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Start date: 2000Subject: search.filters.subject.Neural networks (Computer science)

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Now showing 1 - 10 of 11
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    Learning rate optimisation of an image processing deep convolutional neural network
    (2021) Buthelezi, Sibusiso Blessing; Reddy, Seren; Twala, Bhekisipho
    The major contribution of this dissertation is the proposal of the use of mathematical models to identify an optimal learning rate for an image processing deep convolutional neural network (DCNN). This model is derived from a nonlinear regression relationship between the learning rate and the accuracy of a test DCNN model. This relationship is meant to (A) resolve the problem of arbitrarily selecting the initial learning rate (B) reduce computational resource requirement and (C) reduce training instabilities. An algorithm is developed to analyse an inputted DCNN model and subsequently render output parameters that may be used to aid in the selection of an OLR. The benefit of an OLR includes improved training stability and reduced computational resources. The results rendered by the OLR algorithm proposes that an optimal learning rate improves model performance; this is described by the test model average accuracy of 91%. Furthermore, a model validation graph is also extrapolated. which will illustrate the mathematical model accuracy and the region of interest (ROI). The ROI defines the region in the learning rate spectrum with a positive effect on model performance.
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    Modeling and recognition of faults in smart distribution grid using maching intelligence technique
    (2018) Onaolapo, Adeniyi Kehinde; Akindeji, Timothy Kayode; Adetiba, Emmanuel
    Electrical power systems experience unforeseen faults attributable to diverse arbitrary reasons. Unanticipated failures occurring in power systems are to be prevented from propagating to other parts of the protective system to enhance economic efficacy of electric utilities and provide better service to energy consumers. Since most consumers are directly connected to power distribution networks, there is an increasing research efforts in distribution network fault recognition and fault-types identifications to solve the problem of outages due to faults. This study focuses on fault recognition and fault-types identification in electrical power distribution system based on the Design Science Research (DSR) approach. Diverse simulations of fault types at different locations were applied to the IEEE 13 Node Test Feeder to produce three phase currents and voltages as data set for this study. This was realized by modelling the IEEE 13-node benchmark test feeder in MATLAB-Simulink R2017a. In order to achieve intelligent fault recognition and fault-type identification, different Multi-layer Perceptron Artificial Neural Networks (MLP-ANN) models were designed and subsequently trained using the generated dataset with the Neural Network toolbox in MATLAB R2017a. The fault recognition task verifies if a fault occurs or not while the fault-types identification task determines the fault class as well as the faulty phase(s). Results obtained from the various MLP-ANN models were recorded and statistically analyzed. Acceptable performances were obtained for fault recognition with the 6-25-20-15-1 MLP-ANN architecture, for fault-types identification with the 6-40-4 MLP-ANN architecture and for fault location with the 6-30-15-5-4 MLP-ANN architecture. Given the result obtained in this study, MLP-ANN is adjudged suitable for intelligent fault recognition and fault-types identification in power distribution systems. The trained MLP-ANNs in this study could ultimately be incorporated in power distribution networks within South Africa and beyond in order to enhance energy customers’ satisfaction.
