3. Electronic Theses and Dissertations (ETDs) - All submissions
Permanent URI for this communityhttps://wiredspace.wits.ac.za/handle/10539/45
Browse
3 results
Search Results
Item SummaryNet: two-stream convolutional networks for automatic video summarisation(2020) Jappie, ZiyadVideo summarisation is the task of automatically summarising a video sequence, to extract “important” parts of the video so as to give an overview of what has occurred. The benefit of solving this problem is that it can be applied to a myriad of fields such as the entertainment industry, sports, e-learning and many more. There is a distinct inherent difficulty with video summarisation due to its subjectivity - there is no one defined correct answer. As such, it is particularly difficult to define and measure tangible performance. This is in addition to the other difficulties associated with general video processing. We present a novel two-stream network framework for automatic video summarisation, which we call SummaryNet. The SummaryNet employs a deep two-stream network to model pertinent spatio-temporal features by leveraging RGB as well as optical flow information. We use the Two-Stream Inflated 3D ConvNet (I3D) network to extract high-level, semantic feature representations as inputs to our SummaryNet model. Experimental results on common benchmark datasets show that the considered method achieves comparable or better results than the state-of-the-art video summarisation methodsItem Short-term hourly load forecasting in South Africa using neutral networks(2018) Ilunga, Elvis TshianiAccuracy of the load forecasts is very critical in the power system industry, which is the lifeblood of the global economy to such an extent that its art-of-the-state management is the focus of the Short-Term Load Forecasting (STLF) models. In the past few years, South Africa faced an unprecedented energy management crisis that could be addressed in advance, inter alia, by carefully forecasting the expected load demand. Moreover, inaccurate or erroneous forecasts may result in either costly over-scheduling or adventurous under-scheduling of energy that may induce heavy economic forfeits to power companies. Therefore, accurate and reliable models are critically needed. Traditional statistical methods have been used in STLF but they have limited capacity to address nonlinearity and non-stationarity of electric loads. Also, such traditional methods cannot adapt to abrupt weather changes, thus they failed to produce reliable load forecasts in many situations. In this research report, we built a STLF model using Artificial Neural Networks (ANNs) to address the accuracy problem in this field so as to assist energy management decisions makers to run efficiently and economically their daily operations. ANNs are a mathematical tool that imitate the biological neural network and produces very accurate outputs. The built model is based on the Multilayer Perceptron (MLP), which is a class of feedforward ANNs using the backpropagation (BP) algorithm as its training algorithm, to produce accurate hourly load forecasts. We compared the MLP built model to a benchmark Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX) model using data from Eskom, a South African public utility. Results showed that the MLP model, with percentage error of 0.50%, in terms of the MAPE, outperformed the SARIMAX with 1.90% error performance.Item An experimental system for computer aided bird call recognition(2014-02-07) Colombick, Illan Samson