CN108254000A - A kind of FBG Sensor Network spectrum demodulation methods based on simulated annealing improved - Google Patents
A kind of FBG Sensor Network spectrum demodulation methods based on simulated annealing improved Download PDFInfo
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Abstract
The present invention relates to a kind of FBG Sensor Network spectrum demodulation methods based on simulated annealing improved, include the following steps：S1, according to the mathematical characteristic of FBG Sensor Networks and the reflectance spectrum measured, there is shown the mathematic(al) representation of the reflectance spectrum of entire FBG Sensor Networks, and the mathematic(al) representation of reflectance spectrum is reconstructed, using incident light spectrum and the variance of reflectance spectrum mathematic(al) representation as object function；S2 before obtaining optimal solution, is carried out annealing operation using modified-immune algorithm after each particle position update, the solution being obtained is avoided to be absorbed in locally optimal solution to the solution of object function using particle cluster algorithm into line search；S3, using the optimal solution obtained as the reflectance spectrum centre wavelength value of each FBG in FBG Sensor Networks.Compared with prior art, the present invention improves the demodulation efficiency to FBG spectral multiplexings, increases overlapping of grating spectra demodulation quantity, the limitation for overcoming the wave-length coverage of different gratings that cannot be overlapped.
Description
Technical field
The present invention relates to a kind of FBG Sensor Networks spectrum demodulation methods, and simulated annealing improved is based on more particularly, to one kind
FBG Sensor Network spectrum demodulation methods.
Background technology
Fiber bragg grating (Fiber Bragg grating, FBG) sensor is a kind of fiber optic passive device, is had
Small, light weight, corrosion-resistant, high temperature resistant, high sensitivity, is easily achieved the advantages that distributed network multiplexing at electromagnetism interference,
It is widely used in the fields such as optical communication and optical sensing.Fiber-optic grating sensor utilizes the centre wavelength of its reflectance spectrum wave crest
Correspondence between offset and tested physical quantity realizes the detection to sensing parameter (such as temperature, humidity, strain etc.).
When synchronizing detection to multiple variables, need to set up FBG sensor into the network structure into multiplexing.However work as light source
Bandwidth it is limited when, increase with the multiplexing quantity of grating, it may appear that the problem of FBG spectra overlappings, influence sensor-based system
Demodulate effect.Therefore, it is most important to the multiplexing capacity for improving sensor-based system to distinguish the spectrum being overlapped in FBG sensing networks.
At present in FBG sensing networks are solved the problems, such as in spectra overlapping, the method often used has：Genetic algorithm, difference
Evolution algorithm, simulated annealing etc..Genetic algorithm is applied in FBG Sensor Networks, can precisely detect in sensor-based system and be overlapped
The Bragg grating wavelength of spectrum, but genetic algorithm does not utilize the feedback information of network in time, leads to the search speed of algorithm
Degree is slower, a large amount of calculating time is needed when acquiring accurate solution, and need to encode in Solve problems, is obtained most
It needs to be decoded after excellent solution, increases computational complexity, be not suitable for the wavelength recognition under Practical Project environment；Differential evolution algorithm
It increases to the wavelength accuracy for finding FBG, but as evolution iterative steps increase so that the otherness between individual reduces,
It is easy to cause algorithm and Premature convergence occurs；Its run time of simulated annealing is short, the spectrum weight in sensor-based system is distinguished
When folded, can Real-time Feedback result of calculation, but its global search is poor, and the initial value selection of algorithm can influence its calculating
Precision and calculating take.
The algorithm above can recognize that each FBG of system is passed in the spectra overlapping problem for handling FBG sensing networks
The centre wavelength of sensor improves the demodulation efficiency to FBG spectral multiplexings.But the algorithm above is to realize the light of two gratings
The multiplexing demodulation under overlapping cases is composed, does not account for the situation of multiple overlapping of grating spectra, and there are corresponding algorithm errors.
