To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and ...To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and the hyperparameter optimization of the hybrid neural network(CNN-LSTM)was carried out by using the sparrow search algorithm(SSA).The model utilized the powerful feature extraction and non-linear mapping capabilities of deep learning to effectively handle the complex relationship between input and target variables.The batch normalization technique was used to speed up the training and improve the stability of the soft-sensing model,and the random discard technique was used to prevent the soft-sensing model from overfitting.Finally,the mean absolute error(MAE)was used to assess the accuracy of the soft sensor model predictions.This study compared the proposed model with soft sensor prediction models like Bp,Elman,CNN,LSTM,and CNN-LSTM,using dynamic thermal performance data from the solar collector field of the molten salt linear Fresnel photovoltaic demonstration power plant.The deep learning-based soft sensor model outperformed the other models according to the experimental data.Its coefficients of determination(namely R^(2))are higher by 6.35%,8.42%,5.69%,6.90%,and 3.67%,respectively.The accuracy and robustness have been significantly improved.展开更多
Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and sof...Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.展开更多
The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random t...The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.展开更多
The need for efficient and reproducible development processes for sensor and perception systems is growing with their increased use in modern vehicles. Such processes can be achieved by using virtual test environments...The need for efficient and reproducible development processes for sensor and perception systems is growing with their increased use in modern vehicles. Such processes can be achieved by using virtual test environments and virtual sensor models. In the context of this, the present paper documents the development of a sensor model for depth estimation of virtual three-dimensional scenarios. For this purpose, the geometric and algorithmic principles of stereoscopic camera systems are recreated in a virtual form. The model is implemented as a subroutine in the Epic Games Unreal Engine, which is one of the most common Game Engines. Its architecture consists of several independent procedures that enable a local depth estimation, but also a reconstruction of a whole three-dimensional scenery. In addition, a separate programme for calibrating the model is presented. In addition to the basic principles, the architecture and the implementation, this work also documents the evaluation of the model created. It is shown that the model meets specifically defined requirements for real-time capability and the accuracy of the evaluation. Thus, it is suitable for the virtual testing of common algorithms and highly automated driving functions.展开更多
The stretchable sensor wrapped around a foldable airfoil or embedded inside of it has great potential for use in the monitoring of the structural status of the foldable airfoil.The design methodology is important to t...The stretchable sensor wrapped around a foldable airfoil or embedded inside of it has great potential for use in the monitoring of the structural status of the foldable airfoil.The design methodology is important to the development of the stretchable sensor for status monitoring on the foldable airfoil.According to the requirement of mechanical flexibility of the sensor,the combined use of a layered flexible structural formation and a strain isolation layer is implemented.An analytical higher-order model is proposed to predict the stresses of the strain-isolation layer based on the shear-lag model for the safe design of the flexible and stretchable sensors.The normal stress and shear stress equations in the constructed structure of the sensors are obtained by the proposed model.The stress distribution in the structure is investigated when bending load is applied to the structures.The numerical results show that the proposed model can predict the variation of normal stress and shear stress along the thickness of the strain-isolation（polydimethylsiloxane）layer accurately.The results by the proposed model are in good agreement with the finite element method,in which the normal stress is variable while the shear stress is invariable along the thickness direction of strain-isolation layer.The high-order model is proposed to predict the stresses of the layered structure of the flexible and stretchable sensor for monitoring the status of the foldable airfoil.展开更多
Navigation and positioning is an important and challenging problem in many control engineering applications.It provides feedback information to design controllers for systems.In this paper,a bibliographical review on ...Navigation and positioning is an important and challenging problem in many control engineering applications.It provides feedback information to design controllers for systems.In this paper,a bibliographical review on factor graph based navigation and positioning is presented.More specifically,the sensor modeling,the factor graph optimization methods,and the topology factor based cooperative localization are reviewed.The navigation and positioning methods via factor graph are considered and classified.Focuses in the current research of factor graph based navigation and positioning are also discussed with emphasis on its practical application.The limitations of the existing methods,some solutions for future techniques,and recommendations are finally given.展开更多
