The present paper deals with the development of neural network (NN)-based expert system for modeling of 2024 aluminum tube spinning process. Tube spinning is a highly nonlinear thermo-mechanical process for producing ...
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The present paper deals with the development of neural network (NN)-based expert system for modeling of 2024 aluminum tube spinning process. Tube spinning is a highly nonlinear thermo-mechanical process for producing large-diameter thin-walled shapes. It is interesting to note that the performance of the process depends on various process parameters, such as wall thickness, percentage of thickness reduction, feed rate, mandrel rotational speed, solution treatment time and aging time. Therefore, an NN-based expert system is necessary for modeling the tube spinning process. The input layer of NN consists of six neurons corresponding to the inputs of the tube spinning process. Moreover, the output layer consists of four neurons that represent four responses, namely change in diameter, change in thickness, inner and outer surface roughness. It is to be noted that the performance of NN depends on various factors, such as number of neurons in the hidden layer, coefficients of transfer functions and connecting weights, etc. In the present paper, three algorithms, such as back-propagation, genetic and artificial bee colony algorithms, are used for optimizing the said variables of NN. Further, the developed approaches are tested for their accuracy in prediction with the help of some test cases and found to model the tube spinning process effectively.
This paper proposes a novel image segmentation method based on BP neural network, which is optimized by an enhanced Gravitational Search algorithm (GSA). GSA is a novel heuristic optimization algorithm based on the la...
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This paper proposes a novel image segmentation method based on BP neural network, which is optimized by an enhanced Gravitational Search algorithm (GSA). GSA is a novel heuristic optimization algorithm based on the law of gravity and mass interactions. It has been proven that the GSA has good ability to search for the global optimum, but it suffers from the premature convergence due to the rapid reduction of diversity. This work introduces a cat chaotic mapping into the steps of population initialization and iterative stage of the original GSA, which forms a new algorithm called CCMGSA. Then the CCMGSA is employed to optimize BP neural networks, which forms a combination method called CCMGSA-BP and we use it for image segmentation. To verify the efficiency of this method, the visual and performance experiments are done. The visual results using our proposed method are compared with those using other segmentation methods including an improved k-means clustering algorithm (I-K-means), a hybrid region merging method (H-Region-merging), and manual segmentation. The comparison results show that the proposed method can get good segmentation results on grayscale images with specific characteristics. And we compare the performance of our proposed method with those of IGSA-BP, CLPSO-BP and RGA-BP for image segmentation. The results indicate that the CCMGSA-BP shows better performance in terms of the convergence rate and avoidance of local minima.
Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the ...
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ISBN:
(纸本)9781479978007
Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the designed safety features. Under or over inflated tires adversely affects the stability of vehicles. It is generally the vehicle's user responsibility to ensure the tire inflation pressure is set and maintained to the required value using a tire inflator. In the tire inflator operation, the vehicle's user sets the desired value and the machine has to complete the task. During the inflation process, the pressure sensor does not read instantaneous static pressure to ensure the target value is reached. Hence, the inflator is designed to stop repetitively for pressure reading and avoid over inflation. This makes the inflation process slow, especially for large tires. This paper presents a novel approach using artificial neural network based technique to identify the tire size. Once the tire size is correctly identified, an optimized inflation cycle can be computed to improve performance, speed and accuracy of the inflation process. The developed neural network model was successfully simulated and tested for predicting tire size from the given sets of input parameters. The test results are analyzed and discussed in this paper.
In spite of wide use of projection-based features in handwritten character recognition of several languages, its implementation was somewhat scanty in Bangla handwritten character recognition. This paper introduces th...
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ISBN:
(纸本)9781479964802
In spite of wide use of projection-based features in handwritten character recognition of several languages, its implementation was somewhat scanty in Bangla handwritten character recognition. This paper introduces the usage of projection profile features in recognizing handwritten Bangla basic characters. Alongside it also demonstrates a qualitative and quantitative analysis to visualize the effect of using projection based features on accuracy of recognition of Bangla handwritten characters through a number of approaches. In fact, this particular effort comprises of five different approaches where first one used longest-run, quad-tree and octant centroid features, second one adopted additional shadow features in association with the features of first approach, third one used longest run, quad-tree, shadow and chain code histogram features, next approach used longest-run, quadratic center of mass, shadow and left projection profile features and finally fifth approach with additional right projection profile features along with other features involved in the fourth approach. Throughout this analysis, neural network (trained via back-propagation algorithm) acted as classifier to observe the change in accuracy of recognition. It is seen that, with the increase in number of projection-based features, percentage of accuracy enhances at a greater rate than in case of inclusion of other features. This effective analysis can certainly assist a researcher to choose the optimal feature vector (consisting of several feature sets) for handwritten Bangla basic characters recognition.
Studying dynamic behaviours of a transportation system requires the use of the system mathematical models as well as prediction of traffic flow in the system. Therefore, traffic flow prediction plays an important role...
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Studying dynamic behaviours of a transportation system requires the use of the system mathematical models as well as prediction of traffic flow in the system. Therefore, traffic flow prediction plays an important role in today's intelligent transportation systems. This article introduces a new approach to short-term daily traffic flow prediction based on artificial neural networks. Among the family of neural networks, multi-layer perceptron (MLP), radial basis function (RBF) neural network and wavenets have been selected as the three best candidates for performing traffic flow prediction. Moreover, back-propagation (BP) has been adapted as the most efficient learning scheme in all the cases. It is shown that the coefficients produced by temporal signals improve the performance of the BP learning (BPL) algorithm. Temporal signals provide researchers with a new model of temporal difference BP learning algorithm (TDBPL). The capability and performance of TDBPL algorithm are examined by means of simulation in order to prove that the wavelet theory, with its multi-resolution ability in comparison to RBF neural networks, is a suitable algorithm in traffic flow forecasting. It is also concluded that despite MLP applications, RBF neural networks do not provide negative forecasts. In addition, the local minimum problems are inevitable in MLP algorithms, while RBF neural networks and wavenet networks do not encounter them.
