Дисертації з теми "Pilot plants Data processing"

Demand for Data Transfer between computers has increased ever since the introduction of Personal Computers (PC). Data Communicating on the Personal Computer is much more productive as it is an intelligent terminal that can connect to various hosts on the same I/O hardware circuit as well as execute processes on its own as an isolated system. Yet, the PC on its own is useless for data communication. It requires a hardware interface circuit and software for controlling the handshaking signals and setting up communication parameters. Often the data is distorted due to noise in the line. Such transmission errors are imbedded in the data and require careful filtering. The thesis deals with the development of a Data Acquisition system that collects real time load and weather data and stores them as historical database for use in a load forecast algorithm in a load management system. A filtering technique has been developed here that checks for transmission errors in the raw data. The microcomputers used in this development are the IBM PC/XT and the AT&T 3B2 supermicro computer.
Master of Science

3D time-lapse seismics, also known as 4D seismics, have great potential for monitoring the migration of CO2 at underground storage sites. This thesis focuses on time-lapse analysis of 3D seismic reflection data acquired at the Ketzin CO2 geological storage site in order to improve understanding of the reservoir and how CO2 migrates within it. Four 3D seismic surveys have been acquired to date at the site, one baseline survey in 2005 prior to injection, two repeat surveys in 2009 and 2012 during the injection period, and one post-injection survey in 2015. To accurately simulate time-lapse seismic signatures in the subsurface, detailed 3D seismic property models for the baseline and repeat surveys were constructed by integrating borehole data and the 3D seismic data. Pseudo-boreholes between and beyond well control were built. A zero-offset convolution seismic modeling approach was used to generate synthetic time-lapse seismograms. This allowed simulations to be performed quickly and limited the introduction of artifacts in the seismic responses. Conventional seismic data have two limitations, uncertainty in detecting the CO2 plume in the reservoir and limited temporal resolution. In order to overcome these limitations, complex spectral decomposition was applied to the 3D time-lapse seismic data. Monochromatic wavelet phase and reflectivity amplitude components were decomposed from the 3D time-lapse seismic data. Wavelet phase anomalies associated with the CO2 plume were observed in the time-lapse data and verified by a series of seismic modeling studies. Tuning frequencies were determined from the balanced amplitude spectra in an attempt to discriminate between pressure effects and CO2 saturation. Quantitative assessment of the reservoir thickness and CO2 mass were performed. Time-lapse analysis on the post-injection survey was carried out and the results showed a consistent tendency with the previous repeat surveys in the CO2 migration, but with a decrease in the size of the amplitude anomaly. No systematic anomalies above the caprock were detected. Analysis of the signal to noise ratio and seismic simulations using the detailed 3D property models were performed to explain the observations. Estimation of the CO2 mass and uncertainties in it were investigated using two different approaches based on different velocity-saturation models.

Orientadores: Secundino Soares Filho, João Eduardo Gonçalves Lopes, Marcelo Augusto Cicogna
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
Made available in DSpace on 2018-08-14T06:05:46Z (GMT). No. of bitstreams: 1 Hidalgo_IedaGeriberto_D.pdf: 4414053 bytes, checksum: 69d7171a07cccbc47514404dbb69fb94 (MD5) Previous issue date: 2009
Resumo: Este trabalho apresenta ferramentas e procedimentos para a consolidação de dados de usinas hidrelétricas brasileiras. O objetivo é melhorar a qualidade dos dados que servem como entrada aos modelos computacionais utilizados no planejamento e na programação da operação energética. Como ferramentas de suporte à aplicação da metodologia, são descritos: o gerenciador de dados HydroData, o construtor de consultas HydroConsulta e o simulador da operação de usinas hidrelétricas HydroSim. A seqüência de procedimentos está dividida em duas etapas: análise da coerência da base de dados e consolidação efetiva das funções. No estudo de caso a metodologia foi aplicada a uma usina hidrelétrica que faz parte do Sistema Interligado Nacional e cuja operação está sob a coordenação e controle do Operador Nacional do Sistema Elétrico. A fim de avaliar os procedimentos, foram feitos estudos de reprodução da operação hidrelétrica, para o curtíssimo e curto prazo, alternando dados oficiais e dados consolidados de acordo com a técnica apresentada. Os resultados mostram que a melhoria da qualidade dos dados, obtida com a aplicação da metodologia proposta, aproxima a operação simulada da operação real. Dessa forma, ela contribui para a eficiência, confiabilidade e análise de desempenho dos modelos computacionais em uso no setor elétrico brasileiro.
Abstract: This work presents tools and procedures for the data consolidation from Brazilian hydroelectric plants. The objective is to improve the quality of the input data to the computational models used in the mid and short term operation planning. As support tool to the application of the methodology, are described: the data manager HydroData, the queries builder HydroConsulta and the simulator of the hydroelectric plants operation HydroSim. The procedures sequence is divided into two steps: analysis of the database coherency and effective consolidation of the functions. In the case study the methodology was applied to a hydroelectric plant which is part of the National Interconnected System and whose operation is under the coordination and control of the Independent System Operator. In order to evaluate the procedures, were made studies of hydroelectric operation reproduction, to the mid and short term, alternating official data and consolidated data in accordance with the technique presented. The results show that the improvement of the data quality, obtained with the application of the proposed methodology, brings the simulated and real operation closer. This way, it contributes to the efficiency, reliability and performance analysis of the computational models in use in the Brazilian electric sector.
Doutorado
Energia Eletrica
Doutor em Engenharia Elétrica

