CN114112982A - Fabric fiber component qualitative method based on k-Shape - Google Patents
Fabric fiber component qualitative method based on k-Shape Download PDFInfo
- Publication number
- CN114112982A CN114112982A CN202111178112.2A CN202111178112A CN114112982A CN 114112982 A CN114112982 A CN 114112982A CN 202111178112 A CN202111178112 A CN 202111178112A CN 114112982 A CN114112982 A CN 114112982A
- Authority
- CN
- China
- Prior art keywords
- fabric
- data
- fiber
- fiber component
- shape
- 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
- 239000000835 fiber Substances 0.000 title claims abstract description 95
- 239000004744 fabric Substances 0.000 title claims abstract description 78
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000007781 pre-processing Methods 0.000 claims abstract description 10
- 238000004140 cleaning Methods 0.000 claims abstract description 7
- 238000001514 detection method Methods 0.000 claims abstract description 6
- 239000000203 mixture Substances 0.000 claims description 26
- 238000012545 processing Methods 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 8
- 230000003595 spectral effect Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000000007 visual effect Effects 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims description 2
- 238000003379 elimination reaction Methods 0.000 claims description 2
- 238000013519 translation Methods 0.000 claims description 2
- QVRVXSZKCXFBTE-UHFFFAOYSA-N n-[4-(6,7-dimethoxy-3,4-dihydro-1h-isoquinolin-2-yl)butyl]-2-(2-fluoroethoxy)-5-methylbenzamide Chemical compound C1C=2C=C(OC)C(OC)=CC=2CCN1CCCCNC(=O)C1=CC(C)=CC=C1OCCF QVRVXSZKCXFBTE-UHFFFAOYSA-N 0.000 claims 1
- 238000004422 calculation algorithm Methods 0.000 abstract description 14
- 238000002329 infrared spectrum Methods 0.000 abstract description 9
- 238000004451 qualitative analysis Methods 0.000 abstract description 6
- 239000004753 textile Substances 0.000 description 10
- 238000009499 grossing Methods 0.000 description 5
- 229920004933 Terylene® Polymers 0.000 description 4
- 239000005020 polyethylene terephthalate Substances 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 229920002972 Acrylic fiber Polymers 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 2
- 239000004677 Nylon Substances 0.000 description 2
- 229920002334 Spandex Polymers 0.000 description 2
- 210000000085 cashmere Anatomy 0.000 description 2
- 239000003153 chemical reaction reagent Substances 0.000 description 2
- 238000002485 combustion reaction Methods 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000003912 environmental pollution Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 229920001778 nylon Polymers 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000002310 reflectometry Methods 0.000 description 2
- 239000004759 spandex Substances 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 210000002268 wool Anatomy 0.000 description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 238000004497 NIR spectroscopy Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 125000004432 carbon atom Chemical group C* 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000004090 dissolution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000002844 melting Methods 0.000 description 1
- 230000008018 melting Effects 0.000 description 1
- 239000002861 polymer material Substances 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2201/00—Features of devices classified in G01N21/00
- G01N2201/12—Circuits of general importance; Signal processing
- G01N2201/129—Using chemometrical methods
- G01N2201/1293—Using chemometrical methods resolving multicomponent spectra
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Computational Biology (AREA)
- Health & Medical Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a fabric fiber component qualitative method based on a time series clustering algorithm (k-Shape), which is used for solving the problem that near infrared spectrum data of fabric fiber components are difficult to classify. The method comprises the following steps: collecting the fiber components of various types of fabrics by using near infrared equipment; cleaning, preprocessing and storing the data; calculating and extracting characteristic shapes of the fiber component data of each type of fabric based on a k-Shape clustering algorithm; and calculating intra-cluster difference and inter-cluster difference of the sample data and the characteristic data of the fiber components of the fabrics of various categories based on the distance measure SBD (Shape-based distance), and finishing real-time qualitative detection of the fiber components of the fabrics. The method can realize rapid qualitative analysis of the fabric fiber components, and has the advantages of high accuracy, intuition and strong interpretability.
