CN116295188B - Measuring device and measuring method based on displacement sensor - Google Patents
Measuring device and measuring method based on displacement sensor Download PDFInfo
- Publication number
- CN116295188B CN116295188B CN202310537927.8A CN202310537927A CN116295188B CN 116295188 B CN116295188 B CN 116295188B CN 202310537927 A CN202310537927 A CN 202310537927A CN 116295188 B CN116295188 B CN 116295188B
- Authority
- CN
- China
- Prior art keywords
- matrix
- displacement
- displacement sensor
- superposition
- image
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/02—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring length, width, or thickness
- G01B21/04—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring length, width, or thickness by measuring coordinates of points
- G01B21/045—Correction of measurements
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Measurement Of Length, Angles, Or The Like Using Electric Or Magnetic Means (AREA)
Abstract
The invention belongs to the technical field of displacement sensors, and particularly relates to a measuring device and a measuring method based on a displacement sensor. The device comprises: a plurality of displacement sensors arranged in a matrix and an operation center; each displacement sensor acquires displacement in real time according to different set periods, and the acquired displacement is sent to an operation center; the corresponding period of each displacement sensor is a plurality of small periods obtained by equally dividing one large period; and the operation center constructs a matrix of all displacement amounts in one large period, and after a plurality of large periods with a set number, carries out matrix superposition on all the matrices to obtain a superposition matrix, and finally carries out image-based feature extraction on the superposition matrix to obtain image features. The accuracy of displacement measurement is improved in a matrix correction mode, and the reliability of a measurement result is further improved in a mode that a plurality of displacement sensors stagger periods.
Description
Technical Field
The invention belongs to the technical field of displacement sensors, and particularly relates to an end-to-end event processing engine.
Background
The displacement sensor is also called a linear sensor, which is a linear device with metal induction and is used for converting various measured physical quantities into electric quantity. In the production process, the measurement of displacement is generally divided into two types of measurement of physical size and mechanical displacement. The displacement sensor can be divided into two types of analog type and digital type according to the conversion form of the measured variable. The simulation type can be divided into physical type and structural type. The conventional displacement sensor is of an analog structure type and comprises a potentiometer type displacement sensor, an inductance type displacement sensor, a self-chamfering machine, a capacitance type displacement sensor, an eddy current type displacement sensor, a Hall type displacement sensor and the like. An important advantage of digital displacement sensors is that they facilitate the direct feeding of signals into a computer system. Such sensors are rapidly evolving and increasingly being used.
The existing displacement sensor is easy to have the following problems in the use process:
1. aging problem: if the electronic ruler has been used for a long time and the seal has aged, many impurities are mixed in, and the contact resistance of the brushes is seriously affected by the water mixture and oil, the displayed number can continuously jump. At this time, it can be said that the electronic ruler of the linear displacement sensor is damaged and needs to be replaced.
2. Fluctuation error problem: if the capacity of the power supply is small, many failures occur, so the power supply needs to have sufficient capacity. In general, insufficient capacity may cause the following situations: the movement of the melt adhesive can change the display of the mold clamping electronic ruler, and the mold clamping electronic ruler has fluctuation, or the movement of the mold clamping can cause the display of the mold injection electronic ruler to fluctuate, so that the error of the measurement result is large. If the driving power supply of the electromagnetic valve and the power supply of the linear displacement sensor are simultaneously together, the situation is easier to occur, and even the related fluctuation of the voltage can be measured by using the voltage gear of the universal meter when the situation is serious. If this is not due to high frequency interference, electrostatic interference or insufficient neutrality, it may be the result of too little power from the power supply.
3. Interference problem: if high-frequency interference exists, the voltage measurement of the universal meter is normal, and the display number is continuously jumped; and when the electrostatic interference occurs, the situation is the same as that of the high-frequency interference. When the electronic scale is proved to be static interference, a section of power line can be used for shorting the cover screw of the electronic scale with certain metal on the machine, and the static interference can be eliminated immediately as long as the cover screw is shorted. However, if the high-frequency interference is to be eliminated, the high-frequency interference is difficult to occur to the frequency conversion power saver and the robot, so that the high-frequency interference can be verified by stopping the high-frequency power saver or the robot.
