CN102890201A - Method and device for selecting test points of artificial electromagnetic material unit - Google Patents
Method and device for selecting test points of artificial electromagnetic material unit Download PDFInfo
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- CN102890201A CN102890201A CN2011101116745A CN201110111674A CN102890201A CN 102890201 A CN102890201 A CN 102890201A CN 2011101116745 A CN2011101116745 A CN 2011101116745A CN 201110111674 A CN201110111674 A CN 201110111674A CN 102890201 A CN102890201 A CN 102890201A
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Abstract
The embodiment of the invention provides a method and a device for selecting test points of an artificial electromagnetic material unit. The method comprises the following steps: acquiring parameters of a to-be-measured artificial electromagnetic material unit, value ranges of the parameters and a parameter level; and according to the parameters of the artificial electromagnetic material unit, the value ranges of the parameters and the parameter level, calculating a test point sequence with the Bayesian self-adaptive design method, and selecting test points of the artificial electromagnetic material unit instructed by the test point sequence. The method and the device provided by the embodiment of the invention can be used to complete a test by selecting fewer test points, so that the number of the selected test points is reduced, the test resources are saved, and a test design is optimized.
Description
Technical field
The present invention relates to super Material Field, be specifically related to testing site choosing method and the device of a kind of artificial electromagnetic material unit.
Background technology
Development along with super material technology, the description of artificial electromagnetic material structural unit electromagnetic property plays vital effect to the artificial electromagnetic material the Automation Design, the electromagnetic property of measuring the artificial electromagnetic material structural unit is an important step in the artificial electromagnetic material design process, the shape of artificial electromagnetic material and size can affect its electromagnetic property, therefore, needing to choose a large amount of testing sites in the process of the electromagnetic property of measuring the artificial electromagnetic material unit tests.
At present, the testing site choosing method of artificial material unit is: referring to Fig. 1, be the synoptic diagram of the testing site choosing method of prior art artificial material unit.For example the super material cell of a kind of " worker " type topological structure is tested, the geometric parameter of definition is G vector [a, b, w], supposes parameter a, b, and the span of w three parameters is respectively [Isosorbide-5-Nitrae], [1,3], [0.1,0.2], and sample point number corresponding to each geometric parameter is 4,3 and 2.Carry out choosing of testing site according to orthogonal design method, need to select 24 testing sites as shown in table 1 below to finish test, it is as shown in table 1 below to obtain orthogonal arrage according to the Orthogonal Experiment and Design principle:
Sequence number | a | b | w |
1 | 1 | 1 | 0.1 |
2 | 1 | 1 | 0.2 |
3 | 1 | 2 | 0.1 |
4 | 1 | 2 | 0.2 |
5 | 1 | 3 | 0.1 |
6 | 1 | 3 | 0.2 |
7 | 2 | 1 | 0.1 |
8 | 2 | 1 | 0.2 |
9 | 2 | 2 | 0.1 |
10 | 2 | 2 | 0.2 |
11 | 2 | 3 | 0.1 |
12 | 2 | 3 | 0.2 |
13 | 3 | 1 | 0.1 |
14 | 3 | 1 | 0.2 |
15 | 3 | 2 | 0.1 |
16 | 3 | 2 | 0.2 |
17 | 3 | 3 | 0.1 |
18 | 3 | 3 | 0.2 |
19 | 4 | 1 | 0.1 |
20 | 4 | 1 | 0.2 |
21 | 4 | 2 | 0.1 |
22 | 4 | 2 | 0.2 |
23 | 4 | 3 | 0.1 |
24 | 4 | 3 | 0.2 |
Table 1
In the research and practice process to prior art, the present inventor finds, if the testing site choosing method of existing artificial electromagnetic material structural unit applies to the sample of a plurality of parameters and a plurality of parameters and counts out when more, then to choose a large amount of testing sites.Too much testing site can consume a large amount of resources.
Summary of the invention
The embodiment of the invention provides the testing site choosing method of a kind of artificial electromagnetic material unit, comprising:
Obtain span and the parameter level of the parameter of artificial electromagnetic material to be measured unit, described parameter;
According to span and the parameter level of the parameter of described artificial electromagnetic material unit, described parameter, calculate the testing site sequence by the Bayesian adaptation method for designing;
Choose the testing site of the artificial electromagnetic material unit of described testing site sequence indication.
The testing site selecting device of a kind of artificial electromagnetic material unit is characterized in that, comprising:
Correspondingly, the present invention also provides the testing site selecting device of a kind of artificial electromagnetic material unit, comprising:
Parameter acquisition module is for span and the parameter level of obtaining the parameter of artificial electromagnetic material to be measured unit, described parameter;
The sequence computing module is used for span and parameter level according to the parameter of described artificial electromagnetic material unit, described parameter, calculates the testing site sequence by the Bayesian adaptation method for designing;
Module is chosen in the testing site, for the testing site of the artificial electromagnetic material unit of choosing the sequence indication of described testing site.
