CN111178270B - XRD-based ternary combined material chip structure analysis system and method - Google Patents
XRD-based ternary combined material chip structure analysis system and method Download PDFInfo
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Abstract
A three-element combined material chip structure analysis system and method based on XRD, through XRD data and coordinate data in the analysis material spectrogram, confirm the peak value and get the peak value distribution diagram with the principle of three strong peaks, then divide all peak values into the interval that multiple candidate base vector differentiates the need according to the interval polymerization algorithm of peak value and use the base vector algorithm to obtain all base vectors according to this interval; finally, judging whether each material spectrogram has a basis vector and the composite condition thereof for structure calibration; the method can quickly construct the phase diagram, uses the hierarchical clustering method as a reference object to assist in adjusting and analyzing the phase diagram, and provides a solution for efficient XRD data analysis and quick construction of the phase diagram for scholars in the professional field of materials science.
Description
Technical Field
The invention relates to a technology in the field of computer-aided material analysis, in particular to a ternary combined material chip structure analysis system and method based on XRD.
Background
Clustering spectrogram data by a PCA dimension reduction technology and a hierarchical clustering method according to XRD (X-ray diffraction spectrum) data of the combined material to construct a phase diagram and observe the change trend of the XRD data is the conventional common computer-aided material analysis technology. However, most of the existing analysis techniques only cluster the spectrogram from the data surface, and ignore the original physical characteristics in the material, which makes the phase diagram construction accuracy inaccurate.
In the current material science field, the peak position of a crystal structure possibly existing is artificially calibrated according to prior knowledge in the field, and then whether a spectrogram has a peak value at the diffraction angle of the phase is judged to determine the phase to which the spectrogram belongs, but the prior knowledge is required by the technology to determine the phase possibly existing and the peak to which the phase belongs.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a ternary combination material chip structure analysis system and method based on XRD, which utilize the three-strong peak principle of XRD in the physical characteristics of materials, realize the rapid construction of a phase diagram through a peak value search algorithm, a peak value interval aggregation algorithm and a base vector search algorithm, use a hierarchical clustering method as a reference, assist in adjusting and analyzing the phase diagram, and provide a solution for the students in the professional field of materials science with high-efficiency XRD data analysis and rapid construction of the phase diagram.
The invention is realized by the following technical scheme:
the invention relates to a ternary combined material chip structure analysis system based on XRD, which comprises: the system comprises a data import module for importing coordinate component data of spectrograms and XRD spectrum data, a spectrogram module for displaying a summary view of all spectrograms, a prediction module for calibrating the category to which each spectrogram belongs, an interval adjustment module and a method comparison module, wherein: the data import module generates a phase diagram in a one-key import mode and outputs the phase diagram to the method comparison module, the interval adjustment module respectively outputs observation basis vectors to the spectrogram module and outputs the regenerated phase diagram to the method comparison module, and the observation module outputs the observed single/multi-component spectrogram to the spectrogram module.
The invention relates to a ternary combination material chip structure analysis method based on the system, which comprises the steps of determining peak values and obtaining a peak value distribution graph according to a three-strong peak principle by analyzing XRD data and coordinate data in a material spectrogram, dividing all the peak values into a plurality of intervals required by candidate base vector discrimination according to a peak value interval aggregation algorithm, and obtaining all the base vectors by using a base vector algorithm according to the intervals; and finally, judging whether each material spectrogram has a basis vector and the composite condition thereof for structure calibration.
Technical effects
Compared with the prior art, the method not only clusters the spectrogram by a machine learning clustering means, but also combines the three-strong peak principle of knowledge in the material field by communicating and cooperating with experts in the field of materials, designs various algorithms suitable for XRD data analysis, and ensures the reliability and stability of the algorithms. The invention adopts a Web-based visual interactive system for the auxiliary construction of the phase diagram, not only stays at the display level of the data, but also fully combines the further analysis and processing capability of the visual analysis technology to the data. Compared with the existing method that MATLAB software is usually required to be installed, background service and supporting program operation can be built only by installing python2.7, the system is more convenient and higher in usability, the data mining and XRD data analysis benefits are greatly improved in a multi-graph linkage visual interaction mode, the step of researching XRD data by experts in the material field is greatly simplified by directly generating a phase diagram through one-key type imported data, and the use threshold is also reduced. Meanwhile, the interactive operation and the system control are simple and convenient, the algorithm processing speed is high, and the spectrogram clustering accuracy is high, which is the biggest characteristic of the system.