The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on ...The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines （MSVM）. In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub- models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.展开更多
The catalytic activity of cation exchange resins will be continuously reduced with its use time in a condensation reaction for bisphenol A(BPA).For online estimation of the catalytic activity,a catalytic deactivation ...The catalytic activity of cation exchange resins will be continuously reduced with its use time in a condensation reaction for bisphenol A(BPA).For online estimation of the catalytic activity,a catalytic deactivation model is studied for a production plant of BPA,state equation and observation equation are proposed based on the axial temperature distribution of the reactor and the acetone concentration at reactor entrance.A hybrid model of state equation is constructed for improving estimation precision.The unknown parameters in observation equation are calculated with sample data.The unscented Kalman filtering algorithm is then used for on-line estimation of the catalytic activity.The simulation results show that this hybrid model has higher estimation accuracy than the mechanism model and the model is effective for production process of BPA.展开更多
This paper deals with a new type of multi-angle remotely sensed data--CHRIS(the Compact High Resolution Imaging Spec-trometer),by using rational function models(RFM)and rigorous sensor models(RSM).For ortho-rectifying...This paper deals with a new type of multi-angle remotely sensed data--CHRIS(the Compact High Resolution Imaging Spec-trometer),by using rational function models(RFM)and rigorous sensor models(RSM).For ortho-rectifying an image set,a rigorous sen-sor model-Toutin's model was employed and a set of reported parameters including across track angle,along track angle,IFOV,altitude,period,eccentricity and orbit inclination were input,then,the orbit calculation was started and the track information was given to the raw data.The images were ortho-rectified with geocoded ASTER images and digital elevation(DEM)data.Results showed that with 16 ground control points(GCPs),the correction accuracy decreased with view zenith angle,and the RMSE value increased to be over one pixel at 36 degree off-nadir.When the GCPs were approximately chosen as in Toutin's model,a RFM with three coefficients produced the same accuracy trend versus view zenith angle while the RMSEs for all angles were improved and within about one pixel.展开更多
A novel algorithm for localising a robot in a known two-dimensional environment is presented in this paper. An occupancy grid representing the environment is first converted to a distance function that encodes the dis...A novel algorithm for localising a robot in a known two-dimensional environment is presented in this paper. An occupancy grid representing the environment is first converted to a distance function that encodes the distance to the nearest obstacle from any given location. A Chamfer distance based sensor model to associate observations from a laser ranger finder to the map of the environment without the need for ray tracing, data association, or feature extraction is presented. It is shown that the robot can be localised by solving a non-linear optimisation problem formulated to minimise the Chamfer distance with respect to the robot location. The proposed algorithm is able to perform well even when robot odometry is unavailable and requires only a single tuning parameter to operate even in highly dynamic environments. As such, it is superior than the state-of-the-art particle filter based solutions for robot localisation in occupancy grids, provided that an approximate initial location of the robot is available. Experimental results based on simulated and public domain datasets as well as data collected by the authors are used to demonstrate the effectiveness of the proposed algorithm.展开更多
