150A (en) * | 2021-05-17 | 2021-08-27 | 南京益得冠电子科技有限公司 | Electronic tongue detection method and system based on PCA and random forest |
| CN113537278A (en) * | 2021-05-17 | 2021-10-22 | 南京益得冠电子科技有限公司 | Wine detection method and system based on 1D-CNN electronic tongue |
| CN113486981A (en) * | 2021-07-30 | 2021-10-08 | 西安电子科技大学 | RGB image classification method based on multi-scale feature attention fusion network |
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