Description
Technical Field
The invention relates to a fabric fiber component qualitative method, in particular to a fabric fiber component qualitative method based on k-Shape.
Background
In recent years, the economy of China is vigorously developed, and the requirements of people on the quality of life are continuously improved. As a key part of people's clothing and housing', the textile industry is also gaining more attention. In the textile industry, identification of the type of textile fibre components is essential, and the methods of the prior art for classifying textile fibre components are mainly divided into intelligent and non-intelligent methods.
Non-intelligent methods, i.e., the methods for characterizing the fiber composition of industrially conventional fabrics, generally rely on chemical reagents or physical external forces, such as combustion, melting point, visual hand-feel, microscopic section analysis, etc. Generally, in the conventional method, the fiber sample to be detected needs to be subjected to physical slicing, combustion, chemical dissolution and other operations, and then the fiber components of the fabric are further classified and qualified. The existing non-intelligent method usually uses certain chemical and physical means to destructively measure a sample to be measured when the fabric fiber component is qualitative or quantitative, usually needs professional institutions and personnel to operate and test, is difficult to operate, and has certain safety problems of chemical reagents or physical instruments, which may cause environmental pollution.
With the development of digital technology, some organizations and research departments generally use devices such as a spectrometer and a microscope to classify and characterize components by combining traditional mathematical statistics or artificial intelligence methods in combination with methods for detecting textiles by algorithms.
The near infrared spectral data of the fiber composition of the fabric can be regarded as a kind of time series data. In the aspect of time series clustering analysis, the accuracy of the k-Shape algorithm is superior to that of all scalable algorithms, the efficiency is excellent, and the clustering result of the time series can be obtained quickly. k-Shape introduces a distance measure SBD (Shape-based distance) and computes the centroid of a class from the SBD. Calculating the centroid of a class by conventional methods such as arithmetic averaging is difficult for the sequence to reflect the characteristics of the class, or such averaging is meaningless. The centroids derived from SBD are more able to preserve the shape and features of the class than traditional methods. One of the cores of the qualitative analysis of the fabric fiber components is to obtain the characteristic shape of the data of the fabric fiber components. According to investigation, no invention is dedicated to solving the qualitative problem of the fabric fiber component by using a k-Shape algorithm, and the innovativeness of the patent can be embodied.
Disclosure of Invention
The invention aims to overcome the problems of the prior art and provides a fabric fiber component qualitative method based on k-Shape.
In order to overcome the limitation problem of extracting data characteristics by a classification algorithm in the prior art, the invention innovatively applies a time series clustering analysis algorithm to the qualitative analysis of fabric fiber components, and provides a fabric fiber component qualitative method based on k-Shape. The method can better retain the data characteristics of the near infrared spectrum sequence of the fabric fiber and judge whether the current sample belongs to natural plant fiber and the blend of the natural plant fiber and spandex, acrylic fiber blend, blend of terylene and natural plant fiber, blend of terylene and other fibers which do not belong to natural plant fiber, blend of nylon and other fibers which do not belong to natural plant fiber, blend of wool, cashmere and silk and other categories. And respectively calculating the current sample data and the characteristic data of the eight fabric fiber component categories, and finally determining the properties of the fabric fiber components.
The qualitative process of the fabric fiber components for realizing the near infrared spectrum comprises the following steps:
s1: the various classes of textile fiber components were collected using near infrared equipment. The method comprises the steps of collecting near infrared spectrum data of fabric fiber components of various classes, requiring uniform collection environment without external environment interference such as excessive illumination and recording component class labels of each sample.