Invention content
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind based on improvement simulation
The FBG Sensor Network spectrum demodulation methods of annealing method.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of FBG Sensor Network spectrum demodulation methods based on simulated annealing improved, include the following steps：
S1 according to the grating spectrum Shape invariance principle of FBG Sensor Networks, measures reflectance spectrum when being not affected by stress,
The mathematic(al) representation of the reflectance spectrum of entire FBG Sensor Networks is represented, and reconstructs the mathematic(al) representation of reflectance spectrum, by incident light
Spectrum and the variance of reflectance spectrum mathematic(al) representation are as object function；
S2, using particle cluster algorithm to the solution of object function into line search, before obtaining optimal solution, each particle position is more
Annealing operation is carried out using modified-immune algorithm after new, the solution being obtained is avoided to be absorbed in locally optimal solution；
S3, using the optimal solution obtained as the reflectance spectrum centre wavelength value of each FBG in FBG Sensor Networks.
The step S1 includes the following steps：
S11, the mathematic(al) representation of the independent spectrum according to each FBG reflections, there is shown the reflected light of entire FBG Sensor Networks
The mathematic(al) representation of spectrum is as follows：
Wherein, reflectance spectrums of the R (λ) for entire FBG Sensor Networks, the grating number that n is included for FBG Sensor Networks, RiIt is i-th
The peak reflectivity of FBG, g in chainiFor the independent spectrum mathematic(al) representation that FBG in i-th chain reflects, λ is unknown grating
Centre wavelength value, λBiReflection center wavelength of light for FBG in i-th chain；
R (λ) is reconstructed in S12, and it is as follows to obtain reconstruct reflectance spectrum expression formula：
Wherein, Rv(λ,xBi) be entire FBG Sensor Networks reconstruct reflectance spectrum, xBiTo reconstruct the reflection light center wave of spectrum
It is long；
S13 determines that object function is as follows：
The independent spectrum of each FBG reflections uses Gaussian function approximate expression.
The step S2 includes the following steps：
S21 is randomly provided initial position x to population0With initial velocity v0, setting initial temperature T0With population number M
With greatest iteration step number D；
S22, by current location x0Object function is substituted into, calculates the fitness f (x of each particle0), according to fitness search
Individual optimal solution PiWith globally optimal solution gbest；
S23 updates speed and the position of all particles, updated new explanation is judged whether in solution space, if not
Satisfaction then continues to obtain new explanation, and particle rapidity is as follows with location update formula：
Wherein, subscript n ew represents updated parameter, and subscript and subscript represent old parameter, c1、c2For Studying factors,
r1、r2For the random number in [0,1]；
S24 judges whether to receive update position x using Metropolis criterionnewValue, if receive, xnewAs under
The x of an iterationoldIf not receiving, x is abandonednew, subsequently into step S25；
S25 judges whether current solution is optimal solution, if so, and meet end condition, then export in population optimal solution simultaneously
Terminate, otherwise enter step S26 and carry out annealing operation；
S26 carries out annealing operation using following formula：
Wherein, α is temperature damping's rate, and t is iterative steps, and k is that the temperature T that simulated annealing calculates reaches low temperature
When iteration step numerical value, μ is rises again the factor, 0 ＜ μ ＜ 1, μ and Tt+1Inversely；
S27, return to step S22.
In the step S26,0.7≤α≤1.0 are chosen.
In the step S26, k is iteration step numerical value when temperature T decrease speeds are less than setting speed.
The c1、c2For nonnegative constant, c1+c2>4.
Compared with prior art, the wavelength of each grating is identified during the present invention can be overlapped spectral shape；It is simulating
On the basis of Annealing Particle Swarm Optimization Algorithm, modify to the attenuation function in algorithm annealing process：When temperature value is in high-temperature region, press
Exponential manner is decayed, and improves computational efficiency；When temperature value is in low temperature, makees appropriate tempering heating, algorithm is avoided to be absorbed in " office
Portion is optimal ".Using improved method, the demodulation efficiency to FBG spectral multiplexings is improved, increases overlapping of grating spectra demodulation number
Amount, the limitation for overcoming the wave-length coverage of different gratings that cannot be overlapped.This method can be applied to large-scale optical fiber grating sensing net
In network, a kind of new departure is provided to improve FBG Sensor Network multiplexing capacities.
