Neural Modeling of Square Surface Aerators

This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and overexcitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The proposed algorithm makes use of ratio of voltage to frequency and amplitude of differential current for transformer operating condition detection. This paper presents a comparative study of power transformer differential protection algorithms based on harmonic restraint method, NNPCA, feed forward back propagation neural network (FFBPNN), space vector analysis of the differential signal, and their time characteristic shapes in Park’s plane. The algorithms are compared as to their speed of response, computational burden, and the capability to distinguish between a magnetizing inrush and power transformer internal fault. The mathematical basis for each algorithm is briefly described. All the algorithms are evaluated using simulation performed with PSCAD/EMTDC and MATLAB.

In the present paper, we have employed the application of artificial neural networks (ANN) to predict effective heat storage coefficient (HSC) of building materials. First we prepared a database to train and test the models developed here. Two types of architectures from different networks are developed, one with three inputs and the other with four inputs mixed architecture combining an ANN with a theoretical model developed by us previously. These ANN models are built, trained and tested by the feed forward back propagation algorithm, to obtain the effective properties of building materials from the properties of their constituents. Feed forward back propagation neural network structure has been developed, which includes an input layer, a hidden layer and an output layer. The number of neurons in the input layer is equal to the number of input parameters and the number of neurons in the output layer is equal to the output parameters. A good agreement has been found between the predicted values using ANN and the experimental results reported in the literature.

This study investigates the effectiveness of an Artificial Neural Network (ANN) model in enhancing the classification of dry beans varieties. The dry beans dataset, comprising 13,611 samples and 16 numerical features, was preprocessed using Z-score normalization for outlier removal and Principal Component Analysis (PCA) to remove 702 outliers and reduce dimensionality to 10 components while preserving 99.99% of the original variance. A feedforward ANN architecture was designed, constructed as a feedforward neural network with three fully connected layers namely, an input layer that receives 10 features (as reduced by PCA), a first hidden layer with 16 neurons and ReLU activation, a second hidden layer with 32 neurons and ReLU activation, and an output layer with neurons equal to the number of classes, using softmax activation for multi-class probability prediction. The model was trained over 60 epochs using TensorFlow/Keras. Evaluation on a held-out test set demonstrated that the ANN model achieved a classification accuracy of 94.50%. Performance metrics, including accuracy, precision, recall, F1-score, and confusion matrix analysis, confirmed the model's robustness and generalization capability across all dry beans’ classes. The findings affirm that ANN models, when supported by appropriate preprocessing, can serve as scalable and accurate alternatives to conventional machine learning approaches in agricultural classification tasks.

The present study aims to apply artificial neural networks (ANNs) for reference evapotranspiration (ETo) prediction. Three different feed-forward artifical neural network (ANN) models, each using varied input combinations of previous months ETo, have been trained and tested. The output of the network was the one-month-ahead ETo. The networks learned to forecast one-month-ahead ETo for Mahanadi reservoir project area using the three learning methods namely quasi-Newton algorithm, Levenberg–Marquardt algorithm and backpropagation with adaptive learning rate algorithm. The training results were compared with each other, and performance evaluations were done for untrained data. The performance evaluations measured were standard error of estimates (SEE), raw standard error of estimates (RSEE), and model efficiency. The best ANN architecture for prediction of ETo was obtained for Mahanadi reservoir project area. The monthly reference evapotranspiration data were estimated by the Penman–Monteith method and used for training and testing of the ANN models. Further ANNs predicted results were compared with those obtained using the statistical multiple regression technique. Based on results obtained, the ANN model with architecture of 3–9-1 (three, nine, and one neuron(s) in the input, hidden, and output layers, respectively) trained using quasi-Newton algorithm was found to be the best amongst all the models with minimum SEE and RSEE of 0.45 and 0.45 mm/d respectively and maximum model efficiency of 93%. It is concluded that ANN can be used to predict ETo.

Application of principal component analysis and artificial neural networks to recognize the individual VOCs of methanol/2-propanol in a binary mixture by SAW multi-sensor array

This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN's MSE values are 40 times smaller than the LR's. The test data R value from ANN is 0.9931; thus indicating a high confidence level.

An intelligent system for sorting pistachio nut varieties

This paper describes a new approach for power transformer protection that ensures the security for external faults, magnetizing inrush and over-excitation conditions and provides dependability for internal faults. This approach based on the wave-shape recognition technique. An algorithm based on Neural Network Principal Component Analysis (NNPCA) with back propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and over-excitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The effect of hidden layer neurons on the classification accuracy is analyzed. The proposed algorithm makes use of ratio of voltage-to-frequency and amplitude of differential current for transformer operating condition detection. The algorithm is evaluated using simulation performed with PSCAD/EMTDC and MATLAB.

