CN114675088A - Unsupervised learning radiation source rapid near-field scanning method - Google Patents
Unsupervised learning radiation source rapid near-field scanning method Download PDFInfo
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Abstract
The invention discloses a radiation source fast near-field scanning method for unsupervised learning. Randomly scanning the radiation field values of the points, interpolating the radiation field values of the rest scanning points by multiple interpolation methods, and selecting the scanning points with large variance to divide the scanning points into a plurality of clusters and the central points of the scanning points; then, whether the variance value of the central points of other clusters is obviously changed due to the introduction of the central point of each cluster is detected in sequence for screening, and a radiation field value is obtained by moving a probe by a mechanical arm; the steps are iterated for a plurality of times, and the requirements needed by the user are quickly, accurately and effectively obtained. The invention has high processing efficiency and good robustness, well balances the uncertainty and diversity of point selection and has obvious advantages in the aspects of saving scanning point number and scanning time.
Description
Technical Field
The invention relates to an electromagnetic near field scanning method in the field of electromagnetic compatibility and artificial intelligence, in particular to a radiation source rapid near field scanning method for unsupervised learning.
Background
The high integration of electronic products leads to serious electromagnetic interference problems, which may affect the normal operation of the electronic products. Near field scanning is an effective electromagnetic diagnostic method in industry and academia.
In a typical near field scanning system, a mechanical arm controls a near field probe to move along a certain path above a radiation source to be detected, and information of an electric field or a magnetic field above the radiation source to be detected is obtained through a receiver (usually a frequency spectrograph or a vector network analyzer) and stored in a computer. The time consumed in the whole scanning process comprises three parts, namely the time of moving the mechanical arm, the time of acquiring a scanning field by the receiver and the time of storing field information by the computer. When the number of scanning points is large, the scanning time is long, and the scanning efficiency is greatly reduced.
Disclosure of Invention
In order to solve the problem of overlong scanning time in the near-field scanning process, the invention provides a radiation source rapid near-field scanning method for batch selection of scanning points based on unsupervised learning, which reduces the number of scanning points and directly improves the scanning efficiency.
The invention adopts the following technical scheme:
the method comprises the step S1 of detecting the radiation field value at the initial scanning point on the scanning surface by moving the probe by the mechanical arm aiming at the radiation source;
the method comprises a step S2 of performing near-field scanning detection on the remaining scanning points by using the mechanical arm to move the probe in real time according to the radiation field value of the initial scanning point.
The step S1 specifically includes:
aiming at a given radiation source, a probe is arranged at the tail end of a mechanical arm, the mechanical arm is moved to scan and move on a scanning surface by the probe, the scanning surface is provided with a scanning point, and the mechanical arm is moved to position the probe at one scanning point and detect a radiation field value; and randomly acquiring a group of initial scanning points by using a computer, moving the mechanical arm to detect the position of the probe at each initial scanning point, acquiring the radiation field value of each initial scanning point, and adding the initial scanning points into the detected scanning points.
The radiation source is an active radiation part such as an electronic product or an electronic system. The radiation field value is an electric field or a magnetic field. The scan plane is generally planar.
In step S2, the probe is detected by each wheel of the mobile mechanical arm according to the following method:
s21, under the current round, taking the current undetected scanning point as an undetected point, respectively interpolating and processing the detected radiation field value of the scanning point by different interpolation functions to obtain different preliminary radiation field values of the undetected point, processing each interpolation function to obtain a radiation field value, averaging each preliminary radiation field value of the undetected point to be used as a radiation field estimation value of the undetected point, then calculating the variance of each preliminary radiation field value of the undetected point, taking the variance of different interpolation results as the weight in clustering, extracting the undetected point with the maximum N variances, dividing the undetected point with the maximum N variances into C clusters by using a clustering method, and finding the central point of each cluster; at this point, each center point is essentially an unscanned point, and an estimate of the radiation field and variance are obtained.
By extracting N unscanned points with the largest variance, important scanning points with high uncertainty of radiation field values can be quickly found, the radiation field values of the unscanned points greatly contribute to the richness of the whole radiation field, and the radiation field values contain more information of radiation sources.
The clustering method can be a Kmeans clustering method.
