CN112597923A - Pulse pile-up correction method based on morphology and optimized gray model - Google Patents
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Abstract
The invention discloses a pulse pile-up correction method based on morphology and an optimized gray model, which comprises the steps of preprocessing pile-up pulse data; and then combining mathematical morphology with a gray model, respectively correcting the rising section and the falling section of the pulse, and integrating the pulse into a complete pulse signal to realize the correction of the whole pulse signal accumulation. And according to the defects of the traditional gray model, the hyperbolic tangent transformation function is skillfully utilized to optimize the gray model. The invention utilizes the mathematical morphology transformation and the optimized gray model to respectively correct the ascending section and the descending section of the piled pulse signal, accurately discriminates single pulse pile-up and reconstructs double-peak pulse pile-up signals by combining a mathematical prediction model from the aspect of image processing, improves the nuclear signal analysis and processing capacity, and provides guarantee for the safety application fields of nuclear technology and the like.
Description
Technical Field
The invention relates to the technical field of nuclear signal processing, in particular to a pulse pile-up correction method based on morphology and an optimized gray model.
Background
During the acquisition of nuclear information, if the detector does not react completely to the previous pulse, the next pulse is reached, which may result in partial or even complete coincidence of adjacent pulse signals, which is called pulse pile-up. Under the conditions of high counting rate, high noise and the like, pulse accumulation is particularly easy to occur, and the pulse accumulation can bring a series of negative influences to research work, and the pulse accumulation is specifically embodied in the aspects of pulse waveform deformation, loss of counting rate, prolongation of dead time, further influence on time resolution, space resolution and the like. Pulse accumulation is a key problem existing in a nuclear radiation measurement system and an energy spectrum measurement system, and the occurrence of the pulse accumulation can greatly inhibit the neutron gamma discrimination effect in the neutron detection process. In the past research, the negative influence caused by pulse pile-up is inhibited by directly removing the generated pile-up signals, and in addition, the correction error caused by data missing is large due to the direct method, so that the difficulty is increased for subsequent analysis. Therefore, developing a neutron-gamma pulse accumulation correction algorithm has important significance in the aspects of energy spectrum analysis, neutron-gamma discrimination and the like.
Disclosure of Invention
In view of the above problems in the prior art, the present invention provides a pulse pile-up correction method based on morphology and an optimized gray model, which improves the nuclear signal analysis processing capability.
Morphological Transformations (Morphological Transformations) are simple shape-based Transformations that process binarized images with dilation and erosion operations to connect adjacent elements or separate elements, by which the edge contours of the image are well preserved.
The Grey Model (Grey Model) is a data prediction method for making prediction by establishing a mathematical Model through a small amount of incomplete information, can fully utilize the known information to find the motion law of the system, and is particularly used for processing poor data.
The inventor researches and discovers that for the existing system for detecting the nuclear signal information lost due to accumulation, the accumulation nuclear signal can be detected by combining the two methods, pulse accumulation can be well separated by performing morphological transformation processing on the generated pulse accumulation image, and the separated accumulation signal uses a gray model to predict the missing information in a single pulse signal, so that the separation and completion of the nuclear pulse accumulation signal are realized, the negative influence caused by data missing in the traditional method is reduced, and the detection capability of the nuclear pulse signal accumulation is improved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a pulse pile-up correction method based on morphology and an optimized gray model comprises the following steps:
s10, labeling the stacking pulse signals acquired by the nuclear signal detection device according to the height of the wave peak of the pulse stacking waveform to obtain a pulse sequence with a plurality of wave peaks, and combining every two continuous pulses to form a pulse type;
s20, sequentially preprocessing the stacked pulse data of each pulse type;
s30, in a pulse type, performing mathematical morphology transformation processing on piled-up pulse data based on peak point information of a higher peak and linear structural elements with different scales, determining a pulse waveform ascending section of the higher peak, and performing pile-up correction on the pulse waveform ascending section;
s40, predicting the initial sequence of the pulse waveform descending segment of the higher peak by using a gray model optimized based on hyperbolic tangent function transformation, fitting the prediction result, and finishing the accumulation correction processing of the pulse waveform descending segment;
s50, integrating the correction data of the rising segment and the falling segment of the pulse waveform of the higher wave crest to finish the pulse accumulation correction of the higher pulse waveform peak;
s51, repeating the steps S30-S50, completing the pulse pile-up correction of the lower pulse waveform peak in the pulse type, and completing the pulse pile-up correction of the pulse type;
and S52, repeating the steps S20-S51, and integrating to finish the pulse pile-up correction of the whole pulse sequence.