First part of this thesis presents the design, validation, and implementation of an Orthogonal Frequency Division Multiplexing (OFDM) transmitter and receiver on a Cyclone II FPGA chip using DSP builder and Quartus II high level design tools. The resources in terms of logical elements (LE) including combinational functions and logic registers allocated by the model have been investigated and addressed. The result shows that implementing the basic OFDM transceiver allocates about 14% (equivalent to 6% at transmitter and 8% at receiver) of the available LE resources on an Altera Cyclone II EP2C35F672C6 FPGA chip, largely taken up by the FFT, IFFT and soft decision encoder. Secondly, a new wavelet-based OFDM system based on FDPP-DA based channel estimation is proposed as a reliable ECG Patient Monitoring System, a Personal Wireless telemedicine application. The system performance for different wavelet mothers has been investigated. The effects of AWGN and multipath Rayleigh fading channels have also been studied in the analysis. The performances of FDPP-DA and HDPP-DA-based channel estimations are compared based on both DFT-based OFDM and wavelet-based OFDM systems. The system model was studied using MATLAB software in which the average BER was addressed for randomized data. The main error differences that reflect the quality of the received ECG signals between the reconstructed and original ECG signals are established. Finally a DA-based architecture for 1-D iDWT/DWT based on an OFDM model is implemented for an ECG-PMS wireless telemedicine application. In the portable wireless body transmitter unit at the patient site, a fully Serial-DA-based scheme for iDWT is realized to support higher hardware utilization and lower power consumption; whereas a fully Parallel-DA-based scheme for DWT is applied at the base unit of the hospital site to support a higher throughput. It should be noted that the behavioural level of HDL models of the proposed system was developed and implemented to confirm its correctness in simulation. Then, after the simulation process the design models were synthesised and implemented for the target FPGA to confirm their validation.

First part of this thesis presents the design, validation, and implementation of an Orthogonal Frequency Division Multiplexing (OFDM) transmitter and receiver on a Cyclone II FPGA chip using DSP builder and Quartus II high level design tools. The resources in terms of logical elements (LE) including combinational functions and logic registers allocated by the model have been investigated and addressed. The result shows that implementing the basic OFDM transceiver allocates about 14% (equivalent to 6% at transmitter and 8% at receiver) of the available LE resources on an Altera Cyclone II EP2C35F672C6 FPGA chip, largely taken up by the FFT, IFFT and soft decision encoder. Secondly, a new wavelet-based OFDM system based on FDPP-DA based channel estimation is proposed as a reliable ECG Patient Monitoring System, a Personal Wireless telemedicine application. The system performance for different wavelet mothers has been investigated. The effects of AWGN and multipath Rayleigh fading channels have also been studied in the analysis. The performances of FDPP-DA and HDPP-DA-based channel estimations are compared based on both DFT-based OFDM and wavelet-based OFDM systems. The system model was studied using MATLAB software in which the average BER was addressed for randomized data. The main error differences that reflect the quality of the received ECG signals between the reconstructed and original ECG signals are established. Finally a DA-based architecture for 1-D iDWT/DWT based on an OFDM model is implemented for an ECG-PMS wireless telemedicine application. In the portable wireless body transmitter unit at the patient site, a fully Serial-DA-based scheme for iDWT is realized to support higher hardware utilization and lower power consumption; whereas a fully Parallel-DA-based scheme for DWT is applied at the base unit of the hospital site to support a higher throughput. It should be noted that the behavioural level of HDL models of the proposed system was developed and implemented to confirm its correctness in simulation. Then, after the simulation process the design models were synthesised and implemented for the target FPGA to confirm their validation.

Made available in DSpace on 2014-10-09T12:51:45Z (GMT). No. of bitstreams: 0
Made available in DSpace on 2014-10-09T14:08:59Z (GMT). No. of bitstreams: 0
Tese (Doutoramento)
IPEN/T
Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

Le but de ce travail est de contrôler le mélange des réactifs dans des cuves de précipitation mécaniquement agitées, qui sont les précipiteurs classiques employés dans l'industrie. Outre la compréhension des interactions entre mélange et précipitation, on cherche à déterminer les géométries de précipiteurs et d'agitateurs ainsi que les conditions de fonctionnement qui conduisent à des cristaux à morphologie et granulométrie contrôlées. En sus de la méthode conductimétrique classique pour la mesure des temps de mélange ou des fluctuations de concentration, une nouvelle technique non intrusive basée sur la visualisation des écoulements par nappe laser et traitement d'images a été élaborée pour étudier les mécanismes du mélange dans des réacteurs de tailles diverses donnant des informations précieuses pour la modélisation des effets de mélange sur la précipitation