The embodiment of the invention is by span and the parameter level of the parameter of obtaining artificial electromagnetic material to be measured unit, described parameter, span and parameter level according to the parameter of described artificial electromagnetic material unit, described parameter, calculate the testing site sequence by the Bayesian adaptation method for designing, choose the testing site of the artificial electromagnetic material unit of described testing site sequence indication.Guarantee the homogeneity of testing site, realized choosing the strong testing site of less homogeneity and finished test, reduced the number of choosing of testing site, saved the test resource, optimized test design.
Description of drawings
In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art, the below will do one to the accompanying drawing of required use in embodiment or the description of the Prior Art and introduce simply, apparently, accompanying drawing in the following describes is some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the synoptic diagram of the testing site choosing method of prior art artificial material unit
Fig. 2 is the process flow diagram of the testing site choosing method of the artificial electromagnetic material unit that provides of first embodiment of the invention;
Fig. 3 is the structural representation of the testing site selecting device of the artificial electromagnetic material unit that provides of second embodiment of the invention.
Embodiment
The embodiment of the invention provides testing site choosing method and the device of a kind of artificial electromagnetic material unit.Below be elaborated respectively.
The process flow diagram of the testing site choosing method of a kind of artificial electromagnetic material unit of the embodiment of the invention one can be with reference to figure 2, and the method comprises:
For example, choose geometric parameter and be (a, b), the span of each parameter is respectively [0.25,1], [0.25,1].The horizontal number of the value of each parameter is respectively 4 and 4.
Step 102 according to span and the parameter level of the parameter of described artificial electromagnetic material unit, described parameter, is calculated the testing site sequence by the Bayesian adaptation method for designing.
Utilize the Bayesian adaptation designing technique to generate the testing site sequence, concrete hypothesis, we need to do n Electromagnetic Simulation experiment, and microstructure geometric parameter number is s, adopts step as described below to generate the testing site sequence:
At first, for each parameter j, j=1 ..., s, to integer sequence 1 ..., n carries out random rearrangement one time, obtains random series: π
j(1), L, π
j(n).
Then, definition Y is the electromagnetic response of microstructure, and Ω is the response sample space, and θ is the geometrical parameters space, p
e(y| θ) is probability model, is the probability density that the microstructure of θ obtains electromagnetic response y in order to describe by geometric parameter.The definition test space is E, test sample point e, select like this one the test e after, will electromagnetic response observation data Y.Based on observation data Y and testing site e, from decision set D, choose decision point d, wherein decision set D is determined by the final purpose of test.Process of the test comprises two interactive portions of statistical inference/statistical decision.In test e, given observation data Y, decision-making d obtains by asking for following posteriority expectation entity function,
The mathematical expectation of herein the θ space being asked for characterizes the uncertainty of unknown parameter θ.
Said structure is carried out integration at electromagnetic response sample space Ω, obtains expecting the entity function:
Final selected testing site sequence U (e*) is obtained by following formula
The testing site e* of U (e*) testing site sequence indication possesses preferably homogeneity, choose the testing site of this sequence indication can be in the situation of choosing less testing site warranty test result's degree of accuracy, reduce test number (TN).
The embodiment of the invention is by span and the parameter level of the parameter of obtaining artificial electromagnetic material to be measured unit, described parameter, span and parameter level according to the parameter of described artificial electromagnetic material unit, described parameter, calculate the testing site sequence by the Bayesian adaptation method for designing, choose the testing site of the artificial electromagnetic material unit of described testing site sequence indication.Realize choosing less testing site and finished test, reduced the number of choosing of testing site, saved the test resource, optimized test design.
The process flow diagram of the testing site choosing method of a kind of artificial electromagnetic material unit of the embodiment of the invention two can be with reference to figure 3, and embodiment two compares the method for embodiment one and more optimizes, and the method specifically comprises:
The below also provides a kind of device that extracts electromagnetic property curvilinear characteristic information to be elaborated to the embodiment of the invention, and this device comprises:
Parameter acquisition module 11 is for span and the parameter level of obtaining the parameter of artificial electromagnetic material to be measured unit, described parameter.
Preferably, this sequence computing module specifically comprises:
The embodiment of the invention by obtaining artificial electromagnetic material to be measured unit geometric parameter and the sample point number of described geometric parameter; According to described geometric parameter and sample point number, by good grid point method structure uniform designs table; Choose the testing site of the artificial electromagnetic material unit of described uniform designs table indication.Realize choosing less testing site and finished test, reduced the number of choosing of testing site, saved the test resource, optimized test design.