According to Young inequality of convex function's conjugate properties, a new error function is constructed for feedforward neural network. The function is convex to both connection weight value and hidden layer's output, so it has no local minimum points. Training speed of feedforward neural network based on the new error function is fast, and network's training success rate is high. Its training strategy is: first, fix connection weight values before and after hidden layer, optimize hidden layer's output; then, fix hidden layer's output, optimize connection weight values before and after hidden layer; just like so, until error demand is satisfied. The new network is applied to extract urban information from remote sensing images. Compared with traditional image information extraction method (maximum likelihood method), its extraction accuracy rate is much higher.

In the present work, an effort has been made to explore the potentialities of application of regression analysis and artificial neural network (ANN) in milling process. Optimum setting of horizontal and vertical cutting forces for a particular tool-work piece combination is found using three levels of speed, feed and depth of cut. The parameter combination is worked out using full factorial design of experiment methods (DOE). Experiments are conducted for all the combinations and forces are measured using a milling tool dynamometer. Based on the observations, regression equations are derived. The present investigation was further extended with the application of ANN using an architecture consisting of three input and two output nodes and a hidden layer. The network training is carried out and then trained network is tested with few experimental results, which are not used during training. The results obtained during the study are critically discussed and reported.

The aim of data transformation is to transform the original feature space of data into another space with better properties. This is typically combined with dimensionality reduction, so that the dimensionality of the transformed space is smaller. A widely used method for data transformation and dimensionality reduction is Principal Component Analysis (PCA). PCA finds a subspace that explains most of the data variance. While the new PCA feature space has interesting properties, such as removing linear correlation, PCA is an unsupervised method. Therefore, there is no guarantee that the PCA feature space will be the most appropriate for supervised tasks, such as classification or regression. On the other hand, 3-layer Multi Layer Perceptrons (MLP), which are supervised methods, can also be understood as a data transformation carried out by the hidden layer, followed by a classification/regression operation performed by the output layer. Given that the hidden layer is obtained after a supervised training process, it can be considered that it is performing a supervised data transformation. And if the number of hidden neurons is smaller than the input, also dimensionality reduction. Despite this kind of transformation being widely available (any neural network package that allows access to the hidden layer weights can be used), no extensive experimentation on the quality of 3-layer MLP data transformation has been carried out. The aim of this article is to carry out this research for classification problems. Results show that, overall, this transformation offers better results than the PCA unsupervised transformation method.

Modeling of surface roughness in wire electrical discharge machining of Inconel 718 using artificial neural network

Pulsed Nd:YAG laser has high intensity and high quality beam characteristics, which can be used to produce micro-grooves and micro-turning surface on advanced engineering ceramics. The present research attempts to develop mathematical models by using response surface methodology approach for correlating the machining process parameters and the process responses during laser micro-turning of aluminum oxide (Al2O3) ceramics. The process parameters such as laser average power, pulse frequency, workpiece rotating speed, assist air pressure and Y feed rate were varied during experimentation. The rotatable central composite design experimental planning has been used to design the experimentation. The performance measures considered are surface roughness (Ra) and micro-turning depth deviation. Multi-objective optimization has been carried out for achieving the desired surface roughness as well as minimum depth deviation during laser micro-turning operation. Further, an artificial neural network (ANN) model has been developed to predict the process criteria. Levenberg-Marquadt training algorithm is used for multilayer feed forward backpropagation neural network. The developed ANN model has 5-10-2 feed forward network. There are 5 neurons in the input layer, 10 neurons in the hidden layer and 2 neurons in the output layers corresponding to two output responses, respectively. The developed ANN model has been validated using data obtained by conducting additional set of experiments. It was found that the developed ANN model can predict the process criteria more accurately than response surface methodology (RSM) based developed models.

Forecasting Foreign Exchange Rate Movements with k-Nearest-Neighbour, Ridge Regression and Feed-Forward Neural Networks

The present study addressed the economic analysis of broiler production units. Therefore, required data was collected from 50 broiler production units using personal questionnaires in the winter, 2013. Cronbach alpha coefficient was estimated for these questionnaires to be 0.81, which indicates the reliability of the questionnaire is acceptable. The results showed that the feedforward neural network with two hidden layers (4 and 17 neurons for the economic model) had the best results and it can be used to estimate the energy ratio with high precision. The optimal model performance was performed using measures such as the coefficient of determination (R2 ), MSE, MAPE and MAE. Value of R2 was reported for the economic model as 96%. Keywords: Artificial Neural Networks, Broiler, Economic Analysis, The Ratio of Benefit to Cost