The feasibility of using Artificial Neural Network (ANN) models for application in thermal process calculations was studied. / For a better understanding of the effect of process parameters on the evaluation of thermal process, the accuracy of several formula methods (Steele & Board, Ball, Stumbo and Pham) were studied over a wide range of commercial conditions. A computer simulation based on finite difference method of numerical solutions of heat transfer to packaged foods in cylindrical containers was applied to obtain the time-temperature data for designed conditions (retort and initial temperatures, thermal diffusivity, package sizes and processing time). Moreover, the process time and process lethality from this simulation were used as the reference values for the purpose of comparison. The accuracy of methods was evaluated based on the variation of each parameter over the range of conditions employed in the study. / As the final goal of the study, a multi-layer ANN model was developed as an alternative to thermal process calculations. (Abstract shortened by UMI.)

Graduation date: 1997

Huge amounts of data are being collected as a result of the increased use of mobile telecommunications. Insight into information and knowledge derived from these databases can give operators a competitive edge in terms of customer care and retention,<br /> marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing purpose are unlabeled. Further analysis is thus, required to be able to isolate fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process.<br /> <br /> This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a period of time in a mobile telecommunication network. Our study provides a comparative analysis and application of Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) recurrent neural networks algorithms to user call data records in order to conduct a descriptive data mining on users call patterns.<br /> <br /> Our investigation shows the learning ability of both techniques to discriminate user call patterns / the LSTM recurrent neural network algorithm providing a better discrimination than the SOM algorithm in terms of long time series modelling. LSTM discriminates different types of temporal sequences and groups them according to a variety of features. The ordered features can later be interpreted and labeled according to specific requirements of the mobile service provider. Thus, suspicious call behaviours are isolated within the mobile telecommunication network and can be used to to identify fraudulent call patterns. We give results using masked call data<br /> from a real mobile telecommunication network.

Accelerated degradation testing is widely accepted in competitive industries. As there is no longer the need to test till failures, there are tremendous cost and time benefits on fully capitalizing on such a testing regime. Consequently, this research has aimed for better understanding of the relationship between design and degradation using the degradation data. Existing work in the literature uses the degradation data to improve the reliability of products. The majority of techniques, however, are centred on statistical experimental methods. For problems with increasing complexities such as large multivariable data set, non-linear interactions and dynamic varying processes, conventional methods cannot resolve the problem efficiently. Furthermore, it can not provide the adequate modelling mechanism to learn from the degradation data autonomously for describing the relationship between the design parameters and degradation. Artificial neural network is widely used for complex problems in the literature. This thesis proposes and demonstrates the neural network modelling methodology into capturing the non parametric relationship between design and degradation. / The development of a neural network consists of data preparation, network design and training and testing. This thesis presents a comprehensive description on the data generation and acquisition process. More specifically, the physical tests, experimental designs, equipment configurations, data acquisitions systems and algorithms are elaborated. Single hidden layer multilayered perceptrons are found to be the most suitable network architectures for the problem domains. Detailed descriptions of the training and testing process in determining the suitable number of hidden neurons sufficient for the problem are provided. / In summary, the neural network modelling methodology is demonstrated for the particular problem domain. As a result of the work in this thesis, two models of different practical significance are developed and compiled as Windows executables for predicting material performances. / Thesis (MEng(ManufacturingEngineering)--University of South Australia, 2006.

The area of Agent Technology is a relatively new area and one that has generated great interest within computing circles. The concept of software identities that have the intelligence to perform some of the tasks that humans perform has great potential. Such software identities are slowly making their way into many complex systems ranging from air traffic control systems to web search engines, and removing the direct need for the 'human in the loop'. This type of software that has the ability or intelligence to perform some of the tasks that would otherwise be performed by humans has been coined the term 'Intelligent Agents'. / In many of these agent based systems there may be a series of agents that are operating together to achieve common goals. Such systems are often referred to as a multiagent system. Agents within a multiagent system, usually share information and knowledge between each other in a collaborative manner to achieve a common goal. The information that is shared around is normally learned knowledge. / For the study conducted for this Master's project, collaborative agent learning is examined. The main emphasis of this study is to research how agents can learn. This learned knowledge can then be shared amongst other agents in a system to achieve a common goal. To investigate this, a case study was developed, where the aim was to develop an Automatic Target Recognition (ATR) system containing two main types of agents- a Detection Agent and a Recognition Agent. These agents would have specific roles within the system; however both would be trained to identify the same target images. These agents would then collaborate with each other to positively identify these target images when presented with masses of data in the form of test images as input into the ATR system. / The intelligent and learning aspects of these agents are implemented using Artificial Neural Networks. Two different Artificial Neural Network techniques are used for the two agents. This thesis contains a detailed discussion on Agent Technology, Artificial Neural Networks, the ATR system and descriptions of how the Detection and Recognition Agents were developed and tested. Experimental results are also presented with a discussion on the overall success of the ATR system, the results obtained, problems encountered and future directives for this research. / Thesis (MEng(ElectronicsEngineering))--University of South Australia, 2005.

Traditional Automatic Target Recognition (ATR) Systems often fail when faced with complex recognition tasks involving noise, clutter, and complexity. This work is concerned with implementing a real time, vision based ATR system using an Artificial Neural Network (ANN) to overcome some of the shortcomings of traditional ATR systems. The key issues of this work are vision, pattern recognition and artificial neural networks. The ANN presented in this thesis is inspired by Prof. Stephen Grossberg's work in Adaptive Resonance Theory (ART) and neurophysiological data on the primate brain. An ANN known as Selective Attention Adaptive Resonance Theory (SAART) (Lozo, 1995, 1997) forms the basis of this work. SAART, which is based on Grossberg's ART, models the higher levels of visual processing in the primate brain to provide an ATR system capable of learning and recognising targets in cluttered and complex backgrounds. This thesis contributes an extension to the SAART model to allow a degree of tolerance to imperfections including distortion, changes in size, orientation, or position. In addition to this extension, it is also demonstrated how modulated neural layers can be used for image filtering. A possible extension of the architecture for multi-sensory environments is proposed as a foundation for future research. / Thesis (MEng)--University of South Australia, 2000

Bibliography: leaves 328-352. / xxxi, 352 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This thesis proposes a neural theory, Selective Attention Adaptive Resonance Theory, and a neuro-engineered solution to selective visual attention, memory guided processing and illumination invariant recognition of complete (unoccluded), but distorted 2D shapes of 3D objects in cluttered visual images. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1997?

Bibliography: leaves 125-131. / xi, 131 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This thesis examines the performace of optimisation networks. The main objectives are to determine if there exist any factors which limit the solution quality that may be achieved with optimisation networks, to determine the reasons for any such limitations and to suggest remedies for them. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1996

Bibliographical references: p. 225-234. / xxiv, 285 p. : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1998

Includes bibliographical references (leaves 225-241). / xxviii, 241 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Proposes an architectural framework based on neural networks for visual scene analysis with attentional mechanisms. / Thesis (Ph.D.)--Adelaide University, Dept. of Electrical and Electronic Engineering, 2001