At S22, there may be several center points near the center points of the C clusters, and the merging process is performed. Extracting a central point from the variances of the central points of the C clusters as a candidate central point, forming a reference scanning point set by using the radiation field estimation value of the candidate central point and the radiation field value of the detected scanning point, processing by using different interpolation functions by using the reference scanning point set to obtain a new variance value of each residual central point which is not traversed as the candidate central point, and judging:
if the new variance value of at least one remaining center point is smaller than the standard value VstdIf so, the candidate center point is removed, and the back of the candidate center point is not traversed;
if the new variance values of all the remaining center points except the candidate center point are not less than the standard value VstdIf so, reserving the candidate center point and not traversing behind the candidate center point;
s23, sequentially arranging the variances of the center points of the C clusters from large to small, extracting one center point as a candidate center point, continuously repeating the step S22 to traverse all the center points, scanning and detecting the positions of the current wheel to which the probe is moved by the mechanical arm by using the reserved center points as the current wheel to obtain the radiation field values of the reserved center points, and adding the reserved center points into the detected scanning points;
and S24, continuously repeating the steps S21-S23 to perform multi-round processing until the number of the scanning points obtained by moving the probe through the mechanical arm reaches the number required by the user, finally obtaining a scanning image of a complete scanning plane by utilizing the detected scanning points and an interpolation function, and continuously selecting new scanning points until the scanning image meets the precision requirement after multiple cycles.
In step S22, for each remaining central point except the candidate central point, which is not traversed as the candidate central point, the radiation field values of all points in the reference scanning point set are respectively interpolated by using different interpolation functions, and then averaged to obtain the radiation field estimation value of the central point.
The standard value VstdIt is the minimum variance among the N non-scanned points whose variances are the largest in step S21 as the standard value. By setting the standard value, the standard value is changed dynamically instead of a fixed preset value, so that the algorithm can adapt to different radiation field distributions without human intervention, and the robustness of a near field scanning result is improved.
And screening the scanning points through the variance influence of the newly added central points on other central points, so that the number of the central points for scanning and detecting is reduced. The method sets the variance influence of the newly added central point on the other central point, judges whether the radiation field value information contained in the two central points is similar, removes the central point with smaller variance, and finally reserves the position of the central point after traversing all the scanning points, namely the scanning point needing scanning and detecting in the current round.
The invention arranges the variance of the central point from large to small, and takes the minimum variance as a standard value, thereby accurately obtaining the point with minimum radiation field value change and minimum possibility of representing the richness of the radiation source.
The different interpolation functions are different interpolation functions or different interpolation functions with different function parameters.
The different interpolation functions are Radial Basis Functions (RBFs) with different kernel functions. Other fast interpolation functions may be substituted, but are not limited to such.
Aiming at different radiation sources, the number of different interpolation functions can be adjusted, and the radiation field values of other scanning points are obtained from the radiation field values of the detected scanning points by utilizing the different interpolation functions.
In a specific implementation, in step S21, the non-scanned point with the largest variance in the cluster is selected as the center point of the cluster.
The S23 may specifically be:
s231, arranging the variances of the four interpolation functions of all the central points from large to small, and ordering the central points with the variances arranged from large to small into a sequence PvarSequence PvarThe estimated value of the radiation field of every p (p =1 in the initial period) central point in the system is represented by the mean value of the results obtained by processing four interpolation functions;
s232, obtaining the radiation field value and the sequence P of the scanning points which are detected by moving the probe by the mechanical armvarCenter point P of current maximum variancepmaxFor the sequence PvarAccording to the radiation field value of the probe moved by the mechanical arm at the scanned point and the sequence PvarCentral point P inpmaxThe estimated value of the radiation field is interpolated by four interpolation methods in sequence to obtain the variance of the results of the four interpolation methods, and the variance of the residual central point is judged in sequence: when the variance of the residual center point is less than the standard value VstdIf so, the central point is removed, otherwise, the central point is reserved. The number of center points that are eventually retained may be less than the initial C center points.
And then, if the total number of the scanning points detected by the probe moved by the mechanical arm does not reach the number preset by the user, taking the radiation field value of the scanning point scanned at the moment as the radiation field value of the initial scanning point of a new round, and repeating the step processing until the number of the scanning points obtained by the probe moved by the mechanical arm reaches the number preset by the user.
