CN115561310A - Method for processing non-random coherent noise in grounding electrode defect echo signal - Google Patents
Method for processing non-random coherent noise in grounding electrode defect echo signal Download PDFInfo
- Publication number
- CN115561310A CN115561310A CN202211122636.4A CN202211122636A CN115561310A CN 115561310 A CN115561310 A CN 115561310A CN 202211122636 A CN202211122636 A CN 202211122636A CN 115561310 A CN115561310 A CN 115561310A
- Authority
- CN
- China
- Prior art keywords
- guided wave
- ultrasonic guided
- matrix
- echo signal
- random
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/14—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object using acoustic emission techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/34—Generating the ultrasonic, sonic or infrasonic waves, e.g. electronic circuits specially adapted therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/023—Solids
- G01N2291/0234—Metals, e.g. steel
Abstract
The invention discloses a method for processing non-random coherent noise in a grounding electrode defect echo signal, which comprises the following steps: step 1, transmitting ultrasonic guided waves through an ultrasonic guided wave transmitter, and receiving and detecting ultrasonic guided wave echoes through a sensor to obtain an ultrasonic guided wave echo signal set; step 2, preprocessing an ultrasonic guided wave echo defect detection signal for the ultrasonic guided wave echo signal set to obtain a whitening matrix; and 3, performing optimization calculation on each column of the whitening matrix by adopting a Newton fast iterative algorithm to obtain optimized data. Through the overall structure of the device, coherent noise contained in the echo can be processed, the sensitivity of defect detection is improved, the signal to noise ratio of the reflected echo is improved from the characteristics, and noise caused by the change of the tested piece structure and the soil environment where the tested piece structure is located is filtered out as much as possible, so that the accuracy of the corrosion defect detection of the grounding flat steel is improved in the aspect of ultrasonic guided wave defect signals.
Description
Technical Field
The invention relates to the technical field of ultrasonic guided wave nondestructive testing, in particular to a method capable of processing non-random coherent noise in a grounding electrode defect echo signal.
Background
Ultrasonic guided wave detection has been successfully applied to the field of nondestructive testing as a defect detection means. The detection method has the advantages of non-contact, wide measurement range, high precision and the like, and is widely applied to the corrosion defect detection of the grounding device of the power system, but the grounding device of the power system has various configurations and geometric shapes, such as overlapping, bending and welding among a plurality of grounding electrodes, the detected defect echo can be attenuated due to the characteristics and the change of the soil environment, and the change of a propagation mode can be caused after the ultrasonic guided wave action of the complicated and asymmetric defect shape, so that background noise is generated, the signal-to-noise ratio of an ultrasonic guided wave detection signal is greatly reduced, the sensitivity of defect detection is reduced, meanwhile, the directivity of a sound field can be greatly influenced by the rough detection surface of flat steel, the propagation time and the propagation direction of the guided wave are changed, and the defect positioning is difficult. All of the above causes the corrosion defect detection of the flat steel of the power system to be difficult, so that a method capable of processing the non-random coherent noise in the echo signal of the defect of the grounding electrode is provided.
Disclosure of Invention
The invention aims to provide a method capable of processing non-random coherent noise in a grounding electrode defect echo signal, which can process coherent noise contained in the echo without a large number of observation samples, harsh assumption that a real signal is a deterministic signal and characteristic frequency bands of the processed signal in advance, improve the sensitivity of defect detection and improve the signal-to-noise ratio of a reflected echo from characteristics, thereby improving the accuracy of grounding flat steel corrosion defect detection in the aspect of ultrasonic guided wave defect signals.
The invention discloses a method for processing non-random coherent noise in a grounding electrode defect echo signal, which comprises the following steps:
step 1, transmitting ultrasonic guided waves through an ultrasonic guided wave transmitter, and receiving and detecting ultrasonic guided wave echoes through a sensor to obtain an ultrasonic guided wave echo signal set;
step 2, preprocessing an ultrasonic guided wave echo defect detection signal for the ultrasonic guided wave echo signal set to obtain a whitening matrix;
step 3, performing optimization calculation on each column of the whitening matrix by adopting a Newton fast iterative algorithm to obtain optimized data;
step 4, determining the analyzed optimized data by using a negative entropy method, establishing a criterion for judging whether random vectors y (i) in the optimized data are independent or not, and maximizing the negative entropy by using a maximum entropy principle through an optimal linear function to obtain a linear data set;
and 5, solving the non-Gaussian maximum value among all the components according to the linear data set to remove the coherent noise in the source ultrasonic guided wave.