Description of the drawings
Fig. 1 is the experimental provision schematic diagram that the present embodiment is used to generate Distributed FBG spectra overlapping；
Fig. 2 is for the present embodiment modified-immune algorithm flow chart；
Fig. 3 is algorithm annealing temperature curve figure after the present embodiment improves.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, give detailed embodiment and specific operating process, but protection scope of the present invention is not limited to
Following embodiments.
Embodiment
One kind in the method, is wrapped based on spectral multiplexing method in simulated annealing difference Distributed FBG sensing network is improved
Include following steps：
Step 1：According to the mathematical characteristic of FBG sensor-based systems, determine its relevant mathematic(al) representation and establish rational number
Model is learned, constructs the object function of the spectra overlapping of Distributed FBG sensing network system.
Step 2：The object function of structure is solved with reference to particle cluster algorithm and modified-immune algorithm；Using
Particle cluster algorithm, into line search, can accelerate the convergence rate of initial operating stage to the solution of object function；Using improvement simulated annealing
Algorithm avoids the solution being obtained from being absorbed in locally optimal solution, increases and obtains the probability with optimal solution；Two kinds of algorithms are combined so that shape
Into the existing global optimization of new optimization algorithm again have efficient rapidity.
Step 3:Optimal solution is obtained using new optimization algorithm, and the optimal solution obtained is each FBG in sensor-based system
Centre wavelength value.
Further, it in step 1, determines the object function of the spectra overlapping of Distributed FBG sensing network, is subsequent
It solves and mathematical theory basis is provided, specifically include following steps：
1) assume containing i FBG sensor in Distributed FBG sensing network, and each FBG sensor is not by outer
Under boundary's factor disturbed condition, according to grating spectrum shape invariance principle, the independent spectrum of reflected single FBG sensor
Centre wavelength translation, the independent spectrum mathematic(al) representation according to each FBG, there is shown entire light only has occurred in (being denoted as gi (λ))
Compose the mathematic(al) representation of the reflectance spectrum of multiplex system；
2) it will reflect back into the independent spectrum come and be set as unknown quantity, reconfigure the reflectance spectrum of entire spectral multiplexing system
Mathematic(al) representation；
3) by the spectrum mathematic(al) representation of raw sensory system and the spectrum mathematic(al) representation for reconfiguring rear sensor-based system
Make variance, you can obtain the object function of the spectra overlapping of Distributed FBG sensing network system.
Further, in step 2, the particle cluster algorithm and improvement simulated annealing combined method are whole in order to improve
The computational efficiency of a algorithm, the defects of algorithm is avoided to be absorbed in " local optimum "；Entirely the expression formula of the annealing criterion in algorithm is：
Wherein, α is temperature damping's rate, and t is iterative steps, and k is that the temperature T that simulated annealing calculates reaches low temperature
When iteration step numerical value, μ is rises again the factor, 0 ＜ μ ＜ 1, μ and Tt+1Inversely.
Refering to Fig. 1, Fig. 1 is the structure diagram of the present embodiment Distributed FBG spectra overlapping, as seen from the figure, the present embodiment
Distributed FBG sensor-based system includes：
ASE wideband lasers 1, coupler 2, adjustable attenuators 3, spectroanalysis instrument 4, fiber grating 5；
Each device blocks is described as follows in the present embodiment：
ASE wideband lasers 1 mainly generate optical signal for whole system；The light source used in the present embodiment example for
The situation light source of a model ASE-Cband of Fiberer companies, the wave band of operation wavelength is 1530nm~1565nm,
Spectral flatness is less than 2dB.
Fiber coupler 2 is that general single mode fiber is formed, 1550nm wave bands, port 2 × 2, splitting ratio 50: 50.
Adjustable general's attenuator 3, adjustable damping amount are 0~30dB, return loss PC>45dB, Polarization Dependent Loss<
0.1dB, -40 DEG C~+75 DEG C of operating temperature range.
Spectroanalysis instrument 4 is mainly the reflectance spectrum for showing sensor-based system, and a model of company is found for day intrinsic safety
The desk-top spectroanalysis instrument of MS9740A, measurement wave-length coverage are 600nm~1750nm, maximum input optical power+23dBm.