Specifically, in step S10, the pulse types are divided according to the peak heights of two consecutive pulses:
type 1, in two consecutive pulse waveform peaks, the front peak amplitude is higher than the back peak amplitude;
type 2, in two consecutive pulse waveform peaks, the back peak amplitude is higher than the front peak amplitude.
Specifically, in step S51, the correction data of the upper pulse waveform peak and the lower pulse waveform peak are integrated according to the pulse type, and the pulse pile-up correction for one pulse type is completed.
Specifically, in step S52, the correction data of all the pulse types are integrated according to the order of each pulse type in the pulse train, and the pulse pile-up correction of the entire pulse train is completed.
Specifically, in step S20, the accumulated pulse data is preprocessed by normalization and smoothing to reduce interference between the environmental background and the electronic noise.
Further, the step S30 adopts the following procedure:
s31, extracting peak point information of a higher peak in a pulse type according to the preprocessed accumulated pulse data, and determining the end point of the rising segment of the pulse waveform;
s32, constructing linear structural elements to perform mathematical morphology open operation transformation on the preprocessed accumulated pulse data to obtain variablesTransformed waveform data based on a single pulse waveform peak, wherein the size L of the constructed linear structuring elementSEBy the pre-processed data matrix size Ln/gDetermining: l isSE＝Ln/g-1；
S33, performing difference on two adjacent elements in the waveform data obtained by the conversion of the on operation to obtain a difference matrix, and selecting a pulse position corresponding to a maximum value point in the difference matrix as a starting point of a pulse waveform peak;
s34, determining the rising segment of the pulse waveform according to the end point of the rising segment of the pulse waveform obtained in the step S31 and the starting point of the peak of the pulse waveform obtained in the step S33;
and S35, assigning the waveform data after the on-operation transformation according to the position information of the pulse waveform ascending section to obtain a correction matrix of the pulse waveform ascending section, namely the waveform data after the pulse waveform ascending section is corrected.
The formula adopted by the correction matrix for obtaining the rising section of the pulse waveform is as follows:
Sub＝datao(i+1)-datao(i)
[Max1,Pos1]＝max{Sub}
sub is a difference matrix of two adjacent elements of the matrix after mathematical morphology open operation transformation, dataoFor the accumulated pulse data after the on operation, i is 1,2, …, Ln/g-1，Max1Is the maximum value, Pos, in the difference matrix1Is the position corresponding to the maximum value in the difference matrix, datarFor the correction matrix obtained after evaluation, j is 1,2, …, Ln/g。
Further, the step S40 adopts the following procedure:
s41, selecting data from a peak point to a half-peak height of a higher peak in the preprocessed accumulated pulse data as an initial sequence of a pulse waveform descending segment to perform hyperbolic tangent function transformation to obtain a new accumulated pulse data sequence;
s42, dividing the new stacked pulse data sequence into a training set and a testing set according to the proportion of 85% to 15%, and bringing the training set into a gray model based on hyperbolic tangent function transformation optimization to train to obtain a predicted value;
and S43, fitting the predicted value obtained by training to finish the accumulation correction processing of the pulse waveform descending segment.
More specifically, in step S41, the hyperbolic tangent function is transformed by the following formula:
Y(0)＝{y(0)(1),y(0)(2),…,y(0)(n)}
wherein the initial sequence is X(0)＝{x(0)(1),x(0)(2),…,x(0)(k)},k＝1,2,…,n，Y(0)To obtain a new piled-up pulse data sequence.
More specifically, in step S42, Y is selected(0)Performing first accumulation, solving a gray model whitening differential equation to obtain a gray model predicted value transformed by a hyperbolic tangent function as follows:
compared with the prior art, the invention has the following beneficial effects:
(1) the invention firstly preprocesses the pulse accumulation signal, completes the standardization and the smooth processing of the pulse accumulation signal, respectively corrects the ascending section and the descending section of the pulse signal by utilizing the mathematical morphology transformation and the gray model optimized based on the hyperbolic tangent function transformation, and re-fits the corrected ascending section and the corrected descending section into a complete pulse signal after the correction is completed, thereby realizing the correction of the whole pulse accumulation signal. The method can be used for well discriminating the accumulated pulse signals detected by the nuclear detection device, solves the key problem that the pulse accumulation in the nuclear radiation measurement system and the energy spectrum measurement system greatly inhibits the signal discrimination effect, improves the nuclear signal analysis and processing capacity, and provides guarantee for the safety application fields of nuclear technology and the like.