Preferably, the number of the sensors is n, and assuming that n sensors are placed, each sensor performs p measurements and each sensor has p guided wave signals, the matrix a of the ultrasonic guided wave echo signal sets can be represented as:
A＝B×C
wherein, A is a data matrix of n sensors, C is an original matrix formed by mutually independent signals, and B is called a mixed matrix and is formed by signal weight values.
Preferably, the preprocessing of the ultrasonic guided wave echo defect detection signal on the ultrasonic guided wave echo signal set includes mean value removal and whitening.
As a preferred scheme, the mean value removal is to subtract the mean value from a matrix a of the received ultrasonic guided wave echo signal set, and it is ensured that the processed mean value is 0, which can reduce the complexity of calculation, and may be specifically expressed as:
A-E{A}
where E { A } represents the expectation of replacement by an average value that is often employed in practice.
As a preferred scheme, the whitening processing is to change a covariance matrix of the ultrasonic guided wave echo signal set into a diagonal matrix, make various variables studied linearly independent, remove correlation among the ultrasonic guided wave echo signal sets, thereby simplifying a subsequent extraction process, improving algorithm convergence of ICA, and performing whitening processing by using eigenvalue decomposition of the diagonal matrix to obtain a whitening matrix of a matrix a of the ultrasonic guided wave echo signal set:
F＝G -1/2 H i
in the formula, G and H are respectively an eigen matrix and an eigenvalue diagonal matrix of the covariance matrix E { X, xi }.
As a preferred scheme, the iterative formula for performing optimization calculation on each column of the whitening matrix by using the newton fast iterative algorithm is as follows:
wherein
Representing the elements in the pth column of the kth order iteration result, Z is an orthogonal matrix, which can be represented as Z = AF, g (x) is a nonlinear function with x as a variable, and g (x) = tanh (x) may be taken, g' (x) represents the derivative of g (x),<>the average processing is shown, and the superscript "-" represents the normalization processing.
As a preferred scheme, the negative entropy maximization, that is, determining the analyzed optimized data by using a negative entropy method and establishing a criterion for judging whether the random vector y (i) in the optimized data is independent, and using the negative entropy J (y) as a measure for non-gaussian property of each component, that is, assuming that the probability density of the random vector y is P (y), the entropy thereof is:
H(y)＝-∫P(y)lgP(y)dy
the negative entropy is:
J(y)＝H(y gauss )-H(y)
wherein y is gauss Is a gaussian random component.
Preferably, the maximum value of non-gaussian property between the components is obtained, and according to the maximum entropy principle, the negative entropy J (y) can be estimated as:
J(y i )≈c[E{G(y i )}-E{G(u)}]
where G (x) is any non-quadratic function, c is a normal number, u is a Gaussian variable with mean and unit variance of zero, and E is the mathematically expected operator.
Preferably, G (x) = x 4 /4。
Preferably, the method for processing the non-random coherent noise in the echo signal of the earth pole defect is stored in an application program of a computer framework and is driven to run by a burned program, and the method further comprises a bus framework, a storage and a bus interface, wherein the bus framework can comprise any number of interconnected buses and bridges, the bus framework can link various circuits including one or more processors represented by the processors and a storage represented by the storage, the bus framework can also connect various other circuits such as peripheral devices, voltage regulators, power management circuits and the like together, the bus interface provides an interface between the bus framework and a receiver and a transmitter, and the receiver and the transmitter can be the same element, namely a transceiver, and provide a unit for communicating with various other systems on a transmission medium.
The method for processing the non-random coherent noise in the echo signal with the grounding electrode defect disclosed by the invention has the beneficial effects that:
the method comprises the steps of data preprocessing, component independent criterion establishment and non-Gaussian maximum value among components through optimization calculation of a whitening matrix, no need of a large number of observation samples, no need of rigorous assumption that real signals are deterministic signals, no need of positioning characteristic frequency bands of the processed signals in advance, capability of processing coherent noise contained in echo waves, improvement of sensitivity of defect detection and improvement of signal-to-noise ratio of reflected echo waves from characteristics, and filtering of noise caused by the structure of a tested piece and changes of the soil environment as far as possible, so that accuracy of corrosion defect detection of the grounding flat steel is improved in the aspect of ultrasonic guided wave defect signals.
Drawings
FIG. 1 is a schematic block diagram of an embodiment of a method of the present invention for processing non-random coherent noise in an echo signal with a ground defect;
FIG. 2 is a schematic step diagram illustrating an embodiment of a method for processing non-random coherent noise in an echo signal with a ground fault according to the present invention.
