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    Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods
    (2024-01-01)
    Kanwal, Shazia
    ;
    Jiriwibhakorn, Somchat
    Electric systems are getting more complex with time, and primitive protection methods such as traveling wave and impedance-based methods face limitations and shortcomings. This paper incorporates and presents the applications of an adaptive neuro-fuzzy inference system and compares it with a back propagation neural network, self-organizing map, and hybrid method of discrete wavelet with adaptive neuro-fuzzy inference system for fault detections, classification, and localization in transmission lines. These methods, in comparison with primitive methods, could be capable of detecting, identifying, and predicting the location of the faults more accurately. The IEEE 9-bus system is utilized to obtain data from one end of the transmission line to develop an ANFIS-based model. This system is simulated in MATLAB/Simulink for different fault cases at various locations. The three-phase voltage and current at one end of IEEE 9-bus number seven are taken for training. Three ANFIS models are developed for fault detection, classification, and localization and compared with other models. For verification of the models, mean square error, mean absolute error, and regression analysis have been computed and compared for all the models. All four techniques have performed well for fault classification, detection, and location. However, the percentage error for the ANFIS-based fault model is less compared to backpropagation, self-organizing map, and discrete wavelet transform with ANFIS. Therefore, the proposed ANFIS models can be implemented for deploying in real-time-based protection systems.
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    Using machine learning-based algorithms to analyze erosion rates of a watershed in Northern Taiwan
    (2020-03-01)
    Nguyen, Kieu Anh
    ;
    Chen, Walter
    ;
    Lin, Bor Shiun
    ;
    Seeboonruang, Uma
    This study continues a previous study with further analysis of watershed-scale erosion pin measurements. Three machine learning (ML) algorithms-Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN)-were used to analyze depth of erosion of a watershed (Shihmen reservoir) in northern Taiwan. In addition to three previously used statistical indexes (Mean Absolute Error, Root Mean Square of Error, and R-squared), Nash-Sutcliffe Effciency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, theWilcoxon signed-rank test was used. The research utilized 14 environmental attributes as the input predictors of the ML algorithms. They are distance to river, distance to road, type of slope, sub-watershed, slope direction, elevation, slope class, rainfall, epoch, lithology, and the amount of organic content, clay, sand, and silt in the soil. Additionally, measurements of a total of 550 erosion pins installed on 55 slopes were used as the target variable of the model prediction. The dataset was divided into a training set (70%) and a testing set (30%) using the stratified random sampling with sub-watershed as the stratification variable. The results showed that the ANFIS model outperforms the other two algorithms in predicting the erosion rates of the study area. The average RMSE of the test data is 2.05 mm/yr for ANFIS, compared to 2.36 mm/yr and 2.61 mm/yr for ANN and SVM, respectively. Finally, the results of this study (ANN, ANFIS, and SVM) were compared with the previous study (Random Forest, Decision Tree, and multiple regression). It was found that Random Forest remains the best predictive model, and ANFIS is the second-best among the six ML algorithms.
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    Predicting Conversion from Pyrolysis of Pongmia
    (2015-01-01)
    Lerkkasemsan, Nuttapol
    This research demonstrates the technique of predicting pyrolysis of lignocellulosic biomass. Modeling of pyrolysis of biomass is complex and challenging because of short reaction times, temperatures as high as a thousand degrees Celsius, and biomass of varying or unknown chemical compositions. As such a deterministic model is not capable of representing the pyrolysis reaction system. To be able to predict a pyrolysis reaction of an unknown lignocellulosic biomass without an experimental data support or data fitting is an even more challenging work. In this research, we are trying to predict pyrolysis of Pongmia in Nitrogen to demonstrate that our technique is useful for predicting pyrolysis reaction of other biomass source. There are three main chemical compositions in lignocellulosic biomass which are cellulose, hemicellulose and lignin. We are considering that the total pyrolysis reaction is affected by the reaction of three main compositions. However, these three main chemical compositions of biomass is vary not only by type of biomass but also by other things such as where it is grown or even which part of biomass since the chemical compositions in the leaf can be different from the trunk. Our propose method is an extending study of our previous paper "pyrolysis of biomass-fuzzy modeling". Our model successfully gives a good predicting result. The result shows that our model can predict 91.82% of pyrolysis of Pongmia in Nitrogen correctly without any data from the experiment. Therefore, we could use this method to predict other lignocellulosic biomass before we perform an experiment.
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    Naphtha's price forecasting using neuro-fuzzy system
    (2008-12-01)
    Visetsripong, Porntip
    ;
    Sooraksa, Pitikhate
    ;
    Luenam, Pramote
    ;
    Chaimongkol, Watchareeporn
    Naphtha's price forecasting using Neuro-fuzzy system is a forecasting technique that applied information technology with statistics. 1950 daily prices were collected as a time-series data with trend component. The research found that Neuro-Fuzzy system is more accurate and more reliable than a statistical method; it also works well with continuous data and performs better with more training data. Neuro-Fuzzy system might be used with different data type, but, it might come across with other factors, e.g. seasonal or irregular event. The research also illustrates the multidisciplinary nature in today's world of works in the era of merging among many disciplines. © 2008 SICE.
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    Breakpoint distance los path loss model for indoor communication using anfis
    (2005-12-01)
    Phaiboon, Supachai
    ;
    Phokharatkul, Pisit
    ;
    Somkurnpanich, Suripon
    Breakpoint distance LOS model for indoor wireless communication is presented in this paper. The model is based on the determination of the breakpoint distance and % of wall area between the transmitter and the receiver. The propagation path losses are predicted with adaptive neuro - fuzzy inference systems (ANFIS), trained with measurements at the frequency of 1.8 GHz. The advantage of the ANFIS with hybrid least squares and gradient descent algorithms is fast convergence compared with original neural network. Comparison of our predicted results to measurements indicate that improvements in accuracy over conventional empirical models are achieved.