Temperature drift modeling and compensation of fiber optical gyroscope based on improved support vector machine and particle swarm optimization algorithms

Wang, Wei; Chen, Xiyuan

doi:10.1364/ao.55.006243

Cited by 32 publications

(10 citation statements)

References 14 publications

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“…In the literature, very limited work has been done to show the effectiveness of ML techniques for temperature drift compensation in sensors . In this paper, we proposed to apply different supervised machine learning techniques to learn the characteristics of the temperature drift in ISFET pH sensor based on the collected experimental data.…”

Section: Isfet Temperature Drift Compensation Using ML Techniquesmentioning

confidence: 99%

“…In the literature, very limited work has been done to show the effectiveness of ML techniques for temperature drift compensation in sensors. [48][49][50][51][52][53] In this paper, we proposed to apply different supervised machine learning techniques to learn the characteristics of the temperature drift in ISFET pH sensor based on the collected experimental data. For the purpose of our experiments, we use the following ML techniques: multi-layer perceptron (MLP, a neural network model), decision trees (DT), polynomial regression (PR, a special case of linear regression), random forests (RF), and support Vector Regression (SVR, Support Vector Machine used for Regression analysis).…”

Section: Isfet Temperature Drift Compensation Using ML Techniquesmentioning

confidence: 99%

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Modeling and simulation of temperature drift for ISFET‐based pH sensor and its compensation through machine learning techniques

Bhardwaj

Sinha

Sahu

et al. 2019

Circuit Theory & Apps

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Summary The paper presents modeling and simulation of ion‐sensitive field‐effect transistor (ISFET)‐based pH sensor with temperature‐dependent behavioral macromodel and proposes to compensate the temperature drift in the sensor using intelligent machine learning (ML) models. The macromodel is built using SPICE by introducing electrochemical parameters in a metal‐oxide‐semiconductor field‐effect transistor (MOSFET) model to simulate ISFET characteristics. We account for the temperature dependence of electrochemical and semiconductor parameters in our macromodel to increase its robustness. The macromodel is then exported as a subcircuit element, which is used to design the readout interface circuit. A simple constant‐voltage, constant‐current (CVCC) topology is utilized to generate the data for temperature drift in ISFET pH sensor, which is used to train and test state‐of‐the‐art ML‐based regression models in order to compensate the drift behavior. The experimental results demonstrate that the random forest (RF) technique achieves the best performance with very high correlation and low error rate. Corresponding curves for output signal using the trained models show highly temperature‐independent characteristics when tested for pH 2, 4, 7, 10, and 12, and we obtained a root mean squared error (RMS) variation of ΔpH ≤ 0.024 over a temperature range of 15°C to 55°C in comparison with ΔpH ≤ 1.346 for uncompensated output signal. This work establishes the framework for integration of ML techniques for drift compensation of ISFET chemical sensor to improve its performance.

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Section: Isfet Temperature Drift Compensation Using ML Techniquesmentioning

confidence: 99%

Section: Isfet Temperature Drift Compensation Using ML Techniquesmentioning

confidence: 99%

Modeling and simulation of temperature drift for ISFET‐based pH sensor and its compensation through machine learning techniques

Bhardwaj

Sinha

Sahu

et al. 2019

Circuit Theory & Apps

View full text Add to dashboard Cite

show abstract

“…erefore, some researchers have introduced some optimization algorithms to tackle the limitations. e genetic algorithm (GA) was applied by Pan et al [15], the artificial fish swarm algorithm was applied by Song et al [16], and the particle swarm optimization (PSO) was applied by Wang, Cheng, and Tong et al [17][18][19]. e experimental results indicated that the optimized models are effective and can improve the system's performance.…”

Section: Introductionmentioning

confidence: 99%

A LSTM-RNN-Based Fiber Optic Gyroscope Drift Compensation

Mao

et al. 2021

Mathematical Problems in Engineering

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Fiber optic gyroscope (FOG) inertial measurement unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in positioning and navigation of military and aerospace fields, due to its simple structure, small size, and high accuracy. However, noise such as temperature drift will reduce the accuracy of FOG, which will affect the resolution accuracy of IMU. In order to reduce the FOG drift and improve the navigation accuracy, a long short-term memory recurrent neural network (LSTM-RNN) model is established, and a real-time acquisition method of the temperature change rate based on moving average is proposed. In addition, for comparative analysis, backpropagation (BP) neural network model, CART-Bagging, classification and regression tree (CART) model, and online support vector machine regression (Online-SVR) model are established to filter FOG outputs. Numerical simulation based on field test data in the range of -20°C to 50°C is employed to verify the effectiveness and superiority of the LSTM-RNN model. The results indicate that the LSTM-RNN model has better compensation accuracy and stability, which is suitable for online compensation. This proposed solution can be applied in military and aerospace fields.

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“…Golikov et al [13] proposed a new method to compensate the multiplicative and additive errors caused by temperature changes. Wang et al [14] presented a new method to compensate the error and improved the precision of Fiber-Optic Gyroscopes (FOGs) based on improved Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) algorithms. The regression accuracy of the proposed method increased by 83.81% compared with the traditional SVM.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A surrogate assisted thermal optimization framework for design of pin-fin heat sink for the platform inertial navigation system

Luo

Wang

2020

Engineering Optimization

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In this study, in order to reduce the local high temperature of the platform in inertial navigation system (PINS), a pin-fin heat sink with staggered arrangement is designed. To reduce the dimension of the inputs and improve the efficiency of optimization, a feasible global sensitivity analysis (GSA) based on Kriging-High Dimensional Model Representation with DIviding RECTangles sampling strategy (DIRECT-KG-HDMR) is proposed. Compared with other GSA methods, the proposed method can indicate the effects of the structural and the material parameters on the maximum temperature at the bottom of the heat sink by using both sensitivity and coupling coefficients. From the results of GSA, it can be found that the structural parameters have greater effects on thermal performance than the material ones. Moreover, the coupling intensities between the structural and material parameters are weak. Therefore, the structural parameters are selected to optimize the thermal performance of the heat sink, and several popular optimization algorithms such as GA, DE, TLBO, PSO and EGO are used for the optimization. Moreover, steady thermal response of the PINS with the optimized heat sink is also studied, and its result shows that the maximum temperature of high temperature region of the platform is reduced by 1.09 ℃ compared with the PINS without the heat sink.

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Temperature drift modeling and compensation of fiber optical gyroscope based on improved support vector machine and particle swarm optimization algorithms

Cited by 32 publications

References 14 publications

Modeling and simulation of temperature drift for ISFET‐based pH sensor and its compensation through machine learning techniques

Modeling and simulation of temperature drift for ISFET‐based pH sensor and its compensation through machine learning techniques

A LSTM-RNN-Based Fiber Optic Gyroscope Drift Compensation

A surrogate assisted thermal optimization framework for design of pin-fin heat sink for the platform inertial navigation system

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