Les techniques de séquençage à haut-débit développées ces dernières années ont permis d'identifier des milliers d’ARN non-codants et des événements tels que l’épissage ou l’édition. Cette approche est à l’origine d’une meilleure compréhension des mécanismes régulant l'expression des gènes. Les longs ARN non-codants (lncARN) ont ainsi émergé comme des acteurs clés de la régulation de divers processus développementaux. Ils agissent soit directement sous leur forme longue par des interactions lncARN-protéine(s) soit après une étape de maturation qui génère des siARN ou des miARN régulateurs, menant à l’extinction génique par clivage des ARNm, la répression de la traduction ou en entrainant des modifications épigénétiques (ADN/chromatine) de leurs cibles. L’objectif de cette thèse était d’élucider les mécanismes d'action de lncARNs dans le développement de la plante. J'ai contribué à l'analyse de l'action du lncARN APOLO dans la régulation de la topologie de la chromatine chez Arabidopsis thaliana. Ensuite, j’ai concentré mes efforts sur le lncARN ASCO (Alternative Splicing COmpetitor) qui interagit avec les protéines NSRs (Nuclear Speckles RNA-binding Proteins) et participent au patron d’épissage de certains gènes cibles. Lors d’un traitement par l’auxine, NSRb est induit alors qu’ASCO est réprimé dans les racines. Le même type de traitement, chez le double mutant nsra/b et les lignées surexprimant ASCO, entraine déficience partielle dans la formation des racines latérales. En utilisant un nouvel outil bio-informatique appelé "RNAprof", nous avons détecté 1885 ARN différentiellement maturés entre le mutant nsra/b et la lignée sauvage traités à l’auxine. Parmi ces gènes, nous avons identifié ARF19, un régulateur clé de la voie de signalisation de l’auxine au cours de l'initiation et le développement de la racine. J’ai démontré qu'ARF19 interagit directement avec les NSRs et qu’il est différentiellement polyadénylé dans le double mutant nsra/b, conduisant à une isoforme plus courte du transcrit ARF19. D’autre part, parmi les gènes dérégulés de manière transcriptionnelle chez le mutant des gènes impliqués dans la signalisation par l’éthylène ont été identifiés. J’ai ensuite montré que plusieurs de ces gènes sont aussi dérégulés dans les plantes mutantes arf19-1 et arf19-2 en réponse à l’auxine, soutenant un rôle d'ARF19 dans la réponse croisée entre l’auxine et l’éthylène. Le gène NSRb est induit par l'éthylène et l'inhibition de la synthèse d'éthylène par l'AVG complémente le phénotype de racine latérale du mutant nsra/b en réponse à l’auxine. De plus, l'AVG et la surexpression d’ASCO augmentent l'accumulation de l’isoforme courte d’ARF19. Cette étude met en avant la capacité du lncARN ASCO à moduler l’épissage par le détournement des NSRs et la capacité des ARN non-codants à moduler l’épissage
In the last years, high-throughput sequencing techniques have made possible to identify thousands of noncoding RNAs and a plethora of different mRNA processing events occurring in higher organisms. This led to a better understanding of different regulatory mechanisms controlling gene expression. Long noncoding RNAs (lncRNAs) are emerging as key players in the regulation of varied developmental processes. They can act directly in a long form by lncRNA-protein interactions or be processed into shorter small si/miRNAs, leading to mRNA cleavage, translational repression or epigenetic DNA/chromatin modification of their targets. In this study, we aim to understand the mechanism of action of lncRNAs in plant development. Initially, I contributed to the analysis of the action of the APOLO lncRNA in chromatin topology regulation. Then, I focused my work on the lncRNA ASCO (Alternative Splicing COmpetitor) that interacts with NSRs (Nuclear Speckles RNA-binding Proteins) to modulate the splicing pattern of NSR-regulated mRNA targets. Auxin treatment induces NSRb and represses ASCO expression in roots. The nsra/b double mutant and ASCO overexpressing lines treated with auxin are partially impaired in lateral root formation. Using a new bioinformatic tool called “RNAprof”, we detected 1885 differential RNA processing events genome-wide in auxin-treated nsra/b mutants compared to WT. Among them, we identified ARF19, a key regulator of auxin signaling in lateral root initiation and development. I demonstrated that ARF19 is directly bound by both NSRs and that in the nsra/b double mutant ARF19 is alternatively polyadenylated leading to a short transcript isoform. Furthermore, among the transcriptionally deregulated genes in the nsra/b mutant plants, I identified an important group related to ethylene response. I further showed that several of these genes are also deregulated in the arf19-1 and arf19-2 mutants plants in response to auxin, supporting a role of ARF19 in the auxin-ethylene crosstalk. NSRb is also induced by ethylene and the inhibition of ethylene synthesis by AVG rescues the nsra/b double mutant lateral root phenotype in response to auxin. Moreover, AVG and ASCO overexpression lead to increased accumulation of the ARF19 short isoform. Altogether, this study shed new light on the role of the lncRNA ASCO in the regulation of RNA processing by hijacking NSRs and the capacity of non-coding RNAs to modulate splicing

This dissertation describes the development, design, implementation and evaluation of a data acquisition system, with the main aim of using it for data collection on a laboratory pilot ball mill. An open-source prototype hardware platform was utilised in the implementation of the data acquisition function, however, with limitations. An analogue signal conditioning card has been successfully developed to interface the analogue signals to the dual domain ADC module. Model-based software development was used to design and develop the algorithms to control the DAS acquisition process, but with limited capabilities. A GUI application has been developed and used for the collection and storage of the raw data on the host system. The DAS prototype was calibrated and collected data successfully through all the channels; however, the input signal bandwidth was limited to 2Hz.
Electrical and Mining Engineering
M. Tech. (Electrical Engineering)