And finally, obtaining the radiation field values of the rest scanning element points which are not detected by the radiation field values of the detected scanning points through an RBF linear interpolation method, and obtaining the radiation field values of all the scanning element points of the complete scanning surface.
The unsupervised learning method provided by the invention firstly scans the radiation field values of the points randomly, then interpolates the radiation field values of the rest scanning points by a plurality of interpolation methods, selects the scanning points with larger variance from the results of the plurality of interpolation methods, and divides the scanning points into a plurality of clusters by an artificial intelligence method and finds the central point of each cluster; and then, whether the variance value of the central points of other clusters is obviously changed due to the introduction of the central point of each cluster is sequentially detected, if so, the central points of other clusters are reserved, otherwise, the central points of other clusters are removed, and the waste caused by selecting too many central points at positions with similar radiation field values can be avoided. Obtaining a plurality of clusters of central points at the moment, and obtaining radiation field values through a mechanical arm moving probe; the scanning point required by the user is quickly, accurately and effectively obtained by iterating the steps for a plurality of times.
The invention calculates the variance of the unscanned points through different interpolation functions, classifies and screens the unscanned points through a clustering method, dynamically updates the standard value and other means and operations, realizes the purpose of carrying out high-efficiency near-field scanning through an artificial intelligence technology, and has the advantages of high scanning efficiency and good robustness.
The invention screens most scanning points which do not carry important information of the radiation source in the complete scanning field diagram, only the points carrying the important field information are scanned to reflect the characteristics of the radiation source, and the scanning time can be greatly reduced.
The method is unsupervised to learn, the probe of the mechanical arm calculates the positions of the points needing to be scanned (most points do not need to be scanned) while scanning, and the scanning is automatically determined, so that the method is efficient, saves the scanning time and ensures that part of the point field diagrams can accurately reflect the characteristics of the radiation source.
Compared with the existing near field scanning technology, the invention has the beneficial effects that:
the method can obtain accurate radiation field values by using a near field scanning system at the position with obvious radiation field value change, and obtain the radiation field values by using an interpolation method at other positions, and can greatly improve the near field scanning efficiency compared with the traditional complete scanning method.
The method has high processing efficiency, can be used for real-time near-field scanning, has good robustness, does not need a user to excessively adjust setting parameters, and can well balance the uncertainty and diversity of point selection.
Compared with the traditional full scanning mode or random point selection scanning, the method provided by the invention has obvious advantages in the aspects of saving scanning point number and scanning time.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is an exemplary radiation source for near field scanning in an embodiment of the present invention;
FIG. 3 is a diagram illustrating the magnitude of the complete magnetic field of the radiation source obtained by the mechanical arm moving the probe;
FIG. 4 shows 100 scanning points initially selected in an embodiment of the present invention;
FIG. 5 is a variance distribution diagram of each scanning point on the whole scanning surface calculated according to the initial scanning point and the interpolation function of 4 in the embodiment of the present invention;
fig. 6 shows 200 (N = 200) unscanned points with the largest variance selected in the embodiment of the present invention;
fig. 7 is a graph showing that 200 unscanned points with the largest variance are divided into 50 (C = 50) clusters by using a Kmeans clustering algorithm, and each black point represents a center point of a different cluster in the embodiment of the present invention;
FIG. 8 shows the remaining 14 center points after the 50 center points are merged according to the embodiment of the present invention;
FIG. 9 shows 514 scan points selected by the method proposed in this patent in an embodiment of the present invention;
FIG. 10 is a diagram of a distribution of amplitude of a radiation field of a complete scan plane interpolated by 514 scan points according to an embodiment of the present invention;
fig. 11 is a comparison graph of the mean square error between the method and the randomly selected point, and the mean square error is calculated from the mean square error between the complete scanning plane radiation field after interpolation of the selected scanning point and the complete radiation field in fig. 3.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without any inventive step, are within the scope of the present invention.
As shown in fig. 1, the embodiment of the present invention and its implementation are as follows:
as shown in fig. 2, the radiation source for near field scanning in this embodiment is a "C" shaped metal drawn on a PCB board. The distance between the scanning surface and the upper surface of the C-shaped metal is 3mm, the size of the scanning surface is 180mm multiplied by 180mm, and the scanning interval is 3 mm. The total number of scanning points is therefore 61 × 61= 3721. The full field amplitude value profile of the radiation source is shown in figure 3.