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    Predicting mass transfer in pilot scale external loop airlift reactors using neural networks
    (2018) Naidoo, Nirvana; Pauck, Walter James; Carsky, Milan
    Airlift reactors are a viable means for conducting large scale mass transfer operations. However, due to the difficulty experienced in understanding the complex behavioural characteristics of these reactors, the design of airlift reactors becomes very complicated and largely empirical. Existing correlations and traditional computational fluid dynamic modelling has proven to be mostly reactor dependent and not widely applicable thereby limiting their application. There is therefore a need to develop a model that does not require prior knowledge of relationships between parameters of these reactors but instead uses an alternate method to assist with the design of airlift reactors. An artificial neural network represents this method. The aim of this investigation was to build an artificial neural network using selected input data of Newtonian fluids in pilot scale external loop airlift reactors of varying designs in order to predict the mass transfer coefficient in other external loop airlift reactors with more general geometry. To achieve this, a large base of experimental data (663) was generated using glycerine-air and water-air systems in 5 configurations of external loop airlift reactors with 3 categories of sparger design. The data was modelled using the artificial neural network software, Predict (Version 3.30) by Neuralware. The Coefficient of Correlation for the neural network model was 0.98. The neural network model was tested with unseen external data from various sources of which the R values ranged from 0.91 to 0.99. Additional external data was evaluated with the superficial gas velocity out of the range of the experimental data from this investigation and with a very different design of sparger. The R values for this additional data were 0.85 and 0.67-0.85 respectively. To achieve good correlations it was found necessary to take into account the sparger design and pore size; the actual geometric dimensions of the reactors namely the riser and downcomer diameters and heights; the visual observations of the approximate bubble size and bubble flow patterns and static liquid height in addition to the more usual data of the area and aspect ratios; the fluid properties namely, surface tension, density and viscosity; the superficial gas velocity; the downcomer superficial liquid velocity; the riser gas holdup and the downcomer gas holdup. However, some parameters like the static liquid height although considered important appeared not to be. By considering these as important input variables into the network, the artificial neural network was able to give excellent approximations for both seen and unseen data for some of the reactor configurations. However, the network also had the ability to pick up differences in the reactor configurations were it did not predict well, especially with respect to sparger design. An important conclusion arrived at in this investigation was the significant influence of the sparger and its design on the mass transfer. The sensitivity of the network to the sparger design means that a greater quantification of the influence of the sparger design is required.
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    Assessment of UV index using artificial neural networks
    (2002) Human, Sep; Bajic, Vladimir B.; Duffy, Kevin Jan
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    Statistical pattern recognition based on LVQ artificial neural networks : application to TATA box motif
    (2000) Wang, Haiyan; Bajic, Vladimir B.
    The computational analysis of eukaryotic promoters are among the most important and complex research domains that may contribute to complete gene identification. The current methods for promoter recognition are not sufficiently developed. Eukaryotic promoters contain a number of short motifs that may be used in promoter recognition. Having good computational models for these motifs can be crucial for increased efficiency of promoter recognition programs. This study proposes a combined statistical and LVQ neural network system as a computational model of the TAT A box motif of eukaryotic promoters. The methodology used is universal and applicable to any short functional motif in DNA. The statistical analysis of the core TAT A motif hexamer and its neighboring haxamers show strong regularities that can be used in motif recognition. Moreover, the positional distribution of the TAT A motif in terms of its distance from the transcription start site is very regular and is used in the statistical modeling. Furthermore, the matching score of the position weight matrix for the motif was used as a part of the model. Based on these statistical properties. a novel LV Q classifier for TAT A motif recognition is developed. The characteristics of the method are that the genetic algorithm was used for finding good initial weights of the LV Q system, while fine tuning of two LVQ networks was done by the lvq? algorithm. The final computational model is developed for a recognition level of 67.8o/c correct recognition on the test set with less than 1% false recognition. This model is evaluated in the task of promoter recognition on an independent test set. The results in promoter recognition outperform three other promoter recognition programs. It is shown that the recognition of promoters based on the recognition of the TAT A motifs using this new model is superior to the recognition based on the currently used position weight matrix description of this motif.
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    Design and development of a process control valve diagnostic system based on artificial neural network ensembles
    (2016) Sewdass, Sugith; Govender, Poobalan
    This research discusses the design and development of a computational intelligent based diagnostic system to assess the operating state of a process control valve. Process control valves react to a controller signal and are the main source of faults in a control loop. The elasticity inherent within a valve’s mechanical construction makes it prone to nonlinearities such as backlash, hysteresis and stiction. These nonlinearities negatively affect the performance of a process control loop during a control session. The diagnostic system proposed in this research utilises artificial neural network systems configured as ensembles to classify common control valve faults. Each ensemble functions as a ‘specialist’ trained to identify a specific loop fault. The team of specialized artificial neural networks are configured into a single comprehensive system to detect common control loops problems such as valve hysteresis, backlash, stiction and low air supply. The detection of a specific type of fault is achieved by comparing the mean square error output from each network. The ensemble having the lowest mean square error is the network that has been trained to identify a specific type of fault. Two practical methods to simulate control valve stiction and hysteresis are also presented in this study. These methods make it possible for researchers to investigate dynamics of nonlinear behaviour when these nonlinear effects occur in the control channel.
