Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behavior recognition

2018 ◽  
Vol 58 ◽  
pp. 782-796 ◽  
Author(s):  
Hamid Reza Eftekhari ◽  
Mehdi Ghatee
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Driving Behavior ◽  
Discrete Wavelet ◽  
Behavior Recognition ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals High Impedance Fault Detection in MV Distribution Network using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Izzri Abdul Wahab ◽  
Rajeswari Ramachandran ◽  
Muhammad Mansoor ◽  
Mariammal Thirumeni
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Discrete Wavelet ◽  
Inference System ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Fault ◽  
Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

scholarly journals High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3330 ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Abdul Wahab ◽  
Rajeswari Ramachandran ◽  
Muhammad Mansoor ◽  
Mariammal Thirumeni ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Discrete Wavelet ◽  
Inference System ◽  
Medium Voltage ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Fault ◽  
Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage (MV) distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using MATLAB software R2014b and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three-phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault from other faults in the power system.

scholarly journals Discrete Wavelet Transform with Adaptive Network-Based Fuzzy Inference System for Compression of Images

2019 ◽  
Vol 8 (9) ◽  
pp. 748-756
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Image Encryption ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Median Filter ◽  
Discrete Wavelet ◽  
Hybrid Technique ◽  
Adaptive Network ◽  
Inference System

Image Encryption has a significant role to play in different fields like information security.Images are encryptedfor various purposes. Compression refers to the process that is carried out once the encryption is completed. In this review work, a hybrid technique has been followed for image encryption and decryption. First, input images are sent for preprocessing employing the median filter with the aim of removing the noise that is regarded to be unnecessary. This elimination process aids in improving the quality of the particular image. So the denoised image can be divided into different segments with the goal of encrypting the various blocks of images. This way, the required and unwanted blocks can be found during this above mentioned process. Encryption technique would follow Hybrid Chaos along with Discrete Cosine Transform shortly known as DCT. The encrypted image is then compressed with the help of Discrete Wavelet Transform (DWT) With Adaptive Network-Based Fuzzy Inference System (ANFIS). The experimental results indicate that the newly introduced DWT-ANFIS based compression attains a better performance in comparison with the availablecompression approaches in terms of Compression Ratio (CR) and Peak-Signal-Noise-Ratio (PSNR)

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