scholarly journals INS Error Estimation Based on an ANFIS and Its Application in Complex and Covert Surroundings

2021 ◽  
Vol 10 (6) ◽  
pp. 388
Author(s):  
Yabo Duan ◽  
Huaizhan Li ◽  
Suqin Wu ◽  
Kefei Zhang
Keyword(s):  
Error Estimation ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Sequence Data ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Inference System ◽  
Position Errors ◽  
Position Solution ◽  
Single Sequence

Inertial navigation is a crucial part of vehicle navigation systems in complex and covert surroundings. To address the low accuracy of vehicle inertial navigation in multifaced and covert surroundings, in this study, we proposed an inertial navigation error estimation based on an adaptive neuro fuzzy inference system (ANFIS) which can quickly and accurately output the position error of a vehicle end-to-end. The new system was tested using both single-sequence and multi-sequence data collected from a vehicle by the KITTI dataset. The results were compared with an inertial navigation system (INS) position solution method, artificial neural networks (ANNs) method, and a long short-term memory (LSTM) method. Test results indicated that the accumulative position errors in single sequence and multi-sequences experiments decreased from 9.83% and 4.14% to 0.45% and 0.61% by using ANFIS, respectively, which were significantly less than those of the other three approaches. This result suggests that the ANFIS can considerably improve the positioning accuracy of inertial navigation, which has significance for vehicle inertial navigation in complex and covert surroundings.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

Enhanced Accuracy Navigation Solutions Realized through SINS/GPS Integrated Navigation System

2013 ◽  
Vol 332 ◽  
pp. 79-85
Author(s):  
Outamazirt Fariz ◽  
Muhammad Ushaq ◽  
Yan Lin ◽  
Fu Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Position Errors ◽  
Strapdown Inertial Navigation

Strapdown Inertial Navigation Systems (SINS) displays position errors which grow with time in an unbounded manner. This degradation is due to the errors in the initialization of the inertial measurement unit, and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Improvement to this unbounded growth in errors can be made by updating the inertial navigation system solutions periodically with external position fixes, velocity fixes, attitude fixes or any combination of these fixes. The increased accuracy is obtained through external measurements updating inertial navigation system using Kalman filter algorithm. It is the basic requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertial Navigation System (SINS), Global Positioning System (GPS) is presented using a centralized linear Kalman filter.

Deep-learning based forecasting sampling frequency of biosensors in wireless body area networks

2020 ◽  
Vol 39 (3) ◽  
pp. 3195-3227 ◽  
Author(s):  
Mohammad Mehrani ◽  
Iman Attarzadeh ◽  
Mehdi Hosseinzadeh
Keyword(s):  
Short Term Memory ◽  
Fuzzy Inference ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Sampling Rate ◽  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Sampling Frequency ◽  
Body Area ◽  
Inference System

Wireless Body Area Networks (WBANs) have been introduced as a useful way in controlling health status of the monitored patients, during recent years. Each WBAN includes a number of biosensors attached to the patient’s body, collecting his vital sign features and communicating them to the coordinator to make appropriate decisions. Managing energy consumption of biosensors and continuous monitoring of the patients are two main issues in WBANs. Hence, denoting efficient sampling frequency of biosensors is very important in WBANs. In this paper, we propose a scheme which aims at determining and forecasting sampling rate of active biosensors in WBANs. In this regard, from the first round until a certain round, the sampling rate of biosensors would be determined. Accordingly, we introduce our modified Fisher test, develop spline interpolation method and introduce three main parameters. These parameters are information of patient’s activity, patient’s risk and pivot biosensor’s value. Then, by employing mentioned parameters in addition to the introduced statistical and mathematical based strategies, the sampling rate of active biosensors in the next round would be determined at the end of each entire round. By reaching a pre-denoted round, the sampling rate of biosensors would be predicted through forecasting methods. For this purpose, we develop two machine learning based techniques namely Adaptive Neuro Fuzzy Inference System (ANFIS) and Long Short Term Memory (LSTM). For estimation our approaches we simulate them in MATLAB R2018b software. Simulation results demonstrate that our methods can decrease the number of communicated data by 81%, reduce energy expenditure of biosensors by 73% and forecast the sampling rate of biosensors in the future rounds with 97% accuracy and 2.2753 RMSE.

Studies on an Integrated Navigation Method on the Basis of Adaptive Neuro-Fuzzy Inference System

2012 ◽  
Vol 433-440 ◽  
pp. 4065-4070
Author(s):  
Qiang Gao ◽  
Shan Jin ◽  
Tie Liu Wang
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Integrated System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Inference System ◽  
Integrated Navigation System ◽  
Gps Satellites ◽  
Neuro Fuzzy ◽  
Range Rate

The GPS / INS integrated navigation system performance will significantly decrease during GPS outages. In this paper, we study an new integrated navigation algorithm based on Adaptive Neural- fuzzy Inference System (ANFIS). The algorithm adopted Kalman filter with pseudo-range and pseudo-range rate observations when the number of GPS satellites was not less 4. Otherwise, ANFIS was used to estimate the navigation errors and restrain the increasing INS errors to achieve integrated navigation. The new algorithm can improve the performance of integrated system effectively and enhance the horizontal position accuracy than traditional tight integration algorithms. Especially, the method is applicable to the complex work environment of navigation systems of ships.

