scholarly journals A leakage current estimation based on thermal image of polymer insulator

2019 ◽  
Vol 16 (3) ◽  
pp. 1096
Author(s):  
Darwison Darwison ◽  
Syukri Arief ◽  
Hairul Abral ◽  
Ariadi Hazmi ◽  
M. H. Ahmad ◽  
...  
Keyword(s):  
Thermal Image ◽  
Leakage Currents ◽  
Inference System ◽  
Temperature Detection ◽  
Polymer Insulator ◽  
Color Detection ◽  
Infrared Signature ◽  
Anfis Method ◽  
Polymer Insulators ◽  
Adaptive Neurofuzzy Inference System

Polymer insulators tend to fail because of the climatic and environmental conditions. The failure occurs when the surface of insulator is contaminated by sea salt or cement dust which lead to partial discharge (PD). Leakage currents will increase by PD that causes deterioration of insulation. To predict the insulation failures, an  adaptive neurofuzzy inference system (ANFIS) method using initial color detection processes are proposed to estimate the leakage currents based on the polymer insulator thermal images (infrared signature). In this study, the sodium chloride and kaolin are used as pollutants of the polymer insulator according to IEC 60507 standards. Then, the insulator is tested in the laboratory using AC high voltage applied at 18 kV where the temperature detection is controlled at 26° C and 70% RH (relative humidity). The percentage of colors (Red, Yellow, and Blue) from the thermal image is measured using the color detection method. Correspond to the color percentage, the ANFIS method predicts leakage currents from polymer insulators. Furthermore, this system interprets measured data from insulators that need to be categorized as Safe, Need Maintenance or Harmful. The final application of the system can be a non-contact tool to predict the polymer insulators used by technicians in the field.

scholarly journals Colored object detection using 5 dof robot arm based adaptive neuro-fuzzy method

2019 ◽  
Vol 13 (1) ◽  
pp. 293
Author(s):  
Mujiarto Mujiarto ◽  
Asari Djohar ◽  
Mumu Komaro ◽  
Mohamad Afendee Mohamed ◽  
Darmawan Setia Rahayu ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Kinematic Model ◽  
Inverse Kinematic ◽  
Robot Arm ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Color Detection ◽  
Anfis Method ◽  
Arduino Microcontroller

<p>In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based on Arduino microcontroller is applied to the dynamic model of 5 DoF Robot Arm presented. MATLAB is used to detect colored objects based on image processing. Adaptive Neuro Fuzzy Inference System (ANFIS) method is a method for controlling robotic arm based on color detection of camera object and inverse kinematic model of trained data. Finally, the ANFIS algorithm is implemented in the robot arm to select objects and pick up red objects with good accuracy.</p>

Penerapan Metode ANFIS untuk Prediksi Nilai Akhir dan Kelulusan Uji Kompetensi

2018 ◽  
Vol 4 (1) ◽  
pp. 21-28
Author(s):  
Rayendra
Keyword(s):  
Word Processing ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Information And Communication ◽  
Anfis Method ◽  
Final Score ◽  
Computer Literate ◽  
Competence Test

To improve the graduation of Computer Literate Certified Professional (CLCP) competence test conducted by Competence Test of Information and Communication Technology (TUK-TIK) needs to be done continuous improvement by increasing try out competency test. Past values of the competency test can be used as modeling to predict the final score and the passing of the competency test. With the modeling can be predicted the passing of competency test participants through try out-try out done so that can be known weakness of candidate competency test from three units of CLCP competence. The modeling used to predict the final score and the passing of this competency test is the Adaptive Neuro Fuzzy Inference System (ANFIS) method. Used 20 past data of competency test participants with 6 criteria as input value from three CLCP competence units namely Word Processing, Spreadsheet, and Presentation. The resulting prediction is accurate enough with MAPE (Mean Absolute Percentage Error) value for each competency unit of 0.31492%, 0.284202%, and 0.267167%

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

scholarly journals Prediction of Oilfield-Increased Production Using Adaptive Neurofuzzy Inference System with Smoothing Treatment

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Lin Chen ◽  
Zhibin Liu ◽  
Nannan Ma ◽  
Yi Wang
Keyword(s):  
Initial Data ◽  
Numerical Experiments ◽  
The Novel ◽  
Inference System ◽  
Feasible Method ◽  
Prediction Result ◽  
Well Stimulation ◽  
Adaptive Neurofuzzy Inference System ◽  
Oilfield Development ◽  
Better Than

A novel modified adaptive neurofuzzy inference system with smoothing treatment (MANFIS) is proposed. The MANFIS model considered the smoothing treatment of initial data basing on the adaptive neurofuzzy inference system, and we used it to predict oilfield-increased production under the well stimulation. Numerical experiments show the prediction result of the novel considering smoothing treatment is better than that without smoothing treatment. This study provides a novel and feasible method for prediction of oilfield-increased production under well stimulation, and it can be helpful in the further study of oilfield development measure planning.

scholarly journals Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand)

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Abazar Solgi ◽  
Vahid Nourani ◽  
Amir Pourhaghi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Daily Precipitation ◽  
Inference System ◽  
Short And Long Term ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Original Time ◽  
Adaptive Neurofuzzy Inference System ◽  
Precipitation Modeling

Doubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering high-stochastic property of the process, many models are still being developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently artificial neural network (ANN) as a nonlinear interextrapolator is extensively used by hydrologists for precipitation modeling as well as other fields of hydrology. In the present study, wavelet analysis combined with artificial neural network and finally was compared with adaptive neurofuzzy system to predict the precipitation in Verayneh station, Nahavand, Hamedan, Iran. For this purpose, the original time series using wavelet theory decomposed to multiple subtime series. Then, these subseries were applied as input data for artificial neural network, to predict daily precipitation, and compared with results of adaptive neurofuzzy system. The results showed that the combination of wavelet models and neural networks has a better performance than adaptive neurofuzzy system, and can be applied to predict both short- and long-term precipitations.

Sign in / Sign up

Export Citation Format

Share Document