One of ordinary skill in the art will appreciate that all or part of step in the whole bag of tricks of above-described embodiment is to come the relevant hardware of instruction finish by program, this program can be stored in the computer-readable recording medium, and storage medium can comprise: ROM, RAM, disk or CD etc.
More than method and apparatus that the embodiment of the invention is provided be described in detail, used specific case herein principle of the present invention and embodiment are set forth, the explanation of above embodiment just is used for helping to understand method of the present invention and core concept thereof; Simultaneously, for one of ordinary skill in the art, according to thought of the present invention, all will change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention.
Claims (10)
1. the testing site choosing method of an artificial electromagnetic material unit is characterized in that, comprising:
Obtain span and the parameter level of the parameter of artificial electromagnetic material to be measured unit, described parameter;
According to span and the parameter level of the parameter of described artificial electromagnetic material unit, described parameter, calculate the testing site sequence by the Bayesian adaptation method for designing;
Choose the testing site of the artificial electromagnetic material unit of described testing site sequence indication.
2. the method for claim 1 is characterized in that, describedly calculates the testing site sequence by the Bayesian adaptation method for designing and specifically comprises:
Obtain number and the test number (TN) of the parameter of described artificial electromagnetic material unit;
According to number and the test number (TN) of described parameter, calculate the testing site sequence in predetermined interval.
3. the method for claim 1 is characterized in that, shape or the size of described parameter indication artificial electromagnetic material unit.
4. the method for claim 1 is characterized in that, described parameter has two value levels at least.
5. the method for claim 1 is characterized in that, described artificial electromagnetic material unit comprises two parameters at least.
6. the testing site choosing method of an artificial electromagnetic material unit is characterized in that, comprising:
Parameter acquisition module is for span and the parameter level of obtaining the parameter of artificial electromagnetic material to be measured unit, described parameter;
The sequence computing module is used for span and parameter level according to the parameter of described artificial electromagnetic material unit, described parameter, calculates the testing site sequence by the Bayesian adaptation method for designing;
Module is chosen in the testing site, for the testing site of the artificial electromagnetic material unit of choosing the sequence indication of described testing site.
7. the method for claim 1 is characterized in that, described sequence computing module specifically comprises:
Information acquisition unit is for number and the test number (TN) of the parameter of obtaining described artificial electromagnetic material unit;
Computing unit is used for number and test number (TN) according to described parameter, calculates the testing site sequence in predetermined interval.
8. device as claimed in claim 1 is characterized in that, shape or the size of described parameter indication artificial electromagnetic material unit.
9. device as claimed in claim 1 is characterized in that, described parameter has two value levels at least.
10. device as claimed in claim 1 is characterized in that, described artificial electromagnetic material unit comprises two parameters at least.
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20040210400A1 (en) * | 2003-01-27 | 2004-10-21 | Perlegen Sciences, Inc. | Analysis methods for individual genotyping |
JP2005055190A (en) * | 2003-08-01 | 2005-03-03 | Japan Science & Technology Agency | Electromagnetic-wave radiation source detecting method and device by bayesian network |
CN101105841A (en) * | 2007-02-12 | 2008-01-16 | 浙江大学 | Method for constructing gene controlled subnetwork by large scale gene chip expression profile data |
JP2009003869A (en) * | 2007-06-25 | 2009-01-08 | Just Syst Corp | Machine learning method, machine learning program, and machine learning apparatus |
CN102034029A (en) * | 2010-12-21 | 2011-04-27 | 福建师范大学 | Bayesian network-based signal peptide shearing site prediction method |
-
2011
- 2011-04-30 CN CN201110111674.5A patent/CN102890201B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20040210400A1 (en) * | 2003-01-27 | 2004-10-21 | Perlegen Sciences, Inc. | Analysis methods for individual genotyping |
JP2005055190A (en) * | 2003-08-01 | 2005-03-03 | Japan Science & Technology Agency | Electromagnetic-wave radiation source detecting method and device by bayesian network |
CN101105841A (en) * | 2007-02-12 | 2008-01-16 | 浙江大学 | Method for constructing gene controlled subnetwork by large scale gene chip expression profile data |
JP2009003869A (en) * | 2007-06-25 | 2009-01-08 | Just Syst Corp | Machine learning method, machine learning program, and machine learning apparatus |
CN102034029A (en) * | 2010-12-21 | 2011-04-27 | 福建师范大学 | Bayesian network-based signal peptide shearing site prediction method |
Non-Patent Citations (2)
Title |
---|
刘亚红等: "同时实现介电常数和磁导率为负的H型结构单元左手材料", 《物理学报》, vol. 56, no. 10, 31 October 2007 (2007-10-31) * |
张维铭: "批量较小时的贝叶斯抽样方案的算法", 《纺织高校基础科学学报》, vol. 9, no. 2, 30 June 1996 (1996-06-30) * |
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