(2) The invention divides the accumulated pulse signals into two types of conditions, firstly corrects each pulse waveform peak signal respectively, and then integrates all the pulse signals, thereby better separating and processing the pulse signals and improving the signal analysis and processing efficiency.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Fig. 2 is a schematic overall flow chart of an embodiment of the present invention.
FIG. 3 is a flow chart illustrating a mathematical morphology transformation process performed in an embodiment of the present invention.
FIG. 4 is a flow diagram illustrating a gray model process optimized in an embodiment of the present invention.
FIG. 5 is a schematic diagram of the peak types before and after a pulse in an embodiment of the present invention.
FIG. 6 is a schematic diagram of front and back peak pulse correction in an embodiment of the present invention.
FIG. 7 is a diagram illustrating an integrated pulse signal according to an embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following figures and examples, which include, but are not limited to, the following examples.
Examples
As shown in fig. 1 to 4, the pulse pile-up correction method based on morphology and optimized gray model includes the following steps:
s10, labeling the stacking pulse signals acquired by the nuclear signal detection device according to the height of the wave peak of the pulse stacking waveform to obtain a pulse sequence with a plurality of wave peaks, and combining every two continuous pulses to form a pulse type; the pulse type is divided according to the peak height of two consecutive pulses:
type 1, in two consecutive pulse waveform peaks, the front peak amplitude is higher than the back peak amplitude, as shown in fig. 5 a;
type 2, in two consecutive pulse waveform peaks, the back peak amplitude is higher than the front peak amplitude, as shown in fig. 5 b;
and then pile-up correction is performed on each pulse and pulse type one by one.
S20, sequentially preprocessing the stacked pulse data of each pulse type; the accumulated pulse data is preprocessed by adopting standardization processing and smoothing processing, so that the interference of an environment background and the noise of an electronic system to a real signal is reduced.
S30, for type 1, performing mathematical morphology transformation processing on piled-up pulse data based on peak point information of a pre-peak and linear structural elements with different scales, determining a pulse waveform ascending segment of the pre-peak, and performing pile-up correction on the pulse waveform ascending segment:
and S31, extracting the peak point information of the front peak in the type 1 according to the preprocessed accumulated pulse data, and determining the end point of the rising segment of the pulse waveform.
S32, constructing linear structural elements to perform mathematical morphology open operation transformation on the preprocessed stacking pulse data to obtain transformed waveform data of the front peak, wherein the size L of the constructed linear structural elementsSEBy the pre-processed data matrix size Ln/gDetermining: l isSE＝Ln/g-1；
The mathematical morphology open operation transformation adopts the following formula:
wherein the formulas 1 and 2 are respectively corrosion operation and expansion operation in mathematical morphology transformation, datasFor smoothing individual pulses in the data, SERepresenting structural elements, L representing the original data length, LSERepresenting structural elementsThe length of the element is such that,
for the corrosion operation symbol, ^ is for the expansion operation symbol.
And S33, subtracting two adjacent elements in the waveform data obtained by the conversion of the on operation to obtain a difference matrix, and selecting the pulse position corresponding to the maximum value in the difference matrix as the starting point of the front peak, namely the starting point of the ascending section of the front peak.
And S34, determining the rising segment of the pulse waveform according to the end point of the rising segment of the pulse waveform obtained in the step S31 and the starting point of the rising segment of the pulse waveform obtained in the step S33.
And S35, assigning the waveform data after the on-operation transformation according to the position information of the pulse waveform ascending section to obtain a correction matrix of the pulse waveform ascending section, namely the waveform data after the pulse waveform ascending section is corrected.
The formula adopted by the correction matrix for obtaining the rising section of the pulse waveform is as follows:
Sub＝datao(i+1)-datao(i) (3)
[Max1,Pos1]＝max{Sub} (4)
sub is a difference matrix of two adjacent elements of the matrix after mathematical morphology open operation transformation, dataoFor the accumulated pulse data after the on operation, i is 1,2, …, Ln/g-1，Max1Is the maximum value, Pos, in the difference matrix1Is the position corresponding to the maximum value in the difference matrix, datarFor the correction matrix obtained after evaluation, j is 1,2, …, Ln/g。
In S30, for type 2, the correction processing is performed on the rising segment of the pulse waveform of the latter peak.