Therefore, how to ensure that the probability of these problems is reduced or avoided before and after the operation of the displacement sensor will greatly improve the accuracy and reliability of the displacement sensor.
Disclosure of Invention
In view of the above, the main object of the present invention is to provide a measuring device and a measuring method based on displacement sensors, which improve the accuracy of displacement measurement by means of matrix correction, and further improve the reliability of the measurement result by means of staggering the periods of the plurality of displacement sensors.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
a displacement sensor based measurement device, the device comprising: a plurality of displacement sensors arranged in a matrix and an operation center; each displacement sensor acquires displacement in real time according to different set periods, and the acquired displacement is sent to an operation center; the corresponding period of each displacement sensor is a plurality of small periods obtained by equally dividing one large period; the operation center constructs all displacement amounts in one large period into a matrix, after a plurality of large periods with set quantity, all the matrices are subjected to matrix superposition to obtain a superposition matrix, finally, image-based feature extraction is carried out on the superposition matrix to obtain image features, and according to the obtained image features, the displacement amount measured by each displacement sensor in each subsequent large period is corrected.
Further, the number of displacement sensors is at least greater than 5.
Further, the duration of the large period is at least 2 seconds; the small period is at least 0.2 seconds long.
Further, the matrix stacking process includes: and randomly sequencing all the matrixes, carrying out convolution operation on each matrix and the corresponding correction matrix according to a random sequencing result to obtain a convolution matrix, and then superposing all the convolution matrices obtained by the convolution operation to obtain a superposition matrix.
Further, the correction matrix is expressed using the following formula:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is a correction matrix; />For the number of matrices>For a large period>The value range for correcting the coefficient is as follows: 0.2 to 0.6; />Is a displacement deviation predicted value.
Further, the displacement deviation predicted value is a rank of each matrix.
Further, the method for extracting the features of the superimposed matrix based on the image to obtain the features of the image comprises the following steps: and regarding the superposition matrix as an image matrix, and then carrying out feature extraction on the image matrix to obtain image features.
Further, the method for correcting the displacement measured by each displacement sensor in each subsequent large period according to the image features comprises the following steps: substituting the image characteristics and each displacement into the following formula, and calculating to obtain a corrected result:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>In order to correct the result of the correction,/>for displacement amount->Normalized mean of image features, +.>Is a correction center equal to +.>。
A displacement sensor based measurement method, the method comprising the steps of:
step 1: arranging a plurality of displacement sensors in a matrix, and connecting an operation center with each displacement sensor;
step 2: each displacement sensor acquires displacement in real time according to different set periods, and the acquired displacement is sent to an operation center;
step 3: the corresponding period of each displacement sensor is a plurality of small periods obtained by equally dividing one large period;
step 4: the computing center constructs all displacement amounts in one large period into a matrix, after a plurality of large periods with set quantity, all the matrices are subjected to matrix superposition to obtain a superposition matrix, finally, image-based feature extraction is carried out on the superposition matrix to obtain image features, and according to the obtained image features, the displacement amount measured by each displacement sensor in each subsequent large period is corrected.
Further, the displacement sensor is a strain type, inductance type, differential transformer type, eddy current type or Hall type sensor.
The measuring device and the measuring method based on the displacement sensor have the following beneficial effects:
1. the accuracy is high: according to the invention, the measurement result of the displacement sensor is corrected based on a matrix superposition algorithm in a matrix correction mode, and the accuracy is higher.
2. The reliability is high: the displacement sensors of the invention ensure that each displacement sensor cannot interfere with the measurement results of other displacement sensors in numerical value through the staggering of the measurement periods, thereby ensuring the reliability of the results.