Drawings
FIG. 1 is a system block diagram;
FIG. 2 is a flow chart of the present invention;
FIG. 3 is a flowchart of a basis vector lookup algorithm:
fig. 4 is a flowchart of the peak interval aggregation algorithm.
Detailed Description
As shown in fig. 1, the present embodiment relates to an XRD-based chip structure analysis system for ternary combination materials, which includes: the device comprises a data import module for importing coordinate component data of spectrograms and XRD spectrum data, a spectrogram module for displaying a summary view of all spectrograms, a prediction module for calibrating the class to which each spectrogram belongs, an interval adjustment module for modifying a peak value interval and a method comparison module.
The data import module is used for loading coordinate component data and XRD data, the first line of the coordinate component data needs to specify the material components of each position, then each line represents different component data, the first line of the XRD data represents the horizontal coordinate of a spectrogram, then each line represents different spectrogram data, and the two data files correspond to one another line by line.
The spectrogram module displays a summary view of all spectrograms by taking rectangular coordinates as a frame, the abscissa calibrates the value of the diffraction angle 2theta, the ordinate is an XRD intensity value, and a user can check the interested diffraction angle interval by brushing or by interactive modes such as amplification, reduction and the like. And simultaneously, for a plurality of predicted basis vectors, placing corresponding rectangular labels for interacting with other modules. The module can not only view the spectrogram summary view, but also view a specific spectrogram or a spectrogram of a certain type to increase the interaction mode.
The prediction module obtains the category of each spectrogram through a basis vector search algorithm and calibrates the category with different colors, and the material components of the point can be checked by a mouse suspension spectrogram point; and clicking the coordinate point, the spectrogram module highlights the line graph of the point, and a plurality of spectrograms can be clicked for observation.
The interval adjusting module is used for calibrating the 2theta value range of each specific judgment interval, calibrating the basic vector of each interval, and reconstructing a phase diagram by modifying the value by a material field expert so as to reduce the judgment error.
The method comparison module comprises: the device comprises a base vector phase diagram unit and a hierarchical clustering phase diagram unit which are used for displaying phase diagrams constructed based on a base vector search algorithm and a hierarchical clustering algorithm and a cross comparison unit for comparing two phase diagrams under the same component, wherein: the hierarchical clustering phase diagram unit is used for providing reference for comparison of the constructed phase diagrams, and a material expert can further analyze and adjust a discrimination interval according to different phase boundaries generated by the two phase diagrams, so that the construction accuracy of the phase diagrams is improved; the basis vector phase diagram unit is used for displaying the calculated phase diagram of the ternary composite material, wherein the phase category of each spectrogram is marked by different colors; the two units often have some judgment errors relative to the phase boundary data, and the judgment errors are reflected by the cross comparison unit, and the errors can be used for manually judging whether the interval end points are correct or not and whether the three-strong peak areas of each single phase are correct or not. Therefore, the cross comparison unit can be used for a material expert to perform correction exploration by combining with an interval adjusting module after analyzing each spectrogram and single-phase peak distribution.
The embodiment relates to a ternary combination material chip structure analysis method, which comprises the steps of determining a peak value and obtaining a peak value distribution graph according to a three-strong peak principle by analyzing XRD data and coordinate data in a material spectrogram, dividing all peak values into a plurality of intervals required by candidate base vector discrimination according to a peak value interval aggregation algorithm, and obtaining all base vectors by using a base vector search algorithm according to the intervals; and finally, judging whether each material spectrogram has a basis vector and the composite condition thereof for structure calibration.
The XRD data comprises diffraction angles and X-ray diffraction intensities of specific components under different diffraction angles.
The coordinate data comprises ternary material information and corresponding proportion thereof.
The basis vector in this embodiment specifically means: the basis vector searching algorithm finds out a single phase in the XRD data according to the three strong peaks with the highest peak values, and the peak values in all spectrograms in the same phase diagram are formed by compounding one or more basis vectors, so that the basis vectors are equivalent replacements of the single phase.
The peak value distribution diagram is as follows: each line of XRD data represents the information of a spectrogram, the peak point of the XRD data can be obtained through a peak value searching algorithm, the diffraction angles and diffraction intensities of the peaks of all the spectrograms are highlighted under a rectangular coordinate system, namely a peak value distribution diagram in the spectrogram is formed, and the intensity of the same phase under the same diffraction angle and the transverse deviation condition of the diffraction angle can be observed through the distribution diagram.
