Deep learning applied to the semantic segmentation of tyre stockpiles
- Barfknecht, Nicholas Christopher
- Authors: Barfknecht, Nicholas Christopher
- Date: 2018
- Subjects: Neural networks (Computer science)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10948/23947 , vital:30647
- Description: The global push for manufacturing which is environmentally sustainable has disrupted standard methods of waste tyre disposal. This push is further intensified by the health and safety risks discarded tyres pose to the surrounding population. Waste tyre recycling initiatives in South Africa are on the increase; however, there is still a growing number of undocumented tyre stockpiles developing throughout the country. The plans put in place to eradicate these tyre stockpiles have been met with collection, transport and storage logistical issues caused by the remoteness and distant locales. Eastwood (2016) aimed at optimising the logistics associated with collection, by estimating the number of visible tyres from images of tyre stockpiles. This research was limited by the need for manual segmentation of each tyre stockpile located within each image. This research proposes the use of semantic segmentation to automatically segment images of tyre stockpiles. An initial review of neural network, convolutional network and semantic segmentation literature resulted in the selection of Dilated Net as the semantic segmentation architecture for this research. Dilated Net builds upon the VGG-16 classification architecture to perform semantic segmentation. This resulted in classification experiments which were evaluated using precision, recall and f1-score. The results indicated that regardless of tyre stockpile image dimension, fairly accurate levels of classification accuracy can be attained. This was followed by semantic segmentation experiments which made use of intersection over union (IoU) and pixel accuracy to evaluate the effectiveness of Dilated Net on images of tyre stockpiles. The results indicated that accurate tyre stockpile segmentation regions can be obtained and that the trained model generalises well to unseen images.
- Full Text:
- Date Issued: 2018
- Authors: Barfknecht, Nicholas Christopher
- Date: 2018
- Subjects: Neural networks (Computer science)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10948/23947 , vital:30647
- Description: The global push for manufacturing which is environmentally sustainable has disrupted standard methods of waste tyre disposal. This push is further intensified by the health and safety risks discarded tyres pose to the surrounding population. Waste tyre recycling initiatives in South Africa are on the increase; however, there is still a growing number of undocumented tyre stockpiles developing throughout the country. The plans put in place to eradicate these tyre stockpiles have been met with collection, transport and storage logistical issues caused by the remoteness and distant locales. Eastwood (2016) aimed at optimising the logistics associated with collection, by estimating the number of visible tyres from images of tyre stockpiles. This research was limited by the need for manual segmentation of each tyre stockpile located within each image. This research proposes the use of semantic segmentation to automatically segment images of tyre stockpiles. An initial review of neural network, convolutional network and semantic segmentation literature resulted in the selection of Dilated Net as the semantic segmentation architecture for this research. Dilated Net builds upon the VGG-16 classification architecture to perform semantic segmentation. This resulted in classification experiments which were evaluated using precision, recall and f1-score. The results indicated that regardless of tyre stockpile image dimension, fairly accurate levels of classification accuracy can be attained. This was followed by semantic segmentation experiments which made use of intersection over union (IoU) and pixel accuracy to evaluate the effectiveness of Dilated Net on images of tyre stockpiles. The results indicated that accurate tyre stockpile segmentation regions can be obtained and that the trained model generalises well to unseen images.