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    Hybrid motion detection system using DSP and ANN ensembles
    (IMECS, 2013-03) Moorgas, Kevin Emanuel; Govender, Poobalan
    Hybrid systems are used in engineering systems and scientific applications to enhance and to improve their efficiency. This paper presents a hybrid system using digital signal processing (DSP) systems and artificial neural networks (ANN’s) for object motion detection, extraction and filtering. A summary of a DSP motion detection system is first presented and its performance is compared to that of an ANN ensemble system (AES). The two systems are then combined to form a hybrid motion detection system. Preprocessing of the hybrid system uses wavelet image compression to enhance its computational speed. During the testing of the system the efficiency of the (AES) is demonstrated as a powerful parallel processor in handling large amounts of image data.
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    ANN’s vs. SVM’s for image classification
    (International ASET, 2012-08) Moorgas, Kevin Emanuel; Pillay, Nelendran; Governder, Poobalan
    In this paper the dynamic performance of the artificial neural network is compared to the performance of a statistical method such as the support vector machine. This comparison is made with respect to an image classification application where the performance is compared with regards to generalization and robustness. Image vectors are compressed in order to reduce the dimensionality and the salient feature vectors are extracted with the principle component algorithm. The artificial neural network and the support vector machine are trained to classify images with feature vectors. A comparative analysis is made between the artificial neural network and the support vector machine with respect to robustness and generalization.
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    Object motion detection, extraction and filtering using ANN ensembles
    (2009) Moorgas, Kevin Emanuel; Govender, Poobalan
    This research is devoted to the development of an intelligent image motion detection system based on artificial neural networks (ANN’s). Object motion detection, non-stationary image isolation and extraction, and image filtering is investigated, with the intention of developing a system that will overcome some of the shortcomings associated with the performance of conventional motion detection systems. Motion detection and image extraction finds popular application in medical imagery and engineering based diagnostics systems. Conventional image processing systems utilise Digital Signal Processing (DSP) to perform the non-stationary image motion detection function. Aliasing and filtering are problematic processes in DSP based image processing systems. The proposed ANN motion detection system overcomes some of these shortcomings. The study compares the performance of conventional DSP systems to that of the proposed ANN based system. The excellent noise immunity, ability to generalise and robustness of the ANN system is exploited in the design of the motion detection system. The ANN’s are arranged as ensembles in order to improve the computation time of the proposed motion detection system. A hybrid system comprising DSP and ANN ensembles is also proposed in the study. The hybrid system exploits the positive characteristics of DSP and ANN’s within a single system. The performance of the pure ANN system and the hybrid system is compared to that of DSP systems, using the image’s signal-to-noise ratio and computation times as a basis for comparison.
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    Design and implementation of an intelligent vision and sorting system
    (2009) Li, Zhi; Govender, Poobalan
    This research focuses on the design and implementation of an intelligent machine vision and sorting system that can be used to sort objects in an industrial environment. Machine vision systems used for sorting are either geometry driven or are based on the textural components of an object’s image. The vision system proposed in this research is based on the textural analysis of pixel content and uses an artificial neural network to perform the recognition task. The neural network has been chosen over other methods such as fuzzy logic and support vector machines because of its relative simplicity. A Bluetooth communication link facilitates the communication between the main computer housing the intelligent recognition system and the remote robot control computer located in a plant environment. Digital images of the workpiece are first compressed before the feature vectors are extracted using principal component analysis. The compressed data containing the feature vectors is transmitted via the Bluetooth channel to the remote control computer for recognition by the neural network. The network performs the recognition function and transmits a control signal to the robot control computer which guides the robot arm to place the object in an allocated position. The performance of the proposed intelligent vision and sorting system is tested under different conditions and the most attractive aspect of the design is its simplicity. The ability of the system to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced with missing data made the neural network an attractive option over fuzzy logic and support vector machines.