S40, predicting the initial sequence of the pulse waveform descending segment of the front peak by using a gray model optimized based on hyperbolic tangent function transformation, fitting the prediction result, and finishing the accumulation correction processing of the pulse waveform descending segment:
s41, selecting the data from the peak point to the half-peak height of the front peak in the preprocessed accumulated pulse data as the initial sequence of the pulse waveform descending segment to perform hyperbolic tangent function transformation:
Y(0)＝{y(0)(1),y(0)(2),…,y(0)(n)} (7)
wherein the initial sequence is X(0)＝{x(0)(1),x(0)(2),…,x(0)(k)},k＝1,2,…,n，
Obtaining a new piled-up pulse data sequence Y(0)。
S42, passing pair Y(0)Performing first accumulation, solving a gray model whitening differential equation to obtain a gray model predicted value transformed by a hyperbolic tangent function as follows:
the new piled-up pulse data sequence is divided into a training set and a test set according to a set proportion, in the embodiment, 85% of the sequence is used as the training set, the remaining 15% is used as the test set, and the division proportion standard can be determined according to the actual environment condition. And (3) bringing the training set into a gray model based on hyperbolic tangent function transformation optimization for training to predict data, namely substituting the training set into a formula 8 to solve an actual predicted value reversely to obtain a predicted value.
And S43, fitting the predicted value obtained by training, and finishing the accumulation correction processing of the pulse waveform descending segment of the front peak.
In S40, for type 2, the correction process is performed on the trailing pulse waveform falling segment.
S50, integrating the correction data of the rising segment and the falling segment of the front peak pulse waveform, fitting the correction data into a complete peak shape according to the position information, completing the pulse pile-up correction of the front peak, and obtaining the front peak pulse signal diagram as shown in fig. 6 a.
S51, repeating the steps S30-S50, completing the pulse pile-up correction of the post-peak in the type 1, and obtaining the post-peak pulse signal diagram as shown in FIG. 6 b. And then, fitting the segmented front peak and rear peak correction data into a complete pulse type according to the pulse type and the position information, and completing the pulse pile-up correction of the pulse type, as shown in fig. 7, wherein fig. 7a corresponds to the pulse pile-up correction result of the data of fig. 5a, and fig. 7b corresponds to the pulse pile-up correction result of the data of fig. 5 b.
And S52, repeating the steps S20-S51, and integrating the correction data of all the pulse types according to the sequence of each pulse type in the pulse sequence to finish the pulse pile-up correction of the whole pulse sequence.
In addition, the method can correct other piled signals besides the correction of the pulse pile-up of the nuclear signals, as long as the situation that the types 1 and 2 of the piled signals occur can be converted into images for processing through a morphological transformation mode, so that edge information is kept as much as possible, a morphological transformation processing signal rising section is introduced, an optimized gray model is used for processing a signal falling section, and finally the processed signals are integrated together according to corresponding positions, so that the screening of the piled signals can be completed.
The above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, but all changes that can be made by applying the principles of the present invention and performing non-inventive work on the basis of the principles shall fall within the scope of the present invention.
Claims (10)
1. A pulse pile-up correction method based on morphology and an optimized gray model is characterized by comprising the following steps:
s10, labeling the stacking pulse signals acquired by the nuclear signal detection device according to the height of the wave peak of the pulse stacking waveform to obtain a pulse sequence with a plurality of wave peaks, and combining every two continuous pulses to form a pulse type;
s20, sequentially preprocessing the stacked pulse data of each pulse type;
s30, in a pulse type, performing mathematical morphology transformation processing on piled-up pulse data based on peak point information of a higher peak and linear structural elements with different scales, determining a pulse waveform ascending section of the higher peak, and performing pile-up correction on the pulse waveform ascending section;
s40, predicting the initial sequence of the pulse waveform descending segment of the higher peak by using a gray model optimized based on hyperbolic tangent function transformation, fitting the prediction result, and finishing the accumulation correction processing of the pulse waveform descending segment;
s50, integrating the correction data of the rising segment and the falling segment of the pulse waveform of the higher wave crest to finish the pulse accumulation correction of the higher pulse waveform peak;
s51, repeating the steps S30-S50, completing the pulse pile-up correction of the lower pulse waveform peak in the pulse type, and completing the pulse pile-up correction of the pulse type;
and S52, repeating the steps S20-S51, and integrating to finish the pulse pile-up correction of the whole pulse sequence.
