scholarly journals An Innovative Wavelet Threshold Denoising Method for Environmental Drift of Fiber Optic Gyro

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qian Zhang ◽  
Lei Wang ◽  
Pengyu Gao ◽  
Zengjun Liu
Keyword(s):  
Neural Network ◽  
Fiber Optic ◽  
Back Propagation ◽  
Threshold Value ◽  
Allan Variance ◽  
Discrete Wavelet ◽  
Fiber Optic Gyroscope ◽  
Threshold Method ◽  
Temperature Drift ◽  
Denoising Method

Fiber optic gyroscope (FOG) is a core component in modern inertial technology. However, the precision and performance of FOG will be degraded by environmental drift, especially in complex temperature environment. As the modeling performance is affected by the noises in the output data of FOG, an improved wavelet threshold value based on Allan variance and Classical variance is proposed for discrete wavelet analysis to decompose the temperature drift trend item and noise items. Firstly, the relationship of Allan variance and Classical variance is introduced by analyzing the drawback of traditional wavelet threshold. Secondly, an improved threshold is put forward based on Allan variance and Classical variance which overcomes the shortcoming of traditional wavelet threshold method. Finally, the innovative threshold algorithm is experimentally evaluated on FOG. The mathematical evaluation results show that the new method can get better signal-to-noise ratio (SNR) and gain the reconstruction signal of the higher correlation coefficient (CC). As an experimental validation, the nonlinear capability of error back propagation neural network (BP neural network) is used to fit the drift trend item and find out the complex relationship between the FOG drift and temperature, and the final processing results indicate that the new denoising method can get better root of mean square error (MSE).

scholarly journals Modeling FOG Drift Using Back-Propagation Neural Network Optimized by Artificial Fish Swarm Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Rui Song ◽  
Xiyuan Chen ◽  
Chong Shen ◽  
Hong Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fiber Optic Gyroscope ◽  
Temperature Drift ◽  
Error Sources ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Based on the temperature drift characteristic of fiber optic gyroscope (FOG), a novel modeling and compensation method which integrated the artificial fish swarm algorithm (AFSA) and back-propagation (BP) neural network is proposed to improve the output accuracy of FOG and the precision of inertial navigation system. In this paper, AFSA is used to optimize the weights and threshold of BP neural network which determine precision of the model directly. In order to verify the effectiveness of the proposed algorithm, the predicted results of BP optimized by genetic algorithm (GA) and AFSA are compared and a quantitative evaluation of compensation results is analyzed by Allan variance. The comparison result illustrated the main error sources and the sinusoidal noises in the FOG output signal are reduced by about 50%. Therefore, the proposed modeling method can be used to improve the FOG precision.

scholarly journals A LSTM-RNN-Based Fiber Optic Gyroscope Drift Compensation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ning Mao ◽  
Jiangning Xu ◽  
Jingshu Li ◽  
Hongyang He
Keyword(s):  
Neural Network ◽  
Fiber Optic ◽  
Short Term Memory ◽  
Moving Average ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Measurement Unit ◽  
Support Vector ◽  
Fiber Optic Gyroscope ◽  
Temperature Drift

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.

Application of Improved Back Propagation Neural Network for the Recognition of Composite Insulator Hydrophobicity Grade

2013 ◽  
Vol 373-375 ◽  
pp. 1155-1158
Author(s):  
Kang Yan ◽  
Zhong Yuan Zhang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Median Filter ◽  
Back Propagation ◽  
Histogram Equalization ◽  
Back Propagation Neural Network ◽  
Threshold Method ◽  
Safe Operation ◽  
Composite Insulator ◽  
Adaptive Median Filter

The detection of hydrophobicity is an important way to evaluate the performance of composite insulator, which is helpful to the safe operation of composite insulator. In this paper, the image processing technology and Back Propagation neural network is introduced to recognize the composite insulator hydrophobicity grade. First, hydrophobic image is preprocessed by histogram equalization and adaptive median filter, then the image was segmented by Ostu threshold method, and four features associated with hydrophobicity are extracted. Finally, the improved Back Propagation neural network is adopted to recognize composite insulator hydrophobicity grade. The experimental results show that the improved Back Propagation neural network can accurately recognize the composite insulator hydrophobicity

scholarly journals Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signals

BioResources ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. 2369-2384
Author(s):  
Weihang Dong ◽  
Xiaolei Guo ◽  
Yong Hu ◽  
Jinxin Wang ◽  
Guangjun Tian
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Discrete Wavelet ◽  
Feature Extraction Method ◽  
Monitoring Model ◽  
Plastic Composite ◽  
Spindle Power