To meet the urgent demand of high-speed data rate and to support large number of users, the massive multiple-input multiple-output (MIMO) technology is becoming one of the most promising candidates for the next generation wireless communications, namely the 5G. To realize the full potential of massive MIMO, it is necessary to have the channel state information (CSI) (partially) available at the transmitter. Hence, an efficient channel estimation is one of the key enablers and also critical challenges for 5G communications. Dealing with such problems, this dissertation investigates the design of efficient pilot-data transmission pattern and channel estimation in massive MIMO for both multipair relaying and peer-to-peer systems. Firstly, this dissertation proposes a pilot-data transmission overlay scheme for multipair MIMO relaying systems employing either half- or full-duplex (HD or FD) communications at the relay station (RS). In the proposed scheme, pilots are transmitted in partial overlap with data to decrease the channel estimation overhead. The RS can detect the source data by exploiting the asymptotic orthogonality of massive MIMO channels. Due to the transmission overlay, the effective data period is extended, hence improving system throughput. Both theoretical and simulation results verify that the proposed pilot-data overlay scheme outperforms the conventional separate pilot-data design in the limited coherence interval scenario. Moreover, a power allocation problem is formulated to properly adjust the transmission power of source data transmission and relay data forwarding which further improves the system performance. Additionally, this dissertation proposes and analyzes an efficient HD decode-and-forward (DF) scheme, named sum decode-and-forward (SDF), with the physical layer network coding (PNC) in the multipair massive MIMO two-way relaying system. As comparison, a joint decode-and-forward (JDF) scheme applied to the multipair massive MIMO relaying is also proposed and investigated. In the SDF scheme, a half number of pilots are saved compared to the JDF scheme which in turn increases the spectral efficiency of the system. Both the theoretical analyses and numerical results verifies such superiority of the SDF scheme. Further, the power efficiency of the proposed schemes is also investigated. Simulation results show that the signal transmission power can be rapidly reduced if the massive antenna arrays are equipped on the RS and the required data transmission power can further decrease if the training power is fixed. Finally, this dissertation investigates the general channel estimation problem in the massive MIMO system which employs the hybrid analog/digital precoding structure with limited radio-frequency (RF) chains. By properly designing RF combiners and performing multiple trainings, the performance of the proposed channel estimation can approach that of full-chain estimations depending on the degree of channel spatial correlation and the number of RF chains which is verified by simulation results in terms of both mean square error (MSE) and spectral efficiency. Moreover, a covariance matching method is proposed to obtain channel correlation in practice and the simulation verifies its effectiveness by evaluating the spectral efficiency performance in parametric channel models.
Graduate
0537
0544
leyuanpan@gmail.com

A thesis submi.tted to the Faculty of Science, University of the witwatersrand, Johannesburg I in the fulfillmen,,;"o:f' 't\ :he requirements of the Degree of Magister Scienta~. June 1992
The present study was undertaken; withirl the South African " savannas, to provide "insight into a j;unctional classification of aavanna plants using ecophY$iologiLcal charact~:t's.THe pri.mary r.:>bjective of this study Was to investigate the vari.ation of these tt'aits throughout: the savanna, aridto relate this variation to plant avail.able moisture and nU~l'ie~~s• !t was conclu.ded that~ 1) no formal or specialized strategies have evolved within a number of the study sites, .2) unlike the woody component I neither divergellce nor convergence was demonstrated within the grass layer, c' 3) plant aVailable nutrients did not appear to be a major determinant of either component. Although plant available moistur~ proved to be unimportant in the woody layer, it did playa role as a determinant of the grass layer, and 4) constancy of the plant traits was not demonstrated to \": OCCllr over the gr~~ing season. A succeisf',\lclassification' would require the components to be separat~pl specific determinan.ts be identified for each component, and an element of time be included into both edaphic and biotic measurements.

Modern civil commercial transport aircraft provide the means for the safest of all forms of transportation. While advanced computer technology ranging from flight management computers to warning and alerting devices contributed to flight safety significantly, it is undisputed that the flightcrew represents the most frequent primary cause factor in airline accidents. From a system perspective, machine actors such as the autopilot and human actors (the flightcrew) try to achieve goals (desired states of the aircraft). The set of activities to achieve a goal is called a function. In modern flightdecks both machine actors and human actors perform functions. Recent accident studies suggest that deficiencies in the flightcrew's ability to monitor how well either machines or themselves perform a function are a factor in many accidents and incidents. As humans are inherently bad monitors, this study proposes a method to automatically assess the status of a function in order to increase flight safety as part of an intelligent pilot aid, called the AgendaManager. The method was implemented for the capture altitude function: seeking to attain and maintain a target altitude. Fuzzy systems were used to compute outputs indicating how well the capture altitude function was performed from inputs describing the state of the aircraft. In order to conform to human expert assessments, the fuzzy systems were trained using a genetic algorithm (GA) whose objective was to minimize the discrepancy between system outputs and human expert assessments based on 72 scenarios. The resulting systems were validated by analyzing how well they conformed to new data drawn from another 32 scenarios. The results of the study indicated that even though the training procedure facilitated by the GA was able to improve conformance to human expert assessments, overall the systems performed too poorly to be deployed in a real environment. Nevertheless, experience and insights gained from the study will be valuable in the development of future automated systems to perform function assessment.
Graduation date: 1998

