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.

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 An Adaptive Location-Based Tracking Algorithm Using Wireless Sensor Network for Smart Factory Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Po-Chih Chiu ◽  
Kuo-Wei Su ◽  
Tsung-Yin Ou ◽  
Chih-Lung Yu ◽  
Chen-Yang Cheng ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Signal Transmission ◽  
Signal Strength ◽  
Localization Algorithm ◽  
Inference System ◽  
Accurate Localization ◽  
Localization Algorithms ◽  
New Type ◽  
The Cost ◽  
Smart Factories

In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results.

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.

scholarly journals Adaptive Neural Fuzzy Inference System for Accurate Localization of Wireless Sensor Network in Outdoor and Indoor Cycling Applications

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 38475-38489 ◽  
Author(s):  
Sadik Kamel Gharghan ◽  
Rosdiadee Nordin ◽  
Aqeel Mahmood Jawad ◽  
Haider Mahmood Jawad ◽  
Mahamod Ismail
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Wireless Sensor ◽  
Inference System ◽  
Accurate Localization ◽  
Neural Fuzzy

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

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