The peak value searching algorithm is as follows: determining the peak position by searching the maximum value point of the spectrogram, wherein the specific process comprises the following steps: traversing XRD data of a single spectrogram, continuously updating the maximum point along with the continuous enhancement of the diffraction intensity of the left half of the peak value, and after reaching the peak point, keeping the maximum point because the diffraction intensity of the right half of the peak value is smaller than the diffraction intensity of the peak value until the height difference between the intensity of the right side and the maximum value recorded at the moment is larger than a set threshold value, recording the diffraction angle and the diffraction intensity of the maximum value as a peak point and a corresponding peak value, and then continuously traversing to find out all the peak points.
The threshold value in this embodiment is an average diffraction intensity value of the imported XRD data, and has data adaptivity.
The peak interval aggregation algorithm is as follows: considering that XRD data adaptively obtain an interval for basis vector discrimination due to peak diffraction angle shift caused by structural components of materials, the specific steps include:
1) Initializing an interval set M, and setting minNum = n/20 as the minimum number of peak values contained in a reasonable peak value interval, wherein: n is the number of rows of XRD data, scale =1.5 is the discrimination ratio threshold, and peakNum is the actual number of peaks in the peak interval.
2) As the ternary alloy composite phase is mostly the composite of two phases, according to the principle of three strong peaks, only the highest 6 peaks are needed to be selected as the original peak data for each spectrogram, all the peaks are collected and then sequenced and traversed in sequence: and when the right end point of the previous interval and the currently traversed abscissa are larger than 5 data gaps, setting the right end point of the previous interval as a new starting point of an interval to be added, then continuously traversing peak data until the density is smaller than a threshold value, determining the right end point of the interval to be added, namely realizing the segmentation of the discrimination interval, then adding data to the set M, and continuously traversing until all peak intervals are found out.
The added data refers to: the peak interval can be added when the following 3 conditions are satisfied simultaneously:
①peakNum>＝minNum
②avgTopkPeak/avgPeak>＝scale
③avgTopkPeak/avgSegment>＝scale
3) Calculating the average value of the highest 0.5 × minnum peak values in the peak value data in the interval, and recording the average value as avgttopkpeak; calculating the average peak height in the interval, and marking as avgPeak; the average of all XRD data in this interval was calculated and recorded as avgSegment.
The base vector search algorithm is as follows: for peak value intervals obtained by a peak value interval aggregation algorithm, determining the positions of the peak values of the basis vectors in the intervals according to a three-strong peak principle, and specifically comprising the following steps of:
i) Counting three strong peaks of all spectrograms, and dividing the spectrograms into a plurality of classes according to the falling intervals of the three strong peaks;
ii) the class with the largest number of spectrogram is the first basis vector;
iii) And searching the class with the three strong peak intervals, which is different from the base vectors found previously and has the largest number, in the rest classes as a second base vector, and so on until the requirement is not met in the rest classes, and ending.
The structural calibration refers to: and after determining the corresponding interval of each basis vector according to a basis vector search algorithm, traversing the peak diffraction angles of all spectrograms and predicting the single phase and the complex phase of each point.
The traversal refers to the following steps: a plurality of single phases a, b, c and the like are analyzed according to the current analysis, when peaks exist in the three-strong-peak area of the spectrogram a and the number of the peaks in the three-strong-peak areas of the other single phases is less than 2, the spectrogram is marked as the single phase a; when the spectrogram has at least two peak values in the three-intensity peak region of a and at least two peak values in the three-intensity peak region of b, the spectrogram is marked as a composite phase of a single phase a and a single phase b; the remaining cases are labeled as unknown phases.
The structure calibration is characterized in that a phase diagram is further constructed by adopting a hierarchical clustering algorithm to form a reference so as to assist in improving the accuracy of the phase diagram, and specifically comprises the following steps: initially taking a median array of a three-intensity peak interval of each spectrogram as single category data to form a distance matrix, calculating the similarity between different categories of data, and continuously iterating and combining two data with the closest similarity to create a hierarchical nested clustering tree. The total number of clusters is the total number of classes of pure single-phase and compound phase obtained by the basis vector search algorithm. Wherein the similarity uses Pearson correlation coefficients
In the embodiment, u and v are any two data in the distance matrix.