D.Phil. (Electrical and Electronic Engineering)
Load forecasting is a necessary and an important task for both the electrical consumer and electrical supplier. Whilst many studies emphasize the importance of determining the future demand, few papers address both the forecasting algorithm and computational resources needed to offer a turnkey solution to address the load forecasting problem. The major contribution that, this paper identified is a turnkey load forecasting algorithm. A turnkey forecasting solution is defined by a comprehensive solution that incorporates both the algorithm and processing elements needed to execute the algorithm in the most effective and efficient manner. An electrical consumer, namely the operator of a rapid railway system was faced with a problem of having to forecast the notified network demand and energy consumption. The forecast period was expected to be between a very short term window for maintenance reasons and long term for the requirements warranted by the electrical supplier. The problem was addressed by firstly reviewing the most common forms of load forecasting for which there are two types. These are statistically based methods and methods based upon artificial intelligence. The basic principle of a statistical approach is to approximate or define a curve that best defines the relationship between the load and its parameters. Regression and similar day approach methods use the defined correlation of past values in order to forecast the future behaviour. In other words the future load forecast is forecasted by observing the behaviour of the factors that influenced the load behaviour in the past. The underlying factors that influence the final load may be identified by means of a top down drill down approach. In this way both the load factors and influential variables may be identified. This paper makes use of relevance trees to create a structure of load and influential variables. For a regression forecasting model, the behaviour of the load is modelled according to weather and non-weather variables. The load may be stochastic or deterministic, linear or nonlinear. One of the biggest problems with statistical models is the lack of generality. One model may yield more acceptable results over another model simply because of the sensitivity of the model to one load element that defines the model significantly. Regression type forecast models are an example of this where the elements that define the load are broadly divided into weather and non-weather elements. It is important that the correlation curve reflects the true correlation between the load and its elements. The recursive properties of a statistical based techniques (Kalman filter) allows that the relationship be refined. For methods such as neural networks, the relationship between the load elements that define the future load behaviour is learnt by presenting a series of patterns and then a forecast model is derived. Rigorous mathematical equations are replaced with an artificial neural network where the load curve is learnt. Unlike a statistical based approach (ARMA models), the load does not first need to be defined as a stochastic or deterministic series. In terms of a stochastic approach (non stationery process), the load first would have to be brought to a stationery process. For artificial neural networks, such processes are eliminated and the future forecast is derived faster in terms of a turnkey approach (tested solution). Artificial Neural Networks (ANN) has gained momentum since the eighties. Specifically in the area of forecasting, neural networks have become a common application. In this thesis, data from a railway operator was used to train the neural network and then future data is forecasted. Two embedded processing elements were then evaluated in terms of speed, memory and ability to execute complex mathematical functions (libraries). These were namely a Complex Programmable Logic Device (CPLD) and microcontroller (MCU). The ANN forecasting algorithm was programmed on both a MCU and PLD and compared by means of timing models and hardware platform testing. The most ideal turnkey solution was found to be the ANN algorithm residing on a PLD. The accuracy and speed results surpassed that of a MCU.

The goal of this project was the development of a computer-based system using artificial intelligence to classify the limonoids, protolimonoids and triterpenoids isolated from the family Meliaceae by the Natural Products Research Group of the University of Natal, Durban. A database of samples was obtained between 1991 and 1996, part of which time the author was a member of the group and isolated compounds from Turraea obtusifolia and Turraea floribunda. Over and above the problem of complexity and similarity in structures of the above mentioned natural products, are other difficulties. These include very small amounts of sample being isolated producing very weak peak signals in the C-13 NMR spectra, extraneous peaks in the NMR spectra due to different impurities and instrument noise, non-reproducible spectra due to the pulsed Fourier transform intervals and the nuclear Overhauser effect, impure samples often isolated as stereoisomeric mixtures or as mixed esters and superposition of peak signals in the NMR spectra due to carbons in the same environment within the same compound. These factors make identification by traditional computational and expert systems impossible. As a result of these shortcomings, the author has developed a novel approach using artificial neural network techniques. The artificial neural network system developed used real data from the 300 MHz NMR spectrometer in the Department of Chemistry, Durban. The system was trained to discriminate between limonoids, triterpenoids and flavonoids/coumarins from the C-13 NMR spectra of pure, impure and unseen compounds with an accuracy of better than 90%. Further differentiation of the glabretals from the rest of the protolimonoids as well as from the rest of the triterpenoids showed similarly significant results. Finally, individual limonoid discrimination within the limonoid dataset was extremely successful. Apart from its application to the extractives from Meliaceae, the methodology and techniques developed by the author can be applied to other sets of extractives to provide a robust method for the spectral classification of pre-identified natural products.
Thesis (Ph.D.)-University of Natal, Durban, 1998.

A dissertation submitted to the faculty of Engineering and the Built Environment, University of the Witwatersrand, in fulfillment of the requirements for the degree of Master of Science in Engineering. 21 November 2016
In this work a comprehensive exergy analysis and heat integration study was carried out on a coal based oxy-combustion power plant simulated using ASPEN plus. This is an extension on the work of Fu and Gundersen (2013). Several of the assumptions made in their work have been relaxed here. Their impact was found to be negligible with the results here matching closely with those in the original work. The thermal efficiency penalty was found to be 9.24% whilst that in the original work was 9.4%. The theoretical minimum efficiency penalty was determined to be 3% whilst that in the original work was 3.4%. Integrating the compression processes and the steam cycle was determined to have the potential to increase net thermal efficiency by 0.679%. This was close to the 0.72% potential reported in the original work for the same action.
MT2017

