Study and investigation of long period grating as refractive index sensor

Singh, Amit; Rana, Shashi B.; Singh, Maninder; Sharma, Anish Kumar

doi:10.1016/j.ijleo.2013.10.002

Cited by 15 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ANFIS learning process focused on the adjustment of the antecedent parameters (nonlinear parameters) and the post-component parameters (linear parameters) for the predecessor parameters of equation (7) and the post-part parameters of equation (10). According to the input-output relationship, the backpropagation (BP) algorithm and least squares estimation algorithm were used to adjust parameters.…”

Section: Determination Of the Anfis Modelmentioning

confidence: 99%

See 1 more Smart Citation

A fuzzy neural network model to determine axial strain measured by a long-period fiber grating sensor

Shen

et al. 2020

Measurement and Control

View full text Add to dashboard Cite

The paper reports an adaptive-network-based fuzzy inference system for the measurement of axial strain using long-period fiber grating sensors. The long-period fiber grating sensor supports optical resonances, which are sensitive to the change of axial strain. The axial strain can be quantified based on the wavelength shift and amplitude changes of the optical resonance. To improve the accuracy of axial strain quantification, this paper proposes the adaptive-network-based fuzzy inference system model. The adaptive-network-based fuzzy inference system model is trained using the strain data measured with long-period fiber grating sensors. The parameters of the membership functions used in the adaptive-network-based fuzzy inference system are set adaptively. In the adaptive-network-based fuzzy inference system–based method, the maximum relative error was found to be 1.5%, which is about one-ninth of that when the data fitting method was used. The R-squared statistics using the adaptive-network-based fuzzy inference system model is 0.9872, while that using the linear fitting algorithm is 0.8815. Compared with the conventional data fitting methods, the proposed approach is highly adaptive and versatile with the capability of improving the accuracy of strain quantification.

show abstract

Section: Determination Of the Anfis Modelmentioning

confidence: 99%

“…LPFG sensors are sensitive to many environmental factors, such as temperature, 7 strain, 8 bending, 9 and refractive index. 10,11…”

Section: Introductionmentioning

confidence: 99%

A fuzzy neural network model to determine axial strain measured by a long-period fiber grating sensor

Shen

et al. 2020

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…These periodic perturbations enable light coupling between the propagating core and co-propagating cladding modes [4]. The resonant wavelength formation for LPFG in Eq (1), is based on the phase matching condition between the fundamental core mode and the forward co-propagating cladding mode [5]: where λ res is the resonant wavelength, n eff co , is the effective index of the core mode and n eff cl m , is the effective index of the mth cladding mode and Λ is the grating period.…”

Section: Introductionmentioning

confidence: 99%

Sensitization of hybrid LPFG–FBG refractometer using double-pass configuration

Yong

Lee

Rahman

2015

Optics Communications

View full text Add to dashboard Cite

“…In optical communications, LPFG devices have been used for numerous applications such as in band-rejection filters [2], temperature and strain sensors [3], and any type of refractive index sensors [4,5]. These sensors possess a number of benefits over other conventional sensors.…”

Section: Introductionmentioning

confidence: 99%

Study of modeling aspects of long period fiber grating using three-layer fiber geometry

Singh¹

2014

Photonic Sens

Self Cite

View full text Add to dashboard Cite

The author studied and demonstrated the various modeling aspects of long period fiber grating (LPFG) such as the core effective index, cladding effective index, coupling coefficient, coupled mode theory, and transmission spectrum of the LPFG using three-layer fiber geometry. Actually, there are two different techniques used for theoretical modeling of the long period fiber grating. The first technique was used by Vengsarkar et al who described the phenomenon of long-period fiber gratings, and the second technique was reported by Erdogan who revealed the inaccuracies and shortcomings of the original method, thereby providing an accurate and updated alternative. The main difference between these two different approaches lies in their fiber geometry. Venserkar et al used two-layer fiber geometry which is simple but employs weakly guided approximation, whereas Erdogan used three-layer fiber geometry which is complex but also the most accurate technique for theoretical study of the LPFG. The author further discussed about the behavior of the transmission spectrum by altering different grating parameters such as the grating length, ultraviolet (UV) induced-index change, and grating period to achieve the desired flexibility. The author simulated the various results with the help of MATLAB.

show abstract

Study and investigation of long period grating as refractive index sensor

Cited by 15 publications

References 11 publications

A fuzzy neural network model to determine axial strain measured by a long-period fiber grating sensor

A fuzzy neural network model to determine axial strain measured by a long-period fiber grating sensor

Sensitization of hybrid LPFG–FBG refractometer using double-pass configuration

Study of modeling aspects of long period fiber grating using three-layer fiber geometry

Contact Info

Product

Resources

About