Tool wear conditions monitoring is an important mechanical processing system that can improve the processing quality of wood plastic composite furniture and reduce industrial energy consumption. An appropriate signal, feature extraction method, and model establishment method can effectively improve the accuracy of tool wear monitoring. In this work, an effective method based on discrete wavelet transformation (DWT) and genetic algorithm (GA) – back propagation (BP) neural network was proposed to monitor the tool wear conditions. The spindle power signals under different spindle speeds, depths of milling, and tool wear conditions were collected by power sensors connected to the machine tool control box. Based on the feature extraction method, the approximate coefficients of spindle power signal were extracted by DWT. Then, the extracted approximate coefficients, spindle speeds, depths of milling, and tool wear conditions were taken as samples to train the monitoring model. Threshold and weight of BP neural network were optimized by GA, and the accuracy of monitoring model established by the GA – BP neural network can reach 100%. Thus, the proposed monitoring method can accurately monitor tool wear conditions with different milling parameters, which can achieve the purpose of improving the processing quality of wood plastic composite furniture and reducing energy consumption.

LICENSE-PLATE RECOGNITION USING DWT AND NEURAL NETWORK

2006 ◽  
Vol 04 (04) ◽  
pp. 601-615 ◽  
Author(s):  
TIAN-DING CHEN
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
High Accuracy ◽  
Discrete Wavelet ◽  
License Plate ◽  
License Plate Recognition ◽  
Structure Modification ◽  
New Approach ◽  
Low Pass

This paper presents a new approach for license-plate recognition using Discrete Wavelet Transform (DWT) and Plastic Perception Neural Network (PPNN). It accomplishes the preliminary license-plate localization by applying low-pass wavelet coefficients. Since the amount of data reduces to 1/4, this approach saves a lot of running time, simplifies computational complexity, and economizes memory usage. It adopts the LL and HH sub-bands, which come from a two-dimensional Haar DWT to implement the localization and segmentation for license plates. The proposed methodology provides high accuracy for locating a license plate from an image, and has a high tolerance for license plate displacement in the images. Back-Propagation Neural Network (BPNN) has the advantage of anti-noise and anti-distortion, but the problems of traditional BPNN are a longer learning period, iterations are not prone to convergence, and local minimum. The proposed methods combine the parallel distributive process concept with the BPNN structure modification to solve the above problems. This paper also utilizes PPNN to solve taking position, scale and rotation of the license-plate recognition.

scholarly journals Adaptive Real-Time Wavelet Denoising Architecture Based on Feedback Control Loop

2021 ◽  
Vol 9 (ICRIE) ◽  
Author(s):  
Fars Samann ◽  
◽  
Serwan Ali Bamerni ◽  
Jeeman Ahmed Khorsheed ◽  
Ahmed Khorsheed Al-sulaifanie ◽  
...  
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
Window Size ◽  
Threshold Value ◽  
Wavelet Denoising ◽  
Discrete Wavelet ◽  
Control Loop ◽  
Denoising Method ◽  
Low Snr ◽  
Wide Range

The discrete wavelet transform is commonly used as a denoising step for many applications, like biomedical applications which are usually suffering from low SNR of the recorded signal. However, the choice of appropriate threshold value for DWT coefficients plays significant role in reconstructing the denoised signal. This paper presents a design of real-time wavelet denoising architecture which is suitable for wide range of real-time denoising applications. In this design, an adaptive thresholding approach based on feedback control loop is proposed to make the architecture more applicable for real-time wavelet denoising. This thresholding method considers a noise level estimator module based on first detail coefficients level 𝑑1 to calculate the unknown standard deviation of background noise. The proposed architecture is developed using MATLAB to simulate the suggested denoising method. The performance of the proposed denoising method is studied in terms of integral gain 𝐺 of feedback control and window size 𝑀 with respect to the improvement in SNR and settling time. The results imply that the proposed denoising architecture is suitable for real-time denoising applications with acceptable improvement in SNR approximately 8 dB.

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