CN111091153B - Soft measurement method for bulk of paper sheet - Google Patents
Soft measurement method for bulk of paper sheet Download PDFInfo
- Publication number
- CN111091153B CN111091153B CN201911306620.7A CN201911306620A CN111091153B CN 111091153 B CN111091153 B CN 111091153B CN 201911306620 A CN201911306620 A CN 201911306620A CN 111091153 B CN111091153 B CN 111091153B
- Authority
- CN
- China
- Prior art keywords
- model
- data
- soft measurement
- bulk
- modeling
- 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
- 238000000691 measurement method Methods 0.000 title description 5
- 238000005259 measurement Methods 0.000 claims abstract description 52
- 239000000835 fiber Substances 0.000 claims abstract description 41
- 238000000034 method Methods 0.000 claims abstract description 17
- 238000004519 manufacturing process Methods 0.000 claims abstract description 12
- 238000007781 pre-processing Methods 0.000 claims abstract description 8
- 238000012795 verification Methods 0.000 claims abstract description 8
- 238000003066 decision tree Methods 0.000 claims abstract description 7
- 238000012706 support-vector machine Methods 0.000 claims abstract description 7
- 238000004451 qualitative analysis Methods 0.000 claims abstract description 5
- 238000013480 data collection Methods 0.000 claims abstract description 4
- 238000010219 correlation analysis Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 4
- 238000004537 pulping Methods 0.000 claims description 4
- 238000004804 winding Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000007405 data analysis Methods 0.000 claims description 2
- 238000010606 normalization Methods 0.000 claims description 2
- 238000011002 quantification Methods 0.000 claims description 2
- 239000000126 substance Substances 0.000 claims description 2
- 238000012360 testing method Methods 0.000 claims description 2
- 238000012549 training Methods 0.000 claims description 2
- 238000013507 mapping Methods 0.000 claims 1
- 238000010801 machine learning Methods 0.000 abstract description 2
- 239000000123 paper Substances 0.000 description 32
- 230000006870 function Effects 0.000 description 7
- 238000001514 detection method Methods 0.000 description 5
- 230000007547 defect Effects 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000011087 paperboard Substances 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/34—Paper
- G01N33/346—Paper sheets
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Software Systems (AREA)
- Chemical & Material Sciences (AREA)
- Food Science & Technology (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Medical Informatics (AREA)
- Medicinal Chemistry (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Paper (AREA)
Abstract
The invention discloses a method for measuring the bulk soft of paper, which comprises the following steps: and S1, collecting key modeling data based on the production process, including database and field variable data collection. And S2, performing qualitative analysis to acquire the correlation between the variable data and the bulk, and performing data preprocessing on the variable data to meet the requirements of fiber morphology soft measurement modeling and paper sheet bulk soft measurement modeling. And S3, establishing a fiber morphology soft measurement model by combining a support vector machine algorithm according to the data meeting the modeling requirement. S4, performing feature selection on the data meeting the modeling requirement, establishing a soft measurement model of the paper sheet bulk by combining a gradient lifting decision tree algorithm, and then performing model validity verification by using the on-site data. The invention has the advantages that: the soft measurement model of the paper sheet bulk is established by a machine learning method, the model prediction speed is high, the precision is good, the real-time soft measurement of the bulk of all paper sheets can be accurately realized, and the product percent of pass is improved.
Description
Technical Field
The invention relates to the technical field of papermaking paper physical index prediction, in particular to a soft paper bulk measurement method based on a support vector machine and gradient lifting decision tree combined algorithm.
Background
The bulk of paper and paperboard is an important index of great concern for both enterprises and users, and has important influence on the cost and performance of products.
At present, the detection of paper bulk by enterprises is mainly based on off-line instruments, and the produced products have the problems of high qualification rate, high rate of missing judgments and the like. And (4) production. The quality detection by using the instrument consumes high manpower and material resources, and the quality detection cannot realize the coverage of all products. The enterprise needs to spend more labor and equipment cost to detect the bulk index of the base paper, and the enterprise can also have the hidden trouble that some products which are not detected have unqualified quality in the existing mode of carrying out selective inspection on the base paper, so that the economic loss of the enterprise is increased while the consumer brings poor user experience.
Therefore, the full and real-time quality inspection of the bulk indexes of the base paper products is very important for paper-making enterprises. The traditional instrument-based detection cannot timely and effectively detect the quality of the base paper due to hysteresis, and feeds the quality back to actual production to guide the actual production of high-efficiency and stable qualified products. Meanwhile, the product quality fluctuation is large, and the resource waste of raw materials is caused. Therefore, under the large background of advocating industrial intelligent manufacturing, the accurate online prediction of the bulk of the base paper of the household paper by establishing a mathematical model by using an intelligent algorithm is an urgent problem to be solved.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for measuring the bulk and softness of paper sheets, which solves the defects in the prior art.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
a method for soft measurement of sheet bulk comprising the steps of:
and S1, collecting key modeling data based on the production process, including database and field variable data collection. The variable data includes: pulp fiber morphology measurements, refiner power, throughput, paper machine take-up and fan pump current.
And S2, performing qualitative analysis to acquire the correlation between the variable data and the bulk, and performing data preprocessing on the variable data to meet the requirements of fiber morphology soft measurement modeling and paper sheet bulk soft measurement modeling.
And S3, establishing a fiber morphology soft measurement model by combining a support vector machine algorithm according to the data meeting the modeling requirement.
S4, performing feature selection on data meeting modeling requirements, establishing a soft measurement model of the paper sheet bulk by combining a gradient lifting decision tree algorithm, then performing model validity verification by using on-site data, acquiring production data of the enterprise at another period of time again, performing data analysis and data preprocessing, predicting by using the established model, and comparing with an actual value to verify the validity of the model on new data.
Further, in S2, the relationship between the bulk of the qualitative analysis collected variable data is specifically: the fiber form indexes, the power and the throughput of a pulp grinder in the pulping process, and the key parameters of the speed, the winding speed and the rear temperature of a dry part heater in the paper machine process are collected to be used as selected variable data input by a paper sheet bulk model.
Further, the data preprocessing described in S2 is to perform normalization processing on the input variable data of the model, so that indexes of different units or magnitudes can be compared and weighted conveniently, thereby eliminating the influence of different characteristic dimensions and magnitudes on modeling. The processing mode is to compress the selected characteristic variable data to obtain a new number sequence with the mean value of 0 and the standard deviation of 1. The specific mode is as follows:
where i is 1,2, …, n is the number of samples, j is 1,2, …, and p is the dimension of the samples.
Is the mean value of the samples in the j dimension, xijIs the j-dimensional value of the ith sample,
for the j-dimensional normalized value of the i-th sample, SjIs the standard deviation of the sample in the j dimension. Sj 2Is the variance of the sample in the j dimension.
Further, in S4, selecting a feature of the soft measurement model of the sheet bulk, performing correlation analysis on the preprocessed feature, and selecting variable data having an obvious influence on the bulk to model, wherein a pearson correlation coefficient is used in the correlation analysis, and a specific calculation formula is as follows.
Wherein the content of the first and second substances,
is y and
covariance of (1), Vav [ y [)]
Respectively y and
the variance of (a), E (y),
are respectively y and
the expectation is that.
Further, the specific steps of S3 are as follows:
and S31, establishing a fiber morphology soft measurement model based on the principle of the regression model of the support vector machine. The kernel function may map low-dimensional data to a high-dimensional space, converting a non-linear problem to a linear problem. The kernel function is chosen such that the problem maps to a linear separable inside the high-order feature space. For a given data set, the solution formula for the SVM model is as follows
Where ω is a weight coefficient and x is input variable data [ x ]1，…xp]n×pB is an offset term, αi，
Is Lagrange multiplier, K (x)i，xj) Is a kernel function.
And S32, aiming at the fiber form soft measurement model and the data set, the model input is the original pulp fiber form, the pulp board ratio, the power, the throughput and the concentration of the refiner, and the model output is the ground pulp fiber form. And (3) adopting a cross verification method, randomly taking 80% of training sets to train and adjust model parameters each time, and verifying the precision of the parameters by 20% of testing sets.
And S33, establishing a fiber form soft measurement model by using the parameters with better performance in the step S32, and carrying out soft measurement on the subsequent fiber form.
Further, the specific steps of establishing the sheet bulk soft measurement model in S4 are as follows:
s41, establishing a soft measurement model of the sheet bulk based on the model principle of the gradient enhanced regression tree, and recording Fm(x) The specific formula of the overall model function is as follows:
wherein, Fm(x) As a function of the global model, Fm-1(x) Is the (M-1) th basic regression tree function, M is 1,2 … M is the regression tree, betamIs the mth regression tree weight, L is the loss function of the model, alphamAre parameters in the mth regression tree.
S42, aiming at the speed, the curvature, the quantification and the variable data of the paper machine process selected by the correlation analysis, combining the indexes such as the weight average length of the defibrinated fibers output by the fiber form soft measurement model and the like, and establishing a paper sheet bulk model by utilizing a gradient lifting regression decision tree.
Compared with the prior art, the invention has the advantages that: the soft measurement model of the paper sheet bulk is established by a machine learning method, the model prediction speed is high, the precision is good, and the real-time soft measurement of the bulk of all paper sheets can be accurately realized. The technology not only solves the problem that the bulk of all base paper products cannot be measured in production, reduces the labor detection cost, but also can timely acquire the bulk quality of finished paper, adjusts the paper machine process by monitoring the product quality in real time and finding out abnormality, and improves the product percent of pass.
Drawings
FIG. 1 is a flow chart of a sheet bulk soft measurement method according to an embodiment of the present invention;
FIG. 2 is a graph of the results of a model for establishing soft measurements of fiber morphology in an embodiment of the present invention;
FIG. 3 is a graph of the results of correlation analysis in an embodiment of the present invention;
FIG. 4 is a graph of the results of a soft measurement model of bulk in an embodiment of the invention;
FIG. 5 is a graph showing the results of the soft measurement model of sheet bulk according to the example of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings by way of examples.
As shown in fig. 1, a sheet bulk soft measurement method includes the steps of:
and S1, collecting key modeling data based on the production process, including database and field data collection. Including pulp fiber morphology measurements, refiner power, throughput, machine speed, basis weight, etc.
And S2, qualitatively analyzing and acquiring the correlation between the variables and the bulk, and preprocessing the data of the variables to meet the requirements of fiber morphology soft measurement modeling and paper sheet bulk soft measurement modeling.
And S3, establishing a fiber morphology soft measurement model by combining a support vector machine algorithm according to the data meeting the modeling requirement.
S4, performing feature selection on the data meeting the modeling requirement, establishing a soft measurement model of the paper sheet bulk by combining a gradient lifting decision tree algorithm, and then performing model validity verification by using the on-site data.
In the embodiment, the soft measurement model of the fiber morphology is established based on the SVM algorithm by utilizing the steps, and the output is the fiber morphology after pulp grinding. The results of the soft measurement model of fiber morphology are shown in fig. 2. As can be seen from FIG. 2, the fitting accuracy of the fiber soft measurement model is good, and the evaluation index adopts the average relative error in the specification. The average fiber length fitting average relative error is 2.84%, the average kinked fiber percentage relative error is 3.12%, the average fiber width average relative error is 2.91%, and the average fiber thickness average relative error is 3.09%, all of which are good in performance, after the fiber morphology is soft-measured, a paper sheet bulk soft measurement model is established through the variable selection method based on a GBRT algorithm and combined with paper machine processes such as the speed, the winding speed and the winding rate selected by correlation analysis (the result is shown in figure 3). The results of the bulk soft measurement model are shown in fig. 4. As can be seen from FIG. 4, the average relative error of the bulk is 3.12%, and the model precision is better.
After the soft measurement model of the fiber form and the soft measurement model of the bulk are established, the soft measurement model of the bulk of the paper sheet is combined into a soft measurement model of the bulk of the paper sheet, the fiber form of the originally used pulp board, the pulp board proportion, the pulp grinding process, the paper machine process and the like are input, the loose thickness of the paper sheet is output by the model, production data in another period of time are collected again to carry out universality verification on the established loose thickness model of the paper sheet, and the model verification result is shown in figure 5. As can be seen from FIG. 5, the average relative error of the soft sheet bulk measurement model is 2.77%, and the model accuracy is good.
It will be appreciated by those of ordinary skill in the art that the examples described herein are intended to assist the reader in understanding the manner in which the invention is practiced, and it is to be understood that the scope of the invention is not limited to such specifically recited statements and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (2)
1. A method for measuring the bulk soft of a paper sheet is characterized by comprising the following steps:
s1, collecting key modeling data based on the production process, including database and field variable data collection; the variable data includes: measuring the fiber form of the pulp, the power and the throughput of a pulping machine, the reeling rate of a paper machine and the current of a fan pump;
s2, performing qualitative analysis to acquire the correlation between variable data and bulk, and performing data preprocessing on the variable data to meet the requirements of fiber morphology soft measurement modeling and paper sheet bulk soft measurement modeling;
the data preprocessing in the step S2 is to perform normalization processing on the input variable data of the model, so that the indexes of different units or orders can be compared and weighted conveniently, thereby eliminating the influence of different characteristic dimensions and orders on modeling; the processing mode is to compress the selected characteristic variable data to obtain a new number sequence with the mean value of 0 and the standard deviation of 1; the specific mode is as follows:
wherein i is 1,2, …, n is the number of samples, j is 1,2, …, and p is the dimension of the samples;
is the mean value of the samples in the j dimension, xijIs the j-dimensional value of the ith sample,
for the j-dimensional normalized value of the i-th sample, SjThe standard deviation of the sample in the j dimension is taken; sj 2The variance in the j dimension for the sample;
s3, establishing a fiber morphology soft measurement model by combining a support vector machine algorithm according to data meeting modeling requirements;
the specific steps of S3 are as follows:
s31, establishing a fiber morphology soft measurement model based on the principle of a support vector machine regression model; the kernel function can map low-dimensional data to a high-dimensional space and convert a nonlinear problem into a linear problem; the selection principle of the kernel function is to enable the problem mapping to be linearly separable in the high-order feature space; for a given data set, the solution formula for the SVM model is as follows
Where ω is a weight coefficient and x is input variable data [ x ]1，…xp]n×pB is an offset term, αi，
Is Lagrange multiplier, K (x)i，xj) Is a kernel function;
s32, aiming at the fiber form soft measurement model and the data set, the model input is the original pulp fiber form, the pulp board ratio, the power, the throughput and the concentration of the pulping machine, and the model output is the ground pulp fiber form; adopting a cross verification method, randomly selecting 80% of training sets to train and adjust model parameters each time, and verifying the precision of the parameters by 20% of testing sets;
s33, establishing a fiber form soft measurement model by using the parameters with better performance in the step S32 to perform the soft measurement of the subsequent fiber form;
s4, performing feature selection on data meeting modeling requirements, establishing a soft measurement model of the paper sheet bulk by combining a gradient lifting decision tree algorithm, then performing model validity verification by using on-site data, acquiring production data of an enterprise at another period of time again, performing data analysis and data preprocessing, predicting by using the established model, and comparing with an actual value to verify the validity of the model on new data;
selecting characteristics of a soft measurement model of the paper sheet bulk in S4, performing correlation analysis on the preprocessed characteristics, and selecting variable data which obviously influences the bulk to model, wherein a Pearson correlation coefficient is adopted in the correlation analysis, and a specific calculation formula is as follows;
wherein the content of the first and second substances,
is y and
covariance of (1), Vav [ y [)]，
Respectively y and
the variance of (a), E (y),
are respectively y and
(iii) a desire;
the specific steps of establishing the sheet bulk soft measurement model in the S4 are as follows:
s41, establishing a soft measurement model of the sheet bulk based on the model principle of the gradient enhanced regression tree, and recording Fm(x) The specific formula of the overall model function is as follows:
wherein, Fm(x) As a function of the global model, Fm-1(x) Is the (M-1) th basic regression tree function, M is 1,2 … M is the regression tree, betamIs the mth regression tree weight, L is the loss function of the model, alphamIs the m regression treeA parameter of;
s42, analyzing the selected speed, curvature, quantification and variable data of the paper machine process according to the correlation, combining the defibrinated fiber weight average length index output by the fiber form soft measurement model, and establishing a paper sheet bulk model by using a gradient lifting regression decision tree.
2. The method of claim 1, wherein: in S2, the qualitative analysis of the correlation between the bulk of the collected variable data is specifically: the fiber form indexes, the power and the throughput of a pulp grinder in the pulping process, and the key parameters of the speed, the winding speed and the rear temperature of a dry part heater in the paper machine process are collected to be used as selected variable data input by a paper sheet bulk model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911306620.7A CN111091153B (en) | 2019-12-18 | 2019-12-18 | Soft measurement method for bulk of paper sheet |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911306620.7A CN111091153B (en) | 2019-12-18 | 2019-12-18 | Soft measurement method for bulk of paper sheet |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111091153A CN111091153A (en) | 2020-05-01 |
| CN111091153B true CN111091153B (en) | 2021-03-30 |
Family
ID=70396036
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911306620.7A Active CN111091153B (en) | 2019-12-18 | 2019-12-18 | Soft measurement method for bulk of paper sheet |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111091153B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104881715A (en) * | 2015-05-26 | 2015-09-02 | 华南理工大学 | Paper plant pulp property prediction method based on ratio of waste paper |
| EP3044371A1 (en) * | 2013-09-13 | 2016-07-20 | Stora Enso Oyj | Multiply paperboard |
| CN109440515A (en) * | 2018-11-30 | 2019-03-08 | 华南理工大学 | Soft measurement method for beating degree in papermaking process based on gradient enhanced regression tree |
| CN110455737A (en) * | 2019-07-11 | 2019-11-15 | 中国林业科学研究院林产化学工业研究所 | A method of quickly analyzing Klason content of lignin in pulpwood using near-infrared spectrum technique |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104459089B (en) * | 2014-12-12 | 2016-05-11 | 东北大学 | A kind of flexible measurement method of high consistency refining system freedom |
-
2019
- 2019-12-18 CN CN201911306620.7A patent/CN111091153B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3044371A1 (en) * | 2013-09-13 | 2016-07-20 | Stora Enso Oyj | Multiply paperboard |
| CN104881715A (en) * | 2015-05-26 | 2015-09-02 | 华南理工大学 | Paper plant pulp property prediction method based on ratio of waste paper |
| CN109440515A (en) * | 2018-11-30 | 2019-03-08 | 华南理工大学 | Soft measurement method for beating degree in papermaking process based on gradient enhanced regression tree |
| CN110455737A (en) * | 2019-07-11 | 2019-11-15 | 中国林业科学研究院林产化学工业研究所 | A method of quickly analyzing Klason content of lignin in pulpwood using near-infrared spectrum technique |
Non-Patent Citations (2)
| Title |
|---|
| Basic effects of pulp refining on fiber properties—A review;SamiraGharehkhani等;《Carbohydrate Polymers》;20150122;第115卷;第785-803页 * |
| 基于梯度增强回归树算法的磨浆过程打浆度软测量模型;孟子薇等;《造纸科学与技术》;20190228;第38卷(第1期);第84-87页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111091153A (en) | 2020-05-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110634080A (en) | Abnormal electricity utilization detection method, device, equipment and computer readable storage medium | |
| CN106404441B (en) | A kind of failure modes diagnostic method based on non-linear similarity index | |
| CN107703097B (en) | Method for constructing model for rapidly predicting crude oil property by using near-infrared spectrometer | |
| CN111949939B (en) | Method for evaluating running state of intelligent electric meter based on improved TOPSIS and cluster analysis | |
| CN107885928A (en) | Consider the stepstress acceleration Degradation Reliability analysis method of measurement error | |
| CN102357933B (en) | Real-time quality monitoring method based on rubber mixing process | |
| CN105334185A (en) | Spectrum projection discrimination-based near infrared model maintenance method | |
| CN109034483A (en) | A kind of inspection planning method based on quality function deploying | |
| CN106384256A (en) | Power supplying service satisfaction dynamic testing system based on index system dynamic change | |
| CN105868164A (en) | Soft measurement modeling method based on monitored linear dynamic system model | |
| CN116167640A (en) | LCP film production quality detection data analysis method and system | |
| CN104596981A (en) | Method for distinguishing paper process reconstituted tobacco products via near infrared spectroscopy in combination with PLS-DA | |
| CN110320337A (en) | A kind of automatic evaluating system of strip surface quality and method | |
| CN113158558B (en) | High-speed railway roadbed continuous compaction analysis method, device and analyzer | |
| CN111091153B (en) | Soft measurement method for bulk of paper sheet | |
| CN101650359A (en) | Coal caloric value soft measuring method based on knowledge fusion machine learning algorithm | |
| CN113742248A (en) | Method and system for predicting organization process based on project measurement data | |
| CN107871215A (en) | A kind of standard is to economic development impact evaluation model and verification method | |
| CN112651173A (en) | Agricultural product quality nondestructive testing method based on cross-domain spectral information and generalizable system | |
| CN111024557B (en) | Soft measurement method for water absorption of paper sheet | |
| CN114800036B (en) | Equipment health state assessment method | |
| CN111861271B (en) | Pipeline heat insulation performance evaluation method | |
| CN104596978A (en) | Method for determining quantitative physical indexes of paper-making tobacco sheet by virtue of near-infrared reflectance spectroscopy | |
| CN107179297A (en) | A kind of intelligent category of annatto authentication method and its platform | |
| CN111079344A (en) | Method and system for measuring softness of paper sheet |
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 |