- Full Text:
- Date Issued: 2018
Modelling Ionospheric vertical drifts over the African low latitude region
- Dubazane, Makhosonke Berthwell
- Authors: Dubazane, Makhosonke Berthwell
- Date: 2018
- Subjects: Ionospheric drift , Magnetometers , Functions, Orthogonal , Neural networks (Computer science) , Ionospheric electron density -- Africa , Communication and Navigation Outage Forecasting Systems (C/NOFS)
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/63356 , vital:28396
- Description: Low/equatorial latitudes vertical plasma drifts and electric fields govern the formation and changes of ionospheric density structures which affect space-based systems such as communications, navigation and positioning. Dynamical and electrodynamical processes play important roles in plasma distribution at different altitudes. Because of the high variability of E × B drift in low latitude regions, coupled with various processes that sometimes originate from high latitudes especially during geomagnetic storm conditions, it is challenging to develop accurate vertical drift models. This is despite the fact that there are very few instruments dedicated to provide electric field and hence E × B drift data in low/equatorial latitude regions. To this effect, there exists no ground-based instrument for direct measurements of E×B drift data in the African sector. This study presents the first time investigation aimed at modelling the long-term variability of low latitude vertical E × B drift over the African sector using a combination of Communication and Navigation Outage Forecasting Systems (C/NOFS) and ground-based magnetometer observations/measurements during 2008-2013. Because the approach is based on the estimation of equatorial electrojet from ground-based magnetometer observations, the developed models are only valid for local daytime. Three modelling techniques have been considered. The application of Empirical Orthogonal Functions and partial least squares has been performed on vertical E × B drift modelling for the first time. The artificial neural networks that have the advantage of learning underlying changes between a set of inputs and known output were also used in vertical E × B drift modelling. Due to lack of E×B drift data over the African sector, the developed models were validated using satellite data and the climatological Scherliess-Fejer model incorporated within the International Reference Ionosphere model. Maximum correlation coefficient of ∼ 0.8 was achieved when validating the developed models with C/NOFS E × B drift observations that were not used in any model development. For most of the time, the climatological model overestimates the local daytime vertical E × B drift velocities. The methods and approach presented in this study provide a background for constructing vertical E ×B drift databases in longitude sectors that do not have radar instrumentation. This will in turn make it possible to study day-to-day variability of vertical E×B drift and hopefully lead to the development of regional and global models that will incorporate local time information in different longitude sectors.
- Full Text:
- Date Issued: 2018
- Authors: Dubazane, Makhosonke Berthwell
- Date: 2018
- Subjects: Ionospheric drift , Magnetometers , Functions, Orthogonal , Neural networks (Computer science) , Ionospheric electron density -- Africa , Communication and Navigation Outage Forecasting Systems (C/NOFS)
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/63356 , vital:28396
- Description: Low/equatorial latitudes vertical plasma drifts and electric fields govern the formation and changes of ionospheric density structures which affect space-based systems such as communications, navigation and positioning. Dynamical and electrodynamical processes play important roles in plasma distribution at different altitudes. Because of the high variability of E × B drift in low latitude regions, coupled with various processes that sometimes originate from high latitudes especially during geomagnetic storm conditions, it is challenging to develop accurate vertical drift models. This is despite the fact that there are very few instruments dedicated to provide electric field and hence E × B drift data in low/equatorial latitude regions. To this effect, there exists no ground-based instrument for direct measurements of E×B drift data in the African sector. This study presents the first time investigation aimed at modelling the long-term variability of low latitude vertical E × B drift over the African sector using a combination of Communication and Navigation Outage Forecasting Systems (C/NOFS) and ground-based magnetometer observations/measurements during 2008-2013. Because the approach is based on the estimation of equatorial electrojet from ground-based magnetometer observations, the developed models are only valid for local daytime. Three modelling techniques have been considered. The application of Empirical Orthogonal Functions and partial least squares has been performed on vertical E × B drift modelling for the first time. The artificial neural networks that have the advantage of learning underlying changes between a set of inputs and known output were also used in vertical E × B drift modelling. Due to lack of E×B drift data over the African sector, the developed models were validated using satellite data and the climatological Scherliess-Fejer model incorporated within the International Reference Ionosphere model. Maximum correlation coefficient of ∼ 0.8 was achieved when validating the developed models with C/NOFS E × B drift observations that were not used in any model development. For most of the time, the climatological model overestimates the local daytime vertical E × B drift velocities. The methods and approach presented in this study provide a background for constructing vertical E ×B drift databases in longitude sectors that do not have radar instrumentation. This will in turn make it possible to study day-to-day variability of vertical E×B drift and hopefully lead to the development of regional and global models that will incorporate local time information in different longitude sectors.
