High Impedance Fault Detection Using Artificial Neural Network Techniques

Forecasts of future events are required in many of the activities associated with the planning and operation of the components of a water resource system. For the hydrologic component, there is a need for both short- and long-term forecasts of river flow events in order to optimize the system or to plan for future expansion or reduction. This paper presents the comparison of different artificial neural network (ANN) techniques in short- and long-term continuous and intermittent daily streamflow forecasting. The studies in modelling the intermittent series are quite limited because of the complexity of fitting models in to these series. The available conventional models necessitate the adjustment of numerous parameters for calibration. Three different ANN techniques, namely, feed-forward back propagation (FFBP), generalized regression neural networks, and radial basis function-based neural networks (RBF) are applied to continuous and intermittent river flow data of two Turkish rivers for short-range and long-range forecasting studies. The k-fold partitioning method is employed for preparing the ANN training data successfully. In general, the forecasting performance of RBF is found to be superior to the other two ANN techniques and a time series model in terms of the selected performance criteria. It was observed that the FFBP method had some drawbacks such as a local minima problem and negative flow generation.

Artificial Neural Network (ANN) techniques have recently been applied to many different fields and have demonstrated their capabilities in solving complex problems. In a business environment, the techniques have been applied to predict bond ratings and stock price performance. In these applications, ANN techniques outperformed widely-used multivariate statistical techniques. The purpose of this paper is to compare the ANN method with the Discriminant Analysis (DA) method in order to understand the merits of ANN that are responsible for the higher level of performance. The paper provides an overview of the basic concepts of ANN techniques in order to enhance the understanding of this emerging technique. The similarities and differences between ANN and DA techniques in representing their models are described. This study also proposes a method to overcome the limitations of the ANN approach, Finally, a case study using a data set in a business environment demonstrates the superiority of ANN over DA as a method of classification of observations.

In this paper, the modified single phase nine-level CHB-MLI is proposed based on the selective harmonic elimination pulse width modulation (SHE-PWM) using artificial neural network (ANN) technique. The number of proposed switching devices reduced in a now modified CHB-MLI compared with the conventional structure of CHB-MLI which decreases the switching frequency of the system, leading to a decrease in the total power switching losses. The switching angles of CHB-MLI in this paper are detected and controlled based on the ANN technique into selective harmonic elimination transcendental nonlinear equation which is considered as the popular and powerful method for minimizing the harmonic order at the output voltage waveform of the MLI and produces a low ratio of total harmonic distortion (THD%). This paper aims to design, simulate and build up new modified structure of the single-phase nine-levels CHB-MLI and predict the real-time switch in order to generate superior switching angles by using ANN technique. MATLAB simulation has been used for designing and simulating the nine-levels single-phase CHB-MLI with ANN technique for switching angles prediction. It is found the proposed technique able to reduce harmonic contents of output waveform.

In this paper, Artificial Neural Network (ANN) technique is implemented to improve speed and torque control of a separately excited DC machine drive. The speed and torque sensorless scheme based on ANN is estimated adaptively. The proposed controller is designed to estimate rotor speed and mechanical load torque as a Model Reference Adaptive System (MRAS) method for DC machine. The DC drive system consists of four quadrant DC/DC chopper with MOSFET transistors, ANN, logic gates and routing circuits. The DC drive circuit is designed, evaluated and modeled by Matlab/Simulink in the forward and reverse operation modes as a motor and generator, respectively. The DC drive system is simulated at different speed values (±1200 rpm) and mechanical torque (±7 N.m) in steady state and dynamic conditions. The simulation results illustrate the effectiveness of the proposed controller without speed or torque sensors.

This paper defines a protocol for geometric description and classification of white eggs of chickens using computer vision (CV) and artificial neural network (ANN) techniques. For this, 60 eggs were selected and their dimensions, volumes and surface areas measured using pachymeter, immersion method and mathematical expressions available in the literature, respectively, to compose the reference values. Subsequently, the eggs were imaged with RGB camera and the digital images analyzed using dedicated algorithm. The estimated geometric parameters were compared with the witness values, using descriptive statistics and t test, and used in ANN training for automatic classification of eggs. Perceptron was the algorithm adopted, different configurations were evaluated in the ANN and 20 digital images of white eggs available in the literature used in the classification validation. Prediction of the height, width, height/width, volume and surface area of the eggs using CV showed high correlation and low average square error with the reference values. The best egg classification results occurred with an ANN with three neurons and data of height, width and surface area as input parameters. The proposed protocol has proved feasible for the geometric description and classification of white eggs of Brazilian agro-industries.