Detailed Description
The invention will be further elucidated and described with reference to a specific embodiment and the drawings of the specification:
referring to fig. 1-2, the present invention: a method for processing non-random coherent noise in an echo signal with a defective earthing pole, comprising the following steps:
step 1, transmitting ultrasonic guided waves through an ultrasonic guided wave transmitter, and receiving and detecting ultrasonic guided wave echoes through a sensor to obtain an ultrasonic guided wave echo signal set;
specifically, the method comprises the following steps: the ultrasonic guided wave transmitter and the sensor are all provided with a plurality of existing devices on the market, and the sensor is used for detecting ultrasonic guided wave echoes, and the number of the sensors is increased, so that the attenuation of guided wave signals in the soil environment can be reduced, and the maximum change of an original data set is captured.
Step 2, preprocessing an ultrasonic guided wave echo defect detection signal for the ultrasonic guided wave echo signal set to obtain a whitening matrix;
specifically, the method comprises the following steps: the ultrasonic guided wave echo defect detection signal preprocessing comprises mean value removing and whitening processing.
Step 3, performing optimization calculation on each column of the whitening matrix by adopting a Newton fast iterative algorithm to obtain optimized data;
specifically, the method comprises the following steps: performing an optimized calculation for each column of the whitening matrix enables a more accurate detection of the signal.
Step 4, determining the analyzed optimized data by using a negative entropy method, establishing a criterion for judging whether random vectors y (i) in the optimized data are independent or not, and maximizing the negative entropy by using a maximum entropy principle through an optimal linear function to obtain a linear data set;
specifically, the method comprises the following steps: and finding an optimal linear function so as to conveniently predict the development rule of the data.
And 5, solving the non-Gaussian maximum value among all the components according to the linear data set to remove the coherent noise in the source ultrasonic guided wave.
The sensor is equipped with n, supposing to place n sensors, and every sensor carries out p measurements and every sensor has p guided wave signals, and the matrix A of these supersound guided wave echo signal set can be expressed as:
A＝B×C
wherein, A is a data matrix of n sensors, C is an original matrix formed by mutually independent signals, B is called a mixed matrix and is formed by signal weight values.
Specifically, the method comprises the following steps: each sensor produces a signal without measuring once, and the signal measured each time is relative to the number of measurements.
And the pretreatment of the ultrasonic guided wave echo defect detection signal to the ultrasonic guided wave echo signal set comprises mean value removal and whitening treatment.
The mean value removal means that the mean value is subtracted from a matrix a of a received ultrasonic guided wave echo signal set, and the processed mean value is 0, so that the complexity of calculation can be reduced, which can be specifically expressed as follows:
A-E { A } where E { A } represents the expectation of replacement by an average value that is often employed in practice.
The whitening processing is to change the covariance matrix of the ultrasonic guided wave echo signal set into a diagonal matrix, make the studied variables have linear independence, remove the correlation between the ultrasonic guided wave echo signal sets, thereby simplifying the subsequent extraction process, improving the algorithm convergence of ICA, and obtaining the whitening matrix of the matrix A of the ultrasonic guided wave echo signal set by using the eigenvalue decomposition of the diagonal matrix:
F＝G -1/2 H i
in the formula, G and H are respectively an eigen matrix and an eigenvalue diagonal matrix of the covariance matrix E { X, xi }.
The iterative formula for performing optimization calculation on each column of the whitening matrix by adopting the Newton fast iterative algorithm is as follows:
wherein
Representing the elements in the pth column of the kth order iteration result, Z is an orthogonal matrix, which can be represented as Z = AF, g (x) is a nonlinear function with x as a variable, and g (x) = tanh (x) may be taken, g' (x) represents the derivative of g (x),<>the average processing is shown, and the superscript "-" represents the normalization processing.
The method for maximizing the negative entropy, namely determining the analyzed optimized data by using a negative entropy method and establishing a criterion for judging whether random vectors y (i) in the optimized data are independent or not, and using the negative entropy J (y) as a measure for non-Gaussian property of each component, namely assuming that the probability density of the random vectors y is P (y), the entropy is as follows:
H(y)＝-∫P(y)lgP(y)dy
the negative entropy is:
J(y)＝H(y gauss )-H(y)
wherein y is gauss Is a gaussian random component.
The non-gaussian maxima between the components are found, which according to the maximum entropy principle, the negative entropy J (y) can be estimated as:
J(y i )≈c[E{G(y i )}-E{G(u)}]
where G (x) is any non-quadratic function, c is a normal number, u is a Gaussian variable with a mean and unit variance of zero, and E { } is the mathematically expected operator.
The G (x) = x 4 /4。
It should be noted that: for non-gaussian, higher order statistics are needed to obtain a meaningful representation. Projection pursuit is a technique that uses high order statistics to find interesting data projections. Projection tracking uses a cost function, such as differential entropy, rather than the mean square error used in PCA transformation.