The objective of this study was to provide a technique, based on the residence time distribution of a process, for modelling, assessing and improving flow in the processes of water and waste water treatment works. The technique should be accessible to the staff managing and operating the works. From a review of the literature, a preference was given for the experimental method used for determination of the tracer response, including choice of tracer and tracer addition and monitoring. Data analysis techniques were reviewed, and the method of time domain fitting was developed into a computer program, IMPULSE. IMPULSE provided a tool for analysis of residence time data, and removed the constraint of numerical complexity. Using the building blocks of IMPULSE, a realistic flow model can be constructed from tracer data and evaluated. IMPULSE allows a quantitative comparison of models proposed for a process, and provides the parameters of the models. These parameters quantify the non-idealities in a process. A knowledge of the non-idealities provides a basis for decision-making when modifying a process. The results of tracer experiments performed on some water and waste water treatment processes were analysed using IMPULSE. The results showed that collection of good experimental data was critical to the success of the analysis. It is proposed that a guide be produced which draws out the main points raised in the study, including collection of tracer data and use of IMPULSE. The guide should be accessible and easily understandable to the staff managing and operating water and waste water treatment works.
Thesis (M.Sc.Eng.)-University of Natal, Durban, 1995.

Invasive alien plants have widespread ecological and socioeconomic impacts throughout many parts of the world, including Swaziland where the government declared them a national disaster. Control of these species requires knowledge on the invasion ecology of each species including how they interact with the invaded environment. Species distribution models are vital for providing solutions to such problems including the prediction of their niche and distribution. Various modelling approaches are used for species distribution modelling albeit with limitations resulting from statistical assumptions, implementation and interpretation of outputs. This study explores the usefulness of Bayesian networks (BNs) due their ability to model stochastic, nonlinear inter-causal relationships and uncertainty. Data-driven BNs were used to explore patterns and processes influencing the spatial distribution of 16 priority invasive alien plants in Swaziland. Various BN structure learning algorithms were applied within the Weka software to build models from a set of 170 variables incorporating climatic, anthropogenic, topo-edaphic and landscape factors. While all the BN models produced accurate predictions of alien plant invasion, the globally scored networks, particularly the hill climbing algorithms, performed relatively well. However, when considering the probabilistic outputs, the constraint-based Inferred Causation algorithm which attempts to generate a causal BN structure, performed relatively better. The learned BNs reveal that the main pathways of alien plants into new areas are ruderal areas such as road verges and riverbanks whilst humans and human activity are key driving factors and the main dispersal mechanism. However, the distribution of most of the species is constrained by climate particularly tolerance to very low temperatures and precipitation seasonality. Biotic interactions and/or associations among the species are also prevalent. The findings suggest that most of the species will proliferate by extending their range resulting in the whole country being at risk of further invasion. The ability of BNs to express uncertain, rather complex conditional and probabilistic dependencies and to combine multisource data makes them an attractive technique for species distribution modeling, especially as joint invasive species distribution models (JiSDM). Suggestions for further research are provided including the need for rigorous invasive species monitoring, data stewardship and testing more BN learning algorithms.
Environmental Sciences
D. Phil. (Environmental Science)

