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Student Number : 9800233A - MSc dissertation - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environment / Condition monitoring of machinery has increased in importance as more engineering processes are automated and the manpower required to operate and supervise plants is reduced. The monitoring of the condition of machinery can significantly reduce the cost of maintenance. Firstly, it can allow an early detection of potential catastrophic fault, which could be extremely expensive to repair. Secondly, it allows the implementation of conditions based maintenance rather than periodic or failure based maintenance [1]. In these cases, significant savings can be made by delaying schedule maintenance until convenient or necessary. Although there are numerous efficient methods for modeling of mechanical systems, they all suffer the disadvantage that they are only valid for a particular machine. Changes within the design or the operational mode of the machine normally require a manual adaptation. Using Neural Networks to model technical systems eliminates this major disadvantage. The basis for a successful model is an adequate knowledge base on which the network is "trained". Without prior knowledge of the machines systematic behavior or its history, training of a neural Network is not possible. Therefore, it is a pre-requisite that the knowledge base contains a complete behavior of the machine covering the respective operational modes whereby, not all rather the most important modes are required. Neural networks have a proven ability in the area of nonlinear pattern classification. After being trained, they contain expert knowledge and can correctly identify the different causes of bearing vibration. The capacity of artificial neural networks to mimic and automate human expertise is what makes them ideally suited for handling nonlinear systems. Neural networks are able to learn expert knowledge by being trained using a representative set of data [2]-[6]. At the beginning of a neural network’s training session, the neural network fault detector’s diagnosis of the motor’s condition will not be accurate. An error quantity is measured and used to adjust the neural network’s internal parameters in order to produce a more accurate output. This process is repeated until a suitable error is achieved. Once the network is sufficiently trained and the parameters have been saved, the neural network contains all the necessary knowledge to perform the fault detection. One of the most important aspects of achieving good neural network performance has proven to be the proper selection of training features. The curse of dimensionality states that, as a rule of thumb, the required cardinality of the training set for accurate training increases exponentially with the input dimension [7]. Thus feature selection which is a process of identifying those features that contribute most to the discrimination ability of the neural network is required. Proposed methods for selecting an appropriate subset of features are numerous [8]-[11]. Methods based on generating a single solution, such as the popular forward step wise approach, can fail to select features which do poorly alone but offer valuable information together. Approaches that maintain a population of solutions, such as genetic algorithms (GA) are more likely to speedily perform efficient searches in high dimensional spaces, with strong interdependencies among the features. The emphasis in using the genetic algorithm for feature selection is to reduce the computational load on the training system while still allowing near optimal results to be found relatively quickly. To obtain accurate measure of the condition of machinery, a wide range of approaches can be employed to select features indicative of condition. By comparing these features with features for known normal and probable fault conditions, the machine’s condition can be estimated. The most common approach is that of analysis in the frequency domain by applying a Fast Fourier Transform (FFT) to the time domain history data. The idea is simply to measure the energy (mean square value) of the vibrations. As the machine condition deteriorates, this measure is expected to increase. The method is able to reveal the harmonics around the fundamental frequency of the machine and other predominant frequency component (such as the cage frequency) [12]. Frequency analysis is well established and may be used to detect, diagnose and discriminate a variety of induction motor faults such as broken rotor bars, cage faults, phase imbalance, inner and outer race faults. However, as common in the monitoring of any industrial machine, background noise in recorded data can make spectra difficult to interpret. In addition, the accuracy of a spectrum is limited due to energy leakage [12- 14]. Like many of the new techniques now finding application in machinery condition monitoring, Higher Order Statistics was originally confined to the realms of non-linear structural dynamics. It has of recent however found successful application to the identification of abnormal operation of diesel engines and helicopter gearboxes [5, 7]. Higher Order Statistics provide convenient basis for comparison of data between different measurement instances and are sufficiently robust for on-line use. They are fast in computation compared with frequency or time-domain analysis. Furthermore, they give a more robust assessment than lower orders and can be used to calculate higher order spectra. This dissertation reports work which attempts to extend this capability to induction motors. The aim of this project is therefore to examine the use of Genetic Algorithms to select the most significant input features from a large set of possible features in machine condition monitoring contexts. The results show the effectiveness of the selected features from the acquired raw and preprocessed signals in diagnosis of machine condition. This project consists of the following tasks: #1; Using Fast Fourier transform and higher order signals techniques to preprocess data samples. #1; Create an intelligent engine using computational intelligence methods. The aim of this engine will be to recognize faulty bearings and assess the fault severity from sensor data. #1; Train the neural network using a back propagation algorithm. #1; Implement a feature selection algorithm using genetic algorithms to minimize the number of selected features and to maximize the performance of the neural network. #1; Retrain the neural network with the reduced set of features from genetic algorithm and compare the two approaches. #1; Investigate the effect of increasing the number of hidden nodes in the performance of the computational intelligence engine. #1; Evaluate the performance of the system using confusion matrices. The output of the design is the estimate of fault type and its severity, quantified on a scale between 0-3. Where, 0 corresponds to the absence of the specific fault and 3 the presence of a severe machine bearing fault. This research should make contribution to many sectors of industry such as electricity supply companies, and the railroad industry due to their need of techniques that are capable of accurately recognizing the development of a fault condition within a machine system component. Quality control of electric motors is an essential part of the manufacturing process as competition increases, the need for reliable and economical quality control becomes even more pressing. To this effect, this research project will contribute in the area of faults detection in the production line of electric motor.