scholarly journals Predicting the Amount of Electric Power Transaction Using Deep Learning Methods

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6649
Author(s):  
Gwiman Bak ◽  
Youngchul Bae
Keyword(s):  
Deep Learning ◽  
Electric Power ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Learning Algorithms ◽  
High Accuracy ◽  
Support Vector ◽  
Inference System ◽  
Power Company ◽  
Starting Date

The most important thing to operate a power system is that the power supply should be close to the power demand. In order to predict the amount of electric power transaction (EPT), it is important to choose and decide the variable and its starting date. In this paper, variables that could be acquired one the starting day of prediction were chosen. This paper designated date, temperature and special day as variables to predict the amount of EPT of the Korea Electric Power company. This paper also used temperature data from a year ago to predict the next year. To do this, we proposed single deep learning algorithms and hybrid deep learning algorithms. The former included multi-layer perceptron (MLP), convolution neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), support vector machine regression (SVR), and adaptive network-based fuzzy inference system (ANFIS). The latter included LSTM + CNN and CNN + LSTM. We then confirmed the improvement of accuracy for prediction using pre-processed variables compared to original variables We also assigned two years of test data during 2017–2018 as variable data to measure high prediction accuracy. We then selected a high-accuracy algorithm after measuring root mean square error (RMSE) and mean absolute percent error (MAPE). Finally, we predicted the amount of EPT in 2018 and then measured the error for each proposed algorithm. With these acquired error data, we obtained a model for predicting the amount of EPT with a high accuracy.

scholarly journals A Bronchoscope Localization Method Using an Augmented Reality Co-Display of Real Bronchoscopy Images with a Virtual 3D Bronchial Tree Model

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6997
Author(s):  
Jong-Chih Chien ◽  
Jiann-Der Lee ◽  
Ellen Su ◽  
Shih-Hong Li
Keyword(s):  
Fuzzy Inference ◽  
Bronchial Tree ◽  
Navigation Systems ◽  
Tree Model ◽  
Flexible Bronchoscope ◽  
Inference System ◽  
Registration Method ◽  
Image Guide ◽  
Accurate Localization ◽  
Current Location

In recent years, Image-Guide Navigation Systems (IGNS) have become an important tool for various surgical operations. In the preparations for planning a surgical path, verifying the location of a lesion, etc., it is an essential tool; in operations such as bronchoscopy, which is the procedure for the inspection and retrieval of diagnostic samples for lung-related surgeries, it is even more so. The IGNS for bronchoscopy uses 2D-based images from a flexible bronchoscope to navigate through the bronchial airways in order to reach the targeted location. In this procedure, the accurate localization of the scope becomes very important, because incorrect information could potentially cause a surgeon to mistakenly direct the scope down the wrong passage. It would be a great aid for the surgeon to be able to visualize the bronchoscope images alongside the current location of the bronchoscope. For this purpose, in this paper, we propose a novel registration method to match real bronchoscopy images with virtual bronchoscope images from a 3D bronchial tree model built using computed tomography (CT) image stacks in order to obtain the current 3D position of the bronchoscope in the airways. This method is a combination of a novel position-tracking method using the current frames from the bronchoscope and the verification of the position of the real bronchoscope image against an image extracted from the 3D model using an adaptive-network-based fuzzy inference system (ANFIS)-based image matching method. Experimental results show that the proposed method performs better than the other methods used in the comparison.

scholarly journals Emotional Video to Audio Transformation Using Deep Recurrent Neural Networks and a Neuro-Fuzzy System

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Gwenaelle Cunha Sergio ◽  
Minho Lee
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Audio Signals ◽  
Global Features ◽  
Inference System ◽  
Audio Features ◽  
Neuro Fuzzy ◽  
High Level ◽  
Domain Transformation

Generating music with emotion similar to that of an input video is a very relevant issue nowadays. Video content creators and automatic movie directors benefit from maintaining their viewers engaged, which can be facilitated by producing novel material eliciting stronger emotions in them. Moreover, there is currently a demand for more empathetic computers to aid humans in applications such as augmenting the perception ability of visually- and/or hearing-impaired people. Current approaches overlook the video’s emotional characteristics in the music generation step, only consider static images instead of videos, are unable to generate novel music, and require a high level of human effort and skills. In this study, we propose a novel hybrid deep neural network that uses an Adaptive Neuro-Fuzzy Inference System to predict a video’s emotion from its visual features and a deep Long Short-Term Memory Recurrent Neural Network to generate its corresponding audio signals with similar emotional inkling. The former is able to appropriately model emotions due to its fuzzy properties, and the latter is able to model data with dynamic time properties well due to the availability of the previous hidden state information. The novelty of our proposed method lies in the extraction of visual emotional features in order to transform them into audio signals with corresponding emotional aspects for users. Quantitative experiments show low mean absolute errors of 0.217 and 0.255 in the Lindsey and DEAP datasets, respectively, and similar global features in the spectrograms. This indicates that our model is able to appropriately perform domain transformation between visual and audio features. Based on experimental results, our model can effectively generate an audio that matches the scene eliciting a similar emotion from the viewer in both datasets, and music generated by our model is also chosen more often (code available online at https://github.com/gcunhase/Emotional-Video-to-Audio-with-ANFIS-DeepRNN).

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