Browsing by Subject "Pattern recognition"
Now showing items 1-6 of 6
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The application of signal processing and artificial intelligence techniques in the condition monitoring of rotating machinery
(North-West University, 2003)Condition monitoring of critical machinery has many economic benefits. The primary objective is to detect faults, for example on rolling element bearings, at an early stage to take corrective action prior to the catastrophic ... -
Characterization and modelling of a customs operation
(NWU, 2018)Effective risk management is a prerequisite to find an acceptable balance between the objectives of a customs operation and the streamlined flow of goods. This requires the use of well-designed customs risk management models ... -
A condition based reliability simulator framework based on a heuristic fault model
(NWU, 2018)There are significant concerns as well as remedial efforts by the SA Power Utility to improve the generation plant performance that showed a significant decline over the past number of years. There is general consensus on ... -
An empirical investigation of alternative semi-supervised segmentation methodologies
(ASSAf, 2019)Segmentation of data for the purpose of enhancing predictive modelling is a well-established practice in the banking industry. Unsupervised and supervised approaches are the two main types of segmentation and examples of ... -
Kernel bandwidth estimation for non-parametric density estimation: a comparative study
(Pattern recognition association of South Africa (PRASA), 2013)We investigate the performance of conventional bandwidth estimators for non- parametric kernel density estimation on a number of representative pattern-recognition tasks, to gain a better understanding of the behaviour of ... -
Maximum–likelihood kernel density estimation in high–dimensional feature spaces
(North-West University, 2014)With the advent of the internet and advances in computing power, the collection of very large high-dimensional datasets has become feasible { understanding and modelling high-dimensional data has thus become a crucial ...