This thesis investigates the application of pattern recognition techniques to rock type recognition using monitoring-while-drilling data. The research is focused on data from a large electric blasthole drill operating in an open-pit coal mine. Pre-processing and normalization techniques are applied to minimize potential misclassification issues. Both supervised and unsupervised learning is employed in the classifier design: back-propagation neural networks are used for the supervised learning, while self-organizing maps are used for unsupervised learning. A variety of combinations of drilling data and geophysical data are investigated as inputs to the classifiers. The outputs from these classifiers are evaluated relative to the rock classification made by a commercially available rock type recognition system, as well as relative to independent labelling by a geologist. Classifier performance is improved when drilling data used as inputs are augmented with geophysical data inputs. By using supervised learning with both drilling and geophysical data as inputs, the misclassification of coal, as well as of the non-coal rock types, is reduced compared to results of current commercial recognition methods. Moreover, rock types which were not detected by the previous methods were successfully classified by the supervised models. / Thesis (Master, Mining Engineering) -- Queen's University, 2007-11-28 15:22:17.454 / I would like to thank the financial support provided by the George C. Bateman and J. J. Denny Graduate Fellowship, as well as funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) provided via NSERC grant support to Dr. Daneshmend.

This thesis investigated the abilities of artificial neural networks as rock classifiers in an open-pit hard rock environment using monitoring-while-drilling (MWD) data. Blast hole drilling data has been collected from an open-pit taconite mine. The data was smoothed with respect to depth and filtered for non-drilling data. Preliminary analysis was performed to determine classifier input variables and a method of labelling training data. Results obtained from principal component analysis suggested that the best set of possible classifier input variables was: penetration rate, torque, specific fracture energy, vertical vibration, horizontal vibration, penetration rate deviation and thrust deviation. Specific fracture energy and self-organizing-maps were explored as a means of labelling training data and found to be inadequate. Several backpropagation neural networks were trained and tested with various combinations of input parameters and training sets. Input sets that included all seven parameters achieved the best overall performances. 7-input neural networks that were trained with and tested on the entire data set achieved an average overall performance of 81%. A sensitivity analysis was performed to test the generalization abilities of the neural networks as rock classifiers. The best overall neural network performance on data not included in the training set was 67%. The results indicated that neural networks by themselves are not capable rock classifiers on MWD data in such a hard rock iron ore environment. / Thesis (Master, Mining Engineering) -- Queen's University, 2009-04-23 11:59:07.806

No abstract available / Thesis - University of KwaZulu-Natal, Pietermaritzburg, 2010.

The measurement of vital signs is a risk-free, inexpensive, and reproducible clinical practice that enables identification of physiological deterioration of patients before an adverse event occurs. However, studies show that manual clinical measurements of respiratory rate are intermittent, biased, and inaccurate. Therefore, a contactless planar capacitive sensor was developed and evaluated against a clinical reference method. Results show that the sensor is accurate (i.e. strong agreement with an average ICC value of 0.99 and an average BSI coefficient of 2.76 < 4 breaths/min clinical threshold) and unbiased (i.e. average mean difference of -0.02 breaths/min). The sensor has promise for respiratory rate monitoring of bedridden patients even during shallow breathing. Future work includes addressing technology limitations, conducting a clinical pilot with a diverse patient population, and exploring potential in sleep quality assessment.

The measurement of vital signs is a risk-free, inexpensive, and reproducible clinical practice that enables identification of physiological deterioration of patients before an adverse event occurs. However, studies show that manual clinical measurements of respiratory rate are intermittent, biased, and inaccurate. Therefore, a contactless planar capacitive sensor was developed and evaluated against a clinical reference method. Results show that the sensor is accurate (i.e. strong agreement with an average ICC value of 0.99 and an average BSI coefficient of 2.76 < 4 breaths/min clinical threshold) and unbiased (i.e. average mean difference of -0.02 breaths/min). The sensor has promise for respiratory rate monitoring of bedridden patients even during shallow breathing. Future work includes addressing technology limitations, conducting a clinical pilot with a diverse patient population, and exploring potential in sleep quality assessment.

No description available.

The population ecology of the diseases of the common dab (<I>Limanda limanda</I> L.) has been studied and the use of fish disease as a monitor of pollution in the marine environment reassessed. The diseases included in this study were lymphocystis disease, hyperplasia/papilloma, ulcer disease, inflammatory fat-cell necrosis, and X-cell disease. The interactions responsible for determining disease abundance were assessed by carrying out a detailed longitudinal field survey and interpreting the observed changes in abundance by reference to the population biology of the common dab, the abiotic environment, and the known biology of the diseases. The diseases were not evenly distributed within the host population. Significant differences in prevalence occurred between sexes, ages, and fish of differing maturity status. These were interpreted in terms of varying susceptibility of the host, the response of the host to infection and the dynamics of the diseases. Analysis of the age-prevalence relationships suggests that although some diseases have the potential for their abundance to be regulated this is not fulfilled. The temporal distribution of the diseases consisted of long-term trends, seasonal cycles, and short-term fluctuations. A variety of potential causal factors were highlighted including the spawning behaviour of the dab, temperature, and bacterial abundance related to environmental productivity. The patterns in the spatial distribution of the diseases were reflected in variation in prevalence on both large (˜100nml) and small scales (˜1nml). In this case the causal factors highlighted were salinity, host population density, and again bacterial abundance related to productivity. No effect of pollution on disease prevalence was demonstrated. Neither could the occurrence of lymphocystis disease be related to the concentration of chemical contaminants in the livers of dab.