For non-gaussian numbers, projection pursuit is an effective method. There are similarities and associations between ICA and these technologies. In the noise-free case, ICA is a special case of projection tracking. ICA can also be viewed as a non-gaussian factor analysis. ICA must use higher order statistics, while PCA uses only second order statistics.
A method for processing non-random coherent noise in an echo signal of a defected earth electrode is stored in an application program of a computer framework, and is driven to run by a burning program, and the method further comprises a bus framework, a storage and a bus interface, wherein the bus framework can comprise any number of interconnected buses and bridges, the bus framework links various circuits including one or more processors represented by the processors and a storage represented by the storage, the bus framework can also connect various other circuits such as peripheral equipment, a voltage stabilizer, a power management circuit and the like, the bus interface provides an interface between the bus framework and a receiver and a transmitter, and the receiver and the transmitter can be the same element, namely a transceiver, and a unit for communicating with various other systems on a transmission medium is provided.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for processing non-random coherent noise in an echo signal with a defective earthing electrode, comprising the steps of:
step 1, transmitting ultrasonic guided waves through an ultrasonic guided wave transmitter, and receiving and detecting ultrasonic guided wave echoes through a sensor to obtain an ultrasonic guided wave echo signal set;
step 2, preprocessing an ultrasonic guided wave echo defect detection signal for the ultrasonic guided wave echo signal set to obtain a whitening matrix;
step 3, performing optimization calculation on each column of the whitening matrix by adopting a Newton fast iterative algorithm to obtain optimized data;
step 4, determining the analyzed optimized data by using a negative entropy method, establishing a criterion for judging whether a random vector y (i) in the optimized data is independent or not, and maximizing the negative entropy by using a maximum entropy principle through an optimal linear function to obtain a linear data set;
and 5, solving the non-Gaussian maximum value among all the components according to the linear data set to remove the coherent noise in the source ultrasonic guided wave.
2. The method of claim 1, wherein the method comprises the following steps: the sensor is equipped with n, supposing to place n sensors, and every sensor carries out p measurements and every sensor has p guided wave signals, and the matrix A of these supersound guided wave echo signal set can be expressed as:
A＝B×C
wherein, A is a data matrix of n sensors, C is an original matrix formed by mutually independent signals, B is called a mixed matrix and is formed by signal weight values.
3. The method of claim 1, wherein the pre-processing of the ultrasonic guided wave echo defect detection signals to the set of ultrasonic guided wave echo signals comprises de-averaging and whitening.
4. The method according to claim 3, wherein the averaging is performed by subtracting the mean value from a matrix A of the received ultrasonic guided wave echo signal set, and ensuring that the mean value after processing is 0 reduces computational complexity, which can be expressed as:
A-E{A}
where E { A } represents the expectation of average substitution that is often employed in practice.
5. The method according to claim 3, wherein the whitening process is performed by changing a covariance matrix of the ultrasonic guided wave echo signal set into a diagonal matrix, making each variable studied linearly independent, and removing correlation between the ultrasonic guided wave echo signal sets, thereby simplifying a subsequent extraction process, improving an ICA algorithm convergence, and performing whitening process by using eigenvalue decomposition of the diagonal matrix to obtain a whitening matrix of a matrix A of the ultrasonic guided wave echo signal set:
F＝G -1/2 H i
in the formula, G and H are respectively an eigen matrix and an eigenvalue diagonal matrix of the covariance matrix E { X, xi }.
6. The method of claim 1, wherein the iterative formula for performing the optimized calculation on each column of the whitening matrix by using the Newton fast iterative algorithm is as follows:
wherein
Elements representing the No. P column of the K-th order iteration result; z is an orthogonal matrix, which can be expressed as Z = AF, g (x) is a non-linear function with x as a variable, it is possible to take g (x) = tanh (x), g' (x) represents the derivative of g (x),<>the average processing is shown, and the superscript "-" represents the normalization processing.
7. The method of claim 1, wherein the method for maximizing negative entropy, i.e. determining the analyzed optimized data by using a negative entropy method and establishing a criterion for judging whether the random vector y (i) in the optimized data is independent, uses negative entropy J (y) as a measure of non-gaussian property of each component, i.e. assuming that the probability density of the random vector y is P (y), the entropy is:
H(y)＝-∫P(y)lgP(y)dy
the negative entropy is:
J(y)＝H(y gauss )-H(y)
wherein y is gauss Is a gaussian random component.