Genetic change in Simmental cattle is being accelerated using multiple ovulation and embryo transfer (MOET) techniques. Success of the project depends largely on the ability to generate grade 1 embryos from juvenile heifers following a performance test. Performance testing imposes specific nutritional, management and environmental conditions on heifers which may influence the onset of puberty and subsequent response to superovulation. The objectives of this study were to determine when puberty occurred in Simmental heifers on performance test and to investigate the effects of pubertal development and performance during test on superovulatory response. Heifers were performance tested between 23 and 49 weeks of age. Performance test measurements included: food intake, energy intake, liveweight, backfat depth, muscle depth and muscling score. The onset of puberty was determined in one generation of heifers (n=30) by detecting elevated plasma progesterone levels indicative of first ovulation. Following performance test heifers were superovulated, using ovine FSH, artificially inseminated and embryo recovery carried out at 52 and 61 weeks of age. Puberty occurred in 87% of heifers before the end of test. There was considerable variation in age, liveweight and withers height at puberty, however, daily liveweights gain appeared to be an important determinant of age at puberty. The yield of grade 1 embryos at the first embryo recovery was highest in prepubertal heifers, however, these heifers experienced a substantial decrease in yield at the second recovery apparently due to reduced recovery rates. Investigation of relationships between performance on test and superovulatory response in three generations of heifers (n=110) revealed no significant effect of performance during test on the yield of grade 1 embryos. It was concluded that several factors contributed to reduced embryo yields including: ineffective control of the corpus luteum in cyclic heifers; the incidence of short luteal phases in pre- and peri-pubertal heifers; luteinization of potential ovulatory follicles; and inappropriate timing of exogenous synchrony and superovulation treatments in relation to waves of follicular development.

Upcoming applications of synthetic biology will require access to a wide array of robust genetic components (parts). The logic of a genetic system is encoded with regulatory elements such as pairs of transcription factors:promoters, miRNAs:target sites, or ribozymes:aptamers among others. Due to a relatively simple form and mode of operation of miRNAs, it is possible to design their synthetic variants. Out of all possible miRNA sequences the ones chosen should perform efficiently and should avoid cross-talk with both the host system circuits and within the imported synthetic ones. In this work, a computational method involving a series of heuristics is developed that can be used to design ensembles of such sequences depending on the host transcriptome. As an example, an ensemble of eight such miRNA sequences is produced using this method for use in a human host. Those have then been validated experimentally against the above-mentioned requirements by transfection into HEK 293 cells and flow cytometry measurements of fluorescent markers. The produced sequences are available for use from pENTR vectors of the Gateway cloning system. The required computations were facilitated by a modern cluster computing system—Kaichu—especially developed for this project, but fit for general purpose use and available under an open-source license.

φC31 integrase (Int) and other site-specific recombinases enable controlled and precise genetic manipulations of complex genomes. Int mediates integration of the φC31 genome into the genome of its Streptomyces host. Recombination occurs between specific attachment sites; attB and attP. Int binds attP and attB with similar affinities, despite significant sequence differences. The mechanism through which Int recognises its substrates is not fully understood. To study DNA binding in vivo in the absence of recombination, we employed the challenge phage assay. In this assay, binding by Int to attP or attB results in a high frequency of P22-1000 lysogen formation in Salmonella. When Int has lost binding activity, fewer lysogens are generated. A randomly mutated integrase library has been screened using this assay. A number of the mutants showed a reduction in binding to both attB and attP or just to attB. Point mutations in these integrases largely clustered either a putative zinc finger or to the pfam07508 ‘recombinase' domain. To validate the phage challenge assay data, the binding defective Int mutants were purified and tested in in vitro DNA binding experiments. Int mutants displayed reduced binding to attB and/or attP compared to attL or attR. The purified proteins were used in in vitro recombination assays. Mutants in the recombinase domain generally showed reduced integration whilst demonstrating almost wild type gp3 dependant excision. These data combined with data from others suggested two DNA binding domains in Int; the recombinases domain and the zinc finger. A truncated mutant Int, IntV371SUGA had previously been shown to bind DNA with low affinity. The mutations in the recombinase domain were transferred to IntV371SUGA to test their effect on DNA binding. I suggest that the recombinase motif is intimately involved in DNA recognition and discrimination between the att sites required for phage integratation and excision.