Vigorous particle collisions and mechanical processes occurring during high-velocity pneumatic con- veying often lead to particle degradation. The resulting particle size reduction and particle number increase will im...Vigorous particle collisions and mechanical processes occurring during high-velocity pneumatic con- veying often lead to particle degradation. The resulting particle size reduction and particle number increase will impact on the flow characteristics, and subsequently affect the electrostatic type of flow measurements. This study investigates this phenomenon using both experimental and numerical meth- ods. Particle degradation was induced experimentally by recursively conveying the fillite material within a pneumatic pipeline. The associated particle size reduction was monitored. Three electrostatic sensors were embedded along the pipeline to monitor the flow. The results indicated a decreasing trend in the electrostatic sensor outputs with decreasing particle size, which suggested the attenuation of the flow velocity fluctuation. This trend was more apparent at higher conveying velocities, which suggested that more severe particle degradation occurred under these conditions. Coupled computational fluid dynamics and discrete element methods （CFD-DEM） analysis was used to qualitatively validate these experimental results. The numerical results suggested that smaller particles exhibited lower flow velocity fluctua- tions, which was consistent with the observed experimental results. These findings provide important information for the accurate aoolication of electrostatic measurement devices in oneumatic conveyors.展开更多
Reflective fiber optic sensors have advantages for surface roughness measurements of some special workpieces,but their measuring precision and efficiency need to be improved further. A least-squares support vector mac...Reflective fiber optic sensors have advantages for surface roughness measurements of some special workpieces,but their measuring precision and efficiency need to be improved further. A least-squares support vector machine（LS-SVM）-based surface roughness prediction model is proposed to estimate the surface roughness, Ra, and the coupled simulated annealing（CSA） and standard simplex（SS） methods are combined for the parameter optimization of the mode. Experiments are conducted to test the performance of the proposed model, and the results show that the range of average relative errors is-4.232%–2.5709%. In comparison with the existing models, the LS-SVM-based model has the best performance in prediction precision, stability, and timesaving.展开更多
To solve the problems encountered in practical processes of magneto-optical sensing, the infinitesimal distributed-parameter model and finite-element accumulation of different dielectric properties of micromaterials w...To solve the problems encountered in practical processes of magneto-optical sensing, the infinitesimal distributed-parameter model and finite-element accumulation of different dielectric properties of micromaterials were used to describe the evolution of light polarization states, instead of the previously commonly used method of lumped-parameter simulation, thus essentially explaining the mechanism of sensing, magneto-optical effects, and related factors, and achieving multiphysics coupling using the COMSOL finite-element analysis method. Considering the cases of the Faraday effect without and with line birefringence, the magneto-optical effect and output characteristics of an infinitesimal magneto-optical sensor were simulated and studied. The results verified the effectiveness of the infinitesimal sensor model. Because the magnetic field, stress, and temperature changes alter the dielectric properties of magneto-optical materials, the finite-element accumulation method lays a good foundation for research on theoretical analysis and performance of magneto-optical sensors affected by factors such as the magnetic field, temperature, and stress.展开更多
基金supported by National Natural Science Foundation of China(No.52266012)Gansu Province College Industry Support Plan Project(No.2022CYZC-34)+1 种基金Gansu Province Major Science and Technology Special Project(Nos.20ZD7GF011,22ZD6GA063)Jiuquan City Science and Technology Programme Project(No.2023CA3058).
文摘To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and the hyperparameter optimization of the hybrid neural network(CNN-LSTM)was carried out by using the sparrow search algorithm(SSA).The model utilized the powerful feature extraction and non-linear mapping capabilities of deep learning to effectively handle the complex relationship between input and target variables.The batch normalization technique was used to speed up the training and improve the stability of the soft-sensing model,and the random discard technique was used to prevent the soft-sensing model from overfitting.Finally,the mean absolute error(MAE)was used to assess the accuracy of the soft sensor model predictions.This study compared the proposed model with soft sensor prediction models like Bp,Elman,CNN,LSTM,and CNN-LSTM,using dynamic thermal performance data from the solar collector field of the molten salt linear Fresnel photovoltaic demonstration power plant.The deep learning-based soft sensor model outperformed the other models according to the experimental data.Its coefficients of determination(namely R^(2))are higher by 6.35%,8.42%,5.69%,6.90%,and 3.67%,respectively.The accuracy and robustness have been significantly improved.
基金Supported by the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(2016RCJJ046)the National Basic Research Program of China(2012CB720500)
文摘Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61372156 and 61405053)the Natural Science Foundation of Zhejiang Province of China(Grant No.LZ13F04001)
文摘The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.