在諸多電腦視覺和模式識別的問題中，採集到的圖像和視頻資料通常是高維的。直接計算這些高維資料常常面臨計算可行性和穩定性等方面的困難。然而，現實世界中的資料通常由少數物理因素產生，因而本質上存在退化的結構。例如，它們可以用子空間、子空間的集合、流形或者分層流形等模型來描述。計算並運用這些內在退化結構不僅有助於深入理解問題的本質，而且能夠幫助解決實際應用中的難題。
隨著近些年凸優化理論和應用的發展，一些NP難題諸如低稚矩陣的計算和稀疏表示的問題已經有了近乎完美和高效的求解方法。本論文旨在研究如何應用這些技術來計算高維資料中的退化結構，並著重研究子空間和子空間的集合這兩種結構，以及它們在現實應用方面的意義。這些應用包括:人臉圖像的配准、背景分離以及自動植物辨別。
在人臉圖像配准的問題中，同一人臉在不同光照下的面部圖像經過逐圖元配准後應位於一個低維的子空間中。基於此假設，我們提出了一個新的圖像配准方法，能夠對某未知人臉的多副不同光照、表情和姿態下的圖像進行聯合配准，使得每一幅面部圖像的圖元與事先訓練的一般人臉模型相匹配。其基本思想是追尋一個低維的且位於一般人臉子空間附近的仿射子空間。相比于傳統的基於外觀模型的配准方法(例如主動外觀模型)依賴于準確的外觀模型的缺點，我們提出的方法僅需要一個一般人臉模型就可以很好地對該未知人臉的多副圖像進行聯合配准，即使該人臉與訓練該模型的樣本相差很大。實驗結果表明，該方法的配准精度在某些情況下接近于理想情形，即：當該目標人臉的模型事先已知時，傳統方法所能夠達到的配准精度。
In a wide range of computer vision and pattern recognition problems, the captured images and videos often live in high-dimensional observation spaces. Directly computing them may suffer from computational infeasibility and numerical instability. On the other hand, the data in the real world are often generated due to limited number of physical causes, and thus embed degenerate structures in the nature. For instance, they can be modeled by a low-dimensional subspace, a union of subspaces, a manifold or even a manifold stratification. Discovering and harnessing such intrinsic structures not only brings semantic insight into the problems at hand, but also provides critical information to overcome challenges encountered in the practice.
Recent years have witnessed great development in both the theory and application of convex optimization. Efficient and elegant solutions have been found for NP-hard problems such as low-rank matrix recovery and sparse representation. In this thesis, we study the problem of discovering degenerate structures of high-¬dimensional inputs using these techniques. Especially we focus ourselves on low-dimensional subspaces and their unions, and address their application in overcoming the challenges encoun-tered under three practical scenarios: face image alignment, background subtraction and automatic plant identification.
In facial image alignment, we propose a method that jointly brings multiple images of an unseen face into alignment with a pre-trained generic appearance model despite different poses, expressions and illumination conditions of the face in the images. The idea is to pursue an intrinsic affine subspace of the target face that is low-dimensional while at the same time lies close to the generic subspace. Compared with conventional appearance-based methods that rely on accurate appearance mod-els, ours works well with only a generic one and performs much better on unseen faces even if they significantly differ from those for training the generic model. The result is approximately good as that in an idealistic case where a specific model for the target face is provided.
For background subtraction, we propose a background model that captures the changes caused by the background switching among a few configurations, like traffic lights statuses. The background is modeled as a union of low-dimensional subspaces, each characterizing one configuration of the background, and the proposed algorithm automatically switches among them and identifies violating elements as foreground pixels. Moreover, we propose a robust learning approach that can work with foreground-present training samples at the background modeling stage it builds a correct background model with outlying foreground pixels automatically pruned out. This is practically important when foreground-free training samples are difficult to obtain in scenarios such as traffic monitoring.
For automatic plant identification, we propose a novel and practical method that recognizes plants based on leaf shapes extracted from photographs. Different from existing studies that are mostly focused on simple leaves, the proposed method is de-signed to recognize both simple and compound leaves. The key to that is, instead of either measuring geometric features or matching shape features as in conventional methods, we describe leaves by counting on them the numbers of certain shape patterns. The patterns are learned in a way that they form a degenerate polytope (a spe-cial union of affine subspaces) in the feature space, and can simulate, to some extent, the "keys" used by botanists - each pattern reflects a common feature of several dif-ferent species and all the patterns together can form a discriminative rule for recog-nition. Experiments conducted on a variety of datasets show that our algorithm sig-nificantly outperforms the state-of-art methods in terms of recognition accuracy, ef-ficiency and storage, and thus has a good promise for practicing.
In conclusion, our performed studies show that: 1) the visual data with semantic meanings are often not random - although they can be high-dimensional, they typically embed degenerate structures in the observation space. 2) With appropriate assumptions made and clever computational tools developed, these structures can be efficiently and stably calculated. 3) The employment of these intrinsic structures helps overcoming practical challenges and is critical for computer vision and pattern recognition algorithms to achieve good performance.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
在背景分離的問題中，靜態場景在不同光照情形下的背景可以被描述為一個線性子空間。然而在實際應用中，背景的局部和突然的變化有可能違背此假設，尤其是當背景在幾個狀態之間切換的情形下，例如交通燈在不同組合狀態之間切換。為了解決該問題，本論文中提出了一個新的背景模型，它將背景描述為一些子空間的集合，每個子空間對應一個背景狀態。我們將背景分離的問題轉化為稀疏逼近的問題，因此演算法能夠自動在多個狀態中切換並成功檢測出前景物體。此外，本論文提出了一個魯棒的字典學習方法。在訓練背景模型的過程中，它能夠處理含有前景物體的圖像，並在訓練過程中自動將前景部分去掉。這個優點在難以收集完整背景訓練樣本的應用情形(譬如交通監視等)下有明顯的優勢。
在植物種類自動辨別的問題中，本論文中提出了一個新的有效方法，它通過提取和對比植物葉片的輪廓對植物進行識別和分類。不同于傳統的基於測量幾何特徵或者在形狀特徵之間配對的方法，我們提出使用葉子上某些外形模式的數量來表達樹葉。這些模式在特徵空間中形成一個退化的多面體結構(一種特殊的仿射空間的集合)，而且在某種程度上能夠類比植物學中使用的分類檢索表每個模式都反映了一些不同植物的某個共性，例如某種邊緣、某種形狀、某種子葉的佈局等等;而所有模式組合在一起能夠形成具有很高區分度的分類準則。通過對演算法在四個數據庫上的測試，我們發現本論文提出的方法無論在識別精度還是在效率和存儲方面都相比于目前主流方法有顯著提高，因此具有很好的應用性。
總之，我們進行的一些列研究說明:(1) 有意義的視覺資料通常是內在相關的，儘管它們的維度可能很高，但是它們通常都具有某種退化的結構。(2) 合理的假設和運用計算工具可以高效、穩健地發現這些結構。(3) 利用這些結構有助於解決實際應用中的難題，且能夠使得電腦視覺和模式識別演算法達到好的性能。
Zhao, Cong.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2012.
Includes bibliographical references (leaves 107-121).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
Dedication --- p.i
Acknowledgements --- p.ii
Abstract --- p.v
Abstract (in Chinese) --- p.viii
Publication List --- p.xi
Nomenclature --- p.xii
Contents --- p.xiv
List of Figures --- p.xviii
Chapter Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Motivation --- p.1
Chapter 1.2 --- Background --- p.2
Chapter 1.2.1 --- Subspaces --- p.3
Chapter 1.2.2 --- Unions of Subspaces --- p.6
Chapter 1.2.3 --- Manifolds and Stratifications --- p.8
Chapter 1.3 --- Thesis Outline --- p.10
Chapter Chapter 2 --- Joint Face Image Alignment --- p.13
Chapter 2.1 --- Introduction --- p.14
Chapter 2.2 --- Related Works --- p.16
Chapter 2.3 --- Background --- p.18
Chapter 2.3.1 --- Active Appearance Model --- p.18
Chapter 2.3.2 --- Multi-Image Alignment using AAM --- p.20
Chapter 2.3.3 --- Limitations in Practice --- p.21
Chapter 2.4 --- The Proposed Method --- p.23
Chapter 2.4.1 --- Two Important Assumptions --- p.23
Chapter 2.4.2 --- The Subspace Pursuit Problem --- p.27
Chapter 2.4.3 --- Reformulation --- p.27
Chapter 2.4.4 --- Efficient Solution --- p.30
Chapter 2.4.5 --- Discussions --- p.32
Chapter 2.5 --- Experiments --- p.34
Chapter 2.5.1 --- Settings --- p.34
Chapter 2.5.2 --- Results and Discussions --- p.36
Chapter 2.6 --- Summary --- p.38
Chapter Chapter 3 --- Background Subtraction --- p.40
Chapter 3.1 --- Introduction --- p.41
Chapter 3.2 --- Related Works --- p.43
Chapter 3.3 --- The Proposed Method --- p.48
Chapter 3.3.1 --- Background Modeling --- p.48
Chapter 3.3.2 --- Background Subtraction --- p.49
Chapter 3.3.3 --- Foreground Object Detection --- p.52
Chapter 3.3.4 --- Background Modeling by Dictionary Learning --- p.53
Chapter 3.4 --- Robust Dictionary Learning --- p.54
Chapter 3.4.1 --- Robust Sparse Coding --- p.56
Chapter 3.4.2 --- Robust Dictionary Update --- p.57
Chapter 3.5 --- Experimentation --- p.59
Chapter 3.5.1 --- Local and Sudden Changes --- p.59
Chapter 3.5.2 --- Non-structured High-frequency Changes --- p.62
Chapter 3.5.3 --- Discussions --- p.65
Chapter 3.6 --- Summary --- p.66
Chapter Chapter 4 --- Plant Identification using Leaves --- p.67
Chapter 4.1 --- Introduction --- p.68
Chapter 4.2 --- Related Works --- p.70
Chapter 4.3 --- Review of IDSC Feature --- p.71
Chapter 4.4 --- The Proposed Method --- p.73
Chapter 4.4.1 --- Independent-IDSC Feature --- p.75
Chapter 4.4.2 --- Common Shape Patterns --- p.77
Chapter 4.4.3 --- Leaf Representation by Counts --- p.80
Chapter 4.4.4 --- Leaf Recognition by NN Classifier --- p.82
Chapter 4.5 --- Experiments --- p.82
Chapter 4.5.1 --- Settings --- p.82
Chapter 4.5.2 --- Performance --- p.83
Chapter 4.5.3 --- Shared Dictionaries v.s. Shared Features --- p.88
Chapter 4.5.4 --- Pooling --- p.89
Chapter 4.6 --- Discussions --- p.90
Chapter 4.6.1 --- Time Complexity --- p.90
Chapter 4.6.2 --- Space Complexity --- p.91
Chapter 4.6.3 --- System Description --- p.92
Chapter 4.7 --- Summary --- p.92
Chapter 4.8 --- Acknowledgement --- p.94
Chapter Chapter 5 --- Conclusion and Future Work --- p.95
Chapter 5.1 --- Thesis Contributions --- p.95
Chapter 5.2 --- Future Work --- p.97
Chapter 5.2.1 --- Theory Side --- p.98
Chapter 5.2.2 --- Practice Side --- p.98
Chapter Appendix-I --- Joint Face Alignment Results --- p.100
Bibliography --- p.107