Prediction of thermal stability, crystallinity and thermomechanical properties of poly(ethylene oxide)/clay nanocomposites with artificial neural networks

Utilizing Artificial Neural Networks for Establishing Hearing-Loss Predicting Models Based on a Longitudinal Dataset and Their Implications for Managing the Hearing Conservation Program

A combined approach of artificial neural network, multi-objective genetic algorithm, and response surface methodology for enhanced PMMA micro-channeling in low power fiber laser beam machining

A time and memory efficient methodology for supervised and unsupervised land-cover classification of multispectral remote sensing (MRS) data based on artificial neural network (ANN) techniques is presented. The proposed methodology first performs a vector quantization (VQ) using the self-organizing maps (SOM) algorithm to compress the MRS data followed by either efficient clustering and automatic classification or, when training sets are available, by a forced reduction of the training set size induced by vector quantization resulting to a faster training of the supervised ANN algorithms.

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

This paper illustrates the Artificial Neural Network (ANN) technique to predict the joint torque estimation model for arm rehabilitation device in a clear manner. This device acts as an exoskeleton for people who had failure of their limb. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. In order to prevent the muscles from paralysis becomes spasticity the force of movements should minimize the mental efforts. The objective of this work is to model the muscle EMG signal to torque using ANN technique. The EMG signal is collected from Biceps Brachii muscles to estimate the elbow joint torque. A two layer feed-forward network is trained using Back Propagation Neural Network (BPNN). The experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control.

Purpose This study, grounded in the natural resource-based view (NRBV) theory, aims to investigate how green practices influence firms’ sustainable performance (SP), and to examine the roles of organisational green culture (OGC) and corporate green image (CGI) in this relationship. Design/methodology/approach A quantitative approach was adopted, with data collected from 364 manufacturing enterprises in China. To assess the relationships between OGC, green practices and SP, the study used partial least squares structural equation modelling (PLS-SEM) and artificial neural network (ANN) techniques, combining both linear and non-linear analyses. Findings Organisational green culture significantly promotes the adoption of green human resource management (GHRM) practices, green supply chain management (GSCM) practices and green innovation, all of which positively enhance sustainable performance. Moreover, corporate green image strengthens the impact of these green practices on sustainable performance, indicating that firms with a well-established CGI achieve superior performance outcomes. Originality/value This study contributes to the theoretical development of the NRBV theory by applying it to OGC and SP within the manufacturing sector. It emphasises the importance of GHRM practices in promoting employee engagement with environmental objectives. By combining PLS-SEM and ANN techniques, the study provides deeper insights into how cultural and reputational factors influence sustainable outcomes, offering strategic guidance for human resource practitioners and decision-makers aiming to align organisational, managerial and employee needs for optimal SP.

Accurate estimation of evaporation from agricultural fields and water bodies is needed for the efficient utilisation and management of water resources at the watershed and regional scale. In this study, multiple linear regression (MLR) and artificial neural network (ANN) techniques are used for the estimation of monthly pan evaporation. The modelling approach includes the various combination of six measured climate parameters consisting of maximum and minimum air temperature, maximum and minimum relative humidity, sunshine hours and wind speed of two stations, namely Gangtok in Sikkim and Imphal in the Manipur states of the northeast hill region of India. Average monthly evaporation varies from 0.62 to 2.68 mm/day for Gangtok, whereas it varies from 1.4 to 4.3 mm/day for Imphal during January and June, respectively. Performance of the developed MLR and ANN models was compared using statistical indices such as coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) with measured pan evaporation values. Correlation analysis revealed that temperature, wind speed and sunshine hour had positive correlation, whereas relative humidity had a negative correlation with pan evaporation. Results showed a slightly better performance of the ANN models over the MLR models for the prediction of monthly pan evaporation in the study area.

An improved noise model for pseudomorphic high electron mobility transistors (PHEMT) based on the combination of the artificial neural network (ANN) and conventional equivalent circuit modeling technique is presented. The frequency dispersion of the gate noise model parameter P, drain noise model parameter R, and the correlation coefficient C have been taken into account by using an ANN model. The influence of the gate leakage current can be accommodated by using the proposed noise model. The noise model parameters are determined directly from on wafer noise parameters measurement based on the noise correlation matrix technique. Good prediction for noise parameters and significant improvements of the accuracy of noise parameters are obtained up to 26 GHz for 2 × 40 μm gate width (number of gate fingers × unit gate width) 0.25 μm Double Heterojunction δ-doped PHEMTs over a wide range of bias points. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.

A correlation study between meteorological parameters and COVID-19 pandemic in Mumbai, India