文摘The need for efficient and reproducible development processes for sensor and perception systems is growing with their increased use in modern vehicles. Such processes can be achieved by using virtual test environments and virtual sensor models. In the context of this, the present paper documents the development of a sensor model for depth estimation of virtual three-dimensional scenarios. For this purpose, the geometric and algorithmic principles of stereoscopic camera systems are recreated in a virtual form. The model is implemented as a subroutine in the Epic Games Unreal Engine, which is one of the most common Game Engines. Its architecture consists of several independent procedures that enable a local depth estimation, but also a reconstruction of a whole three-dimensional scenery. In addition, a separate programme for calibrating the model is presented. In addition to the basic principles, the architecture and the implementation, this work also documents the evaluation of the model created. It is shown that the model meets specifically defined requirements for real-time capability and the accuracy of the evaluation. Thus, it is suitable for the virtual testing of common algorithms and highly automated driving functions.
基金Supported by National Natural Science Foundation of China(Grant No.51075327)Open Project of State Key Laboratory for Strength and Vibration of Mechanical Structures of China(Grant No.SV2014-KF-08)Shaanxi Provincial Natural Science Foundation of China(Grant No.2014JM2-5082)
文摘The stretchable sensor wrapped around a foldable airfoil or embedded inside of it has great potential for use in the monitoring of the structural status of the foldable airfoil.The design methodology is important to the development of the stretchable sensor for status monitoring on the foldable airfoil.According to the requirement of mechanical flexibility of the sensor,the combined use of a layered flexible structural formation and a strain isolation layer is implemented.An analytical higher-order model is proposed to predict the stresses of the strain-isolation layer based on the shear-lag model for the safe design of the flexible and stretchable sensors.The normal stress and shear stress equations in the constructed structure of the sensors are obtained by the proposed model.The stress distribution in the structure is investigated when bending load is applied to the structures.The numerical results show that the proposed model can predict the variation of normal stress and shear stress along the thickness of the strain-isolation（polydimethylsiloxane）layer accurately.The results by the proposed model are in good agreement with the finite element method,in which the normal stress is variable while the shear stress is invariable along the thickness direction of strain-isolation layer.The high-order model is proposed to predict the stresses of the layered structure of the flexible and stretchable sensor for monitoring the status of the foldable airfoil.
基金supported by the National Natural Science Foundation of China(No.61873207)the National Science and Technology Major Project,China(No.J2019-I-00210020)+2 种基金the Natural Science Basic Research Program of Shaanxi,China(No.2019JQ-344)the Science and Technology Program of Xi’an City,China(No.2019218314GXRC019CG020-GXYD19.3)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China。
文摘Navigation and positioning is an important and challenging problem in many control engineering applications.It provides feedback information to design controllers for systems.In this paper,a bibliographical review on factor graph based navigation and positioning is presented.More specifically,the sensor modeling,the factor graph optimization methods,and the topology factor based cooperative localization are reviewed.The navigation and positioning methods via factor graph are considered and classified.Focuses in the current research of factor graph based navigation and positioning are also discussed with emphasis on its practical application.The limitations of the existing methods,some solutions for future techniques,and recommendations are finally given.
基金Item Sponsored by National Natural Science Foundation of China (60374003)
文摘The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines （MSVM）. In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub- models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.
基金Supported by the National Natural Science Foundation of China(60674092)
文摘The catalytic activity of cation exchange resins will be continuously reduced with its use time in a condensation reaction for bisphenol A(BPA).For online estimation of the catalytic activity,a catalytic deactivation model is studied for a production plant of BPA,state equation and observation equation are proposed based on the axial temperature distribution of the reactor and the acetone concentration at reactor entrance.A hybrid model of state equation is constructed for improving estimation precision.The unknown parameters in observation equation are calculated with sample data.The unscented Kalman filtering algorithm is then used for on-line estimation of the catalytic activity.The simulation results show that this hybrid model has higher estimation accuracy than the mechanism model and the model is effective for production process of BPA.