Firstly, a computer is used for randomly generating 100 scanning point positions, the mechanical arm controls the near-field probe to move to the 100 scanning point positions for detection, and a frequency spectrograph is used for obtaining magnetic field amplitude values of the corresponding 100 scanning points, wherein the 100 random scanning point positions are shown in fig. 4.
According to the detected magnetic field amplitude values of 100 scanning points, the rest 3621 scanning points are interpolated by using 4 kernel functions linear, cubic, thin _ plate _ spline and quintic of a radial basis function RBF according to the magnetic field amplitude values of 100 scanning points to obtain interpolation results of the 4 magnetic field amplitude values, and the variance of the 4 interpolation results of each scanning point is calculated. The distribution of variance values for all scan positions on the scan plane is shown in fig. 5.
200 maximum variances and 200 corresponding scan points are selected, and the radiation field values of the 200 scan points are considered to have high uncertainty and obvious change, and the variance distribution of the 200 scan points is shown in fig. 6. The smallest variance among the 200 largest variances was extracted as the standard value.
Using the variance of each scanning point as a classification weight, dividing 200 scanning points into 50 clusters by using a Kmeans clustering method, and finding the central point of each cluster. There may be several center points close to each other, which have similar radiation field values and similar contained field information, as shown in fig. 7. Therefore, these central points need to be subjected to a merging process. The specific embodiment is as follows:
1) and (3) carrying out interpolation processing on the central points of the 50 clusters according to the magnetic field amplitude values of 100 scanning points by using 4 interpolation functions of 4 kernel functions linear, cubic, thin _ plate _ spline and quintic of the radial basis function RBF to obtain interpolation results, wherein the interpolation results are used as magnetic field estimation values of the central points, and the variances of the interpolation results are arranged from large to small.
2) Finding the central point corresponding to the maximum variance among the variances of the 50 central points as a candidate central point, wherein the magnetic field estimation value of the candidate central point is obtained, and interpolating each central point of the remaining 49 central points by using 4 interpolation functions according to the magnetic field estimation value of the candidate central point and the magnetic field amplitude value of the scanning point scanned by the mechanical arm moving probe before to obtain a new variance of 4 interpolation results of each central point:
if the new variance of a center point in the remaining 49 center points is smaller than the standard value, the center point is rejected, otherwise, the center point is retained. In this case, the number of center points after screening may be less than 50.
3) Then picking out the next central point from the screened central points, and repeating the step 2);
and (4) processing each central point in the central points by analogy according to the process, and judging whether the introduction of the current candidate central point can obviously influence the variances of 4 interpolation results of other central points (the variances of other central points are smaller than the variances of the introduced candidate central points). If the variance is smaller than the standard value, the influence is considered to be small, the two central points are close to each other, the central point with small variance can be eliminated, and finally, the central point with larger variance is reserved. The 50 center points in fig. 7 are combined, and the remaining 14 center points are shown in fig. 8.
After a group of central points which are far away from each other are obtained through screening, the mechanical arm moves the probe to obtain the magnetic field amplitude value of the group of central points, and at the moment, the number of scanning points of the accurate magnetic field amplitude value obtained through scanning detection at present is the sum of the number of the initial points and the number of the accumulated new central points. If the number is less than the scanning number preset by the user, iterating the process for several times until the number of the positions of the accurate amplitude values obtained by scanning reaches the number preset by the user.
In this embodiment, the total number of scan points obtained by moving the probe with the robotic arm is 514 over several iterations. The magnitude of the magnetic field is shown in fig. 9 for 514 scan points. For the scanning points where the accurate magnetic field amplitude values are not accurately detected, the RBF linear interpolation function is used to perform interpolation processing on the scanning points where the magnetic field amplitude values have been detected to obtain the magnetic field amplitude values of the scanning points, and the magnetic field amplitude values of the complete scanning surface are all obtained, as shown in fig. 10, the magnetic field amplitude distribution is very close to the complete magnetic field amplitude distribution of the radiation source in fig. 3. The rapid near-field scanning method provided by the invention can reduce the number of scanning points to 1/7, and the scanning points are distributed in places with obvious changes of radiation field values, thereby greatly improving the scanning efficiency.
