OVR : a novel architecture for voice-based applications
- Authors: Maema, Mathe
- Date: 2011 , 2011-04-01
- Subjects: Telephone systems -- Research , User interfaces (Computer systems) -- Research , Expert systems (Computer science) , Artificial intelligence , VoiceXML (Document markup language) , Application software -- Development
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:4671 , http://hdl.handle.net/10962/d1006694 , Telephone systems -- Research , User interfaces (Computer systems) -- Research , Expert systems (Computer science) , Artificial intelligence , VoiceXML (Document markup language) , Application software -- Development
- Description: Despite the inherent limitation of accessing information serially, voice applications are increasingly growing in popularity as computing technologies advance. This is a positive development, because voice communication offers a number of benefits over other forms of communication. For example, voice may be better for delivering services to users whose eyes and hands may be engaged in other activities (e.g. driving) or to semi-literate or illiterate users. This thesis proposes a knowledge based architecture for building voice applications to help reduce the limitations of serial access to information. The proposed architecture, called OVR (Ontologies, VoiceXML and Reasoners), uses a rich backend that represents knowledge via ontologies and utilises reasoning engines to reason with it, in order to generate intelligent behaviour. Ontologies were chosen over other knowledge representation formalisms because of their expressivity and executable format, and because current trends suggest a general shift towards the use of ontologies in many systems used for information storing and sharing. For the frontend, this architecture uses VoiceXML, the emerging, and de facto standard for voice automated applications. A functional prototype was built for an initial validation of the architecture. The system is a simple voice application to help locate information about service providers that offer HIV (Human Immunodeficiency Virus) testing. We called this implementation HTLS (HIV Testing Locator System). The functional prototype was implemented using a number of technologies. OWL API, a Java interface designed to facilitate manipulation of ontologies authored in OWL was used to build a customised query interface for HTLS. Pellet reasoner was used for supporting queries to the knowledge base and Drools (JBoss rule engine) was used for processing dialog rules. VXI was used as the VoiceXML browser and an experimental softswitch called iLanga as the bridge to the telephony system. (At the heart of iLanga is Asterisk, a well known PBX-in-a-box.) HTLS behaved properly under system testing, providing the sought initial validation of OVR. , LaTeX with hyperref package
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Predictability of Geomagnetically Induced Currents using neural networks
- Authors: Lotz, Stefanus Ignatius
- Date: 2009
- Subjects: Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5483 , http://hdl.handle.net/10962/d1005269 , Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Description: It is a well documented fact that Geomagnetically Induced Currents (GIC’s) poses a significant threat to ground-based electric conductor networks like oil pipelines, railways and powerline networks. A study is undertaken to determine the feasibility of using artificial neural network models to predict GIC occurrence in the Southern African power grid. The magnitude of an induced current at a specific location on the Earth’s surface is directly related to the temporal derivative of the geomagnetic field (specifically its horizontal components) at that point. Hence, the focus of the problem is on the prediction of the temporal variations in the horizontal geomagnetic field (@Bx/@t and @By/@t). Artificial neural networks are used to predict @Bx/@t and @By/@t measured at Hermanus, South Africa (34.27◦ S, 19.12◦ E) with a 30 minute prediction lead time. As input parameters to the neural networks, insitu solar wind measurements made by the Advanced Composition Explorer (ACE) satellite are used. The results presented here compare well with similar models developed at high-latitude locations (e.g. Sweden, Finland, Canada) where extensive GIC research has been undertaken. It is concluded that it would indeed be feasible to use a neural network model to predict GIC occurrence in the Southern African power grid, provided that GIC measurements, powerline configuration and network parameters are made available.
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An analysis of neural networks and time series techniques for demand forecasting
- Authors: Winn, David
- Date: 2007
- Subjects: Time-series analysis , Neural networks (Computer science) , Artificial intelligence , Marketing -- Management , Marketing -- Data processing , Marketing -- Statistical methods , Consumer behaviour
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5572 , http://hdl.handle.net/10962/d1004362 , Time-series analysis , Neural networks (Computer science) , Artificial intelligence , Marketing -- Management , Marketing -- Data processing , Marketing -- Statistical methods , Consumer behaviour
- Description: This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasting targets as well as Time Series forecasts, suggesting that producers could reduce costs by adopting this more effective method.
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