Drawings
Fig. 1 is a schematic flow chart of a measuring device and a measuring method based on a displacement sensor according to an embodiment of the present invention;
FIG. 2 is a schematic diagram showing the relationship between the opening and closing of a sensor and the distance according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a numerical matrix of a displacement sensor according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a binary matrix of a displacement sensor according to an embodiment of the present invention.
Detailed Description
The method of the present invention will be described in further detail with reference to the accompanying drawings.
In the case of example 1,
as shown in fig. 1, the displacement sensor-based measuring device comprises: a plurality of displacement sensors arranged in a matrix and an operation center; each displacement sensor acquires displacement in real time according to different set periods, and the acquired displacement is sent to an operation center; the corresponding period of each displacement sensor is a plurality of small periods obtained by equally dividing one large period; the operation center constructs all displacement amounts in one large period into a matrix, after a plurality of large periods with set quantity, all the matrices are subjected to matrix superposition to obtain a superposition matrix, finally, image-based feature extraction is carried out on the superposition matrix to obtain image features, and according to the obtained image features, the displacement amount measured by each displacement sensor in each subsequent large period is corrected.
Specifically, since the measurement period of each displacement sensor is staggered, the measurement result of each displacement sensor and the measurement results of other displacement sensors can be ensured not to be overlapped, namely only one displacement sensor is measuring at the same time, and numerical interference between measurement results can be avoided. If two displacement sensors measure values at the same moment, deviation occurs, and therefore, both the two results need to be processed, and a judgment result is more accurate, if the judgment result is wrong, the reliability of the result is greatly affected.
In the case of example 2,
on the basis of the above embodiment, the number of the displacement sensors is at least greater than 5.
Specifically, when the number of the displacement sensors is less than 5, the measurement result is almost the same as the measurement result of a single displacement sensor, and the improvement effect on the accuracy is not obvious.
In the case of example 3,
on the basis of the previous embodiment, the duration of the large period is at least 2 seconds; the small period is at least 0.2 seconds long.
In the case of example 4,
on the basis of the above embodiment, the matrix stacking process includes: and randomly sequencing all the matrixes, carrying out convolution operation on each matrix and the corresponding correction matrix according to a random sequencing result to obtain a convolution matrix, and then superposing all the convolution matrices obtained by the convolution operation to obtain a superposition matrix.
Specifically, the matrix superposition process is essentially a process of processing the matrix to ensure that the displacement values measured by the displacement sensor have smaller errors, and the errors are reduced to ensure the accuracy of the result.
Specifically, in practical application, the displacement has two directions, namely, after one direction is selected, the displacement has positive and negative components, so that the moire fringe signal is measured by using one photoelectric element, and the displacement direction cannot be determined. For orientation, two moire signals with pi/2 phase difference are required. Two photoelectric elements are arranged at the positions with the distance of 1/4 of the stripe distance to obtain two electric signals u01 and u02 with the phase difference pi/2, and the two square wave signals u01 'and u02' are obtained after shaping. The grating moves forward by u01 and advances by u02 90 degrees, and moves backward by u02 and advances by u01 and 90 degrees, so that the movement direction of the grating can be determined by circuit phase discrimination.
In example 5 the process was carried out,
on the basis of the above embodiment, the correction matrix is expressed using the following formula:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is a correction matrix; />For the number of matrices>For a large period>The value range for correcting the coefficient is as follows: 0.2 to 0.6; />Is a displacement deviation predicted value.
Specifically, the correction matrix algorithm of the invention can obviously improve the accuracy of the final result.
In example 6 the process was carried out,
on the basis of the above embodiment, the displacement deviation prediction value is a rank of each matrix.
In example 7,
on the basis of the above embodiment, the method for extracting the features of the superimposed matrix based on the image to obtain the features of the image includes: and regarding the superposition matrix as an image matrix, and then carrying out feature extraction on the image matrix to obtain image features.