S2: and cleaning, preprocessing and storing the data. Performing supplementary acquisition or directly removing error data; carrying out preprocessing such as normalization processing, denoising processing, sequential Fourier transform processing, baseline translation elimination processing, data dimension expansion processing, data enhancement processing of simulated spectral characteristics and the like on data; and storing the cleaned and preprocessed data and the labels thereof into a database.
S3: and extracting the characteristic shape of the fiber component data of each category of the fabric. Based on the distance measure SBD, the feature sequences of each class are calculated separately so that the sum of the squares of all sequences in a class and the SBD of the feature sequence is the minimum, which is also called the centroid of the class.
S4: and (3) carrying out real-time qualitative detection on the fabric fiber components. Scanning the surface of the textile sample by adopting near-infrared equipment; transmitting data to the terminal through Bluetooth or a wired path; the terminal forwards the sample near infrared spectrum data to the server, and the server performs processing on the sample near infrared spectrum data as in S2; respectively calculating the centroids of the fiber components of various fabrics and the distance measure SBD thereof to obtain classification results; the user obtains the classification result, and the classification result can be compared with the component label given by the textile.
Compared with the prior art, the invention has the following advantages: the invention innovatively provides a fabric fiber component qualitative method based on k-Shape. Due to the superiority of the performance of the k-Shape algorithm, the method can realize rapid qualitative analysis of the fabric fiber components, and has the advantages of high accuracy, intuition and strong interpretability.
Drawings
The present application will be described in further detail with reference to the following drawings and detailed description.
FIG. 1 is a diagram of a qualitative scheme of the fiber composition of the fabric of the present invention.
FIG. 2 is a flow chart of a method for characterizing the fiber composition of a fabric according to the present invention.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, the present invention should be understood not to be limited to such an embodiment described below, and the technical idea of the present invention may be implemented in combination with other known techniques or other techniques having the same functions as those of the known techniques.
In the following description of the embodiments, for purposes of clearly illustrating the structure and operation of the present invention, directional terms are used, but the terms "front", "rear", "left", "right", "outer", "inner", "outward", "inward", "axial", "radial", and the like are to be construed as words of convenience and are not to be construed as limiting terms.
The relevant terms are explained as follows:
data cleaning: the process of re-examining and verifying data aims to remove duplicate information, correct existing errors, and provide data consistency.
Near infrared spectroscopy: near Infrared (NIR) is an electromagnetic wave between visible (vis) and mid-Infrared (MIR) and is defined by ASTM (american society for testing and materials testing) as an electromagnetic wave having a wavelength in the range of 780 to 2526 nm. By scanning the near infrared spectrum of the sample, the characteristic information of the hydrogen-containing groups of the organic molecules in the sample can be obtained.
And (3) component analysis: the method refers to a technical method for analyzing the components of a product or a sample by detecting the molecular structure through methods such as a micro-spectrogram, a laser femtosecond and the like and qualitatively and quantitatively analyzing each component. The component analysis technology is mainly used for analyzing unknown substances, unknown components and the like, rapidly determining what various components are in a target sample, qualitatively and quantitatively analyzing the sample, and identifying materials, raw materials, auxiliaries, specific components and contents, foreign matters and the like of high polymer materials such as rubber and the like.
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of a method for characterizing the fiber composition of a fabric includes steps S1-S4 as follows:
s1: the various classes of textile fiber components were collected using near infrared equipment.
S2: and cleaning, preprocessing and storing the data.
S3: and extracting the characteristic shape of the fiber component data of each category of the fabric.
S4: and (3) carrying out real-time qualitative detection on the fabric fiber components.
In the data acquisition in step S1, a near-infrared spectrometer is used to acquire the reflectance of 228 units in the infrared band between 900 nm and 1700 nm. Scanning and sampling one or more sampling points of one sample by the instrument equipment according to an operation specification to acquire near infrared reflectivity data of one dimension and 228 unit lengths. Labeling component classification labels of qualified fabric fiber samples, wherein each spectral data corresponds to one component classification label;
in the data cleaning operation in step S2, the Savitzky-Golay algorithm of signal segmentation and the information standard deviation feature of signal are used. The segmented Savitzky-Golay algorithm of the signal is a polynomial smoothing algorithm based on the principle of least squares, also called convolution smoothing. The principle is to record 5 points with equal wavelength intervals in a section of the spectrum as an X set, the polynomial smoothing is to replace m points by polynomial fitting values of data of the m points left two, m points left one, m points right one and m points right two, and then the polynomial smoothing is moved in sequence until the spectrum signal is traversed. The noisy data is cleaned by calculating curve smoothness and information standard deviation of the overall signal.
The data preprocessing in step S2 includes data normalization, fourier transform, multivariate scattering correction algorithm for eliminating baseline shift, wiener filtering noise reduction, difference enhancement in data enhancement, sampling enhancement, generation of countermeasure network enhancement, and the like. By using the data preprocessing and data enhancement methods, the denoising can be performed according to the actual situation of the near infrared spectrum data, and the data smoothing is realized. In the using process, the data noise reduction and data enhancement algorithms can be selected and combined automatically according to the data characteristics to achieve the best effect.
In step S3, the characteristic shapes of the data of the fiber components of the fabrics of each category are extracted, and the specific algorithm is as follows:
first, the centroids of the classes are calculated, i.e.:
in the above formula, PkIs a data set of the fiber composition of a single type of fabric,
is a member of PkM is the reflectivity data of a single sample point
I.e. 228 units.
Second, according to the following three formulas:
can be combined with
The calculation of (1) is simplified as follows:
it is converted into a vector expression, namely:
introducing matrix M ═ QTS.Q, which converts the above formula to:
at this point, the centroid of the class can be obtained by only obtaining the eigenvalue and eigenvector of the matrix M. Respectively calculating mass centers of eight fabric fiber compositions of natural plant fibers and blends of the natural plant fibers and spandex, acrylic fibers, terylene and natural plant fibers, blends of terylene and other fibers which do not belong to the natural plant fibers, blends of nylon and other fibers which do not belong to the natural plant fibers, blends of wool, cashmere and silk and other types, and carrying out visual analysis on the mass centers to obtain the characteristic shapes of the fabric fibers.
In which the fabric fibre composition in step S4 works qualitatively, using the distance measure SBD, i.e.:
in the above formula, the first and second carbon atoms are,
as near infrared spectral data of the sample fabric,
is the centroid of the data for various fabric fiber compositions. And respectively calculating the SBD of the sample data and the centroid of the eight classes, wherein the smaller the SBD is, the more similar the sample data and the centroid is, and accordingly, the qualitative analysis of the fiber components of the sample fabric can be completed.
In the textile industry, the qualitative composition of the fabric is essential. The traditional method has the defects of environmental pollution, sample damage, complex operation, long time consumption and the like. People use the fabric fiber component qualitative method based on k-Shape, the near infrared equipment is simply operated to scan the fabric fiber sample to be detected, the component category of the fabric fiber sample can be predicted after sample data is calculated in real time, the qualitative problem of the fabric fiber component is solved, and the economic cost and the time cost of detection are reduced.
Compared with the fabric fiber component qualitative method based on k-Shape in the prior art, the fabric fiber component qualitative method based on k-Shape can accurately and quickly realize analysis and calculation of the near infrared spectrum of the fabric fiber, and can perform qualitative analysis on the fabric fiber component with low cost and high efficiency.
The foregoing is only a preferred embodiment of the present application, and it should be noted that, for those skilled in the art, several modifications and substitutions can be made without departing from the technical principle of the present application, and these modifications and substitutions should also be regarded as the protection scope of the present application.
Claims (6)
1. The fabric fiber component qualitative method based on k-Shape is characterized by comprising the following steps of:
s1: collecting the fiber components of various types of fabrics by using near infrared equipment;
s2: marking, cleaning, preprocessing and storing the data;
s3: extracting characteristic shapes of the fabric fiber component data of each category;
s4: and (3) carrying out real-time qualitative detection on the fabric fiber components.
2. The method of claim 1 for characterizing the fiber composition of a k-Shape based fabric, wherein: the data cleansing, preprocessing, grouping and storing in step S2 further includes:
s21: marking and classifying the collected data;
s22: and carrying out the following preprocessing on the marked data: the method comprises the steps of data standardization processing, data denoising processing, sequence Fourier transform processing, baseline translation and drift elimination processing, data dimension expansion processing and data enhancement processing of simulated spectral characteristics.
3. The method of claim 1 for characterizing the fiber composition of a k-Shape based fabric, wherein: the step of extracting the characteristic shape of the data of the fiber components of the fabrics in each category in the step S3 further includes:
s31: and carrying out centroid calculation on the fiber component data of various fabrics.
4. The method of claim 1 for characterizing the fiber composition of a k-Shape based fabric, wherein: the real-time qualitative detection of the fabric fiber component in the step S4 further includes:
s41: collecting fiber components of a target fabric by using near infrared equipment;
s42: cleaning and preprocessing the fiber component data of the target fabric;
s43: and evaluating the fiber component of the target fabric.
5. The method of claim 3, wherein: performing centroid calculations on the various types of fabric fiber component data in step S31 further comprises:
s311: the mass center of the fiber component data of various fabrics is obtained by the following formula:
wherein,
is the centroid, P, of the data of the fiber component of the individual type of fabrickIs a data set of the fiber composition of a single type of fabric,
is a member of PkM is
Of (c) is calculated.
6. The method of claim 4, wherein: evaluating the fiber composition of the target fabric in step S43 further comprises:
s431: and obtaining intra-cluster difference and inter-cluster difference of the mass centers of the target fabric fiber component and various fabric fiber components according to the following formulas:
wherein,
in order to target the fiber composition of the fabric,
is the center of mass of the fiber component of various fabrics;
s432: and classifying the target fabric fiber component, and performing visual analysis on the characteristic shape of the fabric fiber component.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111178112.2A CN114112982A (en) | 2021-10-09 | 2021-10-09 | Fabric fiber component qualitative method based on k-Shape |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111178112.2A CN114112982A (en) | 2021-10-09 | 2021-10-09 | Fabric fiber component qualitative method based on k-Shape |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114112982A true CN114112982A (en) | 2022-03-01 |
Family
ID=80441644
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111178112.2A Pending CN114112982A (en) | 2021-10-09 | 2021-10-09 | Fabric fiber component qualitative method based on k-Shape |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114112982A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115394381A (en) * | 2022-08-24 | 2022-11-25 | 哈尔滨理工大学 | High-entropy alloy hardness prediction method and device based on machine learning and two-step data expansion |
| CN115656096A (en) * | 2022-09-20 | 2023-01-31 | 中山海关技术中心 | Qualitative identification test method for textile fibers |
| CN116413210A (en) * | 2022-11-28 | 2023-07-11 | 山东鑫浩冠新材料科技有限公司 | Lyocell fiber component analysis and detection equipment |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100036795A1 (en) * | 2005-10-13 | 2010-02-11 | Busch Kenneth W | Classification of Fabrics by Near-Infrared Spectroscopy |
| CN104297204A (en) * | 2014-10-08 | 2015-01-21 | 华南理工大学 | Method and device for identifying whether toilet paper is mixed with reclaimed fiber or not |
| CN104376325A (en) * | 2014-10-30 | 2015-02-25 | 中国科学院半导体研究所 | Method for building near-infrared qualitative analysis model |
| CN104931454A (en) * | 2015-06-23 | 2015-09-23 | 浙江理工大学 | Method for rapidly determining Lycra fiber content of textiles using near infrared spectroscopic analysis |
| WO2015146821A1 (en) * | 2014-03-28 | 2015-10-01 | 国立大学法人秋田大学 | Fiber identification method |
| WO2016027792A1 (en) * | 2014-08-22 | 2016-02-25 | 一般財団法人ニッセンケン品質評価センター | Fiber identification method |
| CN106153572A (en) * | 2015-04-14 | 2016-11-23 | 佛山市顺德区美的电热电器制造有限公司 | Yarn fabric detection method |
| CN107219188A (en) * | 2017-06-02 | 2017-09-29 | 中国计量大学 | A kind of method based on the near-infrared spectrum analysis textile cotton content for improving DBN |
| CN107478598A (en) * | 2017-09-01 | 2017-12-15 | 广东省智能制造研究所 | A kind of near-infrared spectral analytical method based on one-dimensional convolutional neural networks |
| CN108120687A (en) * | 2016-11-28 | 2018-06-05 | 西派特（北京）科技有限公司 | A kind of textile fabric device for fast detecting |
| CN108267414A (en) * | 2017-12-26 | 2018-07-10 | 中山出入境检验检疫局检验检疫技术中心 | The near-infrared spectral analytical method of textile fiber content |
| CN110717368A (en) * | 2018-07-13 | 2020-01-21 | 北京服装学院 | Qualitative classification method for textiles |
| CN111007032A (en) * | 2019-11-13 | 2020-04-14 | 上海长海医院 | Near-infrared spectroscopy for rapidly and nondestructively identifying liquorice and pseudo-product glycyrrhiza spinosa |
-
2021
- 2021-10-09 CN CN202111178112.2A patent/CN114112982A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100036795A1 (en) * | 2005-10-13 | 2010-02-11 | Busch Kenneth W | Classification of Fabrics by Near-Infrared Spectroscopy |
| WO2015146821A1 (en) * | 2014-03-28 | 2015-10-01 | 国立大学法人秋田大学 | Fiber identification method |
| WO2016027792A1 (en) * | 2014-08-22 | 2016-02-25 | 一般財団法人ニッセンケン品質評価センター | Fiber identification method |
| CN104297204A (en) * | 2014-10-08 | 2015-01-21 | 华南理工大学 | Method and device for identifying whether toilet paper is mixed with reclaimed fiber or not |
| CN104376325A (en) * | 2014-10-30 | 2015-02-25 | 中国科学院半导体研究所 | Method for building near-infrared qualitative analysis model |
| CN106153572A (en) * | 2015-04-14 | 2016-11-23 | 佛山市顺德区美的电热电器制造有限公司 | Yarn fabric detection method |
| CN104931454A (en) * | 2015-06-23 | 2015-09-23 | 浙江理工大学 | Method for rapidly determining Lycra fiber content of textiles using near infrared spectroscopic analysis |
| CN108120687A (en) * | 2016-11-28 | 2018-06-05 | 西派特（北京）科技有限公司 | A kind of textile fabric device for fast detecting |
| CN107219188A (en) * | 2017-06-02 | 2017-09-29 | 中国计量大学 | A kind of method based on the near-infrared spectrum analysis textile cotton content for improving DBN |
| CN107478598A (en) * | 2017-09-01 | 2017-12-15 | 广东省智能制造研究所 | A kind of near-infrared spectral analytical method based on one-dimensional convolutional neural networks |
| CN108267414A (en) * | 2017-12-26 | 2018-07-10 | 中山出入境检验检疫局检验检疫技术中心 | The near-infrared spectral analytical method of textile fiber content |
| CN110717368A (en) * | 2018-07-13 | 2020-01-21 | 北京服装学院 | Qualitative classification method for textiles |
| CN111007032A (en) * | 2019-11-13 | 2020-04-14 | 上海长海医院 | Near-infrared spectroscopy for rapidly and nondestructively identifying liquorice and pseudo-product glycyrrhiza spinosa |
Non-Patent Citations (3)