The basic principle of the present embodiment is as follows：
ASE wideband light sources generate optical signal, enter in the FBG sensor in each of the links after coupler, via FBG
The narrow band light reflected to form is transferred to spectroanalysis instrument, and reflectance spectrum is finally formed on spectroanalysis instrument.In fiber link
Grating sensor is connected with parallel way, each of the links only there are one FBG sensor, and each FBG reflectance spectrums by
Spectroanalysis instrument processing is formed.Wherein, it is to ensure the reflection spectral shape of every FBG sensor not to add in adjustable attenuators
Together.
The reflectance spectrum for the sensor-based system being made of i FBG sensor shown on spectroanalysis instrument is near by formula (1)
Seemingly it is expressed as：
Wherein Ri(0≤Ri≤ 1) peak reflectivity of FBG in every chain is represented；λBiIt is expressed as the middle cardiac wave of each FBG
It is long；N (λ) represents the random distribution of phylogenetic various noises.
Calculating to spectrum for convenience, needs original spectrum to be reconstructed operation, obtains reconstruct spectrum by formula (2)
It is expressed as：
Wherein xBiTo reconstruct the centre wavelength of spectrum.
Assuming that the reflectance spectrum of all FBG sensors is using Gaussian function approximate expression, the mathematical expression of Gaussian function
Shown in formula such as formula (3)：
Wherein, RiIt is expressed as the reflectivity of i-th of FBG；λBiIt is expressed as the centre wavelength of i-th of FBG；ΔλBRepresent FBG
3db bandwidth.
If obtaining the difference between two spectrum, make the variance of calculating formula (1) and formula (2), be expressed as formula (4)：
According to formula (4), work as xBi→λBiWhen, object function f (xBi) reach minimum value, then it constructs spectrum and is infinitely close to
Original spectrum；At this point, if each x in reconstruct spectrum can be acquiredBiValue, you can obtain original spectrum in each grating wavelength letter
Breath.But in actual mathematical calculating process, due to being constant term after noise item N (λ) integrations in R (λ), do not have to object function
The influence of essence is caused, calculates ignore noise item for convenience.
Object function for minimum value as basic calculating under the conditions of, using improve simulated annealing method solve it is each heavy
Structure spectral centroid wavelength xBiValue, so as to fulfill the identification of overlapping is composed to FBG reflected lights in Distributed FBG sensor-based system.
As Fig. 2 be improve simulated annealing method flow chart, as shown in the figure, its specific implementation step is as follows：
1) administration way initialization condition.
Population setting initial position x is given at random0With initial velocity v0, and initial temperature T is set0With population number M,
And greatest iteration step number D.
2) individual optimal solution and globally optimal solution of particle are calculated
To position x this moment0It is brought into object function, calculates the fitness f (x of each particle0).According to initial position
Fitness, hunt out the individual optimal solution P of initializationiWith globally optimal solution gbest。
3) Position And Velocity updates
The speed and position of all particles are updated, judge updated new explanation whether in solution space, if
It is unsatisfactory for, continues to obtain new explanation.Shown in its particle rapidity and position iterative formula such as formula (5)：
4) receive new explanation probability
Judge whether x after receiving to update using Metropolis standardsnewValue, the acceptance probability of Metropolis standards
Shown in formula such as formula (6):
Wherein, Δ f=f (xnew)-f(xold)；Tt is residing temperature value under present case.As Δ f>When 0, if formula (6) ＞
During rand [0,1], then receive new position xnewPosition x as next iterationold.Otherwise, new position is abandoned.
Formula (6) is computed the numerical value obtained as the numerical value in the range of [0,1], and the value of the formula (6) calculated and [0,1]
Random number is compared, and then carries out corresponding step calculating.This is a kind of probability acceptance criterion.
5) judge end condition
Judge to be obtained whether solution is optimal solution in all solutions；If meeting end condition, optimal solution in population is exported,
Ending method；Otherwise cooling operation is carried out.
6) simulated annealing operates
To Current Temperatures TtCooling processing is carried out, shown in the function such as formula (7) of cooling operation generally used：
Tt+1=α Tt (7)
Wherein α is temperature damping's rate, usually chooses 0.7≤α≤1.0.