2. The method for pulse pile-up correction based on morphology and optimized gray model of claim 1, wherein in step S10, the pulse types are divided according to the peak height of two consecutive pulses into:
type 1, in two consecutive pulse waveform peaks, the front peak amplitude is higher than the back peak amplitude;
type 2, in two consecutive pulse waveform peaks, the back peak amplitude is higher than the front peak amplitude.
3. The method of claim 2, wherein the step S51 integrates the correction data of the peak of the higher pulse waveform and the peak of the lower pulse waveform according to the pulse type, so as to complete the pulse pile-up correction for one pulse type.
4. The method according to claim 3, wherein the step S52 integrates the correction data of all pulse types according to the sequence of each pulse type in the pulse sequence, so as to complete the pulse pile-up correction of the entire pulse sequence.
5. The method for pulse pile-up correction based on morphology and optimized gray model of claim 1, wherein in step S20, the pile-up pulse data is pre-processed by normalization and smoothing to reduce the interference of environmental background and electronic noise.
6. The morphology and optimized gray model-based pulse pile-up correction method according to any one of claims 1-5, wherein the step S30 adopts the following process:
s31, extracting peak point information of a higher peak in a pulse type according to the preprocessed accumulated pulse data, and determining the end point of the rising segment of the pulse waveform;
s32, constructing linear structural elements to perform mathematical morphology open operation transformation on the preprocessed accumulated pulse data to obtain transformed waveform data based on a single pulse waveform peak, wherein the size L of the constructed linear structural elementsSEBy the pre-processed data matrix size Ln/gDetermining: l isSE＝Ln/g-1；
S33, performing difference on two adjacent elements in the waveform data obtained by the conversion of the on operation to obtain a difference matrix, and selecting a pulse position corresponding to a maximum value point in the difference matrix as a starting point of a pulse waveform peak;
s34, determining the rising segment of the pulse waveform according to the end point of the rising segment of the pulse waveform obtained in the step S31 and the starting point of the peak of the pulse waveform obtained in the step S33;
and S35, assigning the waveform data after the on-operation transformation according to the position information of the pulse waveform ascending section to obtain a correction matrix of the pulse waveform ascending section, namely the waveform data after the pulse waveform ascending section is corrected.
7. The method of claim 6, wherein the formula for obtaining the correction matrix of the rising segment of the pulse waveform is as follows:
Sub＝datao(i+1)-datao(i)
[Max1,Pos1]＝max{Sub}
sub is a difference matrix of two adjacent elements of the matrix after mathematical morphology open operation transformation, dataoFor the accumulated pulse data after the on operation, i is 1,2, …, Ln/g-1，Max1Is the maximum value, Pos, in the difference matrix1Is the position corresponding to the maximum value in the difference matrix, datarFor the correction matrix obtained after evaluation, j is 1,2, …, Ln/g。
8. The morphology and optimized gray model-based pulse pile-up correction method according to any one of claims 1-5, wherein the step S40 adopts the following process:
s41, selecting data from a peak point to a half-peak height of a higher peak in the preprocessed accumulated pulse data as an initial sequence of a pulse waveform descending segment to perform hyperbolic tangent function transformation to obtain a new accumulated pulse data sequence;
s42, dividing the new stacked pulse data sequence into a training set and a testing set according to the proportion of 85% to 15%, and bringing the training set into a gray model based on hyperbolic tangent function transformation optimization to train to obtain a predicted value;
and S43, fitting the predicted value obtained by training to finish the accumulation correction processing of the pulse waveform descending segment.
9. The method for pulse pile-up correction based on morphology and optimized gray model of claim 8, wherein in step S41, the hyperbolic tangent function transform is performed by using the following formula:
Y(0)＝{y(0)(1),y(0)(2),…,y(0)(n)}
wherein the initial sequence is X(0)＝{x(0)(1),x(0)(2),…,x(0)(k)},k＝1,2,…,n，Y(0)To obtain a new piled-up pulse data sequence.
10. The method for pulse pile-up correction based on morphology and optimized gray model of claim 9, wherein in step S42, Y is selected(0)Performing first accumulation, solving a gray model whitening differential equation to obtain a gray model predicted value transformed by a hyperbolic tangent function as follows:
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| CN114647814A (en) * | 2022-05-23 | 2022-06-21 | 成都理工大学工程技术学院 | Nuclear signal correction method based on prediction model |
| CN115345208A (en) * | 2022-10-19 | 2022-11-15 | 成都理工大学 | Neutron-gamma pulse accumulation discrimination method based on top-hat conversion |
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