The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence ...
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The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six-step procedure. The back-propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.
This paper describes in details an application of artificial neural networks (ANNs) to predict the performance of a solar thermal energy system (STES) used for domestic hot water and space heating application. Experim...
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This paper describes in details an application of artificial neural networks (ANNs) to predict the performance of a solar thermal energy system (STES) used for domestic hot water and space heating application. Experiments were conducted on the STES under a broad range of operating conditions during different seasons and Canadian weather conditions in Ottawa, over the period of March 2011 through December 2012 to assess the system performance. These experimental data were utilised for training, validating and testing the proposed ANN model. The model was applied to predict various performance parameters of the system, namely the preheat tank stratification temperatures, the heat input from the solar collectors to the heat exchanger, the heat input to the auxiliary propane-fired tank, and the derived solar fractions. The back-propagation learning algorithm with two different variants, the Levenberg-Marguardt (LM) and scaled conjugate gradient (SCG) algorithms were used in the network. It was found that the optimal algorithm and topology were the LM and the configuration with 10 inputs, 20 hidden and 8 output neurons/outputs, respectively. The preheat tank temperature and solar fraction predictions agreed very well with the experimental values using the testing data sets. The ANNs predicted the preheat water tank stratification temperatures and the solar fractions of the STES within less that +/- 3% and +/- 10% errors, respectively. The results confirmed the effectiveness of this method and provided very good accuracy even when the input data are distorted with different levels of noise. Moreover, the results of this study demonstrate that the ANN approach can provide high accuracy and reliability for predicting the performance of complex energy systems such as the one under investigation. Finally, this method can also be exploited as an effective tool to develop applications for predictive performance monitoring system, condition monitoring, fault detection and diagno
Environmental factors, as incident light, temperature and night/day length mainly determine the dynamics of growth and development of dioecious yerba-mate. The complex interactions among these factors and growth respo...
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Environmental factors, as incident light, temperature and night/day length mainly determine the dynamics of growth and development of dioecious yerba-mate. The complex interactions among these factors and growth responses highlight the need for growth model, which describes plant modifications under natural and stress conditions, accounting for the growth unit formations in male and female individuals. The rhythmic growth of yerba-mate considers the existence of two annual growth flushes, (spring and autumn) and two annual growth pauses (summer and winter). We developed an individual-based ecological model (InterpolMateS1) that incorporates some aspects of growth and development of yerba-mate referent to two cultivation environments - monoculture and forest understory. The environmental time series, together with plant morphological time series and information about periods of rhythmic growth and respective growth pauses, were used for artificial neural network (ANN) training. The back-propagation algorithm was implemented to refine the weights generated in ANN from the monthly organized input and to adjust using expected output morphological data sets. The probability of meristem ability to continue the growth in the next growth unit and to preserve the leaf in each internode along the axes of 1st-3rd branching order was calculated and implemented into the model. The cubic splines interpolation was more accurate to define the growth parameters curves of yerba-mate. The InterpolMateS1 was daily-step programmed to calculate the growth of yerba-mate for biennial period between two subsequent prunings. The software was tested to simulate the reduction in growth and biomass production when long-term stress conditions were applied. Virtual females were found to be more sensitive to changes of environmental conditions than males, when low water availability and low temperatures occurred during spring and autumn growth flushes. (C) 2013 Elsevier B.V. All rights reserved.
Owing to the random features of wind generator outputs, power system frequency becomes increasingly variable when considerable wind farms are integrated into a power system. Frequency forecast in a power system is dif...
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Owing to the random features of wind generator outputs, power system frequency becomes increasingly variable when considerable wind farms are integrated into a power system. Frequency forecast in a power system is difficult and challenging. This study develops an improved elastic back-propagation neural network method to forecast system frequency. The effectiveness of the proposed method is verified using field data from a real wind farm in Guangdong, China. Simulation results show that even in different types of wind farms, the proposed method is applicable and can accurately identify changes in system frequency. Therefore, the forecast results can be used to design appropriate frequency control strategies and to enhance security and stability for the whole system. (C) 2014 Elsevier Ltd. All rights reserved.
In this study, an artificial neural-network (ANN)-based space-vector pulse-width modulation (SVPWM) for capacitor voltage balancing of a three-phase three-level neutral-point clamped converter with improved power qual...
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In this study, an artificial neural-network (ANN)-based space-vector pulse-width modulation (SVPWM) for capacitor voltage balancing of a three-phase three-level neutral-point clamped converter with improved power quality is presented. The neural-network-based controller offers the advantage of very fast implementation of the SVPWM algorithm. This makes it possible to use an application specific integrated circuit chip in place of a digital signal processor. The proposed scheme employs single layer feed-forward neural-networks at different stages along with a control algorithm using modified reference vector for capacitor voltage balancing of an improved power quality three-phase neutral-point clamped converter. In other words, the neural-network receives three-phase voltages and currents as input and generates symmetrical pulse-width modulation waves for three phases of the converter. A simulated digital signal processor (DSP)-based modulator generates the data which are used to train the network by a back-propagation algorithm in the MATLAB Neural Network Toolbox. The simulation of converter with ANN-based space-vector modulator shows excellent performance when compared with that of conventional DSP-based modulator.
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