When carrying out cooling operation in view of this method, annealing efficiency is low.If it is desired to improve its annealing efficiency, need pair
(7) α temperature dampings lead and modify in formula.In entire annealing process, since the temperature most of the time is in cold stage, herein
In the case of, if selection temperature value is unreasonable, have the possibility that optimal solution jumps out optimal solution space again.In order to reduce the feelings
Condition occurs, and when temperature is in low-temperature region, iterative steps reach k at this time, suitably take the measure of rising again, improved fast prompt drop
Temperature is as shown in formula (8)：
Wherein μ is the factor of rising again；μ and Tt+1 and inversely (0<μ<1)；The temperature value that Tk is iterative steps when being k.
Improved annealing temperature curve is as shown in Figure 3.
7) circulate operation
When an iteration occurs for temperature, while the individual optimal solution P that more new particle is currentiWith globally optimal solution gbest, and
P in replacement formula (5)old iAnd gold best, carry out speed and location updating again at this time.
The optimal solution of object function is finally solved by aforesaid operations, you can obtain each in Distributed FBG sensor-based system
The centre wavelength of a FBG realizes the identification being overlapped to spectral waveform.
Claims (7)
1. a kind of FBG Sensor Network spectrum demodulation methods based on simulated annealing improved, which is characterized in that include the following steps：
S1 according to the grating spectrum Shape invariance principle of FBG Sensor Networks, measures reflectance spectrum when being not affected by stress, represents
Go out the mathematic(al) representation of the reflectance spectrum of entire FBG Sensor Networks, and reconstruct the mathematic(al) representation of reflectance spectrum, by incident light spectrum with
The variance of reflectance spectrum mathematic(al) representation is as object function；
S2, using particle cluster algorithm to the solution of object function into line search, before obtaining optimal solution, each particle position update with
Annealing operation is carried out using modified-immune algorithm afterwards, the solution being obtained is avoided to be absorbed in locally optimal solution；
S3, using the optimal solution obtained as the reflectance spectrum centre wavelength value of each FBG in FBG Sensor Networks.
2. a kind of FBG Sensor Network spectrum demodulation methods based on simulated annealing improved according to claim 1, feature
It is, the step S1 includes the following steps：
S11, the mathematic(al) representation of the independent spectrum according to each FBG reflections, there is shown the reflectance spectrum of entire FBG Sensor Networks
Mathematic(al) representation is as follows：
Wherein, reflectance spectrums of the R (λ) for entire FBG Sensor Networks, the grating number that n is included for FBG Sensor Networks, RiFor i-th chain
The peak reflectivity of middle FBG, giFor the independent spectrum mathematic(al) representation that FBG in i-th chain reflects, λ is the center of unknown grating
Wavelength value, λBiReflection center wavelength of light for FBG in i-th chain；
R (λ) is reconstructed in S12, and it is as follows to obtain reconstruct reflectance spectrum expression formula：
Wherein, Rv(λ,xBi) be entire FBG Sensor Networks reconstruct reflectance spectrum, xBiTo reconstruct the reflection center wavelength of light of spectrum；
S13 determines that object function is as follows：
3. a kind of FBG Sensor Network spectrum demodulation methods based on simulated annealing improved according to claim 2, feature
It is, the independent spectrum of each FBG reflections uses Gaussian function approximate expression.
4. a kind of FBG Sensor Network spectrum demodulation methods based on simulated annealing improved according to claim 1, feature
It is, the step S2 includes the following steps：
S21 is randomly provided initial position x to population0With initial velocity v0, setting initial temperature T0With population number M and most
Big iterative steps D；
S22, by current location x0Object function is substituted into, calculates the fitness f (x of each particle0), individual is searched according to fitness
Optimal solution PiWith globally optimal solution gbest；
S23 updates speed and the position of all particles, updated new explanation is judged whether in solution space, if be unsatisfactory for
Then continue to obtain new explanation, particle rapidity is as follows with location update formula：
Wherein, subscript n ew represents updated parameter, and subscript and subscript represent old parameter, c1、c2For Studying factors, r1、r2For
[0,1] random number in；
S24 judges whether to receive update position x using Metropolis criterionnewValue, if receive, xnewAs next time
The x of iterationoldIf not receiving, x is abandonednew, subsequently into step S25；
S25 judges whether current solution is optimal solution, if so, and meeting end condition, then export optimal solution in population and tying
Otherwise beam enters step S26 and carries out annealing operation；
S26 carries out annealing operation using following formula：
Wherein, α is temperature damping's rate, and t is iterative steps, and k is when the temperature T that simulated annealing calculates reaches low temperature
Iteration step numerical value, μ is rises again the factor, 0 ＜ μ ＜ 1, μ and Tt+1Inversely；
S27, return to step S22.
