A Leak Detection in Water Pipelines Using Discrete Wavelet Decomposition and Artificial Neural Network

2021 ◽  
pp. 49-65
Author(s):  
Prawit Chumchu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Decomposition ◽  
Leak Detection ◽  
Discrete Wavelet ◽  
Discrete Wavelet Decomposition ◽  
Water Pipelines ◽  
Artificial Neural

Emotion Classification by EEG Signal Generated by Brain using Discrete Wavelet Transform and Artificial Neural Network Backpropagation with Classical Music Stimulus

2019 ◽  
Vol 1 (2) ◽  
pp. 1-4
Author(s):  
Aditya Dimas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Classical Music ◽  
Brain Activity ◽  
Piano Music ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Emotion Classification ◽  
Artificial Neural ◽  
Music Stimulus

People feel different emotions when listening to music on certain levels. Such feelings occur because the music stimuli causing reduced or increased brain activity and producing brainwave with specific characteristics. Results of research indicated that classical piano music can influence one’s emotional intelligent. By using Electroenchephalography (EEG) as a brainwave recording instrument, we can assess the effect of stimulation on the emotions generated through brain activity. This study aimed at developing a method that defines the effect of sound to brain activity using an EEG signal that can be used to identify one's emotion based on classical piano music stimulus reaction. Based on its frequency, this signal was the classified using DWT. To train Artificial Neural Network, some features were taken from the signal. This ANN research was carried out using the process of backpropagation

Thailand tourism forecasting based on a hybrid of discrete wavelet decomposition and NARX neural network

2016 ◽  
Vol 116 (6) ◽  
pp. 1242-1258 ◽  
Author(s):  
Ratree Kummong ◽  
Siriporn Supratid
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wavelet Decomposition ◽  
Tourism Industry ◽  
Relative Price ◽  
Discrete Wavelet ◽  
Content Type ◽  
Input Factor ◽  
Discrete Wavelet Decomposition ◽  
Forecasting Performance

Purpose – Accurate forecast of tourist arrivals is crucial for Thailand since the tourism industry is a major economic factor of the country. However, a nonstationarity, normally consisted in nonlinear tourism time series can seriously ruin the forecasting computation. The purpose of this paper is to propose a hybrid forecasting method, namely discrete wavelet decomposition (DWD)-NARX, which combines DWD and the nonlinear autoregressive neural network with exogenous input (NARX) to cope with such nonstationarity, as a consequence, improve the effectiveness of the demand-side management activities. Design/methodology/approach – According to DWD-NARX, wavelet decomposition is executed for efficiently extracting the hidden significant, temporal features contained in the nonstationary time series. Then, each extracted feature set at a particular resolution level along with a relative price as an exogenous input factor are fed into NARX for further forecasting. Finally, the forecasting results are reconstructed. Forecasting performance measures rely on mean absolute percentage error, mean absolute error as well as mean square error. Model overfitting avoidance is also considered. Findings – The results indicate the superiority of the DWD-NARX over other efficient related neural forecasters in the cases of high forecasting performance rate as well as competently coping with model overfitting. Research limitations/implications – The scope of this study is confined to Thailand tourist arrivals forecast based on short-term projection. To resolve such limitations, future research should aim to apply the generalization capability of DWD-NARX on other domains of managerial time series forecast under long-term projection environment. However, the exogenous input factor is to be empirically revised on domain-by-domain basis. Originality/value – Few works have been implemented either to handle the nonstationarity, consisted in nonlinear, unpredictable time series, or to achieve great success on finding an appropriate and effective exogenous forecasting input. This study applies DWD to attain efficient feature extraction; then, utilizes the competent forecaster, NARX. This would comprehensively and specifically deal with the nonstationarity difficulties at once. In addition, this study finds the effectiveness of simply using a relative price, generated based on six top-ranked original tourist countries as an exogenous forecasting input.

scholarly journals Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

2020 ◽  
Author(s):  
Amit Kumar Gorai ◽  
Simit Raval ◽  
Ashok Kumar Patel ◽  
Snehamoy Chatterjee ◽  
Tarini Gautam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Machine Vision ◽  
Vision System ◽  
Color Space ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Machine Vision System ◽  
Coal Characterization ◽  
Artificial Neural

Abstract Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and converted into five other color spaces (HSI, CMYK, Lab, xyz, Gray) for feature extraction. The intensity component image of HSI color space was further transformed into four frequency components (discrete cosine transform, discrete wavelet transform, discrete Fourier transform, and Gabor filter) for the texture features extraction. A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development. The datasets of the optimized features were used as an input for the model, and their respective coal characteristics (analyzed in the laboratory) were used as outputs of the model. The R-squared values were found to be 0.89, 0.92, 0.92, and 0.84, respectively, for fixed carbon, ash content, volatile matter, and moisture content. The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression, support vector regression, and radial basis neural network models. The study demonstrates the potential of the machine vision system in automated coal characterization.

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