- Full Text:
- Date Issued: 2018
Tomographic imaging of East African equatorial ionosphere and study of equatorial plasma bubbles
- Authors: Giday, Nigussie Mezgebe
- Date: 2018
- Subjects: Ionosphere -- Africa, Central , Tomography -- Africa, Central , Global Positioning System , Neural networks (Computer science) , Space environment , Multi-Instrument Data Analysis System (MIDAS) , Equatorial plasma bubbles
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/63980 , vital:28516
- Description: In spite of the fact that the African ionospheric equatorial region has the largest ground footprint along the geomagnetic equator, it has not been well studied due to the absence of adequate ground-based instruments. This thesis presents research on both tomographic imaging of the African equatorial ionosphere and the study of the ionospheric irregularities/equatorial plasma bubbles (EPBs) under varying geomagnetic conditions. The Multi-Instrument Data Analysis System (MIDAS), an inversion algorithm, was investigated for its validity and ability as a tool to reconstruct multi-scaled ionospheric structures for different geomagnetic conditions. This was done for the narrow East African longitude sector with data from the available ground Global Positioning Sys-tem (GPS) receivers. The MIDAS results were compared to the results of two models, namely the IRI and GIM. MIDAS results compared more favourably with the observation vertical total electron content (VTEC), with a computed maximum correlation coefficient (r) of 0.99 and minimum root-mean-square error (RMSE) of 2.91 TECU, than did the results of the IRI-2012 and GIM models with maximum r of 0.93 and 0.99, and minimum RMSE of 13.03 TECU and 6.52 TECU, respectively, over all the test stations and validation days. The ability of MIDAS to reconstruct storm-time TEC was also compared with the results produced by the use of a Artificial Neural Net-work (ANN) for the African low- and mid-latitude regions. In terms of latitude, on average,MIDAS performed 13.44 % better than ANN in the African mid-latitudes, while MIDAS under performed in low-latitudes. This thesis also reports on the effects of moderate geomagnetic conditions on the evolution of EPBs and/or ionospheric irregularities during their season of occurrence using data from (or measurements by) space- and ground-based instruments for the east African equatorial sector. The study showed that the strength of daytime equatorial electrojet (EEJ), the steepness of the TEC peak-to-trough gradient and/or the meridional/transequatorial thermospheric winds sometimes have collective/interwoven effects, while at other times one mechanism dominates. In summary, this research offered tomographic results that outperform the results of the commonly used (“standard”) global models (i.e. IRI and GIM) for a longitude sector of importance to space weather, which has not been adequately studied due to a lack of sufficient instrumentation.
- Full Text:
- Date Issued: 2018
- Authors: Giday, Nigussie Mezgebe
- Date: 2018
- Subjects: Ionosphere -- Africa, Central , Tomography -- Africa, Central , Global Positioning System , Neural networks (Computer science) , Space environment , Multi-Instrument Data Analysis System (MIDAS) , Equatorial plasma bubbles
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/63980 , vital:28516
- Description: In spite of the fact that the African ionospheric equatorial region has the largest ground footprint along the geomagnetic equator, it has not been well studied due to the absence of adequate ground-based instruments. This thesis presents research on both tomographic imaging of the African equatorial ionosphere and the study of the ionospheric irregularities/equatorial plasma bubbles (EPBs) under varying geomagnetic conditions. The Multi-Instrument Data Analysis System (MIDAS), an inversion algorithm, was investigated for its validity and ability as a tool to reconstruct multi-scaled ionospheric structures for different geomagnetic conditions. This was done for the narrow East African longitude sector with data from the available ground Global Positioning Sys-tem (GPS) receivers. The MIDAS results were compared to the results of two models, namely the IRI and GIM. MIDAS results compared more favourably with the observation vertical total electron content (VTEC), with a computed maximum correlation coefficient (r) of 0.99 and minimum root-mean-square error (RMSE) of 2.91 TECU, than did the results of the IRI-2012 and GIM models with maximum r of 0.93 and 0.99, and minimum RMSE of 13.03 TECU and 6.52 TECU, respectively, over all the test stations and validation days. The ability of MIDAS to reconstruct storm-time TEC was also compared with the results produced by the use of a Artificial Neural Net-work (ANN) for the African low- and mid-latitude regions. In terms of latitude, on average,MIDAS performed 13.44 % better than ANN in the African mid-latitudes, while MIDAS under performed in low-latitudes. This thesis also reports on the effects of moderate geomagnetic conditions on the evolution of EPBs and/or ionospheric irregularities during their season of occurrence using data from (or measurements by) space- and ground-based instruments for the east African equatorial sector. The study showed that the strength of daytime equatorial electrojet (EEJ), the steepness of the TEC peak-to-trough gradient and/or the meridional/transequatorial thermospheric winds sometimes have collective/interwoven effects, while at other times one mechanism dominates. In summary, this research offered tomographic results that outperform the results of the commonly used (“standard”) global models (i.e. IRI and GIM) for a longitude sector of importance to space weather, which has not been adequately studied due to a lack of sufficient instrumentation.
- Full Text:
- Date Issued: 2018
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