A novel real-time control method that minimises linear system vibrations when it is subjected to an arbitrary external excitation is proposed in this study. The work deals with a discrete differential dynamic programming type of problem, in which an external disturbance is controlled over a time horizon by a control force strategy constituted by the well-known convolution approach. The proposed method states that if a control strategy can be established to restore an impulse external disturbance, then the convolution concept can be used to generate an overall control strategy to control the system response when it is subjected to an arbitrary external disturbance. The arbitrary disturbance is divided into impulses and by simply scaling, shifting and summation of the obtained control strategy against the impulse input for each impulse of the arbitrary disturbance, the overall control strategy will be established. Genetic Algorithm was adopted to obtain an optimal control force plan to suppress the system vibrations when it is subjected to a shock disturbance, and then the Convolution concept was used to enable the system response to be controlled in real-time using the obtained control strategy. Numerical tests were carried out on a two-degree of freedom quarter-vehicle active suspension model and the results were compared with results generated using the Linear Quadratic Regulator (LQR) method. The method was also applied to control the vibration of a seven-degree of freedom full-vehicle active suspension model. In addition, the effect of a time delay on the performance of the proposed approach was also studied. To demonstrate the applicability of the proposed method in real-time control, experimental tests were performed on a quarter-vehicle test rig equipped with a pneumatic active suspension. Numerical and experimental results showed the effectiveness of the proposed method in reducing the vehicle vibrations. One of the main contributions of this work besides using the Convolution concept to provide a real time control strategy is the reduction in the number of sensors needed to construct the proposed method as the disturbance amplitude is the only parameter needed to be measured (known). Finally, having achieved what has been proposed above, a generic robust control method is accomplished, which not only can be applied for active suspension systems but also in many other fields.

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A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2016. / The Organic Cation Transporter 2 (OCT2) gene is responsible for facilitating the transport of cationic compounds, which include both endogenous substrates and clinical drugs. Single nucleotide polymorphisms (SNPs) within this gene were extensively explored in the South African black population as little research has been conducted on these individuals so far. We sequenced the OCT2 promoter region of 10 DNA samples from the South African black population and identified four SNPs and one INDEL. We performed a luciferase assay to determine their effects on gene expression and we found two variants (rs59695691 and rs138765638) that showed a statistically significant change in luciferase expression suggesting that they may be associated with a change in OCT2 regulatory function. We also indentified thirteen SNPs and two INDELs within the OCT2 promoter region, and nine SNPs within the OCT2 coding region through analysing various South African population studies. These variations could affect both gene expression and protein function. These findings help contribute to filling the gap pertaining to OCT variation in South African populations. / LG2017

During times of stock market turbulence and crises, monitoring the clustering behaviour of financial instruments allows one to better understand the behaviour of the stock market and the associated systemic risks. In the study undertaken, I apply an effective and performant approach to classify data clusters in order to better understand correlations between stocks. The novel methods aim to address the lack of effective algorithms to deal with high-performance cluster analysis in the context of large complex real-time low-latency data-sets. I apply an efficient and novel data clustering approach, namely the Giada and Marsili log-likelihood function derived from the Noh model and use a Parallel Genetic Algorithm in order to isolate residual data clusters. Genetic Algorithms (GAs) are a very versatile methodology for scientific computing, while the application of Parallel Genetic Algorithms (PGAs) further increases the computational efficiency. They are an effective vehicle to mine data sets for information and traits. However, the traditional parallel computing environment can be expensive. I focused on adopting NVIDIAs Compute Unified Device Architecture (CUDA) programming model in order to develop a PGA framework for my computation solution, where I aim to efficiently filter out residual clusters. The results show that the application of the PGA with the novel clustering function on the CUDA platform is quite effective to improve the computational efficiency of parallel data cluster analysis.