The geographic location of Fezile Dabi District Municipality is unique in the sense that it is one of five district municipalities in the Free State Province that borders on five district municipalities and three provincial boundaries. The Vaal River, a valuable domestic, industrial and agricultural water resource for millions of South Africans, forms one of the administrative boundaries of this district municipality and of the Free State Province. However, despite the vital role the Vaal River plays in the Fezile Dabi region, there is poor water management and assessment system in place. There is a lack of intergovernmental and trans boundary efforts to assess water quality by both district municipalities and provinces. The status of the Vaal River as a visible aquatic boundary line provides an excellent opportunity to develop a trans boundary collaborative water quality monitoring strategy between Fezile Dabi District Municipality and its neighbouring district municipalities. The only way to ensure the safety of water and a healthy environment for all is by addressing the challenges of water quality monitoring in a Trans boundary, integrated and multidisciplinary manner. This study provides a strategy for Fezile Dabi District Municipality and its neighbours by proposing an ideal structure to optimise effective water quality monitoring between them. Firstly, it gives an explanation of what is meant by water quality monitoring as a key performance area of municipal health services. Secondly, information is provided and proposals made on how to integrate water quality monitoring across municipal boundaries. Thirdly, the study suggests how trans boundary collaboration can contribute to improving water quality assessment strategies between Fezile Dabi District Municipality and its adjacent district municipalities. Lastly, an exposition of an ideal organisational structure and methods to optimise effective water quality monitoring between Fezile Dabi District and these municipalities is provided. A way forward is proposed in terms of a structure negotiated and developed at an environmental health workshop held on 28–29 March 2012 in Sasolburg. / Thesis (M. Development and Management (Water Studies))--North-West University, Vaal Triangle Campus, 2013

Killer whales inhabiting coastal waters of the northeastern Pacific are listed under the Canadian Species at Risk Act, which requires the identification of critical habitats for the recovery of their populations. Little is known about their distribution during the winter and what areas are important for their survival during these months. Passive acoustic monitoring is a valuable complementary method to traditional visual and photographic surveys although it has seldom been used to study killer whales and there are limitations in practice. There is a need to develop tools and protocols to maximize the efficiency of such studies. In this thesis, long-term acoustic data collected with autonomous recorders were analyzed 1) to assess the performance of two types of analysis (Manual and Long Term Spectral Averages) for detecting and identifying killer whale calls and to compare the effects of using two different duty cycles (1/3 and 2/3); and 2) to investigate the seasonal occurrence of different killer whale populations at two sites off the west coasts of Vancouver Island and Washington: Swiftsure Bank and Cape Elizabeth. Both the use of Long Term Spectral Averages and a lower duty cycle resulted in a decrease in call detection and resolution of call identification, leading to underestimations of the amount of time the whales spent at the site. A compromise between a lower resolution data processing method and a higher duty cycle (and vice-versa) is therefore suggested for future passive acoustic monitoring studies of killer whales. Killer whale calls were detected on 186 days at Swiftsure Bank and on 39 days at Cape Elizabeth. The seasonal occurrence of killer whales at Swiftsure Bank highlights its importance as a killer whale hotspot, with year-round presence of Southern Residents and British Columbia Transients, Northern Residents in spring and fall, and California Transients on rare occasions. These results support the expansion of Southern Resident’s critical habitat to include Swiftsure Bank. Temporal habitat partitioning between Resident populations was observed at Cape Elizabeth, with Southern Residents detected from January through June and Northern Residents from July to September. These results show that Northern Residents use the southern parts of their range more frequently than previously thought. Both Transient populations were frequently detected throughout the year, suggesting habitat overlapping. / Graduate

The purpose of the current study was to examine the impact of working memory on speech monitoring processes in the primary language of school-age children using the framework of Levelt’s Perceptual Loop Theory of speech production (1983). A community sample of eight children aged 6-8 and fourteen children aged 10-12 completed 4 verbal description tasks under different conditions; control, working memory load, white noise and combined working memory load and white noise. Participants also completed measures of listening span, digit span and spatial span. The results indicate that with increasing working memory load, children make significantly more speech errors, silent pauses and repetitions. No relationship was found between working memory and total repairs per errors or between working memory and total number of editing terms used. Group differences across the conditions were not significant; however, age-related trends were notable. Younger children had greater difficulty monitoring their speech with the introduction of working memory load; whereas, older children had greater difficulty with the introduction of white noise. A revised speech production model incorporating aspects of working memory is recommended and implications for clinical populations are discussed. / Graduate / 0290 / 0620 / 0460 / tlentz@icloud.com