As shown in fig. 2, the system of this embodiment is specifically implemented by a front-end and back-end interactive software architecture of a web page and a back-end server, where: compiling a design webpage by using front-end languages such as html, CSS, javaScript and the like, and processing a vector diagram by using a mainstream data visualization JS library D3. JS; the method comprises the steps that a Web server frame Tornado based on Python is used for front-end and back-end transmission, parameters are transmitted to a server by the front end through an HTTP method of Get, functions of view display and material expert human-computer interaction are provided, data received by the server are converted into json format and are transited to a background function interface for processing, and page value transmission is carried out through ajax.
Loading the data into a server end in a webpage import file mode, calling a peak value search algorithm by the server to obtain a binary data list of 6 peak values with the highest diffraction intensity and corresponding diffraction angles of each spectrogram, and sequencing the two binary data lists from small to large according to the diffraction angles; dividing all peak value binary groups into a plurality of intervals required by candidate base vector discrimination by using a peak value interval aggregation algorithm, displaying the discrimination intervals in a spectrogram view, filling numerical values into an interval adjustment view, simultaneously creating a Boolean matrix Mat for judging whether peak values are contained in all spectrogram intervals, wherein a line represents a spectrogram and is listed as all discrimination intervals for showing whether the spectrogram has the peak value in the discrimination interval; and then, based on the obtained peak value discrimination interval, converting all the basis vectors and the discrimination intervals corresponding to the basis vectors into json files according to a basis vector search algorithm, feeding back the json files to a front-end spectrogram view and an interval adjustment view, finally, combining a Boolean matrix Mat, contrasting the interval where three strong peaks of the basis vectors are located, judging whether each spectrogram has the basis vectors and the composite condition thereof, classifying each spectrogram by using numbers, feeding back the spectrogram to the front-end spectrogram view, and rendering each point color by using the transmitted numbers of the front-end view so as to complete the construction of a final phase diagram.
The invention prevents the condition that the phases of the same substance have the same diffraction angle under a certain offset error through a peak interval polymerization algorithm; the interval aggregation result obtained by the background algorithm is displayed through the interval adjusting module, the algorithm cannot ensure that the left end point and the right end point of the interval are correct, so that an adjusting function is added, if a material science expert thinks that the background calculation result is not in good agreement with reality, the numerical value can be changed, and the system can recalculate according to the adjusted interval. Meanwhile, because the prediction of the system on the basis vector is not hundreds of correct, it is necessary to provide an interval adding and deleting function as an auxiliary means for manually correcting the basis vector after the system is analyzed.
The foregoing embodiments may be modified in many different ways by those skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (8)
1. A ternary combination material chip structure analysis method is characterized in that a peak value is determined according to a three-strong peak principle by analyzing XRD data and coordinate data in a material spectrogram and a peak value distribution graph is obtained, then all the peak values are divided into a plurality of intervals required by candidate basis vector discrimination according to a peak value interval aggregation algorithm, and all basis vectors are obtained by using a basis vector algorithm according to the intervals; finally, judging whether each material spectrogram has a basis vector and the compound condition thereof for structure calibration;
the XRD data comprises diffraction angles and X-ray diffraction intensities of components of the ternary combination material under different diffraction angles;
the coordinate data comprises ternary material information and corresponding proportion thereof;
the base vector refers to: the basis vector searching algorithm finds out a single phase in the XRD data according to the three highest intensity peaks of the peak values, and the peak values in all spectrograms in the same phase diagram are formed by compounding one or more basis vectors, so that the basis vectors are equivalent replacements of the single phase;
the peak value distribution diagram is as follows: each line of XRD data represents information of one spectrogram, a peak point of the XRD data is obtained through a peak searching algorithm, diffraction angles and diffraction intensities of peaks of all the spectrograms are highlighted under a rectangular coordinate system, namely a peak distribution diagram in the spectrogram is formed, and the intensity of the same phase under the same diffraction angle and the lateral deviation condition of the diffraction angle are observed through the distribution diagram;
the peak interval aggregation algorithm is as follows: considering that XRD data causes peak diffraction angle shift due to structural components of materials, the method adaptively obtains an interval for basis vector discrimination, and comprises the following specific steps:
1) Initializing an interval set M, and setting minNum = n/20 as the minimum number of peak values contained in a reasonable peak value interval, wherein: n is the number of rows of XRD data, scale =1.5 is a discrimination ratio threshold, and peakNum is the actual number of peaks in a peak value interval;
2) As the ternary alloy composite phase is mostly the composite of two phases, according to the principle of three strong peaks, only the highest 6 peaks are needed to be selected as the original peak data for each spectrogram, all the peaks are collected and then sequenced and traversed in sequence: setting the right end point of the previous interval and the currently traversed abscissa as a new starting point of the interval to be added when the data interval is larger than 5, then continuously traversing peak data until the density is smaller than a threshold value, determining the right end point of the interval to be added, namely realizing the segmentation of the discrimination interval, then adding data to the set M, and continuously traversing until all peak intervals are found out;
3) Calculating the average value of the highest 0.5 × minnum peak values in the peak value data in the interval, and recording the average value as avgttopkpeak; calculating the average peak height in the interval and recording as avgPeak; calculating the average value of all XRD data in the interval and recording as avgSegment;
the added data refers to: the peak interval can be added when the following 3 conditions are satisfied simultaneously:
①peakNum>＝minNum
②avgTopkPeak/avgPeak>＝scale
③avgTopkPeak/avgSegment>＝scale。
2. the method for analyzing the structure of the ternary combination material chip of claim 1, wherein the peak search algorithm is as follows: determining the peak position by searching the maximum value point of the spectrogram, wherein the specific process comprises the following steps: traversing XRD data of a single spectrogram, continuously enhancing the diffraction intensity of the left half of the peak value, continuously updating the maximum point, and after reaching the peak point, keeping the maximum point because the diffraction intensity of the right half of the peak value is smaller than the diffraction intensity of the peak value until the height difference between the intensity of the right side and the maximum value recorded at the moment is larger than a set threshold value, recording the diffraction angle and the diffraction intensity of the maximum value as a peak point and a corresponding peak value, and then continuously traversing to find out all the peak points.
3. The method for analyzing the structure of a ternary composite material chip as claimed in claim 1, wherein the basis vector search algorithm is as follows: for peak value intervals obtained by a peak value interval aggregation algorithm, determining the positions of the peak values of the basis vectors in the intervals according to a three-strong peak principle, and specifically comprising the following steps:
i) Counting three strong peaks of all spectrograms, and dividing the spectrograms into a plurality of classes according to the falling intervals of the three strong peaks;
ii) the class with the largest number of spectrogram is the first basis vector;
iii) And searching the class with three strong peak intervals which are different from the base vectors found previously and the largest number in the rest classes as a second base vector, and so on until the requirement is not met in the rest classes.
4. The method for analyzing the structure of the ternary composite material chip as claimed in claim 1, wherein the structure calibration is as follows: after determining the corresponding interval of each basis vector according to a basis vector search algorithm, traversing the peak diffraction angles of all spectrograms, and predicting the single phase and the complex phase of each point;
the traversal is as follows: a plurality of single phases a, b, c and the like are analyzed according to the current analysis, when peaks exist in the three-strong-peak area of the spectrogram a and the number of the peaks in the three-strong-peak areas of the other single phases is less than 2, the spectrogram is marked as the single phase a; when the spectrogram has at least two peak values in the three-intensity peak region of a and at least two peak values in the three-intensity peak region of b, the spectrogram is marked as a composite phase of a single phase a and a single phase b; the remaining cases are marked as unknown phases.
5. The method for analyzing the structure of the ternary composite material chip according to claim 1 or 4, wherein the structure calibration is carried out by further adopting a hierarchical clustering algorithm to construct a phase diagram to form a reference to assist in improving the accuracy of the phase diagram, and specifically comprises the following steps: initially taking a median array of a three-intensity peak interval of each spectrogram as single category data to form a distance matrix, calculating the similarity between different categories of data, and continuously iterating and combining two data with the closest similarity to create a hierarchical nested clustering tree;
the total classification number of the pure single-phase and the composite phase obtained by the basis vector searching algorithm is used as the clustering number;
the similarity is based on Pearson correlation coefficient
In the embodiment, u and v are any two data in the distance matrix.