And regarding the superposition matrix as an image matrix, and then carrying out feature extraction on the image matrix to obtain the image features. One commonly used image feature extraction algorithm is Convolutional Neural Network (CNN), and the process of image feature extraction using CNN will be described in detail below.
CNNs have strict requirements on the input data format, and normalization and standardization of the image matrix are required. Specifically, it is necessary to scale the image pixel values to within the range of [0,1], and subtract the mean value and divide by the standard deviation so that the data conforms to a standard normal distribution.
Constructing the CNN model requires selecting appropriate model structures, super parameters, optimizers, and the like. Taking the classical LeNet model as an example, the model includes two convolutional layers, two pooling layers, and three fully-connected layers. Each convolution layer and full connection layer is typically followed by an activation function, such as ReLU, etc. The specific model structure is as follows:
Input ->Conv1 ->Pool1 ->Conv2 ->Pool2 ->Flatten ->FC1 ->FC2 ->FC3 ->Output；
wherein Input is an Input layer, conv1 and Conv2 are convolution layers, pool1 and Pool2 are pooling layers, flat is a flattening layer, FC1, FC2 and FC3 are full connection layers, and Output is an Output layer.
In the CNN model, the convolution layer and the pooling layer may extract feature information of an image. The convolution layer scans the image matrix through the convolution kernel, and image features at different positions are extracted. The pooling layer maintains main characteristic information by reducing the dimension of the convolution result. In this process, the features extracted by the different layers can be visualized, and what features correspond to different image information is observed.
In the case of example 8,
referring to fig. 4, the method for correcting the displacement measured by each displacement sensor in each subsequent large period according to the image features according to the previous embodiment includes: substituting the image characteristics and each displacement into the following formula, and calculating to obtain a corrected result:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>To correct the result->For displacement amount->Normalized mean of image features, +.>Is a correction center equal to +.>。
After the feature vector is obtained, it needs to be normalized. The common normalization method is L2 norm normalization, mean variance normalization and the like. In this example, we use mean variance normalization. The specific method is to subtract the mean value of the feature vector and then divide the feature vector by the standard deviation. The specific formula is as follows:
N = (F - mean(F)) / std(F)；
wherein F is a feature vector, and N is a normalized feature vector.
After normalization, a normalized mean of the image features may be calculated. The normalized mean value is used for calculation in the subsequent correction. The specific calculation formula is as follows:
S = mean(N)；
wherein S is the normalized mean value of the image features, and N is the normalized feature vector.
After the normalized mean value of the image features is obtained, the value may be used to correct the displacement measured by each displacement sensor in each subsequent large period. Specific correction methods according to the description of the patent, the following formula is used:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>To correct the result->For displacement amount->Normalization of image featuresMean value of transformation (Ten)>Is a correction center equal to +.>。
The formula uses a limit sign to indicate that as S approaches X, the value in the formula approaches R. Specifically, the formula corrects each displacement amount, and calculates a corresponding correction result. The correction center X is half of the displacement D, and represents symmetry, and the positive and negative values of the displacement are corrected in the same way according to the principle of symmetry. S is the normalized mean value of the image features and represents the influence of the image features on the displacement correction. The 1+1/S in the formula is a weighting process for preventing the denominator from being 0, wherein the smaller S is, the larger the denominator is, and the smaller the weighting coefficient is.
In summary, the method uses CNN to extract image features, then normalizes and calculates the mean value, and finally corrects the displacement by using a correction formula.
Specifically, the characteristics are corrected again to improve the accuracy of the displacement measured in each large period.