8. The method of claim 1, wherein the non-gaussian property between each component is maximized, and the negative entropy J (y) is estimated as:
J(y i )≈c[E{G(y i )}-E{G(u)}]
where G (x) is any non-quadratic function, c is a normal number, u is a Gaussian variable with mean and unit variance of zero, and E is the mathematically expected operator.
9. The method of claim 8, wherein G (x) = x 4 /4。
10. The method of claim 1, capable of dealing with non-random coherent noise in a ground pole defect echo signal, characterized in that; the method capable of handling the non-random coherent noise in the earth pole defect echo signal is stored inside an application program of a computer architecture, driven by a burned program, and includes a bus architecture, which may include any number of interconnected buses and bridges, linking together various circuits including one or more processors represented by processors and a memory represented by a memory, and a bus interface that may also link together various other circuits such as peripherals, voltage regulators, power management circuits, etc., the bus interface providing an interface between the bus architecture and a receiver and transmitter, which may be the same element, i.e., a transceiver, providing a unit for communicating with various other systems over a transmission medium.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211122636.4A CN115561310A (en) | 2022-09-15 | 2022-09-15 | Method for processing non-random coherent noise in grounding electrode defect echo signal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211122636.4A CN115561310A (en) | 2022-09-15 | 2022-09-15 | Method for processing non-random coherent noise in grounding electrode defect echo signal |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115561310A true CN115561310A (en) | 2023-01-03 |
Family
ID=84740456
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211122636.4A Pending CN115561310A (en) | 2022-09-15 | 2022-09-15 | Method for processing non-random coherent noise in grounding electrode defect echo signal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115561310A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116818914A (en) * | 2023-08-30 | 2023-09-29 | 东光县津东玻璃工艺制品有限公司 | Glass and nondestructive testing method for processed product thereof |
-
2022
- 2022-09-15 CN CN202211122636.4A patent/CN115561310A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116818914A (en) * | 2023-08-30 | 2023-09-29 | 东光县津东玻璃工艺制品有限公司 | Glass and nondestructive testing method for processed product thereof |
| CN116818914B (en) * | 2023-08-30 | 2023-11-14 | 东光县津东玻璃工艺制品有限公司 | Glass and nondestructive testing method for processed product thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113888471B (en) | High-efficiency high-resolution defect nondestructive testing method based on convolutional neural network | |
| CN109557429A (en) | Based on the GIS partial discharge fault detection method for improving wavelet threshold denoising | |
| CN113050075B (en) | Underwater sound source matching field positioning method based on diffusion mapping | |
| CN111965632A (en) | Radar target detection method based on Riemann manifold dimensionality reduction | |
| CN109581516B (en) | Denoising method and system for data of curvelet domain statistic adaptive threshold value ground penetrating radar | |
| CN115561310A (en) | Method for processing non-random coherent noise in grounding electrode defect echo signal | |
| CN114972339B (en) | Data enhancement system for bulldozer structural member production abnormity detection | |
| CN115685096B (en) | Secondary radar side lobe suppression method based on logistic regression | |
| CN113159447B (en) | Laser radar electromagnetic environment effect prediction method and system | |
| Yoon et al. | Efficient edge‐preserved sonar image enhancement method based on CVT for object recognition | |
| CN115469179A (en) | Submarine cable defect positioning method, device, storage medium and system | |
| CN117406190A (en) | Non-excavation pull rod corrosion detection method, device and equipment based on radar signals | |
| CN111371436A (en) | Radar antenna scanning period measuring method, device, equipment and storage medium | |
| CN113567916B (en) | Correlation interferometer direction finding method, system, equipment and storage medium | |
| Chen et al. | Intercomparisons of Site VSWR Measurement Methods using Mode Filtering, Time Domain and Spatial Sampling Techniques | |
| CN115311164A (en) | Synthetic beam effect eliminating method for sparse interference array observation imaging | |
| CN110579534B (en) | Non-baseline detection and positioning method for defects of steel plate with welding seam based on reciprocity damage | |
| CN113409283B (en) | Defect quantification method, device and medium based on super-resolution ultrasonic image | |
| Huang et al. | A method for extracting fingerprint feature of communication satellite signal | |
| Travassos et al. | Noise reduction in a non-homogenous ground penetrating radar problem by multiobjective neural networks | |
| CN115236415B (en) | Antenna gain acquisition method and device, electronic equipment and storage medium | |
| CN116400200B (en) | Cross verification method for electromagnetic side channel information of vehicle-gauge security chip | |
| WO2024016572A1 (en) | Noise identification method and apparatus, and device and storage medium | |
| CN117422713B (en) | Method and system for extracting laser welding penetration curve according to OCT point cloud | |
| CN109932697B (en) | Weak target detection method based on compressed sensing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |