CN109886250A - A kind of exacerbation blanket experience evaluation method and system based on KF-PLS - Google Patents
A kind of exacerbation blanket experience evaluation method and system based on KF-PLS Download PDFInfo
- Publication number
- CN109886250A CN109886250A CN201910178668.8A CN201910178668A CN109886250A CN 109886250 A CN109886250 A CN 109886250A CN 201910178668 A CN201910178668 A CN 201910178668A CN 109886250 A CN109886250 A CN 109886250A
- Authority
- CN
- China
- Prior art keywords
- matrix
- pls
- expression vector
- user
- blanket
- 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
Abstract
The present invention provides a kind of exacerbation blanket experience evaluation method and system based on KF-PLS, by developing Mobile phone App, obtains user and be transferred to cloud using different types of exacerbation blanket process video (can be taken on site or read video file by mobile phone A pp)；The video is resolved into continuous serial-gram beyond the clouds；Using face recognition technology, identify the corresponding human face expression type of the serial-gram, the code vector that changes over time of expression is obtained, in cloud platform, the complex nonlinear relational model that is scored by KF-PLS neural network user experience data with corresponding user experience process；The typing for carrying out video can automatically obtain the user experience evaluation result of the user experience process, aggravate as enterprise the foundation of blanket product up-gradation optimization.
Description
Technical field
The present invention relates to big data fields, and in particular to a kind of exacerbation blanket experience based on KF-PLS evaluation method and is
System.
Background technique
Nowadays, the period that positive value mental health crisis is got worse, especially young man.According to " high religion in 2015
Educate record event report " a report in point out, suicide is the second largest killer of university student's death, is only second to traffic accident；From 1999
Since year, the whole homicide rate in the U.S. has risen violently about 25%.For those with self-closing disease, SPD (feel disorder), depression,
The excessive user of hypoevolutism crowd or only pressure.Some researches show that moderately squeezing body by foreign object can be very big
Alleviate anxiety to releive pressure, claims to mitigate psychological pressure by back abdomen bilateral massage as aggravating blanket, improve limb
Body locomitivity can effectively mitigate the generally existing intense strain of patient, and user is allowed to be easier to learn in the state of loosening
With other people interaction.It is embedded in Emotion identification system, the emotional change during Patient Experience is acquired, is calculated, is divided
Analysis still can be used as enterprise in most instances and carry out the foundation that self-closing disease aggravates the optimization of blanket product up-gradation.
For the prior art in aggravating blanket product optimization development, engineers and technicians are unable to the exacerbation of quick obtaining modified
The user experience data of blanket, and then Fast Evaluation cannot be made to product optimization result.
Summary of the invention
In order to solve in present R & D of complex, research staff is unable to quick obtaining modified and aggravates blanket user experience number
According to the problem of, the application provide it is a kind of based on KF-PLS exacerbation blanket experience evaluation method, which is characterized in that including following step
Suddenly,
S1: acquisition user obtains the first process according to first process video using the first process video for aggravating blanket
Serial-gram carries out recognition of face to the first process families photo and obtains user's human face expression vector, according to the user
Human face expression vector obtains input matrix；
S2: acquisition user investigation data obtain matrix of consequence Y according to the user investigation data, construct KF-PLS model,
KF-PLS model is trained using the input matrix and the matrix of consequence.
S3: acquisition user is using the second process video for aggravating blanket, and the KF-PLS model completed using training is to the use
It is analyzed using the second process video for aggravating blanket and obtains user experience data in family.
Further, the step S1 includes,
S11: using abscissa as the time, ordinate is that expression type code generation user's human face expression vector changes over time
Two-dimentional expression spectrum, wherein " indignation " corresponding expression vector be [0,0,0,0,0,0,1]T, " detest " corresponding expression vector
For [0,0,0,0,0,2,0]T, " fear " corresponding expression vector be [0,0,0,0,3,0,0]T, " happiness " corresponding expression vector
For [0,0,0,4,0,0,0]T, " sad " corresponding expression vector be [0,0,5,0,0,0,0]T, " surprised " corresponding expression vector
For [0,6,0,0,0,0,0]T, " loss of emotion " corresponding expression vector be [7,0,0,0,0,0,0]T, compose to obtain matrix using expression
A=[e1,e2,e3,…,en]7×n；
S12: matrix A progress transposed transform is obtained into AT=[e1,e2,e3,…,en]n×7；
S13: structural matrix M=AAT；
S14: the characteristic value of calculating matrix M, eigenvalue matrix λ=[λ of generator matrix M1,λ2,λ3,…,λ7]1×7；
S15: it generates input matrix X=[λ, N, B]1×9, wherein N is the age, and B is gender.
Further, input matrix X and matrix of consequence Y are decomposed using following formula
X=TPt+E
Y=UQt+F
The input paramete information data after the influence of component environment variable are removed in matrix T expression, and part ring is removed in matrix U expression
User experience result information data after the influence of border variable, E and F indicate error.
Further, the step S2 includes the following steps,
S21: setting PLS initial model number of main factor is l, and factor coefficient is w1, t1, v1, p1；w2, t2, v2, p2；……；
wi, ti, vi, pi(i=1,2,3 ..., l), in which:
vi=(tTy)/(tTT)=[vi1 vi2 ... vip],
S22: all coefficient values in model are formed into state vector
W=[w1 Tt1 Tv1p1 T...wi Tti Tvipi T]T(i=1,2,3 ..., l),
S23: generating state equation and observational equation,
Wherein YekFor standard specimen test result；WkThe main gene coefficient at moment is corrected for k-th of standard specimen；XkIt is inputted for k-th
Parameter matrix；YrkTo predict test result.
S24: environmental variance V is obtainedkStatistical property
It enables
S25: obtaining observational equation,
Yek=HkWk+Dk+Vk,
S26: Kalman filter model is generated；
The step S26 includes,
S261: calculating forward weight variable and measurement updaue,
S262: calculating forward error covariance,
WhereinAnd Pk-1For initial estimation,
S263: calculating kalman gain,
S264: by desired output YekMore new estimation,
S265: updating error covariance,
S266: K is done and adds 1 assignment and turns S262.
Further, the step S3 further includes,
User experience data is sent to administrator's mobile terminal and is shown.
The present invention also provides a kind of in order to guarantee the implementation of the above method, and the exacerbation blanket based on KF-PLS experiences evaluation system,
It comprises the following modules
Acquisition module is obtained for acquiring user using the first process video for aggravating blanket according to first process video
To the first process families photo, recognition of face is carried out to the first process families photo and obtains user's human face expression vector, according to
Input matrix is obtained according to user's human face expression vector,；
Training module obtains result square according to the first user investigation data for acquiring the first user investigation data
Battle array Y, the KF-PLS model of building are trained KF-PLS model using the input matrix and the matrix of consequence.
As a result output module, for acquiring user using the second process video for aggravating blanket, using the KF- of training completion
PLS model is analyzed the user using the second process video for aggravating blanket and obtains storage user experience data.
Further, the acquisition module obtains input matrix using following steps,
S11: using abscissa as the time, ordinate is that expression type code generation user's human face expression vector changes over time
Two-dimentional expression spectrum, wherein " indignation " corresponding expression vector be [0,0,0,0,0,0,1]T, " detest " corresponding expression vector
For [0,0,0,0,0,2,0]T, " fear " corresponding expression vector be [0,0,0,0,3,0,0]T, " happiness " corresponding expression vector
For [0,0,0,4,0,0,0]T, " sad " corresponding expression vector be [0,0,5,0,0,0,0]T, " surprised " corresponding expression vector
For [0,6,0,0,0,0,0]T, " loss of emotion " corresponding expression vector be
[7,0,0,0,0,0,0]T, compose to obtain matrix A=[e using expression1,e2,e3,…,en]7×n；
S12: matrix A progress transposed transform is obtained into AT=[e1,e2,e3,…,en]n×7；
S13: structural matrix M=AAT；
S14: the characteristic value of calculating matrix M, eigenvalue matrix λ=[λ of generator matrix M1,λ2,λ3,…,λ7]1×7；
S15: it generates input matrix X=[λ, N, B]1×9, wherein N is the age, and B is gender.
Further, input matrix X and matrix of consequence Y are decomposed using following formula
X=TPt+E
Y=UQt+F
The input paramete information data after the influence of component environment variable are removed in matrix T expression, and part ring is removed in matrix U expression
User experience result information data after the influence of border variable, E and F indicate error.
Further, the training module is trained KF-PLS model using following steps:
S21: setting PLS initial model number of main factor is l, and factor coefficient is w1, t1, v1, p1；w2, t2, v2, p2；……；
wi, ti, vi, pi(i=1,2,3 ..., l), in which:
vi=(tTy)/(tTT)=[vi1 vi2 ... vip],
S22: all coefficient values in model are formed into state vector
W=[w1 Tt1 Tv1p1 T...wi Tti Tvipi T]T(i=1,2,3 ..., l),
S23: generating state equation and observational equation,
Wherein YekFor standard specimen test result；WkThe main gene coefficient at moment is corrected for k-th of standard specimen；XkIt is inputted for k-th
Parameter matrix；YrkTo predict test result.
S24: environmental variance V is obtainedkStatistical property
It enables
S25: obtaining observational equation,
Yek=HkWk+Dk+Vk,
S26: Kalman filter model is generated；
The step S26 includes,
S261: calculating forward weight variable and measurement updaue,
S262: calculating forward error covariance,
WhereinAnd Pk-1For initial estimation,
S263: calculating kalman gain,
S264: by desired output YekMore new estimation,
S265: updating error covariance,
S266: K is done and adds 1 assignment and turns S262.
Further, the result output module is also used to, and user experience data is sent to administrator's mobile terminal simultaneously
It is shown.The invention has the advantages that
1 follows the anatomy such as nerves and muscles, has common trait；Expression Recognition is under a kind of unconscious, free state
Data capture method, ensure that the reliability and objectivity of data.
2, which are easily integrated into data analysis system, is analyzed and is visualized.
3 allow the data collection of other software real time access facial expression analysis system.
4 can analyze the facial expression of all races, the facial expression including children.
5 present invention are divided user using the video for aggravating blanket process by the neural network model that training is completed
Analysis quickly obtains user experience data, can be convenient research staff and quickly assesses modified exacerbation blanket, improves exacerbation
The efficiency of research and development of blanket.
Detailed description of the invention
Fig. 1 is that a kind of exacerbation blanket based on KF-PLS of the present invention experiences evaluation method flow chart.
Fig. 2 is that a kind of exacerbation blanket based on KF-PLS of the present invention experiences evaluation system structural schematic diagram.
Fig. 3 is one embodiment of the invention two dimension expression spectrum.
Fig. 4 is one embodiment of the invention KF-PLS neural network schematic diagram.
Specific embodiment
In the following description, for purposes of illustration, it in order to provide the comprehensive understanding to one or more embodiments, explains
Many details are stated.It may be evident, however, that these embodiments can also be realized without these specific details.
For in R & D of complex, research staff is unable to quick obtaining modified and aggravates asking for blanket user experience data
Topic, a kind of exacerbation blanket experience evaluation method and system based on KF-PLS of the present invention.In conjunction with Kalman filtering (Kalman
Filter, KF) with the Dynamic Evolution Model bearing calibration of PLS algorithm (Partial Least Squares, PLS).It will use first
Family experience data and user experience process score data obtain initial quantitative calibration models by PLS progress regressing calculation；Blocked
The enlightenment of Kalman Filtering Dynamic Evolution, establishes the PLS main gene coefficient iterative learning method based on Kalman filter, establishes dynamic
State corrects calibration model, using the main gene coefficient of Partial Least Squares Regression as the state variable of KF algorithm, newest moment standard
The sample observational variable to be measured as KF increases a standard specimen newly, corrects a PLS model, and introduce forgetting factor, gradually lose
Problem reduction is the estimation procedure of state parameter by the effect for forgetting outmoded sample.
The present invention is trained KF-PLS model by acquisition user video and user investigation data, is completed by training
KF-PLS model to user using modified aggravate blanket video identification, the user experience data of quick obtaining user.
Wherein, it should be noted that KF-PLS network, which can be regarded as one, has local memory unit and LOCAL FEEDBACK
The recurrent neural network of connection.
Hereinafter, specific embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Evaluation method is experienced in order to illustrate the exacerbation blanket provided by the invention based on KF-PLS, Fig. 1 shows the present invention one
Exacerbation blanket of the kind based on KF-PLS experiences evaluation method flow chart.
As shown in Figure 1, the present invention provide it is a kind of based on KF-PLS exacerbation blanket experience evaluation method include: S1: acquisition use
Family obtains the first process families photo according to first process video using the first process video for aggravating blanket, to described the
One process families photo carries out recognition of face and obtains user's human face expression vector, obtains according to user's human face expression vector defeated
Enter matrix；
S2: acquisition user investigation data obtain matrix of consequence Y, the KF-PLS mould of building according to the user investigation data
Type is trained KF-PLS model using the input matrix and the matrix of consequence；
S3: acquisition user is using the second process video for aggravating blanket, and the KF-PLS model completed using training is to the use
It is analyzed using the second process video for aggravating blanket and obtains user experience data in family.
First process video, the first process families photo are the training data for training neural network model, and second
Process video is data to be tested, and trained neural network carries out analysis the second mistake of acquisition to the second process video for use
The corresponding user experience data of journey video.
Step S1 includes in implementation process of the present invention, using mobile phone A pp obtain user using different colours, model,
The exacerbation blanket process video (can be taken on site or read video file by mobile phone A pp) of pressure is transferred to cloud, in cloud
The video is resolved into continuous serial-gram by end, using face recognition technology, identifies the corresponding human face expression of the serial-gram,
Obtaining the code vector that expression changes over time, (7 kinds of expression type indignation are detested, frightened, glad, sad, surprised, loss of emotion
Corresponding code is 1,2,3,4,5,6,7), age N (year), gender B (it is 1/0 that male/female, which corresponds to code) is to the data square
Battle array makees following processing, obtains input matrix X；
Specifically, step S1 includes in an embodiment of the present invention,
S11: the two-dimentional expression spectrum that expression code vector changes over time is drawn, wherein abscissa is the time, and ordinate is
Expression type code 1-7, obtaining " indignation " corresponding expression vector is [0,0,0,0,0,0,1]T, " detest " corresponding expression to
Amount is [0,0,0,0,0,2,0]T, " fear " corresponding expression vector be [0,0,0,0,3,0,0]T, " happiness " corresponding expression to
Amount is [0,0,0,4,0,0,0]T, " sad " corresponding expression vector be [0,0,5,0,0,0,0]T, " surprised " corresponding expression to
Amount is [0,6,0,0,0,0,0]T, " loss of emotion " corresponding expression vector be [7,0,0,0,0,0,0]T；It composes to obtain square using expression
Battle array A=[e1,e2,e3,…,en]7×n(enFor one of seven kinds of expression vectors).For example, as n=10, E=[5,7,6,6,4,4,
4,4,6,7]；The expression of expression code matrices at any time is drawn to compose as shown in figure 3, being composed to obtain expression spectrum matrix A by expression:
S12: matrix A progress transposed transform is obtained into AT=[e1,e2,e3,…,en]n×7；
S13: constructing new matrix is M=AAT；
S14: calculating the characteristic value of matrix M, and value indicative matrix is λ=[λ1,λ2,λ3,…,λ7]1×7；
S15: input parameter matrix is by matrix exgenvalue, gender, age composition X=[λ, N, B]1×9。
Step S2 includes in implementation process of the present invention, the real user experience of investigation user's video process, selection point
Number 1 divides, 2 points, 3 points, one of 4 points, 5 points (it is very poor, poor, general, good, fine to respectively correspond experience of the process) as experience test knot
Fruit, and as output result y；Using a large amount of input matrix X and corresponding output matrix of consequence Y, Kalman filtering and PLS phase are utilized
In conjunction with method (KF-PLS) establish dynamic corrections calibration model.Calculated the main gene coefficient of Partial Least Squares Regression as KF
The state variable of method, the newest moment standard sample observational variable to be measured as KF increase a standard specimen newly, correct a PLS
Model, and forgetting factor is introduced, gradually forget the effect of outmoded sample, i.e., is the estimation procedure of state parameter by problem reduction.
In implementation process of the present invention, in step S2, input matrix X and matrix of consequence Y are divided using following formula
Solution
X=TPt+E
Y=UQt+F
The input paramete information data after the influence of component environment variable are removed in matrix T expression, and part ring is removed in matrix U expression
After border variable influences and user experience result information data, E and F indicate error.
Step S2 includes the following steps in implementation process of the present invention,
S21: PLS initial model number of main factor is set as l, factor coefficient is w1, t1, v1, p1；w2, t2, v2, p2；……；wi,
ti, vi, pi(i=1,2,3 ..., l), in which:
vi=(tTy)/(tTT)=[vi1 vi2 ... vip],
S22: all coefficient values in model are formed into state vector
W=[w1 Tt1 Tv1p1 T...wi Tti Tvipi T]T(i=1,2,3 ..., l),
S23: generating state equation and observational equation,
Wherein YekFor standard specimen test result；WkThe main gene coefficient at moment is corrected for k-th of standard specimen；XkIt is inputted for k-th
Parameter matrix；YrkTo predict test result.
S24: environmental variance V is obtainedkStatistical property
It enables
S25: obtaining observational equation,
Yek=HkWk+Dk+Vk,
S26: Kalman filter model is generated；
The step S26 includes,
S261: calculating forward weight variable and measurement updaue,
S262: calculating forward error covariance,
WhereinAnd Pk-1For initial estimation,
S263: calculating kalman gain,
S264: by desired output YekMore new estimation,
S265: updating error covariance,
S266: K is done and adds 1 assignment and turns S262.
In implementation process of the present invention, step S3 includes that above-mentioned trained KF-PLS model is put into cloud, the process
Develop into software；For newly developed exacerbation blanket, as long as typing video can automatically obtain the user's body of the user experience process
Evaluation result is tested, product up-gradation optimum results are carried out to company and are evaluated, efficiency of research and development is improved.
It should be pointed out that the above description is not a limitation of the present invention, the present invention is also not limited to the example above,
Variation, modification, addition or the replacement that those skilled in the art are made within the essential scope of the present invention, are also answered
It belongs to the scope of protection of the present invention.
Claims (10)
1. a kind of exacerbation blanket based on KF-PLS experiences evaluation method, which is characterized in that include the following steps
S1: acquisition user obtains the first process families according to first process video using the first process video for aggravating blanket
Photo carries out recognition of face to the first process families photo and obtains user's human face expression vector, according to user's face
Expression vector obtains input matrix；
S2: acquisition user investigation data obtain matrix of consequence Y according to the user investigation data, construct KF-PLS model, use
The input matrix and the matrix of consequence are trained KF-PLS model.
S3: acquisition user makes the user using the KF-PLS model that training is completed using the second process video for aggravating blanket
It is analyzed with the second process video for aggravating blanket and obtains user experience data.
2. a kind of exacerbation blanket based on KF-PLS as described in claim 1 experiences evaluation method, which is characterized in that the step
S1 includes,
S11: using abscissa as the time, ordinate is that expression type code generates user's human face expression vector changes over time two
Dimension table feelings spectrum, wherein " indignation " corresponding expression vector is [0,0,0,0,0,0,1]T, " detest " corresponding expression vector be
[0,0,0,0,0,2,0]T, " fear " corresponding expression vector be [0,0,0,0,3,0,0]T, " happiness " corresponding expression vector be
[0,0,0,4,0,0,0]T, " sad " corresponding expression vector be [0,0,5,0,0,0,0]T, " surprised " corresponding expression vector be
[0,6,0,0,0,0,0]T, " loss of emotion " corresponding expression vector be [7,0,0,0,0,0,0]T, compose to obtain matrix A using expression
=[e1,e2,e3,…,en]7×n；
S12: matrix A progress transposed transform is obtained into AT=[e1,e2,e3,…,en]n×7；
S13: structural matrix M=AAT；
S14: the characteristic value of calculating matrix M, eigenvalue matrix λ=[λ of generator matrix M1,λ2,λ3,…,λ7]1×7；
S15: it generates input matrix X=[λ, N, B]1×9, wherein N is the age, and B is gender.
3. a kind of exacerbation blanket based on KF-PLS as claimed in claim 2 experiences evaluation method, which is characterized in that using following
Formula decomposes input matrix X and matrix of consequence Y
X=TPt+E
Y=UQt+F
The input paramete information data after the influence of component environment variable are removed in matrix T expression, and component environment change is removed in matrix U expression
User experience result information data after amount influence, E and F indicate error.
4. a kind of exacerbation blanket based on KF-PLS as claimed in claim 3 experiences evaluation method, which is characterized in that the step
S2 includes the following steps,
S21: setting PLS initial model number of main factor is l, and factor coefficient is w1, t1, v1, p1；w2, t2, v2, p2；……；wi, ti,
vi, pi(i=1,2,3 ..., l), in which:
vi=(tTy)/(tTT)=[vi1 vi2 ... vip],
S22: all coefficient values in model are formed into state vector
W=[w1 Tt1 Tv1p1 T...wi Tti Tvipi T]T(i=1,2,3 ..., l),
S23: generating state equation and observational equation,
Wherein YekFor standard specimen test result；WkThe main gene coefficient at moment is corrected for k-th of standard specimen；XkFor k-th of input parameter square
Battle array；YrkTo predict test result.
S24: environmental variance V is obtainedkStatistical property
It enables
S25: obtaining observational equation,
Yek=HkWk+Dk+Vk,
S26: Kalman filter model is generated；
The step S26 includes,
S261: calculating forward weight variable and measurement updaue,
S262: calculating forward error covariance,
WhereinAnd Pk-1For initial estimation,
S263: calculating kalman gain,
S264: by desired output YekMore new estimation,
S265: updating error covariance,
S266: K is done and adds 1 assignment and turns S262.
5. a kind of exacerbation blanket based on KF-PLS as claimed in claim 4 experiences evaluation method, which is characterized in that the step
S3 further includes,
User experience data is sent to administrator's mobile terminal and is shown.
6. a kind of exacerbation blanket based on KF-PLS experiences evaluation system, which is characterized in that comprise the following modules
Acquisition module obtains the according to first process video for acquiring user using the first process video for aggravating blanket
One process families photo carries out recognition of face to the first process families photo and obtains user's human face expression vector, according to institute
It states user's human face expression vector and obtains input matrix,；
Training module obtains matrix of consequence Y according to the first user investigation data for the first user investigation data of acquisition,
The KF-PLS model of building is trained KF-PLS model using the input matrix and the matrix of consequence.
As a result output module, for acquiring user using the second process video for aggravating blanket, using the KF-PLS mould of training completion
Type is analyzed the user using the second process video for aggravating blanket and obtains storage user experience data.
7. a kind of exacerbation blanket based on KF-PLS as claimed in claim 6 experiences evaluation system, which is characterized in that the acquisition
Module obtains input matrix using following steps,
S11: using abscissa as the time, ordinate is that expression type code generates user's human face expression vector changes over time two
Dimension table feelings spectrum, wherein " indignation " corresponding expression vector is [0,0,0,0,0,0,1]T, " detest " corresponding expression vector be
[0,0,0,0,0,2,0]T, " fear " corresponding expression vector be [0,0,0,0,3,0,0]T, " happiness " corresponding expression vector be
[0,0,0,4,0,0,0]T, " sad " corresponding expression vector be [0,0,5,0,0,0,0]T, " surprised " corresponding expression vector be
[0,6,0,0,0,0,0]T, " loss of emotion " corresponding expression vector be [7,0,0,0,0,0,0]T, compose to obtain matrix A using expression
=[e1,e2,e3,…,en]7×n；
S12: matrix A progress transposed transform is obtained into AT=[e1,e2,e3,…,en]n×7；
S13: structural matrix M=AAT；
S14: the characteristic value of calculating matrix M, eigenvalue matrix λ=[λ of generator matrix M1,λ2,λ3,…,λ7]1×7；
S15: it generates input matrix X=[λ, N, B]1×9, wherein N is the age, and B is gender.
8. a kind of exacerbation blanket based on KF-PLS as claimed in claim 7 experiences evaluation system, which is characterized in that using following
Formula decomposes input matrix X and matrix of consequence Y
X=TPt+E
Y=UQt+F
The input paramete information data after the influence of component environment variable are removed in matrix T expression, and component environment change is removed in matrix U expression
User experience result information data after amount influence, E and F indicate error.
9. a kind of exacerbation blanket based on KF-PLS as claimed in claim 8 experiences evaluation system, which is characterized in that the training
Module is trained KF-PLS model using following steps:
S21: setting PLS initial model number of main factor is l, and factor coefficient is w1, t1, v1, p1；w2, t2, v2, p2；……；wi, ti,
vi, pi(i=1,2,3 ..., l), in which:
vi=(tTy)/(tTT)=[vi1 vi2 ... vip],
S22: all coefficient values in model are formed into state vector
W=[w1 Tt1 Tv1p1 T...wi Tti Tvipi T]T(i=1,2,3 ..., l),
S23: generating state equation and observational equation,
Wherein YekFor standard specimen test result；WkThe main gene coefficient at moment is corrected for k-th of standard specimen；XkFor k-th of input parameter square
Battle array；YrkTo predict test result.
S24: environmental variance V is obtainedkStatistical property
It enables
S25: obtaining observational equation,
Yek=HkWk+Dk+Vk,
S26: Kalman filter model is generated；
The step S26 includes,
S261: calculating forward weight variable and measurement updaue,
S262: calculating forward error covariance,
WhereinAnd Pk-1For initial estimation,
S263: calculating kalman gain,
S264: by desired output YekMore new estimation,
S265: updating error covariance,
S266: K is done and adds 1 assignment and turns S262.
10. a kind of exacerbation blanket based on KF-PLS as claimed in claim 9 experiences evaluation system, which is characterized in that the knot
Fruit output module is also used to, and user experience data is sent to administrator's mobile terminal and is shown.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910178668.8A CN109886250A (en) | 2019-03-11 | 2019-03-11 | A kind of exacerbation blanket experience evaluation method and system based on KF-PLS |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910178668.8A CN109886250A (en) | 2019-03-11 | 2019-03-11 | A kind of exacerbation blanket experience evaluation method and system based on KF-PLS |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109886250A true CN109886250A (en) | 2019-06-14 |
Family
ID=66931628
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910178668.8A Pending CN109886250A (en) | 2019-03-11 | 2019-03-11 | A kind of exacerbation blanket experience evaluation method and system based on KF-PLS |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109886250A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105548068A (en) * | 2015-12-23 | 2016-05-04 | 重庆科技学院 | Dynamic evolving model correcting method and system |
| CN106404712A (en) * | 2016-10-19 | 2017-02-15 | 重庆城市管理职业学院 | Adaptive model correcting method and system based on GT-KF-PLC near infrared spectrum |
| CN106600667A (en) * | 2016-12-12 | 2017-04-26 | 南京大学 | Method for driving face animation with video based on convolution neural network |
-
2019
- 2019-03-11 CN CN201910178668.8A patent/CN109886250A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105548068A (en) * | 2015-12-23 | 2016-05-04 | 重庆科技学院 | Dynamic evolving model correcting method and system |
| CN106404712A (en) * | 2016-10-19 | 2017-02-15 | 重庆城市管理职业学院 | Adaptive model correcting method and system based on GT-KF-PLC near infrared spectrum |
| CN106600667A (en) * | 2016-12-12 | 2017-04-26 | 南京大学 | Method for driving face animation with video based on convolution neural network |
Non-Patent Citations (2)
| Title |
|---|
| 王得胜: "气味用户体验测试评价技术研究及应用", 《中国优秀硕士学位论文全文数据库（工程科技Ⅱ辑）》 * |
| 赵飞祥: "基于PLS结构方程的在线零售平台（网站）用户体验测度研究", 《中国优秀博硕士学位论文全文数据库(硕士) 经济与管理科学辑》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Cheung | Fixed-and random-effects meta-analytic structural equation modeling: Examples and analyses in R | |
| CN110009210B (en) | Comprehensive assessment method for student class listening level based on attention degree and concentration degree | |
| CN108228674B (en) | DKT-based information processing method and device | |
| Hirose et al. | Automatically growing dually adaptive online IRT testing | |
| CN109919102A (en) | A kind of self-closing disease based on Expression Recognition embraces body and tests evaluation method and system | |
| CN110192860B (en) | Brain imaging intelligent test analysis method and system for network information cognition | |
| CN109919099A (en) | A kind of user experience evaluation method and system based on Expression Recognition | |
| Song et al. | Pluggable reputation systems for peer review: A web-service approach | |
| Hirose | Dually Adaptive Online IRT Testing System | |
| CN108229688A (en) | A kind of information processing method and device based on IRT | |
| CN117122303A (en) | Brain network prediction method, system, equipment and storage medium | |
| CN109886250A (en) | A kind of exacerbation blanket experience evaluation method and system based on KF-PLS | |
| CN111429044A (en) | Intelligent science and technology teaching system based on learning behaviors and control method | |
| CN109919101A (en) | A kind of user experience evaluation method and system based on cell phone client | |
| CN109920514A (en) | A kind of self-closing disease based on Kalman filtering neural network embraces body and tests evaluation method and system | |
| CN116090879A (en) | Flight training quality assessment method based on eye movement data | |
| CN105046193A (en) | Human motion identification method based on fusion sparse expression matrixes | |
| CN115205072A (en) | Cognitive diagnosis method for long-period evaluation | |
| CN109934156A (en) | A kind of user experience evaluation method and system based on ELMAN neural network | |
| CN109919100A (en) | A kind of user experience evaluation method and system based on cell phone client Yu cloud service technology | |
| CN111985793A (en) | Online student evaluation and education method | |
| DE102020129018A1 (en) | DEEP USER MODELING THROUGH BEHAVIOR | |
| Bataev et al. | Artificial intelligence technologies in higher education institutions: a model of adaptive education | |
| Huang et al. | RESEARCH ON INDEPENDENT COLLEGE TEACHERS’TEACHING ABILITY BASED ON FACTOR ANALYSIS IN SPSS | |
| CN109920539A (en) | It is a kind of to embrace body in self-closing disease unconscious, under free state and test evaluation method and system |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190614 |