5. a kind of FBG Sensor Network spectrum demodulation methods based on simulated annealing improved according to claim 4, feature
It is, in the step S26, chooses 0.7≤α≤1.0.
6. a kind of FBG Sensor Network spectrum demodulation methods based on simulated annealing improved according to claim 4, feature
It is, in the step S26, k is iteration step numerical value when temperature T decrease speeds are less than setting speed.
7. a kind of FBG Sensor Network spectrum demodulation methods based on simulated annealing improved according to claim 4, feature
It is, the Studying factors c1、c2For nonnegative constant, c1+c2>4.
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Cited By (3)
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|---|---|---|---|---|
| CN109141488A (en) * | 2018-07-26 | 2019-01-04 | 福州大学 | A kind of Bragg optical-fiber grating sensor overlapped spectra demodulation method based on exchange population |
| CN109489699A (en) * | 2019-01-07 | 2019-03-19 | 福州大学 | A kind of fiber grating distortion spectrum demodulation method |
| CN110596894A (en) * | 2019-10-25 | 2019-12-20 | 清华大学深圳国际研究生院 | Method and system for designing diffractive optical element |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1998004892A1 (en) * | 1996-07-26 | 1998-02-05 | Advanced Micro Devices, Inc. | A rapid thermal anneal system and method including improved temperature sensing and monitoring |
| US20020141699A1 (en) * | 2001-02-13 | 2002-10-03 | Broptics Communication Corporation | Optical fiber bragg grating thermal compensating device and method for manufacturing same |
| CN102183814A (en) * | 2011-05-27 | 2011-09-14 | 哈尔滨工业大学深圳研究生院 | Reversal design method of optical fibre Bragg grating based on mix optimization algorithm |
| CN104764414A (en) * | 2015-04-16 | 2015-07-08 | 三峡大学 | FBG heterogeneous strain reconstruction method utilizing swarm optimization |
-
2017
- 2017-12-08 CN CN201711298779.XA patent/CN108254000A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1998004892A1 (en) * | 1996-07-26 | 1998-02-05 | Advanced Micro Devices, Inc. | A rapid thermal anneal system and method including improved temperature sensing and monitoring |
| US20020141699A1 (en) * | 2001-02-13 | 2002-10-03 | Broptics Communication Corporation | Optical fiber bragg grating thermal compensating device and method for manufacturing same |
| CN102183814A (en) * | 2011-05-27 | 2011-09-14 | 哈尔滨工业大学深圳研究生院 | Reversal design method of optical fibre Bragg grating based on mix optimization algorithm |
| CN104764414A (en) * | 2015-04-16 | 2015-07-08 | 三峡大学 | FBG heterogeneous strain reconstruction method utilizing swarm optimization |
Non-Patent Citations (3)
| Title |
|---|
| 李彩玲: "基于粒子群模拟退火算法的光纤光栅复用解调技术的研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 陈华根等: "改进的非常快速模拟退火算法", 《同济大学学报（自然科学版）》 * |
| 陈华根等: "模拟退火算法机理研究", 《同济大学学报（自然学报）》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109141488A (en) * | 2018-07-26 | 2019-01-04 | 福州大学 | A kind of Bragg optical-fiber grating sensor overlapped spectra demodulation method based on exchange population |
| CN109489699A (en) * | 2019-01-07 | 2019-03-19 | 福州大学 | A kind of fiber grating distortion spectrum demodulation method |
| CN109489699B (en) * | 2019-01-07 | 2021-06-29 | 福州大学 | Fiber grating distortion spectrum demodulation method |
| CN110596894A (en) * | 2019-10-25 | 2019-12-20 | 清华大学深圳国际研究生院 | Method and system for designing diffractive optical element |
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