A vegetation survey was conducted on the newly acquired farm portions of the farm Doornrandje 386 JR, which are being incorporated into Gauteng Department of Agriculture and Rural Development’s protected area expansion. This study provides an ecological basis for establishing an efficient management programme for the area. From a TWINSPAN classification, refined by Braun-Blanquet procedures, five plant communities were identified. A classification and description of the major plant communities is presented. Descriptions of the plant communities include characteristic species, as well as prominent and less conspicuous species of the tree, shrub, herb and grass strata. Floristic analyses of the vegetation and a biodiversity comparison of the different plant communities found on the farm are also undertaken. This study proves that the extended land incorporated into the Reserve contributes to the biological diversity of the study area.
Environmental Sciences
M. Sc. (Environmental Science)

The vegetation of the Nooitgedacht section of the Loskop Dam Nature Reserve resembles Bankenveld vegetation and differs from the other areas of the reserve. This study was undertaken to identify, classify, and describe the plant communities present on this section, and to determine their veld condition. The Braun-Blanquet approach was followed to classify the different plant communities. A total number of 170 sample plots (100m2) were placed in all homogeneous vegetation units in a randomly stratified basis. The Ecological Index Method (EIM) was used to determine the veld condition. Data were collected using the steppoint method and incorporated into the GRAZE model from where the veld condition was calculated. A minimum of 400 step points were surveyed in each community with more points in the larger communities. Plant community data was analysed using the JUICE software program. A total of 11 plant communities were identified. The overall veld condition score indicates the vegetation to be in a good condition, resulting in a high grazing capacity.
Environmental Sciences
M. Sc. (Nature Conservation)