6. An XRD-based ternary composite material chip structure analysis system for implementing the method according to any preceding claim, comprising: the device comprises a data import module for importing coordinate component data of spectrograms and XRD spectrum data, a spectrogram module for displaying summary views of all spectrograms, a prediction module for calibrating the class to which each spectrogram belongs, an interval adjustment module and a method comparison module, wherein: the data import module generates a phase diagram in a one-key import mode and outputs the phase diagram to the method comparison module, the interval adjustment module respectively outputs observation basis vectors to the spectrogram module and outputs the regenerated phase diagram to the method comparison module, and the observation module outputs the observed single/multi-component spectrogram to the spectrogram module.
7. The system of claim 6, wherein the data import module is configured to load coordinate component data and XRD data, wherein a first row of the coordinate component data is required to specify the material components at each location, and each subsequent row represents different component data, and the first row of the XRD data represents the abscissa of the spectrogram, and each subsequent row represents different spectrogram data, and the two data files correspond to each other row by row;
the spectrogram module displays a summary view of all spectrograms by taking a rectangular coordinate as a frame, the abscissa calibrates the value of the diffraction angle 2theta, the ordinate is an XRD intensity value, and a user checks an interested diffraction angle interval by brushing or an interactive mode such as amplification, reduction and the like; meanwhile, for a plurality of predicted basis vectors, placing corresponding rectangular labels for interaction with other modules;
the prediction module calibrates the classes of all spectrograms obtained by a basis vector search algorithm by using different colors, and the material components of the spectrogram are checked by mouse suspension spectrogram points; clicking the coordinate point, highlighting the line graph of the point by the spectrogram module, and clicking a plurality of spectrograms for observation;
the interval adjusting module is used for calibrating the 2theta value range of each specific judgment interval, calibrating the base vector of each interval, and reconstructing a phase diagram by modifying the value to reduce the judgment error.
8. The system of claim 6, wherein said method comparison module comprises: the device comprises a base vector phase diagram unit and a hierarchical clustering phase diagram unit which are used for showing phase diagrams constructed based on a base vector search algorithm and a hierarchical clustering algorithm, and a cross comparison unit for comparing the two phase diagrams under the same components, wherein: the hierarchical clustering phase diagram unit is used for providing reference for comparison of the constructed phase diagrams, further analyzing and adjusting the discrimination interval according to different phase boundaries generated by the two phase diagrams, and improving the accuracy rate of the construction of the phase diagrams; the basis vector phase diagram unit is used for displaying the calculated phase diagram of the ternary composite material, wherein the phase category of each spectrogram is marked by different colors; the cross comparison unit reflects the judgment errors of the basis vector phase diagram unit and the hierarchical clustering phase diagram unit on the phase boundary data, is used for manually judging whether the interval end point is correct or not and whether the three-strong peak area of each single phase is correct or not, analyzes the peak distribution of each spectrogram and each single phase and then combines an interval adjustment module to perform correction exploration.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101630736A (en) * | 2009-08-17 | 2010-01-20 | 深圳市天骄科技开发有限公司 | Improvement method of cycle performance of lithium battery tertiary cathode material |
| WO2017155119A1 (en) * | 2016-03-11 | 2017-09-14 | 国立大学法人東京工業大学 | Sulfide solid electrolyte |
| CN107818298A (en) * | 2017-09-27 | 2018-03-20 | 厦门大学 | General Raman spectral characteristics extracting method for machine learning material recognition |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030078739A1 (en) * | 2001-10-05 | 2003-04-24 | Surromed, Inc. | Feature list extraction from data sets such as spectra |
-
2019
- 2019-12-30 CN CN201911396757.6A patent/CN111178270B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101630736A (en) * | 2009-08-17 | 2010-01-20 | 深圳市天骄科技开发有限公司 | Improvement method of cycle performance of lithium battery tertiary cathode material |
| WO2017155119A1 (en) * | 2016-03-11 | 2017-09-14 | 国立大学法人東京工業大学 | Sulfide solid electrolyte |
| CN107818298A (en) * | 2017-09-27 | 2018-03-20 | 厦门大学 | General Raman spectral characteristics extracting method for machine learning material recognition |
Non-Patent Citations (2)
| Title |
|---|
| Wei Liu等.Immobilization of elemental mercury in non-ferrous metal smelting gas using ZnSe1-xSx nanoparticles.Fuel .2019,第254卷1-7. * |
| 张毅凡等.分布式拒绝服务的可视分析.网络与信息安全学报.2017,57-69. * |
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