| Title |
|---|
| JOHN PAPARRIZOS等: "k-Shape: Efficient and Accurate Clustering of Time Series", 《ACM SIGMOD RECORD》, vol. 45, no. 1, 2 June 2016 (2016-06-02), pages 69 - 76 * |
| 李艳坤等: "《模式识别方法及其在复杂体系中的应用》", vol. 1, 31 March 2020, 燕山大学出版社 * |
| 陆婉珍等: "《现代近红外光谱分析技术》", vol. 1, 30 April 2000, 中国石化出版社 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115394381A (en) * | 2022-08-24 | 2022-11-25 | 哈尔滨理工大学 | High-entropy alloy hardness prediction method and device based on machine learning and two-step data expansion |
| CN115394381B (en) * | 2022-08-24 | 2023-08-22 | 哈尔滨理工大学 | High-entropy alloy hardness prediction method and device based on machine learning and two-step data expansion |
| CN115656096A (en) * | 2022-09-20 | 2023-01-31 | 中山海关技术中心 | Qualitative identification test method for textile fibers |
| CN116413210A (en) * | 2022-11-28 | 2023-07-11 | 山东鑫浩冠新材料科技有限公司 | Lyocell fiber component analysis and detection equipment |
| CN116413210B (en) * | 2022-11-28 | 2023-11-21 | 浙江浩铭机械科技有限公司 | Lyocell fiber component analysis and detection equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114112982A (en) | Fabric fiber component qualitative method based on k-Shape | |
| US8190551B2 (en) | Classification of fabrics by near-infrared spectroscopy | |
| JP6333326B2 (en) | Analysis method of biological samples by spectral imaging | |
| Fernandez et al. | Infrared spectroscopic imaging for histopathologic recognition | |
| WO2022116594A1 (en) | Fiber quality grade online test system and application thereof | |
| CN110717368A (en) | Qualitative classification method for textiles | |
| KR20130056886A (en) | Method for analyzing biological specimens by spectral imaging | |
| Lavine et al. | Criteria for comparing infrared spectra–A review of the forensic and analytical chemistry literature | |
| KR20170118032A (en) | Methods and systems for classifying biological samples, including optimization of analyses and use of correlation | |
| MX2014004004A (en) | Method and system for analyzing biological specimens by spectral imaging. | |
| CN104568778A (en) | Textile component identification method based on hyperspectral imaging | |
| CN113686804B (en) | Textile fiber component nondestructive cleaning analysis method based on deep regression network | |
| Fortier et al. | Identification of cotton and cotton trash components by Fourier transform near-infrared spectroscopy | |
| De Frond et al. | What determines accuracy of chemical identification when using microspectroscopy for the analysis of microplastics? | |
| CN115406852A (en) | Fabric fiber component qualitative method based on multi-label convolutional neural network | |
| Klaasse et al. | TraceBase; A database structure for forensic trace analysis | |
| CN113970532B (en) | Fabric fiber component detection system and prediction method based on near infrared spectrum | |
| CN103076300B (en) | Method for judging and analyzing traditional Chinese medicine resource fingerprint information by specific mode identification model | |
| CN113970528B (en) | Textile component mixing method based on complete constraint least square method | |
| CN114088659B (en) | Method for detecting abnormal near infrared spectrum waveform of fabric fiber component | |
| Ge et al. | Surface temperature measurement based on reflectance spectrum of temperature indicating paint and machine learning algorithms | |
| CN113887317A (en) | Semi-supervised classification method for fabric fiber components based on pi model | |
| CN107179297A (en) | A kind of intelligent category of annatto authentication method and its platform | |
| CN114324294A (en) | Multi-probe gas Raman spectrum system and mixed gas quantitative analysis method | |
| CN114264626A (en) | Fabric nondestructive quantitative analysis method based on time series residual error network |
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 |