In example 9 the process was carried out,
a displacement sensor based measurement method, the method comprising the steps of:
step 1: arranging a plurality of displacement sensors in a matrix, and connecting an operation center with each displacement sensor;
step 2: each displacement sensor acquires displacement in real time according to different set periods, and the acquired displacement is sent to an operation center;
step 3: the corresponding period of each displacement sensor is a plurality of small periods obtained by equally dividing one large period;
step 4: the computing center constructs all displacement amounts in one large period into a matrix, after a plurality of large periods with set quantity, all the matrices are subjected to matrix superposition to obtain a superposition matrix, finally, image-based feature extraction is carried out on the superposition matrix to obtain image features, and according to the obtained image features, the displacement amount measured by each displacement sensor in each subsequent large period is corrected.
In the example 10 of the present invention,
based on the above embodiment, the displacement sensor is a strain type, inductance type, differential transformer type, eddy current type or hall type sensor.
In particular, displacement is an amount related to the movement of the position of an object during movement, and the range in which the manner of measurement of displacement is related is quite broad. Small displacements are usually detected by strain, inductive, differential transformer, eddy current, hall sensors, and large displacements are usually measured by sensing techniques such as inductive synchronizers, gratings, capacitive gratings, magnetic gratings, etc. The grating sensor has the advantages of easy realization of digitalization, high precision (the highest resolution can reach the nanometer level at present), strong anti-interference capability, no human reading error, convenient installation, reliable use and the like, and is increasingly widely applied to the industries of machine tool processing, detection instruments and the like.
It should be noted that, in the system provided in the foregoing embodiment, only the division of the foregoing functional units is illustrated, in practical application, the foregoing functional allocation may be performed by different functional units, that is, the units or steps in the embodiment of the present invention are further decomposed or combined, for example, the units in the foregoing embodiment may be combined into one unit, or may be further split into multiple sub-units, so as to complete all or the functions of the units described above. The names of the units and the steps related to the embodiment of the invention are only used for distinguishing the units or the steps, and are not to be construed as undue limitation of the invention.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the storage device and the processing device described above and the related description may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.
Those of skill in the art will appreciate that the various illustrative elements, method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the program(s) corresponding to the software elements, method steps may be embodied in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, QD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Those skilled in the art may implement the described functionality using different approaches for each particular application, but such implementation is not intended to be limiting.
The terms "first," "another portion," and the like, are used for distinguishing between similar objects and not for describing a particular sequential or chronological order.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or unit/apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or unit/apparatus.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related art marks may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will fall within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention.
Claims (8)
1. A displacement sensor based measurement device, the device comprising: a plurality of displacement sensors arranged in a matrix and an operation center; each displacement sensor acquires displacement in real time according to different set periods, and the acquired displacement is sent to an operation center; the corresponding period of each displacement sensor is a plurality of small periods obtained by equally dividing one large period; the operation center constructs a matrix of all displacement amounts in one large period, after a plurality of large periods with a set number, carries out matrix superposition on all the matrixes to obtain a superposition matrix, finally carries out image-based feature extraction on the superposition matrix to obtain image features, and corrects the displacement amount measured by each displacement sensor in each subsequent large period according to the obtained image features;
the matrix superposition process comprises the following steps: randomly sequencing all the matrixes, carrying out convolution operation on each matrix and a corresponding correction matrix according to a random sequencing result to obtain a convolution matrix, and then superposing all the convolution matrixes obtained by the convolution operation to obtain a superposition matrix;
the correction matrix is expressed using the following formula:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is a correction matrix; />For the number of matrices>For a large period>The value range for correcting the coefficient is as follows: 0.2 to 0.6;is a displacement deviation predicted value.
2. The apparatus of claim 1, wherein the number of displacement sensors is at least greater than 5.
3. The apparatus of claim 2, wherein the large period is at least 2 seconds long; the small period is at least 0.2 seconds long.
4. The apparatus of claim 3, wherein the displacement bias predictor is a rank of each matrix.
5. The apparatus of claim 4, wherein the means for image-based feature extraction from the superimposed matrix comprises: and regarding the superposition matrix as an image matrix, and then carrying out feature extraction on the image matrix to obtain image features.