基金Chinese Program for High Technology Research and Development(2006AA12Z114)National Natural Science Foundation of China(40601070)
文摘This paper deals with a new type of multi-angle remotely sensed data--CHRIS(the Compact High Resolution Imaging Spec-trometer),by using rational function models(RFM)and rigorous sensor models(RSM).For ortho-rectifying an image set,a rigorous sen-sor model-Toutin's model was employed and a set of reported parameters including across track angle,along track angle,IFOV,altitude,period,eccentricity and orbit inclination were input,then,the orbit calculation was started and the track information was given to the raw data.The images were ortho-rectified with geocoded ASTER images and digital elevation(DEM)data.Results showed that with 16 ground control points(GCPs),the correction accuracy decreased with view zenith angle,and the RMSE value increased to be over one pixel at 36 degree off-nadir.When the GCPs were approximately chosen as in Toutin's model,a RFM with three coefficients produced the same accuracy trend versus view zenith angle while the RMSEs for all angles were improved and within about one pixel.
文摘A novel algorithm for localising a robot in a known two-dimensional environment is presented in this paper. An occupancy grid representing the environment is first converted to a distance function that encodes the distance to the nearest obstacle from any given location. A Chamfer distance based sensor model to associate observations from a laser ranger finder to the map of the environment without the need for ray tracing, data association, or feature extraction is presented. It is shown that the robot can be localised by solving a non-linear optimisation problem formulated to minimise the Chamfer distance with respect to the robot location. The proposed algorithm is able to perform well even when robot odometry is unavailable and requires only a single tuning parameter to operate even in highly dynamic environments. As such, it is superior than the state-of-the-art particle filter based solutions for robot localisation in occupancy grids, provided that an approximate initial location of the robot is available. Experimental results based on simulated and public domain datasets as well as data collected by the authors are used to demonstrate the effectiveness of the proposed algorithm.
文摘Vigorous particle collisions and mechanical processes occurring during high-velocity pneumatic con- veying often lead to particle degradation. The resulting particle size reduction and particle number increase will impact on the flow characteristics, and subsequently affect the electrostatic type of flow measurements. This study investigates this phenomenon using both experimental and numerical meth- ods. Particle degradation was induced experimentally by recursively conveying the fillite material within a pneumatic pipeline. The associated particle size reduction was monitored. Three electrostatic sensors were embedded along the pipeline to monitor the flow. The results indicated a decreasing trend in the electrostatic sensor outputs with decreasing particle size, which suggested the attenuation of the flow velocity fluctuation. This trend was more apparent at higher conveying velocities, which suggested that more severe particle degradation occurred under these conditions. Coupled computational fluid dynamics and discrete element methods （CFD-DEM） analysis was used to qualitatively validate these experimental results. The numerical results suggested that smaller particles exhibited lower flow velocity fluctua- tions, which was consistent with the observed experimental results. These findings provide important information for the accurate aoolication of electrostatic measurement devices in oneumatic conveyors.
文摘Reflective fiber optic sensors have advantages for surface roughness measurements of some special workpieces,but their measuring precision and efficiency need to be improved further. A least-squares support vector machine（LS-SVM）-based surface roughness prediction model is proposed to estimate the surface roughness, Ra, and the coupled simulated annealing（CSA） and standard simplex（SS） methods are combined for the parameter optimization of the mode. Experiments are conducted to test the performance of the proposed model, and the results show that the range of average relative errors is-4.232%–2.5709%. In comparison with the existing models, the LS-SVM-based model has the best performance in prediction precision, stability, and timesaving.
基金supported by the National Natural Science Foundation of China(Grant No.51277066)
文摘To solve the problems encountered in practical processes of magneto-optical sensing, the infinitesimal distributed-parameter model and finite-element accumulation of different dielectric properties of micromaterials were used to describe the evolution of light polarization states, instead of the previously commonly used method of lumped-parameter simulation, thus essentially explaining the mechanism of sensing, magneto-optical effects, and related factors, and achieving multiphysics coupling using the COMSOL finite-element analysis method. Considering the cases of the Faraday effect without and with line birefringence, the magneto-optical effect and output characteristics of an infinitesimal magneto-optical sensor were simulated and studied. The results verified the effectiveness of the infinitesimal sensor model. Because the magnetic field, stress, and temperature changes alter the dielectric properties of magneto-optical materials, the finite-element accumulation method lays a good foundation for research on theoretical analysis and performance of magneto-optical sensors affected by factors such as the magnetic field, temperature, and stress.