To further illustrate the advantages of the proposed method of the present invention quantitatively, fig. 11 compares the advantages of the method of the present invention with the random selection of points on the mean square error. The mean square error is calculated from the mean square error between the complete scan plane radiation field from the selected scan point after interpolation and the complete radiation field in fig. 3. It can be seen that, with the increase of the number of points, the mean square error of the method is far smaller than that of random point selection, which proves the effectiveness of the method provided by the invention, and the scanning area with more important information can be found with fewer scanning points, thereby greatly improving the near-field scanning efficiency, and meanwhile, the method has high robustness.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equally replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. A radiation source fast near-field scanning method for unsupervised learning is characterized by comprising the following steps:
the method comprises the step S1 of detecting the radiation field value at the initial scanning point on the scanning surface by moving the probe by the mechanical arm aiming at the radiation source;
the method comprises a step S2 of performing near-field scanning detection on the remaining scanning points by using the mechanical arm to move the probe in real time according to the radiation field value of the initial scanning point.
2. The unsupervised learning fast near-field scanning method of a radiation source according to claim 1, characterized in that: the step S1 specifically includes: aiming at a given radiation source, a probe of a movable mechanical arm scans and moves on a scanning surface, the scanning surface is provided with scanning points, a group of initial scanning points are randomly obtained, the probe is positioned at each initial scanning point to be detected by the movable mechanical arm, and the radiation field value of each initial scanning point is obtained.
3. The unsupervised learning fast near-field scanning method of a radiation source according to claim 1, characterized in that: the step S2 specifically includes:
s21, under the current wheel, taking the current undetected scanning point as an undiscanned point, respectively interpolating and processing the radiation field value of the detected scanning point by different interpolation functions to obtain different preliminary radiation field values of the undiscanned point, averaging the preliminary radiation field values of the undiscanned point to be used as a radiation field estimation value of the undiscanned point, then solving the variance of the preliminary radiation field values of the undiscanned point, extracting N undiscanned points with the largest variance, dividing the N undiscanned points with the largest variance into C clusters by a clustering method, and finding the central point of each cluster;
s22, extracting a central point from the variance of the central points of the C clusters as a candidate central point, forming a reference scanning point set by the radiation field estimation value of the candidate central point and the radiation field value of the detected scanning point, processing by using different interpolation functions by using the reference scanning point set to obtain a new variance value of each residual central point, and judging:
if the new variance value of at least one remaining center point is smaller than the standard value VstdThen eliminating the candidate central point;
if the new variance value of all the remaining center points except the candidate center point is not less than the standard value VstdIf yes, the candidate center point is reserved;
s23, sequentially arranging the variances of the center points of the C clusters from large to small, extracting one center point as a candidate center point, continuously repeating the step S22 to traverse all the center points, scanning and detecting the positions of the current wheel to which the probe is moved by the mechanical arm by using the reserved center points as the current wheel to obtain the radiation field values of the reserved center points, and adding the reserved center points into the detected scanning points;
and S24, continuously repeating the steps S21-S23 to perform multi-round processing until the number of the scanning points detected by the probe moved by the mechanical arm reaches the number required by the user, and finally obtaining a scanning image of a complete scanning plane by using the detected scanning points and an interpolation function.
4. The unsupervised learning fast near-field scanning method of the radiation source according to claim 3, characterized in that: in step S22, for each remaining central point except the candidate central point, the radiation field values of all points in the reference scanning point set are interpolated by different interpolation functions, and then averaged to obtain the radiation field estimation value of the central point.
5. The unsupervised learning fast near-field scanning method of the radiation source according to claim 3, characterized in that: the standard value VstdIt is the minimum variance among the N non-scanned points whose variances are the largest in step S21 as the standard value.
6. The unsupervised learning fast near-field scanning method of the radiation source according to claim 3, characterized in that: the different interpolation functions are different interpolation functions or the same interpolation function with different function parameters.
7. The unsupervised learning fast near-field scanning method of the radiation source according to claim 3, characterized in that: the different interpolation functions are radial basis functions with different kernel functions.
8. The unsupervised learning fast near-field scanning method of the radiation source according to claim 3, characterized in that: in step S21, the unscanned point with the largest variance in the cluster is selected as the center point of the cluster.
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