6. The apparatus of claim 5, wherein the method for correcting the displacement measured by each displacement sensor in each subsequent large period based on the image characteristics comprises: substituting the image characteristics and each displacement into the following formula, and calculating to obtain a corrected result:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>To correct the result->For displacement amount->Normalization of image featuresValue of->Is a correction center equal to +.>。
7. The apparatus of claim 6, wherein the displacement sensor is a strain, inductive, differential transformer, eddy current, or hall sensor.
8. A displacement sensor based measurement method based on a device according to one of claims 1 to 7, characterized in that the method comprises the steps of:
step 1: arranging a plurality of displacement sensors in a matrix, and connecting an operation center with each displacement sensor;
step 2: each displacement sensor acquires displacement in real time according to different set periods, and the acquired displacement is sent to an operation center;
step 3: the period corresponding to each displacement sensor is a plurality of small periods obtained by equally dividing one large period:
step 4: the operation center constructs all displacement amounts in one large period into a matrix, after a plurality of large periods with set quantity, all the matrices are subjected to matrix superposition to obtain a superposition matrix, finally, image-based feature extraction is carried out on the superposition matrix to obtain image features, and according to the obtained image features, the displacement amount measured by each displacement sensor in each subsequent large period is corrected;
the matrix superposition process comprises the following steps: randomly sequencing all the matrixes, carrying out convolution operation on each matrix and a corresponding correction matrix according to a random sequencing result to obtain a convolution matrix, and then superposing all the convolution matrixes obtained by the convolution operation to obtain a superposition matrix;
the correction matrix is expressed using the following formula:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is a correction matrix; />For the number of matrices>For a large period>The value range for correcting the coefficient is as follows: 0.2 to 0.6; />Is a displacement deviation predicted value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310537927.8A CN116295188B (en) | 2023-05-15 | 2023-05-15 | Measuring device and measuring method based on displacement sensor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310537927.8A CN116295188B (en) | 2023-05-15 | 2023-05-15 | Measuring device and measuring method based on displacement sensor |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116295188A CN116295188A (en) | 2023-06-23 |
| CN116295188B true CN116295188B (en) | 2023-08-11 |
Family
ID=86789101
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310537927.8A Active CN116295188B (en) | 2023-05-15 | 2023-05-15 | Measuring device and measuring method based on displacement sensor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116295188B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006136696A1 (en) * | 2005-06-22 | 2006-12-28 | 3Dfeel | Method and device for rendering interactive a volume or surface |
| CA2713305A1 (en) * | 2010-08-23 | 2012-02-23 | Justin D. Pearlman | Method of and system for signal separation during multivariate physiological monitoring |
| CN108088845A (en) * | 2017-12-07 | 2018-05-29 | 武汉精测电子集团股份有限公司 | A kind of image-forming correction method and device retained based on Weak Information |
| WO2019119301A1 (en) * | 2017-12-20 | 2019-06-27 | 华为技术有限公司 | Method and device for determining feature image in convolutional neural network model |
| CN111272366A (en) * | 2020-03-02 | 2020-06-12 | 东南大学 | Bridge displacement high-precision measurement method based on multi-sensor data fusion |
| CN115016645A (en) * | 2022-06-15 | 2022-09-06 | 哈尔滨工业大学 | Multi-degree-of-freedom acquired data glove for cooperative control of artificial fingers |
| CN115311624A (en) * | 2022-08-16 | 2022-11-08 | 广州市吉华勘测股份有限公司 | Slope displacement monitoring method and device, electronic equipment and storage medium |
| CN116068883A (en) * | 2023-03-06 | 2023-05-05 | 山东慧点智能技术有限公司 | Remote intelligent control method and system for water conservancy gate |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI321503B (en) * | 2007-06-15 | 2010-03-11 | Univ Nat Taiwan Science Tech | The analytical method of the effective polishing frequency and number of times towards the polishing pads having different grooves and profiles |
-
2023
- 2023-05-15 CN CN202310537927.8A patent/CN116295188B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006136696A1 (en) * | 2005-06-22 | 2006-12-28 | 3Dfeel | Method and device for rendering interactive a volume or surface |
| CA2713305A1 (en) * | 2010-08-23 | 2012-02-23 | Justin D. Pearlman | Method of and system for signal separation during multivariate physiological monitoring |
| CN108088845A (en) * | 2017-12-07 | 2018-05-29 | 武汉精测电子集团股份有限公司 | A kind of image-forming correction method and device retained based on Weak Information |
| WO2019119301A1 (en) * | 2017-12-20 | 2019-06-27 | 华为技术有限公司 | Method and device for determining feature image in convolutional neural network model |
| CN111272366A (en) * | 2020-03-02 | 2020-06-12 | 东南大学 | Bridge displacement high-precision measurement method based on multi-sensor data fusion |
| CN115016645A (en) * | 2022-06-15 | 2022-09-06 | 哈尔滨工业大学 | Multi-degree-of-freedom acquired data glove for cooperative control of artificial fingers |
| CN115311624A (en) * | 2022-08-16 | 2022-11-08 | 广州市吉华勘测股份有限公司 | Slope displacement monitoring method and device, electronic equipment and storage medium |
| CN116068883A (en) * | 2023-03-06 | 2023-05-05 | 山东慧点智能技术有限公司 | Remote intelligent control method and system for water conservancy gate |
Non-Patent Citations (1)
| Title |
|---|
| 基于图像熵的直线电机动子位置精密测量方法;张凯;董菲;赵吉文;董思兴;;仪器仪表学报(12);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116295188A (en) | 2023-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3341223B1 (en) | Method for determining tyre characteristic influencing variables and control device therefore | |
| CN103759758B (en) | A kind of method for detecting position of the automobile meter pointer based on mechanical angle and scale identification | |
| CN108593260B (en) | Optical cable line fault positioning and detecting method and terminal equipment | |
| CN100559131C (en) | A kind of method for automatically detecting pointer instrument | |
| DE102018101441A1 (en) | Magnetic sensor position measurement with phase compensation | |
| Xu et al. | Application of a modified fuzzy ARTMAP with feature-weight learning for the fault diagnosis of bearing | |
| CN110455222B (en) | High-precision rotation angle measuring method, device and equipment | |
| CN113516226A (en) | Hybrid model multivariate time sequence anomaly detection method based on graph neural network | |
| CN109060836A (en) | High-pressure oil pipe joint external screw thread detection method based on machine vision | |
| CN104019834B (en) | Single code channel absolute position coding method and reading measuring system | |
| CN105389817A (en) | Two-time phase remote sensing image change detection method | |
| DE102014015978B4 (en) | Position detecting device and lens device and image pick-up device including the same | |
| CN105823504A (en) | Zero-point-crossing processing method of encoder | |
| Tang et al. | Fault diagnosis of rolling bearing based on probability box theory and GA-SVM | |
| CN116295188B (en) | Measuring device and measuring method based on displacement sensor | |
| CN112066862A (en) | Position calibration method and device for linear displacement steering engine and terminal | |
| CN115840120A (en) | High-voltage cable partial discharge abnormity monitoring and early warning method | |
| Mai et al. | An automatic meter reading method based on one-dimensional measuring curve mapping | |
| EP1861681B9 (en) | Method and circuit arrangement for recording and compensating a tilt angle when detecting a rotation movement or angle | |
| CN105701471A (en) | Method for correcting laser scanning waveform abnormal data | |
| CN116401535B (en) | Time sequence data coarse and fine recognition method and system based on difference method | |
| CN102521874B (en) | Normal sampling recalculation method based on image reconstruction three-dimensional data | |
| CN115601713A (en) | Steam drum water level image recognition method and device | |
| CN113591875B (en) | High-precision pointer type instrument identification method | |
| CN108917632B (en) | High-efficiency high-precision digital image correlation displacement post-processing method |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |